Miracle-dz/UniFAR-FTUs huggingface dataset:
Barite in north-central New Mexico

Barite is an industrial mineral with hundreds of commercial uses ranging from medicines to weighting materials, which is its most important use (Fields, 1983; Williams et al., 1,964). Barite (BaSOn), with a specific gravity up to 4.5 (commercial grade is 4.2), quently, the production of barite is heavily dependent upon the needs of the petroleum exploration industry. Several economically promising barite occurrences are in north-central New Mexico in the Placitas and Tijeras Canyon districts (Sandia Mountains), in the Manzano Mountains near Edgewood, and at El Cuervo (Crow) Butte in southern Santa Fe County (Fig. 1; Table 1). Barite also occurs as a trace or gangue mineral at other localities in north-cenhalNewMefco (Table 1). However, according to preliminary data, no significant barite deposits have been found in New Mexico north of the Sandia Mountains probablv because sulphates are absent in the iubsurfice. Barite defosits are common in central and southern New Mexico in or near the Rio Grande rift (Smith, 1982; Williams eI aI., 7964).
A Randomized Hyperparameter Tuning of Adaptive Moment Estimation Optimizer of Binary Tree-Structured LSTM

Adam (Adaptive Moment Estimation) is one of the promising techniques for parameter optimization of deep learning. Because Adam is an adaptive learning rate method and easier to use than Gradient Descent. In this paper, we propose a novel randomized search method for Adam with randomizing parameters of beta1 and beta2. Random noise generated by normal distribution is added to the parameters of beta1 and beta2 every step of updating function is called. In the experiment, we have implemented binary tree-structured LSTM and adam optimizer function. It turned out that in the best case, randomized hyperparameter tuning with beta1 ranging from 0.88 to 0.92 and beta2 ranging from 0.9980 to 0.9999 is 3.81 times faster than the fixed parameter with beta1 = 0.999 and beta2 = 0.9. Our method is optimization algorithm independent and therefore performs well in using other algorithms such as NAG, AdaGrad, and RMSProp. Keywords—Adaptive moment estimation; gradient descent; treestructured LSTM; hyperparameter tuning
Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now with the advent of wearable, mobile, and ubiquitous technologies that we can aim at sensing and recognizing emotions, continuously and in the wild. This Special Issue aims at bringing together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and recognition of human emotions.
Big Metadata : When Metadata is Big Data

The rapid emergence of cloud data warehouses like Google Big-Query has redefined the landscape of data analytics. With the growth of data volumes, such systems need to scale to hundreds of EiB of data in the near future. This growth is accompanied by an increase in the number of objects stored and the amount of metadata such systems must manage. Traditionally, Big Data systems have tried to reduce the amount of metadata in order to scale the system, often compromising query performance. In Google BigQuery, we built a metadata management system that demonstrates that massive scale can be achieved without such tradeoffs. We recognized the benefits that fine grained metadata provides for query processing and we built a metadata system to manage it effectively. We use the same distributed query processing and data management techniques that we use for managing data to handle Big metadata. Today, BigQuery uses these techniques to support queries over billions of objects and their metadata.
EPICGen: An Experimental Platform for Indoor Congestion Generation and Forecasting

Effectively and accurately forecasting the congestion in indoor spaces has become particularly important during the pandemic in order to reduce the risk of exposure to airborne viruses. However, there is a lack of readily available indoor congestion data to train such models. Therefore, in this demo paper we propose EPICGen , an experimental platform for indoor congestion generation to support congestion forecasting in indoor spaces. EPICGen consists of two components: (i) Grid Overlayer , which models the floor plans of buildings; and (ii) Congestion Generator , a realistic indoor congestion generator. We demonstrate EPICGen through an intuitive map-based user interface that enables end-users to customize the parameters of the system and visualize generated datasets
Successful Treatment of Metastatic Gastric Carcinoma Deficient in Mismatch Repair Proteins with Immune Checkpoint Blockade: A Case Report and Review of the Literature

