Miracle-dz/UniFAR-FTUs huggingface dataset:
Resonance-channel quantum numbers in electron-hydrogen and proton-hydrogen scattering from group theory of the long-range dipole interaction

We derive quantum numbers $K$ and $T$ which label asymptotic potential-energy curves in ${e}^{\ensuremath{-}}$-H scattering. Zero-order channel states are constructed group theoretically so that the dipole interaction ${\mathcal{r}}_{1}cos{\ensuremath{\theta}}_{12}$ is diagonal in a product space of hydrogenic orbitals for 1 and spherical harmonics for 2. $K$ and $T$ are analogous to the "electric quantum number" and the $z$ component of angular momentum in the linear Stark effect for H, and are closely related to quantum numbers described recently for doubly excited He. Channels supporting resonances are predicted from a perturbation expansion of eigenvalues of the effective centrifugal barrier operator $2{r}_{1}cos{\ensuremath{\theta}}_{12}+{l}_{2}^{2}$ in terms of $K$ and $T$. Comparison with exact ${\mathrm{H}}^{\ensuremath{-}}$ results and experiment is excellent, and the method accounts for previously unexplained degeneracies with respect to total parity. A rigorous result is that only channels with $Kg0$ can support an infinity of states below threshold. An approximate selection rule for coupling between neighboring channels suggests that decay of ${\mathrm{H}}^{\ensuremath{-}}$ resonances goes preferentially to channels such that $\ensuremath{\Delta}K=\ifmmode\pm\else\textpm\fi{}1$, $\ensuremath{\Delta}T=0$. Application of the method is also made to $p$-H scattering channels for which the zero-order basis nearly diagonalizes the long-range centrifugal barrier.
Numerical Study of the Three‐Body Ion—Ion Recombination Rate

The three‐body ion—ion recombination rate is studied at low density, using the Monte Carlo calculational method. Good agreement with experiment is obtained using Brueckner's recently proposed model which includes the effect of ``dissociative collisions. '' Agreement of earlier theory with experiment is shown to be largely fortuitous.
How should childhood acute lymphoblastic leukemia relapses in low‐income and middle‐income countries be managed: The AHOPCA‐ALL study group experience

Children with relapsed acute lymphoblastic leukemia (ALL) in low‐income and middle‐income countries rarely survive. The Pediatric Hematology‐Oncology Association of Central America (AHOPCA) developed the AHOPCA‐ALL REC 2014 protocol to improve outcomes in resource‐constrained settings without access to stem cell transplantation.
On Using Distorted Sensors for Set-based Multi-scale Actuator Fault Diagnosis

Abstract The off-line fault detection, isolation and identification of multiplicative actuator faults using sensors providing measurements affected by unknown nonlinear distortions and bounded noises are addressed. The only assumption about the unknown functions modeling the sensor distortions is their monotonicity. Bounded disturbances are also considered as unknown inputs of the system time-varying dynamics. Polytopic faulty parameter sets resulting from the search of robust output pairs and from zonotope computations are the basis for a decision scheme which makes use of multi-scale temporal windows. A numerical example modeling part of a distillation column illustrates the off-line diagnosis of three multiplicative actuator faults from a single low-cost noisy sensor with unknown nonlinear distortion and constant bias.
Optimized Switching Frequency Voltage Balancing Schemes for Flying Capacitor Based Multilevel Converters

The redundant switching states of flying capacitor-based (FC-based) multilevel converters are used to balance the voltages of the FCs. Attempts to balance capacitor voltages have ignored the switching transitions between converter switching states. In this article, we propose a generalized voltage balancing scheme with an optimized switching frequency scheme for FC-based multilevel converters, including the classic flying capacitor multicell (FCM) converter, which needs less computation. All the switching transitions between the switching states of adjacent voltage levels are considered by the overall priority index of switching frequency. In addition, an overall priority index is utilized to balance the voltages of FCs. Using these two indexes, three criteria are developed to select the proper switching state of the converter. Using these criteria, the proposed decoupled space vector modulation scheme is implemented intuitively and simply for FC-based multilevel converters. The proposed scheme is evaluated by simulation results of a five-level FCM converter. Moreover, experimental results demonstrate the robustness of the proposed method in the static load and transient conditions.
Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate

Most proteins function as part of various complexes, forming via stable and dynamic protein–protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters (affinity, molecular-recognition specificity, kinetic rate constants, and thermodynamic-parameters determination); (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors’ experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed.
Asymptotic expansions and Lie algebras for some nonlinear filtering problems

State estimation problems for systems involving small parameters are treated by both analytical and Lie algebraic approximation techniques. An asymptotic expansion for the unnormalized conditional density corresponding to the case of observations of a Gauss-Markov process through a (weak) polynomial nonlinearity is computed and a convergence result is derived. The expansion is related to certain approximations of the associated estimation Lie algebra. The convergence result is based on arguments used recently to prove existence and uniqueness and to estimate the tail behavior of solutions to nonlinear filtering problems with unbounded coefficients. These arguments are, in turn, adapted from the analytical theory of parabolic PDE's. Simulation results are presented to further assess the performance of the resulting approximate filters.
Network Topology Can Explain Differences in Pleiotropy Between Cis- and Trans-regulatory Mutations

Abstract A mutation's degree of pleiotropy (i.e., the number of traits it alters) is predicted to impact the probability of the mutation being detrimental to fitness. For mutations that impact gene expression, mutations acting in cis have been hypothesized to generally be less pleiotropic than mutations affecting the same gene's expression in trans, suggesting that cis-regulatory mutations should be less deleterious and more likely to fix over evolutionary time. Here, we use expression and fitness data from Saccharomyces cerevisiae gene deletion strains to test these hypotheses. By treating deletion of each gene as a cis-regulatory mutation affecting its own expression and deletions of other genes affecting expression of this focal gene as trans-regulatory mutations, we find that cis-acting mutations do indeed tend to be less pleiotropic than trans-acting mutations affecting expression of the same gene. This pattern was observed for the vast majority of genes in the data set and could be explained by the topology of the regulatory network controlling gene expression. Comparing the fitness of cis- and trans-acting mutations affecting expression of the same gene also confirmed that trans-acting deletions tend to be more deleterious. These findings provide strong support for pleiotropy playing a role in the preferential fixation of cis-regulatory alleles over evolutionary time.
gldc Is Essential for Renal Progenitor Patterning during Kidney Development

The glycine cleavage system (GCS) is a complex located on the mitochondrial membrane that is responsible for regulating glycine levels and contributing one-carbon units to folate metabolism. Congenital mutations in GCS components, such as glycine decarboxylase (gldc), cause an elevation in glycine levels and the rare disease, nonketotic hyperglycinemia (NKH). NKH patients suffer from pleiotropic symptoms including seizures, lethargy, mental retardation, and early death. Therefore, it is imperative to fully elucidate the pathological effects of gldc dysfunction and glycine accumulation during development. Here, we describe a zebrafish model of gldc deficiency that recapitulates phenotypes seen in humans and mice. gldc deficient embryos displayed impaired fluid homeostasis suggesting renal abnormalities, as well as aberrant craniofacial morphology and neural development defects. Whole mount in situ hybridization (WISH) revealed that gldc transcripts were highly expressed in the embryonic kidney, as seen in mouse and human repository data, and that formation of several nephron segments was disrupted in gldc deficient embryos, including proximal and distal tubule populations. These kidney defects were caused by alterations in renal progenitor populations, revealing that the proper function of Gldc is essential for the patterning of this organ. Additionally, further analysis of the urogenital tract revealed altered collecting duct and cloaca morphology in gldc deficient embryos. Finally, to gain insight into the molecular mechanisms underlying these disruptions, we examined the effects of exogenous glycine treatment and observed analogous renal and cloacal defects. Taken together, these studies indicate for the first time that gldc function serves an essential role in regulating renal progenitor development by modulating glycine levels.
Molecular Diagnosis of Encephalitis/Meningoencephalitis Caused by Free-Living Amoebae from a Tertiary Center in India

