Miracle-dz/UniFAR-FTUs huggingface dataset:
Perturbed Microbial Ecology in Myasthenia Gravis: Evidence from the Gut Microbiome and Fecal Metabolome

Myasthenia gravis (MG) is a devastating acquired autoimmune disease. Emerging evidence indicates that the gut microbiome plays a key role in maintaining immune system homeostasis. This work reports that MG is characterized by decreased α‐phylogenetic diversity, and significantly disturbed gut microbiome and fecal metabolome. The altered gut microbial composition is associated with fecal metabolome changes, with 38.75% of altered bacterial operational taxonomic units showing significant correlations with a range of metabolite biomarkers. Some microbes are particularly linked with MG severity. Moreover, a combination of microbial makers and their correlated metabolites enable discriminating MG from healthy controls (HCs) with 100% accuracy. To investigate whether disturbed gut mcirobiome might contribute to the onset of MG, germ‐free (GF) mice are initially colonized with MG microbiota (MMb) or healthy microbiota (HMb), and then immunized in a classic mouse model of MG. The MMb mice demonstrate substantially impaired locomotion ability compared with the HMb mice. This effect could be reversed by cocolonizing GF mice with both MMb and HMb. The MMb mice also exhibit similar disturbances of fecal metabolic pathways as found in MG. Together these data demonstrate disturbances in microbiome composition and activity that are likely to be relevant to the pathogenesis of MG.
Accurate and Robust Localization Techniques for Wireless Sensor Networks

The report focuses on three areas in particular: the first is the Received Signal Strength indicator technique, Direction of Arrival technique, and the integration of two algorithms, RSS and DOA, in order to build a hybrid, more robust algorithms. In the Received Signal Strength (RSS), the unknown node location is estimated using trilateration. This report examines the performance of different estimators such as Least Square, Weighted Least Square, and Huber robustness in order to obtain the most robust performance. In the direction of arrival (DOA) method, the estimation is carried out using Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimation of Signal Parameters Via Rotational Invariance Technique (ESPRIT) algorithms. We investigate multiple signal scenarios utilizing various antenna geometries, which includes uniform linear array (ULA) and uniform circular array (UCA). Specific attention is given for multipath scenarios in which signals become spatially correlated (or coherent). This required the use of pre-processing techniques, which include phase mode excitation (PME), spatial smoothing (SS), and Toeplitz. Further improvements of existing localization techniques are demonstrated through the use of a hybrid approach in which various combinations of RSS and DOA are explored, simulated, and analyzed. This has led to two major contributions: the first contribution is a combined RSS/DOA method, based on UCA, which has the tolerance of detecting both uncorrelated and coherent signals simultaneously. The second major contribution is a combined Root-MUSIC/Toepltiz method, based on UCA, which is outperforms other techniques in terms of increased number of detected signals and reduced computationally load.
Effect of Electrical Resistance of TiO2 Layer Characteristics of Dye Sensitized Solar Cell Using Chlorophyll as a Sensitizer

The third generation of solar cells found by Gratzel in 1991 was called Dye-sensitized Solar Sell (DSSC). DSSC is composed of five parts, namely transparent conductive glass as a substrat; titanium dioxide as a semiconductor materials; chlorophyll as a dye sensitizer, carbon as a catalyst which can convert energy of light into electrical energy. Characterization of DSSC will be carried out in this research covering electrical characterization using RLC meter & I-V meter, optical characterization using Spectrophotometer. TiO2 nanoparticle in in anatase phase having size of 13 nm used in this experiment was synthesized by co-precipitation method. Photoanode with thickness of 10 μm has the lowest resistance compared to the other thickness of 30 μm and 50 μm. The device applying photoanode of 10 μm shows the highest efficiency up to 0.1141 %, while those with other thicknesses respectively gives 0.0378%, and 0,0283%, revealing the efficiency becomes lower with the increasing thickness of TiO2.
Design of a Modified Charcoal Production Kiln for Thermal Therapy and Evaluation of the Charcoal Characteristics from this Kiln