Treatment of locally advanced and metastatic gastric cancer has continued to be challenging and overall prognosis from this disease has remained poor with conventional chemotherapy. Through prospective and retrospective investigations, gastric carcinomas deficient in mismatch repair proteins have proven to be responsive to immune checkpoint blockade in the metastatic setting. We present a case of a patient with diffuse peritoneal metastatic gastric carcinoma and deficient in mismatch repair proteins who is effectively treated with single agent immune checkpoint blockade. This case is of interest as we have interval biopsy of disease demonstrating a robust inflammatory response within the tumor stroma and over 17 months of response and dramatic improvement in performance status while on immunotherapy. While this may only be applicable to a small subgroup of patients it represents a therapy with significant improvement in anti-tumor activity compared to conventional chemotherapy. This also highlights the importance of checking clinically meaningful biomarkers as well as the need to identify science-based tumor and patient specific markers that will allow us to better select treatment for each patient in the future. Anthony Dominick1* and Weijing Sun2 1Department of Hematology and Oncology, University of Kansas Medical Center, USA 2Department Medical Oncology, University of Kansas School of Medicine, USA
Extracting ConceptNet Knowledge Triplets from Japanese Wikipedia

This paper presents a method of acquiring IsA assertions (hyponymy relations) AtLocation assertions (informing of location of objects) and LocatedNear assertions (informing of neighboring locations) automatically from Japanese Wikipedia XML dump files. To extract IsA assertions, we use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles. The tool also produces information-rich taxonomy from which, using our original method, we can extract additional information, in this case AtLocation and LocatedNear type of assertions. Experiments showed that both methods produce positive results: we were able to acquire 5,866,680 IsA assertions with 99.0% reliability, 131,760 AtLocation assertion pairs with 93.0% reliability and 6,217 LocatedNear assertion pairs with 99.0% reliability. Our method exceeded the baseline sys-tem considering both precision and the number of acquired assertions.
Systematic Design of Multimode Antennas for MIMO Applications by Leveraging Symmetry

A systematic procedure for constructing orthogonal antenna ports on arbitrary symmetric multimode antennas is proposed. The procedure is based on the projection operator method from group theory. With this method, optimal orthogonal port configurations are generated analytically. Electromagnetic simulations, modal analyses, or optimization techniques are not required. The optimal port configurations are solely governed by the symmetry of an antenna and can thus be considered as a priori knowledge. Accordingly, this knowledge is available right from the beginning of the design process and can be purposefully used to compare different suitable antenna geometries with respect to the practical implementation effort. The procedure is illustrated by means of an application example resulting in a complete prototype of a six-port multimode antenna.
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain

Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L’Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available temporal taggers are trained only on substantially different domains, such as news and clinical text. Since the cost of annotating large datasets is prohibitive, we investigate the trade-off between in-domain data and performance in DA-Time, a hybrid temporal tagger for the English VA domain which combines a neural architecture for robust TE recognition, with a parser-based TE normalizer. We find that transfer learning goes a long way even with as little as 25 in-domain sentences: DA-Time performs at the state of the art on the news domain, and substantially outperforms it on the VA domain.
Down in arms: Marine climate stress inhibits growth and calcification of regenerating <i>Asterias forbesi</i> (Echinodermata: <i>Asteroidea</i>) arms

Anthropogenic CO2 is changing the pCO2, temperature, and carbonate chemistry of seawater. These processes are termed ocean acidification (OA) and ocean warming. Previous studies suggest two opposing hypotheses for the way in which marine climate stress will influence echinoderm calcification, metabolic efficiency, and reproduction: either an additive or synergistic effect. Sea stars have a regenerative capacity, which may be particularly affected while rebuilding calcium carbonate arm structures, leading to changes in arm growth and calcification. In this study, Asterias forbesi were exposed to ocean water of either ambient, high temperature, high pCO2, or high temperature and high pCO2 for 60 days, and the regeneration length of the amputated arm was measured weekly. Ocean acidification conditions (pCO2 ~1180 μatm) had a negative impact on regenerated arm length, and an increase in temperature of +4°C above ambient conditions (Fall, Southern Gulf of Maine) had a positive effect on regenerated arm length, but the additive effects of these two factors resulted in smaller regenerated arms compared to ambient conditions. Sea stars regenerating under high pCO2 exhibited a lower proportion of calcified mass, which could be the result of a more energetically demanding calcification process associated with marine climate stress. These results indicate that A. forbesi calcification is sensitive to increasing pCO2, and that climate change will have an overall net negative effect on sea star arm regeneration. Such effects could translate into lower predation rates by a key consumer in the temperate rocky intertidal of North America.
CARDIOVASCULAR DISEASES ASSOCIATED WITH INFLUENZA INFECTION: SYSTEMATIC REVIEW AND META-ANALYSIS

