Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Different Ways in Which the CODIS 7.0 May be Used in Mass Disaster Identification

DNA analysis is a key method for the identification of human remains in mass disasters. Reference samples from relatives may be used to identify missing persons by kinship analysis. Different methods of applying the CODIS in disaster victim identification (DVI) were investigated. Two searches were evaluated: (i) relating family relatives to a pedigree tree (FPT) and (ii) relating unidentified human remains to a pedigree tree (UPT). A joint pedigree likelihood ratio (JPLR) and rank were calculated for each search. Both searches were similar in average JPLR and rank. In exceptional cases, namely the existence of a mutation different from the CODIS model, a nonbiological father, a mistake in STR, or incorrect profile association, the UPT search returned one true rank, whereas the FPT search returned no results. This paper suggests a novel strategy to overcome these limitations and increase efficiency in conducting identification of mass disaster victims.
An Adaptive Feedback Turning SIR Particle Filter and Its Application on Fault Diagnosis of Auxiliary Power Unit

With the development of aero-engine control technology, the complexity of the control system is getting higher and higher, and the requirements of high security and reliability have made fault diagnosis more urgent. Because of the bad working environment, the sensor is very prone to malfunction. Because SIR particle filter can be used to implement the estimation of the engine state parameters in the fault diagnosis of the sensor and is inefficient and inaccuracy in state estimation, an adaptive feedback correction mechanism is provided to improve particle degradation in SIR particle filter in the estimation. The simulations show that the proposed method improves the estimation precision of exhaust temperature of Auxiliary Power Unit (APU) during bias failure, drift failure, and power failure of temperature sensor.
Identification of Antioxidant Proteins With Deep Learning From Sequence Information

Antioxidant proteins have been found closely linked to disease control for its ability to eliminate excess free radicals. Because of its medicinal value, the study of identifying antioxidant proteins is on the upsurge. Many machine-learning classifiers have performed poorly owing to the nonlinear and unbalanced nature of biological data. Recently, deep learning techniques showed advantages over many state-of-the-art machine learning methods in various fields. In this study, a deep learning based classifier was proposed to identify antioxidant proteins based on mixed g-gap dipeptide composition feature vector. The classifier employed deep autoencoder to extract nonlinear representation from raw input. The t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction. Support vector machine was finally performed for classification. The classifier achieved F1 score of 0.8842 and MCC of 0.7409 in 10-fold cross validation. Experimental results show that our proposed method outperformed the traditional machine learning methods and could be a promising tool for antioxidant protein identification. For the convenience of others' scientific research, we have developed a user-friendly web server called IDAod for antioxidant protein identification, which can be accessed freely at http://bigroup.uestc. edu.cn/IDAod/.
Design of Substrate Integrated Waveguide (SIW) BPF using Circular Ring, Split Ring and Inductive Posts

Design of Substrate Integrated Waveguide (SIW) Band-pass Filter using circular rings, slotted rings and inductive posts are presented in this paper. These structures are analyzed separately to formulate their effects on creating higher cut-off within the microwave Ku bands. Significant isolation is achieved by using array of split ring structures embedded with reflection cancelling inductive vias. Major filter parameters of all configurations are studied and presented with comparative analysis. Maximum and minimum Insertion Loss of 1. SdB and O.4dB respectively has been achieved. Return loss and isolation > 10dB is also attained by the proposed technique.
Use of Utility Based Interestingness Measures to Predict the Academic Performance of Technology Learners in Sri Lanka

Knowledge extracted from educational data can be used by the educators to obtain insights about how the quality of teaching and learning must be improved, how the factors a $\square$ ect the performance of the students and how qualified students can be trained for the industry requirements. This research focuses on classifying a knowledge based system using a set of rules. The main purpose of the study is to analyse the most influencing attributes of the students for their module performance in tertiary education in Sri Lanka. The study has gathered data about students in a reputed degree awarding institute in Sri Lanka and used three different data mining algorithms to predict the influential factors and they have been evaluated for interestingness using objective oriented utility based method. The findings of this study will positively a $\square$ ect the future decisions about the progress of the students' performance, quality of the education process and the future of the education provider.
Non-Orthogonal Multiple Access: Common Myths and Critical Questions

