Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of environmental and ecological risks caused by metals in agricultural areas: an example in the Amik Plain of South Turkey

ABSTRACT The works of literature evaluating the eco-environmental risks posed by metals in agricultural areas in developing countries remains limited. This study sought to evaluate the environmental and ecological risks posed by metals in the intensively cultivated areas of the Amik Plain as well as to determine the origins of the metals. For this purpose, 137 soil samples were taken from agricultural production areas of the Amik Plain, and 11 metals (Al, Fe, Ni, Pb, Co, Cr, Cu, Mn, Zn, Cd and As) were examined in the samples. As Ni had the highest average enrichment factor (EF) value (8.04) when compared with the other metals, the soils were found to be significantly enriched with Ni. The Pearson correlation analysis and principal component analysis showed that the Zn concentration was controlled by lithogenic sources, while the Ni, Pb, Cd, Cr and Cu concentrations were controlled by both anthropogenic and lithogenic sources.
LRH‐1/NR5A2 interacts with the glucocorticoid receptor to regulate glucocorticoid resistance

Nuclear receptors are transcription factors with important functions in a variety of physiological and pathological processes. Targeting glucocorticoid receptor (GR) activity using glucocorticoids is a cornerstone in the treatment of patients with T cell acute lymphoblastic leukemia (T‐ALL), and resistance to GC‐induced cell death is associated with poor outcome and a high risk for relapse. Next to ligand‐binding, heterodimerization with other transcription factors presents an important mechanism for the regulation of GR activity. Here, we describe a GC‐induced direct association of the Liver Receptor Homolog‐1 (LRH‐1) with the GR in the nucleus, which results in reciprocal inhibition of transcriptional activity. Pharmacological and molecular interference with LRH‐1 impairs proliferation and survival in T‐ALL and causes a profound sensitization to GC‐induced cell death, even in GC‐resistant T‐ALL. Our data illustrate that direct interaction between GR and LRH‐1 critically regulates glucocorticoid sensitivity in T‐ALL opening up new perspectives for developing innovative therapeutic approaches to treat GC‐resistant T‐ALL.
Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network

Most industrial processes feature high nonlinearity, non-Gaussianity, and time correlation. Models based on overcomplete broad learning system (OBLS) have been successfully applied in the fault monitoring realm, which may relatively deal with the nonlinear and non-Gaussian characteristics. However, these models barely take time correlation into full consideration, hindering the further improvement of the monitoring accuracy of the network. Therefore, an effective dynamic overcomplete broad learning system (DOBLS) based on matrix extension is proposed, which extends the raw data in the batch process with the idea of “time lag” in this article. Subsequently, the OBLS monitoring network is employed to continue the analysis of the extended dynamic input data. Finally, a monitoring model is established to tackle the coexistence of nonlinearity, non-Gaussianity, and time correlation in process data. To illustrate the superiority and feasibility, the proposed model is conducted on the penicillin fermentation simulation platform, the experimental result of which illustrates that the model can extract the feature of process data more comprehensively and be self-updated more efficiently. With shorter training time and higher monitoring accuracy, the proposed model can witness an improvement of average monitoring accuracy by 3.69% and 1.26% in 26 process fault types compared to the state-of-the-art fault monitoring methods BLS and OBLS, respectively.
The spectroscopic variability of T$_{\rm eff}$ and $\log g$ in $\beta$ Cep

Time resolved spectra of $\beta$ Cep yield the following average results: T$_{\rm eff}$ =24000 $\pm$ 250 K, $\log g$ = 3.91 $\pm$ 0.05 and $\xi$ = 8.1 $\pm$ 0.9 km s$^{-1}$. N, O, Ne, Al, Si and S abundances are solar while C, Mg and Fe are slightly under-abundant. Pulsational amplitudes of $\Delta$T$_{\rm eff}$ $\sim$ 700 K and $\Delta$$\log g$ $\sim$ 0.2 dex are found from H$\beta$. The metal lines give similar amplitudes but centred on T$_{\rm eff}$ $\sim$ 25000 K. An upper limit of 1.0 km s$^{-1}$ to the variability of the microturbulence is derived from the Si {\sc iii} triplet at 455 nm. The radial velocity amplitude derived from the core of H$\beta$ is $\sim$ 15% greater than that from the metal lines.
Evaluation of the mortality of neonates and children related to the use of central venous catheters: a systematic review