Background: Cardiovascular diseases represent important diagnoses that can become fatal if an early and adequate management is not carried out. Since 1930, a possible relationship between these events and influenza virus infection has been proposed. Objectives: To determine the association between cardiovascular diseases and previous infection by influenza virus. Materials and methods: A systematic review was carried out according to PRISMA. The electronic search was carried out in the databases of EMBASE, PubMed, Global Index Medicus, Google Scholar and Cochrane Library. The included studies had laboratory-confirmed influenza in patients over 18 years of age; studies that considered pregnant patients or animals were excluded. The quality of the studies was verified using the standardized tool of the National Heart Lung and Blood Institute and the certainty of the results was assessed with GRADE. In addition, 2 meta-analysis of the association measures were performed using the Cochrane Software Review Manager 5.4. 1. Results: 31 records were identified, of which 4 were included (n = 1101). These studies were conducted in Finland, China, and Australia. The estimated combined OR for the positive association between influenza A and acute myocardial infarction obtained was 2.52 (95% CI 1.59 - 4); and in the association with influenza B, an OR of 4.78 ( 95% CI 1.57-14.61). The OR obtained for the association with myocarditis was 5.23 (95% CI 1.00 - 27.32). Conclusions: Evidence of almost 5 times the risk of suffering acute myocardial infarction was found in those who had a previous episode of influenza B, and almost 3 with influenza A. Better quality longitudinal studies are required to confirm this finding.
Mineral paragenesis, structure, and "ore shoot" geometry at the U.S. Treasury mine, Chloride mining district, New Mexico

f\9 U.S. Treasury mine is located in the southeastern portion of the Chloride min_ing district, Sierra County, New Mexico (Fig. 1). A se-gment of the St. Cloud-U.S. Treasury epithermal vein s stem, which contains copper, lead, zinc, silver, and gold mineralization, was exploited by mining activity. The entire vein system is approximately 7,700 ft in strike length. Near the U.S. Treasury mine, the vein generally trends N45'W, dips 65'SW, and is hosted by debrisflow breccias and other volcaniclastic deposits of the Eoiene-OliB! ene, lower Rubio Peak Formation. A K-Ar radiometric age date of 26.5 -r 1.1 m.y. (late Oligocene) was obtained from vein adularia in the St. Cloud portion of the vein system (Mike Bauman, First Mississippi Corp., pers. comm. 1984). The U.S. Treasury deposit was discovered and initially worked in the 1880's and early 1890's; additional development and mining were done just before World War L Production from this early aitivity was estimated at 920,000 by Harley (1934). In the early iSAO's Si. C_loud,Mining Company (Goldfield Co.p. ) produced approximately 235,000 ounces of silver and 4,000 ounces of gold from about 60,000 tons of ore and development rock.
On Eulerian-Eulerian Large Eddy Simulation of Polydispersed, Reacting Spray Flows with Moment Methods

The present work contributes to the challenge of finding and establishing an appropriate approach for the Large Eddy Simulation of polydisperse spray flows with and without chemical reaction. First, a stochastic approach is adopted which describes the particle population with the help of a number density function (NDF) spanned over the particle phase space including particle size, velocity and temperature. It is gained from a theoretical infinite number of particle phase realisations for a given flow realisation of the gas phase. The transport equation of the NDF is then spatially filtered due to the numerical method applied and subsequently integrated over the particle phase space. For closure of the unknown terms, which are related to the dynamics of the particles itself but also their interaction with the gas phase, a moment method is applied, which presumes a certain type of the shape of the NDF. The temporal and spatial development of the NDF is calculated by transporting up to four moments of the NDF, each with its respective moment transport velocity. The latter are determined from a relaxation time approach. An adapted Thickened-Flame Model in conjunction with a simple Arrhenius mechanism is used to model the combustion. Additionally a method is proposed for single droplet combustion, which is adapted to be applicable in the framework of the moment model. The numerical simulations are realised with OpenFOAM. A swirling flow configuration and a particle-laden jet have been investigated. Results obtained from the polydisperse approach show improved spray dynamics and evaporation rates compared to those resulting from a monodisperse description. The applicability of the combustion model is verified, however, the simplicity of the model demonstrates the need for more sophisticated approaches and further extensive research in this field. The same is true for the particle-turbulence interaction.
Effects of Cumin/Maize Intercropping on Crop Productivity, Interspecific Interaction and Water Use Efficiency in the Semi-arid Area of Northwest China

