Miracle-dz/UniFAR-FTUs huggingface dataset:
Grazer‐induced aggregation in diatoms

Sinking is part of diatom life history. Typically, by the end of a bloom, diatoms form aggregates that sink rapidly to the bottom. This opportunistic lifestyle can be considered an adaptation to reduce predation losses, because predation pressure in the sediment is less than that in the water column. In this study, we demonstrate that two out of six examined diatoms become sticky and coagulate into aggregates when exposed to chemical copepod cues. Cell stickiness increases proportionally to the concentration of copepod cues and duration of exposure until saturation. Surprisingly, nutrient limitation (Si and N) did not increase stickiness. Furthermore, we demonstrate a chain length reduction of Skeletonema marinoi, when exposed to copepod cues. We argue that the increase in stickiness is adaptive when grazing mortality exceeds growth rate. This ensures that the maximum number of cells survive in the sediment, ready to utilize the next window of growth opportunity.
High Cerebrospinal Fluid CX3CL1 Levels in Alzheimer’s Disease Patients but Not in Non-Alzheimer’s Disease Dementia

Alzheimer’s disease (AD) is the most common form of cognitive decline worldwide, occurring in about 10% of people older than 65 years. The well-known hallmarks of AD are extracellular aggregates of amyloid β (Aβ) and intracellular neurofibrillary tangles (NFTs) of tau protein. The evidence that Aβ overproduction leads to AD has paved the way for the AD pathogenesis amyloid cascade hypothesis, which proposes that the neuronal damage is sustained by Aβ overproduction. Consistently, AD cerebrospinal fluid (CSF) biomarkers used in clinical practice, including Aβ 1–42, Aβ 1–40, Aβ 42/40 ratio, and pTau, are related to the amyloid hypothesis. Recently, it was suggested that the Aβ deposition cascade cannot fully disclose AD pathogenesis, with other putative players being involved in the pathophysiology of the disease. Among all, one of the most studied factors is inflammation in the brain. Hence, biomarkers of inflammation and microglia activation have also been proposed to identify AD. Among them, CX3 chemokine ligand 1 (CX3CL1) has taken center stage. This transmembrane protein, also known as fractalkine (FKN), is normally expressed in neurons, featuring an N-terminal chemokine domain and an extended mucin-like stalk, following a short intra-cytoplasmatic domain. The molecule exists in both membrane-bound and soluble forms. It is accepted that the soluble and membrane-bound forms of FKN evoke differential signaling within the CNS. Given the link between CX3XL1 and microglial activation, it has been suggested that CX3CL1 signaling disruption could play a part in the pathogenesis of AD. Furthermore, a role for chemokine as a biomarker has been proposed. However, the findings collected are controversial. The current study aimed to describe the cerebrospinal fluid (CSF) levels of CX3XL1 and classical biomarkers in AD patients.
Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration

The creation of novel anticancer treatments for a variety of human illnesses, including different malignancies and dangerous microbes, also potentially depends on nanoparticles including silver. Recently, it has been successful to biologically synthesize metal nanoparticles using plant extracts. The natural flavonoid 3,3′, 4′, 5,5′, and 7 hexahydroxyflavon (myricetin) has anticancer properties. There is not much known about the regulatory effects of myricetin on the possible cell fate-determination mechanisms (such as apoptosis/proliferation) in colorectal cancer. Because the majority of investigations related to the anticancer activity of myricetin have dominantly focused on the enhancement of tumor cell uncontrolled growth (i.e., apoptosis). Thus, we have decided to explore the potential myricetin interactors and the associated biological functions by using an in-silico approach. Then, we focused on the main goal of the work which involved the synthesis of silver nanoparticles and the labeling of myricetin with it. The synthesized silver nanoparticles were examined using UV-visible spectroscopy, dynamic light scattering spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy. In this study, we have investigated the effects of myricetin on colorectal cancer where numerous techniques were used to show myricetin’s effect on colon cancer cells. Transmission Electron Microscopy was employed to monitor morphological changes. Furthermore, we have combined the results of the colorectal cancer gene expression dataset with those of the myricetin interactors and pathways. Based on the results, we conclude that myricetin is able to efficiently kill human colorectal cancer cell lines. Since, it shares important biological roles and possible route components and this myricetin may be a promising herbal treatment for colorectal cancer as per an in-silico analysis of the TCGA dataset.
Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.
Towards a new hybrid approach for building document-oriented data warehouses