Objective: To analyze mortality related to infections in the use of central venous catheters in newborns and hospitalized children. Methods: This was a systematic review, using studies that were identified in the databases of MEDLINE, EMBASE, Lilacs, CINAHL, SciELO and Cochrane, in bibliographical references of articles, and other reviews. Two reviewers independently identified relevant studies, analyzed the methodological quality, and subsequently, extracted data. Results: We encountered 1,000 articles, of which 16 were related to catheter infection and only two mentioned mortality. Findings of these two studies verified that sepsis was the principal complication related to the use of central venous catheters, and the most prevalent microorganisms in these infections were Candida sp. and Enterococcus sp. , with significant impact on neonatal and child mortality. Conclusion: It was observed in this review that sepsis was most prevalent complication related to central catheters.
The MIMO Antenna Array with Mutual Coupling Reduction and Cross-polarization Suppression by Defected Ground Structures

This paper proposes two types of defected ground structures (DGSs) for achieving the mutual coupling (MC) reduction and the antenna cross-polarization (XP) suppression respectively in a MIMO (multiple input multiple output) wireless communication system. The novel periodic fractal DGS (PFDGS) are presented to reduce the MC between antenna elements. About 20 dB MC reduction is achieved, which contributes to improve the antenna efficiency and increases the MIMO system channel capacity. However, the method of using DGS or other decoupling structures for MC reduction will degrade the antenna XP level unnecessarily. For solving this problem, another arc-shaped DGS is etched under each patch to suppress the antenna XP level. In this way, the XP level is suppressed from −10 dB to −34.6 dB in the boresight direction. Moreover, the arc-shaped DGS will not degrade the MC reduction performance.
New Species, Taxonomy and Faunistics of East Asian and Papuan Lagriinae Latreille, 1825 (Coleoptera: Tenebrionidae)

Abstract. In the present paper Bothrichara anggi sp. nov. (New Guinea), B. grisea sp. nov. (New Guinea), Bothrionota ruzzieri sp. nov. (Sulawesi), Exostira transmontana sp. nov. (China, India, Myanmar), Kaindilagria opposita sp. nov. (New Guinea), Macrocasnonidea bifurcata sp. nov. (Borneo), and Oreogria arfak sp. nov. (New Guinea) are described and illustrated. New synonymy is proposed for Bothrichara Borchmann, 1916 (= Calogria Borchmann, 1916 syn. nov.), Borchmannia violaceocincta (Borchmann & Pic, 1912) (= Rouyerus (Borchmannia) violaceocincta var. diversicollis Borchmann & Pic, 1912 syn. nov., = R. (B.) dissimilis Borchmann in Borchmann & Pic, 1912 syn. nov., = R. (B.) dissimilis var. rufithorax Pic in Borchmann & Pic, 1912 syn. nov., = R. (B.) dissimilis var. lineaticollis Borchmann & Pic, 1912 syn. nov., = Borchmannia akiyamai Merkl, 1988 syn. nov.; = Borchmannia masumotoi Merkl, 1988 syn. nov.) and Chlorophila gemma Telnov, 2021 (= C. carinadorsum Liang, Zhou & Chen, 2022 syn. nov.). Taxonomic position of Costiferolagria grossepunctata (Borchmann, 1916) and C. octocostata (Borchmann, 1916) is discussed and lectotype is designated for Auristira octocostata. Interspecific variability is described and discussed for Sora marmoreipennis Merkl, 2019. Images of Borchmannia violaceocincta, Costiferolagria grossepunctata (Borchmann, 1916), C. octocostata (Borchmann, 1916), Macrocasnonidea quadrimaculata (Pic, 1912), and Sora bimucronata (Pic, 1926) are presented for the first time. New information is provided on the distribution of 25 East Asian and Papuan Lagriinae Latreille, 1825 (1820) including several corrections and additions.
An expansion on prioritized experience replay with round robin scheduling