Non-orthogonal multiple access (NOMA) has received tremendous attention for the design of radio access techniques for fifth generation (5G) wireless networks and beyond. The basic concept behind NOMA is to serve more than one user in the same resource block, for example, a time slot, subcarrier, spreading code, or space. With this, NOMA promotes massive connectivity, lowers latency, improves user fairness and spectral efficiency, and increases reliability compared to orthogonal multiple access (OMA) techniques. While NOMA has gained significant attention from the communications community, it has also been subject to several widespread misunderstandings, such as "NOMA is based on allocating higher power to users with worse channel conditions. As such, cell-edge users receive more power in NOMA and due to this biased power allocation toward celledge users inter-cell interference is more severe in NOMA compared to OMA. NOMA also compromises security for spectral efficiency. " The above statements are actually false, and this article aims at identifying such common myths about NOMA and clarifying why they are not true. We also pose critical questions that are important for the effective adoption of NOMA in 5G and beyond and identify promising research directions for NOMA, which will require intense investigation in the future.
Planning Fully Renewable Powered Charging Stations on Highways: A Data-Driven Robust Optimization Approach

This paper proposes a comprehensive two-stage method for siting and sizing stand-alone electric-vehicle charging stations on highway networks. In the first stage, locations, where individual vehicles require charging services, are obtained from Monte Carlo simulation provided with the traffic demand and battery data; an integer programming model is proposed to determine the optimal sites of charging stations from potential candidates, ensuring that every vehicle is able to visit at least one charging station without depleting the battery; afterward, the spatial and temporal distribution of charging demand at individual selected sites can be simulated. In the second stage, a data-driven distributionally robust optimization model is developed to optimize the capacities of renewable generations and energy storage units in each charging station. The uncertain generation and demand are described by a family of inexact distributions around an empirical distribution, and their distance in the sense of Kullback–Leibler divergence is controlled by an adjustable scalar. Two reformulations of the robust model are suggested based on risk theory. The first one relies on Value-at-Risk (VaR) and gives rise to a mixed-integer linear program (MILP), which is more accurate; the second one offers a conservative approximation based on Conditional VaR and comes down to a linear program, which is more tractable. Numerical study on a test system demonstrates the effectiveness of the proposed methods.
Multi-Objective Optimal Method for Three Energy Entities in Integrated Community Energy System

As the underlying terminal of multi-energy system, integrated community energy system (ICES) is featured with multi-energy complementary and integrated optimization operation. It is important to research on its optimal operation method for the development of integrated energy system and the consumption of renewable energy. Firstly, a model of ICES is established, including energy operator, user with distributed PV and EV charging agent. Secondly, a multi-objective optimal model is established, in which the three types of energy entities pursuit their own maximized interests rationally. Finally, the proposed model is solved in a typical winter scenario of the business ICES. Results demonstrate that the energy operator can get a certain profit by selling energy to the others. User with distributed photovoltaic (PV) participating in the profit competition is able to increase the utilization of PV resource and reduce the cost of energy using by selling its remaining PV resource. By choosing the bids of other entities and setting the best charging strategy, electric vehicle (EV) charging agent can not only reduce the cost of energy using, but also help users to consume the excessive PV power and reduce the peak load of distribution network in early peak hours.
A Feature Fusion Based Approach for Handwritten Bangla Character Recognition Using Extreme Learning Machine

Optical Character Recognition (OCR) is an abstruse field of pattern recognition. An active branch of OCR is handwritten character recognition. This paper presents Bangla handwritten character recognition based on a feature fusion endeavor. Character recognition mostly depends on impeccable features extracted from input images. Coupling of two distinct feature vectors obtained by Histogram of Oriented Gradients (HOG) and Gabor filter is illustrated here. To evaluate the recognition rate of input characters Extreme Learning Machine (ELM) is used which is a feed-forward neural network. A 5-fold cross-validation scheme has been applied to measure the fulfillment of the organization. While using individual feature extraction technique, HOG and Gabor filter show 90.5% and 91.2% accuracy respectively. However, using feature fusion approach provides a better accuracy of 96.1%.
Reliable and  $Q$ -Enhanced Floating Active Inductors and Their Application in RF Bandpass Filters

