Magnetism and Charge Dynamics in Iron Pnictides

In a wide variety of materials, such as copper oxides, heavy fermions, organic salts, and the recently discovered iron pnictides, superconductivity is found in close proximity to a magnetically ordered state. The character of the proximate magnetic phase is thus believed to be crucial for understanding the differences between the various families of unconventional superconductors and the mechanism of superconductivity. Unlike the AFM order in cuprates, the nature of the magnetism and of the underlying electronic state in the iron pnictide superconductors is not well understood. Neither density functional theory nor models based on atomic physics and superexchange, account for the small size of the magnetic moment. Many low energy probes such as transport, STM and ARPES measured strong anisotropy of the electronic states akin to the nematic order in a liquid crystal, but there is no consensus on its physical origin, and a three dimensional picture of electronic states and its relations to the optical conductivity in the magnetic state is lacking. Using a first principles approach, we obtained the experimentally observed magnetic moment, optical conductivity, and the anisotropy of the electronic states. The theory connects ARPES, which measures one particle electronic states, optical spectroscopy, probing the particle hole excitations of the solid and neutron scattering which measures the magnetic moment. We predict a manifestation of the anisotropy in the optical conductivity, and we show that the magnetic phase arises from the paramagnetic phase by a large gain of the Hund's rule coupling energy and a smaller loss of kinetic energy, indicating that iron pnictides represent a new class of compounds where the nature of magnetism is intermediate between the spin density wave of almost independent particles, and the antiferromagnetic state of local moments.Comment: 4+ pages with additional one-page supplementary materia

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Kinetic frustration and the nature of the magnetic and paramagnetic states in iron pnictides and iron chalcogenides

The iron pnictide and chalcogenide compounds are a subject of intensive investigations due to their high temperature superconductivity.\cite{a-LaFeAsO} They all share the same structure, but there is significant variation in their physical properties, such as magnetic ordered moments, effective masses, superconducting gaps and T$_c$. Many theoretical techniques have been applied to individual compounds but no consistent description of the trends is available \cite{np-review}. We carry out a comparative theoretical study of a large number of iron-based compounds in both their magnetic and paramagnetic states. We show that the nature of both states is well described by our method and the trends in all the calculated physical properties such as the ordered moments, effective masses and Fermi surfaces are in good agreement with experiments across the compounds. The variation of these properties can be traced to variations in the key structural parameters, rather than changes in the screening of the Coulomb interactions. Our results provide a natural explanation of the strongly Fermi surface dependent superconducting gaps observed in experiments\cite{Ding}. We propose a specific optimization of the crystal structure to look for higher T$_c$ superconductors.Comment: 5 pages, 3 figures with a 5-page supplementary materia

Date Palm Leaflet-Derived Carbon Microspheres Activated Using Phosphoric Acid for Efficient Lead (II) Adsorption

\ua9 2024 by the authors.The removal of lead metals from wastewater was carried out with carbon microspheres (CMs) prepared from date palm leaflets using a hydrothermal carbonization process (HTC). The prepared CMs were subsequently activated with phosphoric acid using the incipient wetness impregnation method. The prepared sample had a low Brunauer–Emmet–Teller (BET) surface area of 2.21 m2\ub7g−1, which increased substantially to 808 m2\ub7g−1 after the activation process. Various characterization techniques, such as scanning electron microscopy, BET analysis, Fourier transform infrared, and elemental analysis (CHNS), were used to evaluate the morphological structure and physico-chemical properties of the CMs before and after activation. The increase in surface area is an indicator of the activation process, which enhances the absorption properties of the material. The results demonstrated that the activated CMs had a notable adsorption capacity, with a maximum adsorption capacity of 136 mg\ub7g−1 for lead (II) ions. This finding suggests that the activated CMs are highly effective in removing lead pollutants from water. This research underscores the promise of utilizing activated carbon materials extracted from palm leaflets as an eco-friendly method with high potential for water purification, specifically in eliminating heavy metal pollutants, particularly lead (II), contributing to sustainability through biomass reuse

Innovator resilience potential: A process perspective of individual resilience as influenced by innovation project termination