In the semi-arid areas of Xinjiang in China, the agricultural community is facing long-term challenges in improving crop productivity and economic profits. A potential way to improve crop productivity is by intercropping food crops with cash crops. In this two-year field experimental study, we analyzed the advantages of the cumin/maize intercropping system as compared to cultivating them separately from three perspectives: crop growth, interspecific interactions, and water use efficiency. At the experimental site, each cropping systems (i.e., the cumin/maize intercropping system, monocropping cumin system, and monocropping maize system) had three replicates. In the experimental of 2019 and 2020, the three cropping systems showed significant differences in plant height, stem diameter, and leaf area index (LAI). The yields of the cumin/maize intercropping system were both lower than monocropping maize and cumin. However, the cumin/maize intercropping system had a higher land equivalent ratio (LER) than either of the monocropping systems, with a two-year average value of 1.65. Intercropped cumin was less dominant; as shown by aggressivity (Acm; averaging—0.22) and relative crowding coefficient (Rcm; averaging—3.15). In addition, the cumin/maize intercropping system significantly improved the Water equivalent ratio (WER) as compared to the monocropping systems, with a two-year average value of 1.67. Therefore, the cumin/maize intercropping system is a promising agricultural strategy to improve the utilization rate of agricultural resources and economic benefits in Xinjiang.
Performance management – a strategic and integrated approach to ensuring the success of organizations

The performance of an organization is determined by: its level of coherence and stability; its ability to procure and use resources; of his reputation and image; the synergy of multiplying determinants such as employee relations, communication, mission, philosophy and leadership style, recognition, quality of organizational processes. Performance is defined by behaviors, but especially by results. A mixed performance management model is concerned with both inputs and outputs. The selection of the best model to improve performance is based on a self-assessment process. The complexity of managing an organization today requires that managers be able to measure performance and analyze the impact of different dimensions of performance on organizational excellence. Many experts in the field have started talking about the importance of measurement as a means of communication. The institution’s performance is analyzed on the basis of key internal and external performance indicators, established targets and existing trends, judged constructively, with the establishment of improvement targets. Any performance measurement indicator is also used as a tool to control it, effectively for the implementation of continuous performance improvement actions.
Analysis on Network Traffic Features for Designing Machine Learning based IDS

An intrusion detection system (IDS) is the most important technology for securing network systems. It can dynamically monitor network traffic for malicious activities that are aimed to violate confidentiality, integrity, authenticity, and availability of the network. Currently, several Machine Learning (ML) techniques are used to design and implement IDS since ML techniques can capture the complex nature of cyberattacks. However, network traffic information usually contains unimportant features that can deteriorate the efficacy of ML-based IDS. This research analyses the critical features in network traffic to be used for design/implementing the effective ML-based IDS. The selected features are applied to different ML methods to test the effectiveness. This research is conducted on the CICIDS2017 dataset generated by the Canadian Institute of Cybersecurity, using 30 percent of the full datasets and 100 percent of the Wednesday set. The best result achieved for 30 percent of the full set is by using 30 chosen features with the Bagging ensemble classifier giving the accuracy of 99.9 percent with the false-positive rate as low as 0.03 percent. The best result achieved for Wednesday set is by using the Random Forest Classifier which achieves an accuracy of 99.9 percent and a false-positive rate (FPR) of 0.02 percent.
Estimation of forest lands value by David Ricardo differential rent theory, linear programming model and GIS technologies

Integrated research approach joining the David Ricardo theory of differential rent, linear programming theory and GIS technology used for developing the methodology for differential rent determination and calculations for forest lands in the way similar as to agricultural ones. Special linear programming model was developed and resolved to obtain the rent estimations on the base of information offered by GIS data base on forest plots productivity and location regarding transportation network characteristics. Implementations of forest rent estimations for forest lands value assessments, forest management and economics was discussed. Rent values was calculated as an example for Lisino training and experimental forest of Saint-Petersburg State Forest Technical University. As a result of implementation of rental approach for forest resources assessments the value of forest lands significantly elevates as much as approximately on 5 times. Forest rent as an extra income originated from forest plots natural characteristics suggested to be used as a main source of so-called “long-money” needed for investments in forestry especially in boreal zone with long rotation period.
Simple production of medium density fiberboards (MDF) reinforced with chitosan

Abstract A simple and efficient method was developed for preparing medium density fiberboard (MDF) reinforced with chitosan via the traditional hot-press manufacturing process. The mechanical and dimensional properties of the MDF were investigated as a function of the chitosan amount. At the 4% level of added chitosan, the MDF reached the optimal performance and met completely the requirements of the Chinese national standard GB/T 11718-2009. Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS) and X-ray powder diffraction (XRD) characterizations revealed that wood fibers and chitosan might interact with each other through the formation of hydrogen and amide bonds during the hot-pressing process. The fracture surfaces of the MDFs are indicative for strong bonds at the interface, which explain the excellent MDF performance.