Schemaless databases offer a large storage capacity while guaranteeing high performance in data processing. Unlike relational databases, which are rigid and have shown their limitations in managing large amounts of data. However, the absence of a well-defined schema and structure in not only SQL (NoSQL) databases makes the use of data for decision analysis purposes even more complex and difficult. In this paper, we propose an original approach to build a document-oriented data warehouse from unstructured data. The new approach follows a hybrid paradigm that combines data analysis and user requirements analysis. The first data-driven step exploits the fast and distributed processing of the spark engine to generate a general schema for each collection in the database. The second requirement-driven step consists of analyzing the semantics of the decisional requirements expressed in natural language and mapping them to the schemas of the collections. At the end of the process, a decisional schema is generated in JavaScript object notation (JSON) format and the data loading with the necessary transformations is performed.
Anisotropy of iron-platinum-arsenide Ca10(PtnAs8)(Fe2−xPtxAs2)5 single crystals

The upper critical field Hc2 anisotropy of Ca10(PtnAs8)(Fe2−xPtxAs2)5 (n = 3, 4) single crystals with long FeAs interlayer distance (d) was studied by angular dependent resistivity measurements. A scaling of the angular dependent resistivity was realized for both single crystals using the anisotropic Ginzburg-Landau (AGL) approach with an appropriate anisotropy parameter γ. The AGL scaling parameter γ increases with decreasing temperature and reaches a value of about 10 at 0.8Tc for both single crystals. These values are much larger than those of other iron-based superconductors (FeSCs). Remarkably, the values of γ2 show an almost linear increase with the FeAs/FeSe interlayer distance d for FeSCs. Compared to cuprates, FeSCs are less anisotropic, indicating that two dimensionality of the superconductivity is intrinsically weak.
Optimization-based distributed safety control with applications to collision avoidance for mobile robotic networks

This paper presents a distributed input rectification methodology to guarantee the safety of a multi-agent system over its pre-given cooperative control task. We start with defining a multi-agent system as a control target, which consists of a group of agents with general control affine nonlinear dynamics and an interconnection topology between agents. The research objective, safety, is next defined via a set invariance property, where a safe set is formed by control barrier functions. Then, an optimization-based distributed safety control method is presented. In the proposed approach, each agent rectifies its control input by solving an individual optimization problem to achieve a pre-given cooperative control strategy as much as possible while satisfying constraints for the safety derived by investigating the control barrier functions. Here, the optimization problem is formulated in a distributed manner by dividing the safety constraint between agents. To show the utility of the present approach, collision avoidance problems of a mobile robotic network are investigated as case studies. GRAPHICAL ABSTRACT
Investigating GAIM-GM's Capability to Sense Ionospheric Irregularities via Walker Satellite Constellations

Abstract : GAIM-GM is a modularized physics based data assimilation model, which ingests data from multiple data sources. One data source is slant total electron content (TEC) from a ground station network to satellites, and along the occultation path between multiple satellites. This study examines GAIM-GM's capability to sense a depleted region in the ionosphere, overlaid on an IFM electron density grid, from satellite constellations ingesting the slant TEC values into GAIM-GM. Satellite constellations were developed in STK(registered trademark). A real ground station network, generated from IGS, was ingested into STK(registered trademark), to compute access times to the satellite constellation, and compute slant TEC values on the perturbed IFM grid. The size of the feature was varied along with the number of satellites in the Walker constellation. 25 different scenarios with these parameters varied were created to determine the sensitivity of GAIM-GM to sense the feature. A simple heuristic algorithm was applied comparing the truth data, in this case the perturbed IFM grid, to the GAIM-GM output in each scenario across the entire grid, and for those grid points within the feature.
A sparse-sampled redshift survey of IRAS galaxies. I. The convergence of the IRAS dipole and the origin of our motion with respect to the microwave background.

Un nouveau survey profond des redshifts des galaxies IRAS est utilise pour analyser l'origine du mouvement du Groupe Local par rapport au fond microonde et pour predire l'ecoulement dans 100 h -1 Mpc. Cet ecoulement est modelise en terme de l'attraction gravitationnelle d'un petit nombre de superamas.
Suppression of Star Formation in the central 200 kpc of a z = 1.4 Galaxy Cluster [Erratum added]