Innovation projects fail at an astonishing rate. Yet, the negative effects of innovation project failures on the team members of these projects have been largely neglected in research streams that deal with innovation project failures. After such setbacks, it is vital to maintain or even strengthen project members’ innovative capabilities for subsequent innovation projects. For this, the concept of resilience, i.e. project members’ potential to positively adjust (or even grow) after a setback such as an innovation project failure, is fundamental. We develop the second-order construct of innovator resilience potential, which consists of six components – self-efficacy, outcome expectancy, optimism, hope, self-esteem, and risk propensity – that are important for project members’ potential of innovative functioning in innovation projects subsequent to a failure. We illustrate our theoretical findings by means of a qualitative study of a terminated large-scale innovation project, and derive implications for research and management

Deep convolutional neural network with 2D spectral energy maps for fault diagnosis of gearboxes under variable speed.

For industrial safety, correct classification of gearbox fault conditions is necessary. One of the most crucial tasks in data-driven fault diagnosis is determining the best set of features by analyzing the statistical parameters of the signals. However, under variable speed conditions, these statistical parameters are incapable of uncovering the dynamic characteristics of different fault conditions of gearboxes. Later, several deep learning algorithms are used to improve the performance of the feature selection process, but domain knowledge expertise is still necessary. In this paper, a combination domain knowledge analysis and a deep neural network is proposed. By using the input acoustic emission (AE) signal, a two-dimensional spectrum energy map (2D AE-SEM) is created to form an identical fault pattern for various speed conditions of gearboxes. Then, a deep convolutional neural network (DCNN) is proposed to investigate the detailed structure of the 2D input for final fault classification. This 2D AE-SEM offers a graphical depiction of acoustic emission spectral characteristics. Our proposed system offers vigorous and dynamic classification performance through the proposed DCNN with a high diagnostic fault classification accuracy of 96.37% in all considered scenarios

Muscle 4EBP1 activation modifies the structure and function of the neuromuscular junction in mice

Dysregulation of mTOR complex 1 (mTORC1) activity drives neuromuscular junction (NMJ) structural instability during aging; however, downstream targets mediating this effect have not been elucidated. Here, we investigate the roles of two mTORC1 phosphorylation targets for mRNA translation, ribosome protein S6 kinase 1 (S6K1) and eukaryotic translation initiation factor 4E-binding protein 1 (4EBP1), in regulating NMJ structural instability induced by aging and sustained mTORC1 activation. While myofiber-specific deletion of S6k1 has no effect on NMJ structural integrity, 4EBP1 activation in murine muscle induces drastic morphological remodeling of the NMJ with enhancement of synaptic transmission. Mechanistically, structural modification of the NMJ is attributed to increased satellite cell activation and enhanced post-synaptic acetylcholine receptor (AChR) turnover upon 4EBP1 activation. Considering that loss of post-synaptic myonuclei and reduced NMJ turnover are features of aging, targeting 4EBP1 activation could induce NMJ renewal by expanding the pool of post-synaptic myonuclei as an alternative intervention to mitigate sarcopenia

Lineage-Specific Methyltransferases Define the Methylome of the Globally Disseminated Escherichia coli ST131 Clone.