This paper presents three new floating active inductor (FAI)-based bandpass filter (BPF) topologies, namely, the nominal-<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> BPF (NQF), <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-enhanced BPF-1 (QF<sub>1</sub>), and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-enhanced BPF-2 (QF<sub>2</sub>). The filters utilize voltage differencing transconductance amplifier (VDTA) as an active building block and are implemented in a 45-nm CMOS process. Negative resistance techniques are used to enhance the quality factor and an addition-based current reference technique reduces the sensitivity of filter’s design metrics to process, voltage, and temperature (PVT) variations. The performances of the filters are analyzed in the presence of parasitics and analytical expressions are derived. The proposed filters consume minimal power, possess a high <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> and good dynamic range, and are reliable despite being implemented in the nanoscale regime. The NQF/QF<sub>1</sub>/QF<sub>2</sub> exhibits a center frequency of 4.36/3.63/3.02 GHz, quality factor of 17.84/54/46.2, the 3-dB bandwidth of 244.4/67.22/65.36 MHz, the dynamic range of 122.49/118.58/119.46 dB, and a 1-dB compression point of −3.06/−5.48/−5.45 dBm. The filter topologies consume 0.552/0.608/0.634 mW from ±1-V supply voltage. The above-mentioned parameters translate into a figure-of-merit FOM<sub>1</sub>(FOM<sub>2</sub>) of 125.07 dB-Hz/mW (80.59 dB)/120.74 dB-Hz/mW (81.33 dB)/121.44 dB-Hz/mW (79.81 dB) for NQF/QF<sub>1</sub>/QF2.
Stabilization of a Cascaded AC–DC–DC System With Small Bus Capacitance Based on Small Signal Analysis

In this paper, the source converter (voltage source rectifier) of a cascaded system is modeled based on small signal analysis, and the closed-loop transfer functions of the converter are obtained. Then, the output impedance of the source converter is calculated. The analysis of the output impedance indicates that the smaller the bus capacitance, the worse the stability of the cascaded system, and the smaller the power converted by the cascaded system. Three kinds of virtual impedance control methods are proposed to reduce the output impedance of the source converter; all of them improve the stability and increase the maximum power of the cascaded system. The parameters of the three kinds of methods are designed and their effects are compared. Simulation and experimental results show a good agreement with the theoretical analysis, which proves the validity of the virtual impedance control methods.
SNPP VIIRS reflective solar bands on-orbit calibration six-year update: extension and improvements

The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) has been on-orbit for more than 6 years. VIIRS has 22 spectral bands, among which fourteen are reflective solar bands (RSB) covering a spectral range from 0.410 to 2.25 μm. The SNPP VIIRS RSB have performed very well since launch. The radiometric calibration for the RSB has also reached a mature stage after almost six years since its launch. Numerous improvements have been made in the standard RSB calibration methodology. Additionally, a hybrid calibration method, which takes the advantages of both solar diffuser calibration and lunar calibration and avoids the drawbacks of the two methods, successfully finalizes the highly accurate calibration for VIIRS RSB. The successfully calibrated RSB data record significantly impacts the ocean color products, whose stringent requirements are especially sensitive to calibration accuracy, and helps the ocean color products to reach maturity and high quality. Nevertheless, there are still many challenge issues to be investigated for further improvements of the VIIRS sensor data records (SDR). In this presentation, SNPP VIIRS RSB calibration and performance in the past six-year since launch will be presented.
Do Complexity Measures of Frontal EEG Distinguish Loss of Consciousness in Geriatric Patients Under Anesthesia?

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration—differences which future work may reveal to improve geriatric patient monitoring.
A Phase II Trial of the Aurora Kinase A Inhibitor Alisertib for Patients with Castration-resistant and Neuroendocrine Prostate Cancer: Efficacy and Biomarkers