We present the results of an extended narrow-band H{\alpha} study of the massive galaxy cluster XMMU J2235.3-2557 at z = 1.39. This paper represents a follow up study to our previous investigation of star-formation in the cluster centre, extending our analysis out to a projected cluster radius of 1.5 Mpc. Using the Near InfraRed Imager and Spectrograph (NIRI) on Gemini North we obtained deep H narrow-band imaging corresponding to the rest-frame wavelength of H{\alpha} at the cluster's redshift. We identify a total of 163 potential cluster members in both pointings, excluding stars based on their near-IR colours derived from VLT/HAWK-I imaging. Of these 163 objects 14 are spectroscopically confirmed cluster members, and 20% are excess line-emitters. We find no evidence of star formation activity within a radius of 200 kpc of the brightest cluster galaxy in the cluster core. Dust-corrected star formation rates (SFR) of excess emitters outside this cluster quenching radius, RQ \sim 200 kpc, are on average = 2.7 \pm 1.0 M\odot yr-1, but do not show evidence of increasing star-formation rates toward the extreme 1.5 Mpc radius of the cluster. No individual cluster galaxy exceeds an SFR of 6 M\odot yr-1 . Massive galaxies (log M\ast /M\odot > 10.75) all have low specific SFRs (SSFRs, i.e. SFR per unit stellar mass). At fixed stellar mass, galaxies in the cluster centre have lower SSFRs than the rest of the cluster galaxies, which in turn have lower SSFRs than field galaxies at the same redshift by a factor of a few to 10. For the first time we can demonstrate through measurements of individual SFRs that already at very early epochs (at an age of the Universe of \sim4.5 Gyr) the suppression of star-formation is an effect of the cluster environment which persists at fixed galaxy stellar mass. [Erratum added after the original paper]
Al-Sn-Al Bonding Strength Investigation Based on Deep Learning Model

Al-Sn-Al wafer bonding is a new semiconductor manufacturing technology that plays an important role in device manufacturing. Optimization of the bonding process and testing of the bonding strength remain key issues. However, using only physical experiments to study the above problems presents difficulties such as repeating many experiments, high costs, and low efficiency. Deep learning algorithms can quickly simulate complex physical correlations by training large amounts of data, which is a good solution to the difficulties in studying wafer bonding. Therefore, this paper proposes the use of deep learning models (2-layer CNN and 50-layer ResNet) to achieve autonomous recognition of bonding strengths corresponding to different bonding conditions, and the results from a comparative test set show that the ResNet model has an accuracy of 99.17%, outperforming the CNN model with an accuracy of 91.67%. Then, the identified images are analyzed using the Canny edge detector, which showed that the fracture surface morphology of the wafer is a hole-shaped structure, with the smaller the area of hole movement on the wafer surface, the higher the bonding strength. In addition, the effects of bonding time and bonding temperature on bonding strength are verified, showing that relatively short bonding times and relatively low bonding temperatures resulted in better wafer bonding strength. This research demonstrates the potential of using deep learning to accelerate wafer bonding strength identification and process condition optimization.
Bioinformatic analysis and relative expression of hyd and fdx during H2 production in microalgae

Microalgae have been positioned as excellent models for producing new sources of energy (biofuels and biohydrogen). Some investigations in these biological models have been directed to know if the enzymes ferredoxin (FDX) and hydrogenase (HYD) are involved in the algae producing different concentrations of molecular hydrogen (H2). To date, little is known about the concomitant transcriptional regulation of both enzymes during H2 evolution in algae. In this research, we evaluated the relative expression of hdy and fdx genes during the evolution of H2 in three microalgae (Chlorella vulgaris, Scenedesmus obliquus, and Chlamydomonas reinhardtii) in N‐deprived anaerobic cultures in the presence of Fe, and 12:24 and 24:24 h dark:light cycles. We also detected structural differences in the enzymes. The 3D modeling indicated that the 3D structure of HYD and FDX are conserved in most algal genera, and the results of our grouping according to the aa characteristics of the proteins showed two grouping trends: One, according to the algae's phylogenetic classification, and another one according to the species‐specific enzyme's characteristics, and the grouping could perhaps be more influenced by the algae's ability to produce H2. The three microalgae species reached maximum H2 accumulation values in 24h:24 h dark:light conditions in Fe‐supplemented media (4.2 ± 0.12 mL L−1 in C. vulgaris, 3.9 ± 0.10 mL L−1 in S. obliquus, and 4.5 ± 0.10 mL L−1 in C. reinhardtii), and the highest global relative expression of hyd and fdx genes was reached during the first hour of exposure to light, which suggests concomitant expression of both enzymes at the beginning of H2 production. The behavior of the expression of the hyd and fdx genes in these algal species proved to be similar between species. A better understanding of the concomitant regulation of both enzymes could lay the groundwork for the future use of both enzymes to improve H2 yields in microalgae.
A Low-Profile, Circularly Polarized, Metasurface-Based Antenna With Enhanced Bandwidth and Stable High Gain

A low-profile circularly polarized (CP) metasurface-based (MS-based) antenna with enhanced axial-ratio bandwidth (ARBW) and stable high gain is developed. The developed antenna has a stacked layout with the MS containing 3 × 3 patch lattices, which are placed above a custom-designed driven element. The driven element comprises a patch with an etched slot and a parasitic ring strip with shorting pins, which can excite the MS to generate multiple resonances and three AR minimum points, thus resulting in enhanced ARBW and stable high gain. Finally, an antenna prototype is designed to confirm the simulation results. The experimental (simulated) results demonstrate that the MS-based antenna is low-profile: 0.03 λ0, where λ0 corresponds to the operating central frequency at 2.63 GHz, achieving a −10 dB fractional bandwidth (FBW) of 20.48 (19.81)%, and 3 dB ARBW of 10.2 (11)% along with a stable gain of 8.1–8.8 (8.0–9.2) dBic, respectively.
Integrated Bioinformatics and Validation of lncRNA-Mediated ceRNA Network in Myocardial Ischemia/Reperfusion Injury