Background: Pathogenic free-living amoeba (FLA) such as Acanthamoeba spp. , Naegleria fowleri, and Balamuthia mandrillaris are causative agents of fatal amoebic encephalitis/meningoencephalitis. The diagnosis of such infections is challenging due to a lack of clinical suspicion and expertise in microscopic identification. We evaluated the performance of molecular assays for the timely and accurate detection of FLA-causing central nervous system (CNS) afflictions. Methods: This study included samples from 156 patients with suspected encephalitis/meningoencephalitis, including 149 cerebrospinal fluid (CSF) samples, 5 brain tissue biopsies, and 2 brain abscess samples. All the samples were subjected to PCR-based detection of Acanthamoeba spp. , N. fowleri, and B. mandrillaris. The diagnostic characteristics and the inter-rater reliability scores were evaluated for parasite-specific polymerase chain reaction (PCR) using culture on non-nutrient agar (NNA)/microscopy or histopathological examination as a confirmatory test for Acanthamoeba spp. and N. fowleri and histopathology for B. mandrillaris. Results: We detected 11 samples positive for FLA, including 6 Acanthamoeba spp. , 3 B. mandrillaris, and 2 N. fowleri. Furthermore, all 11 samples were positive according to the confirmatory tests, i.e., culture on NNA/microscopy/histopathology in the case of Acanthamoeba spp. and N. fowleri and histopathology of tissue biopsies for B. mandrillaris. The inter-rater reliability between the PCRs and the confirmatory tests for the detection of Acanthamoeba spp. , N. fowleri, and B. mandrillaris was 100%. Conclusions: The PCR-based detection of FLA in patients suspected of encephalitis/meningoencephalitis was found to be fast, efficient, and reliable in our study. We suggest the use of these PCRs in laboratories to obtain additional data on their efficiency in diagnosing FLA infections of the CNS. The present study was conducted with a small sample size of 156 patient samples, and we found only six Acanthamoeba spp. , three B. mandrillaris, and two N. fowleri. The present study should be conducted on a larger sample size for better evaluation of the primer pairs.
Performance Improvement for Double-Stator Axial Flux SRM Using a New Winding Reconfigurable Power Converter

This article presents a new power converter topology that enables reconfigurable winding operation in a double-stator axial flux switched reluctance motor (DSAFSRM). The proposed power converter allows the winding working in series and parallel connection methods and requires fewer components. By applying the reconfigurable winding converter and implementing the mode selection scheme, both the wide speed range of the parallel mode and the high efficiency of the series mode are obtained, thus achieving high-efficiency operation over a wide speed range. At first, the topology and operating modes are introduced. The six available working states are also detailed. Then the drive system simulation model with the reconfigurable winding power converter is established in the MATLAB/Simulink environment. The steady-state performance of the two modes and the dynamic switching performance is simulated and analyzed. Finally, the DSAFSRM drive based on the reconfigurable winding converter is built and experimentally tested. The performance results verify the validity and correctness of the presented power converter.
TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING

Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40
Sex-Specific Multiparameter Blood Test for the Early Diagnosis of Alzheimer’s Disease

Blood-based biomarkers are needed for the early diagnosis of Alzheimer’s disease (AD). We analyzed longitudinal human plasma samples from AD and control cases to identify biomarkers for the early diagnosis of AD. Plasma samples were grouped based on clinical diagnosis at the time of collection: AD, mild cognitive impairment (MCI), and pre-symptomatic (preMCI). Samples were analyzed by ELISA using a panel of reagents against nine different AD-related amyloid-β (Aβ), tau, or TDP-43 variants. Receiver operating characteristic (ROC) curves of different biomarker panels for different diagnostic sample groups were determined. Analysis of all of the samples gave a sensitivity of 92% and specificity of 76% for the diagnosis of AD. Early-stage diagnosis of AD, utilizing only the preMCI and MCI samples, identified 88% of AD cases. Using sex-biased biomarker panels, early diagnosis of AD cases improved to 96%. Using the sex-biased panels, we also identified 6 of the 25 control group cases as being at high risk of AD, which is consistent with what is expected given the advanced age of the control cases. Specific AD-associated protein variants are effective blood-based biomarkers for the early diagnosis of AD. Notably, significant differences were observed in biomarker profiles for the early detection of male and female AD cases.
Global optimization of the bifunctional catalyst problem

As shown by Luus et al. (1992), determination of the optimum catalyst blend along a tubular reactor to maximize the yield of a particular component is a difficult problem due to the large number of local optima. By using iterative dynamic programming, the effects of the region reduction factor and the choice of the initial region size are analyzed in detail for this problem. Even if a single grid-point for the state is used, the likelihood of obtaining the global optimum is very high if the initial region size is chosen to be sufficiently large. Comme l'ont montre Luus et al. (1992), la determination du melange optimal de catalyseurs dans un reacteur tubulaire pour maximiser le rendement d'un composant particulier est un probleme difficile en raison du grand nombre d'optimums locaux. Les effets du facteur de reduction de la region et la selection de la taille de la region initiale ont ete analyses en detail pour ce probleme par la programmation dynamique iterative. Měme si un point unique est utilise pour l'etat, la probabilite d'obtenir un optimum global est tres bonne lorsque la taille choisie pour la region initiale est suffisamment grande.
HEAO 1 measurements of the galactic ridge