A modified charcoal kiln was developed for both thermal therapy and charcoal production. The design of a modified kiln for thermal therapy focused on safety and cleanliness, plus the production of good quality charcoal. Two entrances in the kiln were designed for convenient charcoal production and thermal therapy. A barrier wall designed for noxious gases was installed between the adjoining charcoal kilns for safe thermal therapy. Additionally, a fine dust collector was installed to remove the fine dust generated during charcoal production. To verify the safety of the kiln, fine dust and harmful gases, such as carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), and radon (Rn), were measured after the charring process. The quality of the charcoal produced in the thermal therapy kiln was also examined. To evaluate charcoal quality, some physical properties and results from a proximate analysis were evaluated using Korean standards. The measurements of harmful substances and fine dust in the modified charcoal kiln met the criterion of the Ministry of Environment Clean Air Conservation Act in Korea. In addition, there were no noticeable differences in the monthly charcoal characteristics prepared from the thermal therapy kiln, and the charcoal characteristics also met the reference values of the Korea Forest Service.
Three‐Dimensional S Velocity Structure of the Mantle Transition Zone Beneath Central America and the Gulf of Mexico Inferred Using Waveform Inversion

Travel time tomography studies have reported various modes of subduction of slabs around the mantle transition zone, but the causes of this variability are still controversial. In order to place additional constraints on the style of subduction in and near the mantle transition zone, we apply waveform inversion to S wave triplications due to the 410‐ and 660‐km discontinuities, and infer the regional three‐dimensional S velocity structure in the depth range 370–820 km beneath Central America and the Gulf of Mexico. We use ~3,400 transverse component records at epicentral distances 17–30° from 20 intermediate focus earthquakes beneath Central America recorded at stations of the USArray and other smaller networks. We filter the records between 20 and 100 s, and use the portions of the waveforms from 10 s before to ~40–80 s after the arrival of the direct S wave, thereby including the S wave triplications while excluding the sS depth phase. We correct the data for the structure above our target region using previous S velocity models, and we show that the results of the inversion do not depend heavily on which correction is used. Resolution tests confirm that we can resolve small‐scale structure in the target region. Our inferred models show that the Cocos slab penetrates into the lower mantle, but with important variations along the Middle America Trench in the subduction style of the slab. We suggest that this variability could be due to the thermal structure of the Cocos slab, and to the interaction of the slab with a possible lower mantle plume.
Exploring the hybrid effects of short glass/basalt fibers on the mechanical, thermal and gamma-radiation shielding properties of DCBA/BA-a resin composites

ABSTRACT As the huge demands for advanced engineering materials with specific properties, this study aimed to fabricate novel lightweight materials possessing ameliorated characteristics. The novel hybrid composites were prepared by reinforcing both the bisphenol-A based dicyanate ester and benzoxazine (DCBA/BA-a) resin blends using silane-treated short glass/basalt hybrid fibers through a compression molding technique, and then experimentally characterised. Results from the mechanical and thermal analyses revealed that the mechanical and thermal parameters were highly improved upon increasing the reinforcing phases’ amount. The shielding evaluations disclosed that the integration of hybrid fibers helped the DCBA/BA-a blend to possess excellent nuclear shielding properties. Graphical-Abstract
A Novel Realistic Approach of Adaptive Beamforming Based on Deep Neural Networks