Purpose: Neuroendocrine prostate cancer (NEPC) is an aggressive variant of prostate cancer that may develop de novo or as a mechanism of treatment resistance. N-myc is capable of driving NEPC progression. Alisertib inhibits the interaction between N-myc and its stabilizing factor Aurora-A, inhibiting N-myc signaling, and suppressing tumor growth. Patients and Methods: Sixty men were treated with alisertib 50 mg twice daily for 7 days every 21 days. Eligibility included metastatic prostate cancer and at least one: small-cell neuroendocrine morphology; ≥50% neuroendocrine marker expression; new liver metastases without PSA progression; or elevated serum neuroendocrine markers. The primary endpoint was 6-month radiographic progression-free survival (rPFS). Pretreatment biopsies were evaluated by whole exome and RNA-seq and patient-derived organoids were developed. Results: Median PSA was 1.13 ng/mL (0.01–514.2), number of prior therapies was 3, and 68% had visceral metastases. Genomic alterations involved RB1 (55%), TP53 (46%), PTEN (29%), BRCA2 (29%), and AR (27%), and there was a range of androgen receptor signaling and NEPC marker expression. Six-month rPFS was 13.4% and median overall survival was 9.5 months (7.3–13). Exceptional responders were identified, including complete resolution of liver metastases and prolonged stable disease, with tumors suggestive of N-myc and Aurora-A overactivity. Patient organoids exhibited concordant responses to alisertib and allowed for the dynamic testing of Aurora–N-myc complex disruption. Conclusions: Although the study did not meet its primary endpoint, a subset of patients with advanced prostate cancer and molecular features supporting Aurora-A and N-myc activation achieved significant clinical benefit from single-agent alisertib.
Design and Evaluation of Tactile Number Reading Methods on Smartphones

In this paper, we propose two active tactile texture interaction methods enabling eye-free tactile number (0–9) reading on a variable-friction surface for a smartphone equipped with a TPad device. We employ four discriminative stripe-based tactile textures to represent different numbers, namely, 0, 1, 2, and 5, and combine them with the other numbers. For the first interaction method, we adopt simple left/right slider motion gestures (LRSM), and for the second, we utilize up to down slider motion gestures (UToDSM). We examined the interactive efficiency, recognition accuracy, and user-subjective satisfaction of both techniques. We also compared the two methods with an approach that converts a variable-friction perception into a typical vibrotactile perception. Our results indicate that UToDSM leads to the highest interactive efficiency and achieves a high recognition accuracy.
Imaging and analysis of a three-dimensional spider web architecture

Spiders are abundantly found in nature and most ecosystems, making up more than 47 000 species. This ecological success is in part due to the exceptional mechanics of the spider web, with its strength, toughness, elasticity and robustness, which originate from its hierarchical structures all the way from sequence design to web architecture. It is a unique example in nature of high-performance material design. In particular, to survive in different environments, spiders have optimized and adapted their web architecture by providing housing, protection, and an efficient tool for catching prey. The most studied web in literature is the two-dimensional (2D) orb web, which is composed of radial and spiral threads. However, only 10% of spider species are orb-web weavers, and three-dimensional (3D) webs, such as funnel, sheet or cobwebs, are much more abundant in nature. The complex spatial network and microscale size of silk fibres are significant challenges towards determining the topology of 3D webs, and only a limited number of previous studies have attempted to quantify their structure and properties. Here, we focus on developing an innovative experimental method to directly capture the complete digital 3D spider web architecture with micron scale resolution. We built an automatic segmentation and scanning platform to obtain high-resolution 2D images of individual cross-sections of the web that were illuminated by a sheet laser. We then developed image processing algorithms to reconstruct the digital 3D fibrous network by analysing the 2D images. This digital network provides a model that contains all of the structural and topological features of the porous regions of a 3D web with high fidelity, and when combined with a mechanical model of silk materials, will allow us to directly simulate and predict the mechanical response of a realistic 3D web under mechanical loads. Our work provides a practical tool to capture the architecture of sophisticated 3D webs, and could lead to studies of the relation between architecture, material and biological functions for numerous 3D spider web applications.
Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overlapping, and potentially erroneous DNA segments from environmental samples into the accurate representation of the underlying microbiomes's genomes. State-of-the-art tools require big shared memory machines and cannot handle contemporary metagenome datasets that exceed Terabytes in size. In this paper, we introduce the MetaHipMer pipeline, a high-quality and high-performance metagenome assembler that employs an iterative de Bruijn graph approach. MetaHipMer leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is end-to-end parallelized using the Unified Parallel C language and therefore can run seamlessly on shared and distributed-memory systems. Experimental results show that MetaHipMer matches or outperforms the state-of-the-art tools in terms of accuracy. Moreover, MetaHipMer scales efficiently to large concurrencies and is able to assemble previously intractable grand challenge metagenomes. We demonstrate the unprecedented capability of MetaHipMer by computing the first full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion reads — size 2.6 TBytes.