UNLABELLED: Escherichia coli sequence type 131 (ST131) is a clone of uropathogenic E. coli that has emerged rapidly and disseminated globally in both clinical and community settings. Members of the ST131 lineage from across the globe have been comprehensively characterized in terms of antibiotic resistance, virulence potential, and pathogenicity, but to date nothing is known about the methylome of these important human pathogens. Here we used single-molecule real-time (SMRT) PacBio sequencing to determine the methylome of E. coli EC958, the most-well-characterized completely sequenced ST131 strain. Our analysis of 52,081 methylated adenines in the genome of EC958 discovered three (m6)A methylation motifs that have not been described previously. Subsequent SMRT sequencing of isogenic knockout mutants identified the two type I methyltransferases (MTases) and one type IIG MTase responsible for (m6)A methylation of novel recognition sites. Although both type I sites were rare, the type IIG sites accounted for more than 12% of all methylated adenines in EC958. Analysis of the distribution of MTase genes across 95 ST131 genomes revealed their prevalence is highly conserved within the ST131 lineage, with most variation due to the presence or absence of mobile genetic elements on which individual MTase genes are located. IMPORTANCE: DNA modification plays a crucial role in bacterial regulation. Despite several examples demonstrating the role of methyltransferase (MTase) enzymes in bacterial virulence, investigation of this phenomenon on a whole-genome scale has remained elusive until now. Here we used single-molecule real-time (SMRT) sequencing to determine the first complete methylome of a strain from the multidrug-resistant E. coli sequence type 131 (ST131) lineage. By interrogating the methylome computationally and with further SMRT sequencing of isogenic mutants representing previously uncharacterized MTase genes, we defined the target sequences of three novel ST131-specific MTases and determined the genomic distribution of all MTase target sequences. Using a large collection of 95 previously sequenced ST131 genomes, we identified mobile genetic elements as a major factor driving diversity in DNA methylation patterns. Overall, our analysis highlights the potential for DNA methylation to dramatically influence gene regulation at the transcriptional level within a well-defined E. coli clone

Compensated right ventricular function of the onset of pulmonary hypertension in a rat model depends on chamber remodeling and contractile augmentation.

Right-ventricular function is a good indicator of pulmonary arterial hypertension (PAH) prognosis; however, how the right ventricle (RV) adapts to the pressure overload is not well understood. Here, we aimed at characterizing the time course of RV early remodeling and discriminate the contribution of ventricular geometric remodeling and intrinsic changes in myocardial mechanical properties in a monocrotaline (MCT) animal model. In a longitudinal study of PAH, ventricular morphology and function were assessed weekly during the first four weeks after MCT exposure. Using invasive measurements of RV pressure and volume, heart performance was evaluated at end of systole and diastole to quantify contractility (end-systolic elastance) and chamber stiffness (end-diastolic elastance). To distinguish between morphological and intrinsic mechanisms, a computational model of the RV was developed and used to determine the level of prediction when accounting for wall masses and unloaded volume measurements changes. By four weeks, mean pulmonary arterial pressure and elastance rose significantly. RV pressures rose significantly after the second week accompanied by significant RV hypertrophy, but RV stroke volume and cardiac output were maintained. The model analysis suggested that, after two weeks, this compensation was only possible due to a significant increase in the intrinsic inotropy of RV myocardium. We conclude that this MCT-PAH rat is a model of RV compensation during the first month after treatment, where geometric remodeling on EDPVR and increased myocardial contractility on ESPVR are the major mechanisms by which stroke volume is preserved in the setting of elevated pulmonary arterial pressure. The mediators of this compensation might themselves promote longer-term adverse remodeling and decompensation in this animal model

Normal-State Spin Dynamics and Temperature-Dependent Spin Resonance Energy in an Optimally Doped Iron Arsenide Superconductor

The proximity of superconductivity and antiferromagnetism in the phase diagram of iron arsenides, the apparently weak electron-phonon coupling and the "resonance peak" in the superconducting spin excitation spectrum have fostered the hypothesis of magnetically mediated Cooper pairing. However, since most theories of superconductivity are based on a pairing boson of sufficient spectral weight in the normal state, detailed knowledge of the spin excitation spectrum above the superconducting transition temperature Tc is required to assess the viability of this hypothesis. Using inelastic neutron scattering we have studied the spin excitations in optimally doped BaFe1.85Co0.15As2 (Tc = 25 K) over a wide range of temperatures and energies. We present the results in absolute units and find that the normal state spectrum carries a weight comparable to underdoped cuprates. In contrast to cuprates, however, the spectrum agrees well with predictions of the theory of nearly antiferromagnetic metals, without complications arising from a pseudogap or competing incommensurate spin-modulated phases. We also show that the temperature evolution of the resonance energy follows the superconducting energy gap, as expected from conventional Fermi-liquid approaches. Our observations point to a surprisingly simple theoretical description of the spin dynamics in the iron arsenides and provide a solid foundation for models of magnetically mediated superconductivity.Comment: 8 pages, 4 figures, and an animatio