A new deep neural network (NN) approach applied to antenna array adaptive beamforming is presented in this article. A recurrent NN (RNN) based on the gated recurrent unit (GRU) architecture is used as a beamformer in order to produce proper complex weights for the feeding of the antenna array. The proposed RNN utilizes four hidden GRU layers and one extra layer for linear transformation. The produced weights are subsequently compared with respective weights derived by a null steering beamforming (NSB) technique in order to measure the accuracy of the RNN. The RNN training is performed by using a large dataset derived from an NSB technique applied to a realistic microstrip linear antenna array, in order to consider real-world effects, such as the nonisotropic radiation pattern of an array element and the mutual coupling between the array elements. The RNN performance is examined by using the root-mean-square error metric, whereas its beamforming performance is evaluated by estimating the mean value and the standard deviation of the divergences of the main lobe and nulls directions from their respective desired directions. A comparison between various NN structures and an overall study of the proposed RNN-based beamformer are also presented.
Corrosion behaviour of copper - nickel alloys in LiBr solutions: A comparative study

THE ANODIC potentiodynamic cyclic polarization behavior of Cu/10 Ni and Cu/30 Ni alloys in different wide range of LiBr concentrations from 10-1 to 9 M was investigated. The results obtained showed that the breakdown potential, hysteresis loop area and passive current were recorded at higher value of potentials in case of Cu/10 Ni in comparison with Cu/30 Ni. The current density showed higher values during the first few moment of polarization of both alloys studied as result of the changes the taking place on the surface. At 5x10-1 M of LiBr, in case of Cu/10 Ni, two breakdown potentials yielded two hysteresis loops, one of them was at 700 mV and the second was at more anodic potential. These are not recorded in case of Cu/30 Ni where pitting corrosion was manifested at 2 and 4 M LiBr only. The current – time measurement and surface examination of the electrode in case of Cu/30 Ni at 3x10-1 and 5x10-1M LiBr showed that the film formed at low anodic potential of 150 mV clarify the formation of small number of initiated pits which are surrounded by a passive and protective film of doped Cu2O and the surface shown enrichment of Ni. While at all the potential studied for the Cu/10 Ni and Cu/30 Ni the surface of the alloy suffered from denickelification.
Understanding and predicting nitrogen dynamics in the Midwest United States Corn Belt

Understanding nitrogen (N) dynamics in agricultural systems is very critical for profitable corn production while minimizing the N losses from agroecosystems which cause environmental degradation and increase cost of production. The nitrogen cycle in agricultural systems is complex; it includes interactions among many pools and fluxes. These pools and fluxes are controlled by climate, soil properties and crop management. The overall objective of this research is to better understand the impacts of crop management practices on cropping systems N dynamics. Potentially mineralizable N (PMN), measures the release of plant-available N from soil organic matter, is controlled by the soil properties, climate and past crop management practices. However, the effect size and relationship with the crop yield across different conservation and conventional crop management practices remain uncertain. Using a metaanalysis approach, chapter two examined the effects of various conservation and conventional crop management practices on PMN in soil. This quantitative review suggests that, as compared with no fertilizer, cropping systems with inorganic N fertilizer had 22%, and systems with manure had 34% higher PMN. Three or more different crops in rotation had 44% higher PMN than continuous cropping systems. Cropping systems with leguminous cover crops had 211% higher PMN than systems without cover crops. Compared with till systems, no-till systems had 13% higher PMN. Although few studies reported PMN and crop yield, in those that did report both variables, conservation crop management practices consistently increased both PMN and yield. Consistent with the use of PMN as a soil health indicator, this study suggests that practices benefiting PMN also benefit yield.
Parametric Design of Resilient Complex Networked Systems

Engineered systems are becoming increasingly complex and interconnected, requiring consideration of their resilience to potential network threats. We therefore explore a parametrically defined network design space to further understand properties and design choices that promote resilience in complex networked systems. This design space focuses on three aspects of a network: its initial topology, adaptation strategy, and threat faced. We assess network resilience using complex network properties and a resilience engineering approach. The proposed methods are applied to an agent-based information exchange network simulation, developed to represent a general class of networked systems relying on message passing among nodes. We find that resilient networks tend to have more random, rather than scale-free initial topologies, regardless of the threat faced. However, the optimal adaptation strategy shows a sharp transition from fully random to fully degree-based once a critical point is reached for threat randomness. These results demonstrate the value of thoroughly exploring the network design space, expanding upon insights gained from existing point design analyses. This work further confirms the importance of understanding fundamental behaviors of complex networked systems.
The role of local communities in sustainable tourism development - Noteć River Valley case study