With the outstanding accomplishments achieved in recent years, Reinforcement Learning (RL) has become an area where researchers have flocked to in order to find innovative ideas and solutions to challenge and conquer some of the most difficult tasks. While the bulk of the research has been focused on the learning algorithms such as SARSA, Q-Learning, and Genetic, not much attention has been paid to tools used to help these algorithms (e.g. the Experience Replay Buffer). This paper goes over what is believed to be the most accurate Taxonomy of the AI field and briefly covers the Q-Learning algorithm, as it is the base algorithm for this study. Most importantly, it proposes a new Experience Replay Buffer technique, the Round Robin Prioritized Experience Replay Buffer (RRPERB), which aims to help RL agents learn quicker and generalize better to rarely seen states by not completely depriving them of experiences which are ranked as less priority.
Near-real-time flood mapping of the Amur River basin from sentinel-2 MSI data using deep learning

In this paper, the authors propose an algorithm for automatic near-real-time flood mapping of the Amur River basin from Sentinel-2 MSI data using a U-net convolutional neural network adapted to the task. As a training set, we used Sentinel-2 Level-2A data and vector maps of river floods, created manually by specialists from the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. According to the training results, Precision was 94.91%, Recall - 90.76%, F1-measure - 92.79%. High accuracy estimates and fast operation speed make it possible to use the developed algorithm for automatic near-real-time flood mapping of the Amur River basin in complex monitoring problems.
Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations

Future Multi-Domain Operations (MDO) will require the coordination of hundreds—even thousands—of devices and component services. This will demand the capability to rapidly discover the distributed devices/services and combine them into different workflow configurations, thereby creating the applications necessary to support changing mission needs. Motivated by neuromorphic processing models, in previous work it was shown that this can be achieved by using hyperdimensional symbolic semantic vector representations of the services/devices and workflows. Using a process of vector exchange the required services are dynamically discovered and inter-connected to achieve the required tasks. In network edge environments, the capability to perform these tasks with minimum energy consumption is critical. This paper describes how emerging spiking neural network (SNN) neuromorphic processing devices can be used to perform the required hyperdimensional vector computation (HDC) with significant energy savings compared to what can be achieved using traditional CMOS implementations.
Various 3D printing materials with different nozzle design in biomedical area

Three-dimensional (3D) printing is now becoming a huge discipline and has innovated the manufacturing method in biomedical engineering over these years. By applying this 3D printing technique, more biomedical products (such as surgical implants, orthopedics products and anatomical models) can be produced more accurately, efficiently, and economically. In this study, we will simulate the 3D printing process of one sample of bone tissue and another sample of anatomical model in virtual environment. We found that the mechanical properties is still largely determined by the choice of biomaterials and will not change significantly through the 3D printing process. By comparing printing time, yield strength of the products and other mechanical properties, we found that the material is still the determining factor of the mechanical properties of the product while different nozzle design and multi-material combinations can also influence the biomedical 3D printing process. The nozzle diameter will determine the 3D printing speed and the smoothness of surface texture. By choosing the material combinations that have better chemical affinity, the biomedical 3D printing products have better mechanical properties and less likely to fail. By applying this study, we can develop methods to optimize biomedical 3D printing process through picking different materials and different nozzle design.
Self-supervised pre-training improves fundus image classification for diabetic retinopathy

This paper assesses the efficacy of self-supervised learning in the DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD). Recently, self-supervised learning has achieved great success in the field of Computer Vision. Particularly, self-supervised learning can effectively serve the field of medical imaging where a large amount of labeled data is usually limited. In this paper, we apply the Bootstrap Your Own Latent (BYOL) approach to grade diabetic retinopathy which scores the lowest among the MedMNIST dataset. With the pre-trained model using BYOL, we evaluate the efficacy of the BYOL approach on DeepDRiD following fine-tuning protocols. Further, we compare the performance of the model with the model from scratch and proved the effectiveness of BYOL in DeepDRiD. Our experiment shows that BYOL can boost the performance of grading diabetic retinopathy.
Virtual network resource allocation algorithm based on high reliability and intelligent offloading strategy under 5G network slicing