Background Myocardial ischemia/reperfusion (MI/R) injury is a common pathology in ischemia heart disease. Long noncoding RNAs (lncRNAs) are significant regulators related to many ischemia/reperfusion conditions. This study is aimed at exploring the molecule mechanism of lncRNA-mediated competing endogenous RNA (ceRNA) network in MI/R. Methods The dataset profiles of MI/R and normal tissues (GSE130217 and GSE124176) were obtained from the GEO database. Integrated bioinformatics were performed to screen out differentially expressed genes (DEGs). Thereafter, an lncRNA-mediated ceRNA network was constructed by the starBase database. The GO annotations and KEGG pathway analysis were conducted to study action mechanism and related pathways of DEGs in MI/R. A model of hypoxia/reoxygenation- (H/R-) treated HL-1 cell was performed to verify the expression of lncRNAs through qRT-PCR. Results 2406 differentially expressed- (DE-) mRNAs, 70 DE-lncRNAs, and 156 DE-miRNAs were acquired. These DEGs were conducted to construct an lncRNA-mediated ceRNA network, and a subnetwork including lncRNA Xist/miRNA-133c/mRNA (Slc30a9) was screen out. The functional enrichment analyses revealed that the lncRNAs involved in the ceRNA network might functions in oxidative stress and calcium signaling pathway. The lncRNA Xist expression is reduced under H/R conditions, followed by the increased level of miRNA-133c, thus downregulating the expression of Slc30a9. Conclusion In sum, the identified ceRNA network which included the lncRNA Xist/miR-133c/Slc30a9 axis might contribute a better understanding to the pathogenesis and development of MI/R injury and offer a novel targeted therapy way.
Robust Adaptive Fuzzy Control of Nonlinear Systems with Unknown and Time‐Varying Saturation

In this paper, a robust adaptive fuzzy control approach is proposed for a class of nonlinear systems in strict‐feedback form with the unknown time‐varying saturation input. To deal with the time‐varying saturation problem, a novel controller separation approach is proposed in the literature to separate the desired control signal from the practical constrained control input. Furthermore, an optimized adaptation method is applied to the dynamic surface control design to reduce the number of adaptive parameters. By utilizing the Lyapunov synthesis, the fuzzy logic system technique and the Nussbaum function technique, an adaptive fuzzy control algorithm is constructed to guarantee that all the signals in the closed‐loop control system remain semiglobally uniformly ultimately bounded, and the tracking error is driven to an adjustable neighborhood of the origin. Finally, some numerical examples are provided to validate the effectiveness of the proposed control scheme in the literature.
Thermodynamic and kinetic characterization of salt hydrates for thermochemical energy storage

Inorganic salt hydrates that undergo reversible solid–gas thermochemical reactions can be used for thermal energy storage in buildings. However, characterization of the reaction enthalpy (energy storage capacity) has been a challenge owing to their microstructure and hygrothermal stability, which results in variations between literature data for the same salt and underperforming theoretical predictions. To address this, a parametric study of the simultaneous thermal analysis variables is performed to characterize phase transitions in six thermochemical materials (TCMs). This yields a set of standard experimental conditions for each TCM that maximizes energy density, while establishing design rules for salt mixtures. Graphical abstract
Design and Implementation of an Anthropomorphic Robotic Arm Prosthesis

The development and manufacture of prosthetic limbs is one of the important tendencies of the development of medical techniques. Taking into account the development of modern electronic technology and automated systems and its mobility and compactness, the actual task is to create a prosthesis that will be close to a fully functioning human limb in its anthropomorphic properties and will be capable of reproducing its basic actions with a high accuracy. The paper analyzes the main directions in the development of a control system for electronic limb prostheses. The description and results of the practical implementation of a prototype of an anthropomorphic prosthetic arm and its control system are presented in the paper. We developed an anthropomorphic multi-finger artificial hand for utilization in robotic research and teaching applications. The designed robotic hand is a low-cost alternative to other known 3D printed robotic hands and has 21 degrees of freedom—4 degrees of freedom for each finger, 3 degrees for the thumb and 2 degrees responsible for the position of the robotic hand in space. The open-source mechanical design of the presented robotic arm has mass-dimensional and motor parameters close to the human hand, with the possibility of autonomous battery operation, the ability to connect different control systems, such as from a computer, an electroencephalograph, a touch glove.