Artykul ma na celu ukazanie zakresu zaangazowania spoleczności lokalnych i lokalnych przedsiebiorstw w rozwoj uslug ekosystemu kulturowego na obszarach wiejskich, w szczegolności w odniesieniu do zrownowazonej turystyki. Autorzy omawiają, czy, w jaki sposob i dlaczego, spoleczności lokalne tworzą produkty i infrastrukture ekoturystyczną na obszarach, na ktorych zasoby przyrodnicze i kulturowe podlegają ścislej kontroli. Niniejsza praca opiera sie na badaniach terenowych prowadzonych w cennym przyrodniczo rejonie doliny Noteci, bedącej cześcią Miedzynarodowej Drogi Wodnej E70. Przeprowadzono 6 ukierunkowanych wywiadow z pracownikami wskazanych organizacji oraz z mieszkancami zaangazowanymi w tworzenie oferty turystyki zrownowazonej i ekoturystyki. Wyniki wskazują, ze spolecznośc lokalna coraz cześciej zdaje sobie sprawe z korzyści związanych z funkcjonowaniem na obszarach chronionych i wykorzystuje lokalne zasoby naturalne i kulturowe w celu rozwoju na swoim obszarze zrownowazonej turystyki, w tym ekoturystyki. Staje sie to dodatkowym, a dla niektorych glownym źrodlem dochodu. Jednocześnie chronione jest lokalne dziedzictwo kulturowe, bedące priorytetowym miernikiem Unii Europejskiej. Na szerszą skale takie dzialania mają szanse rozwinąc sie w dolinie Noteci, przy specjalistycznym i finansowym wsparciu, a takze, przy wzroście wiedzy mieszkancow na temat idei ekoturystyki oraz ich świadomym zaangazowaniu w dzialania na rzecz jej rozwoju.
Depression Detection on Social Media With User Network and Engagement Features Using Machine Learning Methods

Depression is a complicated mental health disorder with many different forms and symptoms. Traditional methods face barriers when detecting and diagnosing depression, including social stigma and societal labeling. As social media platforms became commonplace for information sharing, their anonymity meant that the barriers had considerably lessened. An alternative method to depression detection being researched is using social media data to build machine learning models for depression detection. To that end, this research uses machine learning models to incorporate new user networks and user engagement features into depression detection on Twitter users. These two features provide an additional understanding of users and may significantly affect depression detection. A Twitter dataset is constructed to include additional data on users' following list and the history of liked tweets not examined in prior studies. Ten machine learning models are constructed using five different machine learning algorithms tested on two sets of features. Models with proposed features outperformed other machine learning models without proposed features, with the best model yielding 82.05% performance for both accuracy and F1 score. This study discovered that the most important feature is the number of depression keywords in liked tweets, with at least twice the gain compared to 88% of other features used. Topic modelling features for liked tweets also have high gain and are important in detecting depression. Additionally, features derived from original tweets, replies, and liked tweets have higher gain and are more important than retweets and quote tweets in detecting depression.
Multiplicity vs. Composition Study to Understand the Field Evaporation of Polar AlxGa1−xN Heterostructures: A New Approach

Polar AlGaN/GaN heterostructures have proven to be a potential material system in the advancement of high-voltage, high-power, and high-temperature microwave electron device technology [1]. Obtaining compositional and spatial data from these complex heterostructure systems requires an advanced analysis method; such as Atom Probe Tomography (APT). However, there are critical challenges to analyze them in APT; such as trajectory aberration and multi-hit events due to the dissimilar field evaporation. As a result, chemical quantification and determination of interfacial nature become uncertain. Hence a sincere effort to understand the field evaporation mechanism and how it is impacted by heterostructure chemistry is essential. Our aim is to design a protocol for measuring multiplicity and its correlation with the composition to understand the field evaporation behavior across polar heterostructure interfaces. We propose that tracking the occurrence and count of multi-hit events during atom probe analysis can elucidate interfacial chemistry characteristics related to field evaporation. This work will aid in the understanding of how the polarity of the crystals plays a role in the field evaporation.
Prescriptive Training Courseware: IS-Design Methodology