The 5G network slicing technology can greatly improve the network transmission efficiency of users and significantly reduce the transmission delay. Network Functions Virtualization (NFV) provides a new solution for more flexible network deployment and more efficient resource integration. The allocation of virtual resources is often realized through the deployment of Service Function Chain (SFC). This paper studies the problem of virtual network resource allocation under 5G network slicing. By designing an efficient SFC deployment model and VFN backup method, the virtual network resource allocation with high reliability and intelligent offload is realized.
Metrics of financial effectiveness: Return On Investment in XSEDE, a national cyberinfrastructure coordination and support organization

This paper explores the financial effectiveness of a national advanced computing support organization within the United States (US) called the eXtreme Science and Engineering Discovery Environment (XSEDE). XSEDE was funded by the National Science Foundation (NSF) in 2011 to manage delivery of advanced computing support to researchers in the US working on non-classified research. In this paper, we describe the methodologies employed to calculate the return on investment (ROI) for governmental expenditures on XSEDE and present a lower bound on the US government’s ROI for XSEDE from 2014 to 2020. For each year of the XSEDE project considered, XSEDE delivered measurable value to the US that exceeded the cost incurred by the Federal Government to fund XSEDE. That is, the US Federal Government’s ROI for XSEDE is at least 1 each year. Over the course of the study period, the ROI for XSEDE rose from 0.99 to 1.78. This increase was due partly to our ability to assign a value to more and more of XSEDE’s services over time and partly to the value of certain XSEDE services increasing over time. From 2014 to 2020, XSEDE offered an ROI of more than $1.5 in value for every $1.0 invested by the US Federal Government. Because our estimations were very conservative, this figure represents the lower bound of the value created by XSEDE. The most important part of ”returns” created by XSEDE are the actual outcomes it enables in terms of education, enabling new discoveries, and supporting the creation of new inventions that improve quality of life. In future work we will use newly developed accounting methodologies to begin assessing the value of the outcomes of XSEDE.
Multi-point acquisition function for constraint parallel efficient multi-objective optimization

Bayesian optimization is often used to optimize expensive black box optimization problems with long simulation times. Typically Bayesian optimization algorithms propose one solution per iteration. The downside of this strategy is the sub-optimal use of available computing power. To efficiently use the available computing power (or a number of licenses etc.) we introduce a multi-point acquisition function for parallel efficient multi-objective optimization algorithms. The multi-point acquisition function is based on the hypervolume contribution of multiple solutions simultaneously, leading to well spread solutions along the Pareto frontier. By combining this acquisition function with a constraint handling technique, multiple feasible solutions can be proposed and evaluated in parallel every iteration. The hypervolume and feasibility of the solutions can easily be estimated by using multiple cheap radial basis functions as surrogates with different configurations. The acquisition function can be used with different population sizes and even for one shot optimization. The strength and generalizability of the new acquisition function is demonstrated by optimizing a set of black box constraint multi-objective problem instances. The experiments show a huge time saving factor by using our novel multi-point acquisition function, while only marginally worsening the hypervolume after the same number of function evaluations.
Power Optimization and Performance Analysis of Underwater Optical Wireless Communication System with MIMO-NOMA under Weak Oceans Turbulence

In this paper, an underwater wireless optical MIMO-NOMA (multiple-input multiple-output, non-orthogonal multiple access) communication system with optimized power allocation is proposed, and the expressions of outage probability and sum rate of the system under the ocean weak turbulence channel are derived. In addition, the impacts of scintillation indexes for different turbulences on the outage probability and sum rate of the system are evaluated, and the relationship between the outage probability and the setting target rate or the number of receiving nodes analyzed. It is shown by our simulation that the proposed underwater wireless optical MIMO-NOMA communication system with progressive feedback power allocation method (PF-MIMO-NOMA) outperforms the existing MIMO-OFDM communication system and conventional MIMO-NOMA (C-MIMO-NOMA) communication system in terms of outage probability and sum rate performance of the system.
High Performance MPI over the Slingshot Interconnect: Early Experiences