The HEAO A2 experiment data was systematically searched for unresolved galactic disc emission. Although there were suggestions of non-uniformities in the emission, the data were consistent with a disc of half-thickness 241 + 22 pc and surface emissivity (2-10 keV) at galactic radius R(kpc) of 2.2 10 to the minus 7th power exp(-R/3.5) erg/sq cm to the (-2)power/s (R 7.8 kpc). giving a luminosity of approximately 4.4 10 to the 37th power erg S to the (-1) power. If the model is extrapolated to radii less than 7.8 kpc, the unresolved disc emission is approximately 1.4 10 to the 38th power erg S to the (-1) power (2-10 keV) i.e., a few percent of the luminosity of the galaxy in resolved sources. the disc emission has a spectrum which is significantly softer than that of the high galactic latitude diffuse X-ray background and it is most probably of discrete source origin.
Progress in system design using integrated multi-element interferometric switches

In the present communication we discuss recent advances in the development of Semiconductor optical amplifier (SOA)-based interferometric optical gates and their use through the implementation of functional high-speed optical systems. SOA Mach-Zehnder interferometers (SOA-MZI) show great potential for being used as fundamental building blocks in developing intelligent high speed all-optical sub-systems. In this context we discuss the development of optical systems that perform diverse and non-trivial network functionalities that find application in Optical Packet/Burst Switching networks (OPS/OBS). The use of generic building blocks to develop a variety of optical sub-systems is essential, as this avoids the requirement for custom-made technological solutions and allows for a common fabrication procedure for all subsystems. In this context, we discuss latest research on integration of arrays of such optical switches onto the same photonic chip using hybrid integration technology. The development of such arrays reduces the cost of photonic devices by sharing the packaging and pigtailing costs. By using an integrated quadruple array of SOA-MZI switches we demonstrate the front-end unit of an All-optical Label Switched node that performs clock recovery, data recovery and label/payload separation, and a scalable Time Slot Interchanger (TSI), proving the multi-functionality and processing power of such device. Both functional systems exhibit comparable performance compared to implementations using single switches with significantly lower device costs. The cost reduction offered by the integration of multiple switches on the same chip is also evident in high-speed WDM networks, where multi-wavelength regeneration can be achieved with the use of several integrated switch arrays.
Caring for providers to improve patient experience (CPIPE): intervention development process

ABSTRACT A growing body of research has documented disrespectful, abusive, and neglectful treatment of women in facilities during childbirth, as well as the drivers of such mistreatment. Yet, little research exists on effective interventions to improve Person-Centred Maternal Care (PCMC)—care that is respectful and responsive to individual women's preferences, needs, and values. We sought to extend knowledge on interventions to improve PCMC, with a focus on two factors – provider stress and implicit bias – that are driving poor PCMC and contributing to disparities in PCMC. In this paper we describe the process towards the development of the intervention. The intervention design was an iterative process informed by existing literature, behaviour change theory, formative research, and continuous feedback in consultation with key stakeholders. The intervention strategies were informed by the Social Cognitive Theory, Trauma Informed System framework, and the Ecological Perspective. This process resulted in the ‘Caring for Providers to Improve Patient Experience (CPIPE)’ intervention, which has 5 components: provider training, peer support, mentorship, embedded champions, and leadership engagement. The training includes didactic and interactive content on PCMC, stress, burnout, dealing with difficult situations, and bias, with some content integrated into emergency obstetric and neonatal care (EmONC) simulations to enable providers apply concepts in the context of managing an emergency. The other components create an enabling environment for ongoing individual behavior and facility culture change. The pilot study is being implemented in Migori County, Kenya. The CPIPE intervention is an innovative theory and evidence-based intervention that addresses key drivers of poor PCMC and centers the unique needs of vulnerable women as well as that of providers. This intervention will advance the evidence base for interventions to improve PCMC and has great potential to improve equity in PCMC and maternal and neonatal health.