Information systems (IS) research is found in many diverse communities. This paper explores the human-dimension of human-computer interaction (HCI) to present IS-design practice in the light of courseware development. Assumptions are made that online courseware provides the perfect solution for maintaining a knowledgeable, well skilled workforce. However, empirical investigations into the effectiveness of information technology (IT)-induced training solutions are scarce. Contemporary research concentrates on information communications technology (ICT) training tools without considering their effectiveness. This paper offers a prescriptive IS-design methodology for managing the requirements for efficient and effective courseware development. To develop the methodology, we examined the main instructional design (ID) factors that affect the design of IT-induced training programs. We also examined the tension between maintaining a well-skilled workforce and effective instructional systems design (ISD) practice by probing the current ID models used by courseware developers since 1990. An empirical research project, which utilized this IS-design methodology investigated the effectiveness of using IT to train government employees in introductory ethics; this was a study that operationalized the interactive effect of cognitive preference and instructional format on training performance outcomes. The data was analysed using Rasch item response theory (IRT) that models the discrimination of people’s performance relative to each other’s performance and the test-items’ difficulty relative to each test-item on the same logit scale. The findings revealed that IS training solutions developed using this IS-design methodology can be adapted to provide trainees with their preferred instructional mode and facilitate cost effective eTraining outcomes. Supplementary files : Please note this article has supplementary files accessible via the Article Tools menu to the right.
DeepFlexiHLS: Deep Neural Network Flexible High-Level Synthesis Directive Generator

Deep Neural Networks (DNNs) are now widely adopted to solve various problems ranging from speech recognition to image classification. Since DNNs demand a large amount of processing power, their implementation on hardware, i.e., FPGA or ASIC, has received much attention. High-level synthesis is widely used since it significantly boosts productivity and flexibility and requires minimal hardware knowledge. However, when HLS transforms a C implementation to a Register-Transfer Level one, the high parallelism capability of the FPGA is not well-utilized. HLS tools provide a feature called directives through which designers can guide the tool using some defined C pragma statements to improve performance. Nevertheless, finding appropriate directives is another challenge, which needs considerable expertise and experience. This paper proposes DeepFlexiHLS, a two-stage design space exploration flow to find a set of directives to achieve minimal latency. In the first stage, a partition-based method is used to find the directives corresponding to each partition. Aggregating all these directives leads to minimal latency. Experimental results show 54% more speed-up than similar work on VGG neural network. In the second stage, an estimator is implemented to find the latency and resource utilization of various combinations of the found directives. The results form a Pareto-frontier from which the designer can choose if FPGA resources are limited or are not to be entirely used by the DNN module.
Enhanced Tropical Cyclone Intensity in the Western North Pacific During Warm Periods Over the Last Two Millennia

It is assumed that the potential intensity of tropical cyclones (TC) will increase with rising global temperature. The western North Pacific is one of the three principal TC centers, but TC records from the region are scarce and sometimes controversial. Here we present grain‐size distributions and element contents of sediment cores from the East China Sea, in the western North Pacific. We interpret changes in the mean grain size of the coarse fraction as a proxy for TC intensity, and we infer a linkage of TC intensity to temperature changes over the last two millennia. Supported by model simulations, our results show that TC intensity increased (decreased) during relatively warm (cool) periods, confirming the control of temperature on TC intensity on a multicentennial scale. Our results suggest that long‐term TC intensity in the western North Pacific may increase with continued global warming.