The Slingshot interconnect designed by HPE/Cray is becoming more relevant in High-Performance Computing with its deployment on the upcoming exascale systems. In particular, it is the interconnect empowering the first exascale and highest-ranked supercomputer in the world, Frontier. It offers various features such as adaptive routing, congestion control, and isolated workloads. The deployment of newer interconnects raises questions about performance, scalability, and any potential bottlenecks as they are a critical element contributing to the scalability across nodes on these systems. In this paper, we will delve into the challenges the slingshot interconnect poses with current state-of-the-art MPI libraries. In particular, we look at the scalability performance when using slingshot across nodes. We present a comprehensive evaluation using various MPI and communication libraries including Cray MPICH, OpenMPI + UCX, RCCL, and MVAPICH2-GDR on GPUs on the Spock system, an early access cluster deployed with Slingshot and AMD MI100 GPUs, to emulate the Frontier system.
The (1 + (λ, λ)) global SEMO algorithm

The (1 + (λ, λ)) genetic algorithm is a recently proposed single-objective evolutionary algorithm with several interesting properties. We show that its main working principle, mutation with a high rate and crossover as repair mechanism, can be transported also to multi-objective evolutionary computation. We define the (1 + (λ, λ)) global SEMO algorithm, a variant of the classic global SEMO algorithm, and prove that it optimizes the OneMinMax benchmark asymptotically faster than the global SEMO. Following the single-objective example, we design a one-fifth rule inspired dynamic parameter setting (to the best of our knowledge for the first time in discrete multi-objective optimization) and prove that it further improves the runtime to O(n2), whereas the best runtime guarantee for the global SEMO is only O(n2 log n).
Institutional Value of a Nobel Prize

The Nobel Prize is awarded each year to individuals who have conferred the greatest benefit to humankind in Physics, Chemistry, Medicine, Economics, Literature, and Peace, and is considered by many to be the most prestigious recognition for one’s body of work. Receiving a Nobel prize confers a sense of financial independence and significant prestige, vaulting its recipients to global prominence. Apart from the prize money (approximately US$1,145,000), a Nobel laureate can expect to benefit in a number of ways, including increased success in securing grants, wider adoption and promulgation of one’s theories and ideas, increased professional and academic opportunities, and, in some cases, a measure of celebrity. A Nobel laureate’s affiliated institution, by extension, also greatly benefits. Because of this, many institutions seek to employ Nobel Prize winners or individuals who have a high likelihood of winning one in the future. Many of the recent discoveries and innovations recognized with a Nobel prize were made possible only because of advanced computing capabilities. Understanding the ways in which advanced research computing facilities and services are essential in enabling new and important discoveries cannot be overlooked in examining the value of a Nobel Prize. This paper explores an institution’s benefits of having a Nobel Prize winner among its ranks.
Understanding the cost of fitness evaluation for subset selection: Markov chain analysis of stochastic local search

With a focus on both the fitness and cost of subset selection, we study stochastic local search (SLS) heuristics in this paper. In particular, we consider subset selection problems where the cost of fitness function evaluation needs to be accounted for. Here, cost can be fitness evaluation's computation time or energy cost. We propose and study an SLS method, SLS4CFF, tailored to such problems. SLS4CFF ("SLS for costly fitness functions") is an amalgamation of several existing heuristics. We develop a homogeneous Markov chain model that explicitly represents both fitness and cost of subset selection with SLS4CFF. This Markov chain, which can be lumped or compressed for certain fitness and cost functions, enables us to better understand and analyze hyperparameter optimization in a principled manner, via expected hitting times. Studies with synthetic and real-world problems improve the understanding of SLS and demonstrate the importance of cost-awareness.