(E,E)-N1,N2-Bis(2,6-di­fluoro­benzyl­idene)ethane-1,2-di­amine.

The asymmetric unit of the title compound, C16H12F4N2, comprises half of the potentially bidentate Schiff base ligand, with an inversion centre located at the mid-point of the central C—C bond. The crystal packing is stabilized by inter­molecular C—H⋯N and π–π inter­actions [centroid–centroid distance = 3.6793 (12) Å and inter­planar spacing = 3.4999 (7) Å]

Sequence analysis of the second internal Transcribed spacer (ITS2) region of rDNA for species identification of trichostrongylus nematodes isolated from domestic livestock in Iran

Background: Infectivity of herbivores with Trichostrongylus nematodes is widespread in many countries, having a major economic impact on breeding, survivability, and productivity of domestic livestock. This study was carried out on Trichostrongylus species isolated from domestic livestock in order to develop an easy-to-perform method for species identification. Methods: Trichostrongylus isolates were collected from sheep, goat, cattle, and buffaloes in Khuzestan Province, southwest Iran. Primary species identification was carried out based on morphological characterization of male worms. PCR amplification of ITS2-rDNA region was performed on genomic DNA and the products were sequenced. Phylogenetic analysis of the nucleotide sequence data was conducted employing Bayesian Inference approach. Consequently, a restriction fragment length polymorphism (RFLP) profile was designed to differentiate Trichostrongylus species. Results: A consensus sequence of 238 nucleotides was deposited in the GenBank for Iranian isolates of Trichostrongylus species including T. colubriformis, T. capricola, T. probolurus and T. vitrinus. The designated RFLP using restriction enzyme TasI could readily differentiate among species having different ITS2 sequence. The molecular analysis was in concordance with morphological findings. Conclusion: Phylogenetic analysis indicated a close relationship among the sequences obtained in this study and reference sequence of relevant species. ITS2-RFLP with TasI is recommended for molecular differentiation of common Trichostrongylus species

Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents

This paper proposes an algorithm for Federated Learning (FL) with a two-layer structure that achieves both variance reduction and a faster convergence rate to an optimal solution in the setting where each agent has an arbitrary probability of selection in each iteration. In distributed machine learning, when privacy matters, FL is a functional tool. Placing FL in an environment where it has some irregular connections of agents (devices), reaching a trained model in both an economical and quick way can be a demanding job. The first layer of our algorithm corresponds to the model parameter propagation across agents done by the server. In the second layer, each agent does its local update with a stochastic and variance-reduced technique called Stochastic Variance Reduced Gradient (SVRG). We leverage the concept of variance reduction from stochastic optimization when the agents want to do their local update step to reduce the variance caused by stochastic gradient descent (SGD). We provide a convergence bound for our algorithm which improves the rate from $O(\frac{1}{\sqrt{K}})$ to $O(\frac{1}{K})$ by using a constant step-size. We demonstrate the performance of our algorithm using numerical examples

Negative linear compression and expanding NH N bonds in an imidazoline compound.

The 3-dimensional network of NHN hydrogen bonds and ClCl hydrogen contacts in the crystal structure of 2-(3′-chlorophenyl)imidazoline exhibits an anomalous hydrostatic compression. The lengthening of hydrogen bonds NHN and some CHN contacts as well as their supramolecular architecture lead to anomalous expansion of the crystal along [x] and [y] on increasing pressure to 0.1 GPa. The mechanism of this phenomenon is due to the ‘stiffness’ of the NHN and ClCl interactions and ‘softness’ of other van der Waals contacts. Above 0.1 GPa all crystal directions become compressed. However, up to 1.20 GPa, the crystal remains in the same orthorhombic phase of polar space group Fdd2

4-Bromo-2-[(E)-(2-{2-[(2-{[(E)-5-bromo-2-hydroxybenzylidene]amino}phenyl)sulfanyl]ethylsulfanyl}phenyl)iminomethyl]phe-nol.

The asymmetric unit of the title compound, C28H22Br2N2O2S2, comprises half of a Schiff base ligand, the whole mol­ecule being generated by a crystallographic inversion center located at the mid-point of the C—C bond of the central methyl­ene segment. Intra­molecular O—H⋯N and O—H⋯S hydrogen bonds make S(6) and S(5) ring motifs, respectively. In the crystal, there are no significant inter­molecular inter­actions

2-{[(4-{[(2-Hydroxyphenyl)(phenyl)methylidene]amino}butyl)imino](phenyl) methyl}phenol.

The asymmetric unit of the title compound, C 30H 28N 2O 2, comprises half of a potential tetra-dentate Schiff base ligand; an inversion centre is situtated at the center of the butane-diamine spacer. The central methylene segment of the diamine spacer is disordered over two positions with a refined siteoccupancy ratio of 0.651 (7):0.349 (7). The phenyl ring and the hydroxysubstituted benzene ring are almost perpendicular to each other, with a dihedral angle of 87.90 (8) Å. intramolecular O - H⋯N hydrogen bonds make S(6) ring motifs

A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults

The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical alarm logs and their labels to extract numerical representations for each log, effectively addressing the issue of imbalanced classification due to a small proportion of anomalies in the dataset without employing one-hot encoding. The robustness of the embedding is evaluated by plotting the two most significant principle components of the embedded alarm logs, revealing that anomalies form distinct clusters with similar embeddings. Multivariate normal Gaussian clustering is then applied to these components, identifying clusters with a high ratio of anomalies to normal alarms (above 90%) and labeling them as the anomaly group. To classify new alarm logs, we check if their embedded vectors' two most significant principle components fall within the anomaly-labeled clusters. If so, the log is classified as an anomaly. Performance evaluation demonstrates that SEMC-AD outperforms conventional random forest and gradient boosting methods without embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively. While supervised classification methods may excel in labeled datasets, the results demonstrate that SEMC-AD is more efficient in classifying anomalies in datasets with numerous categorical features, significantly enhancing anomaly detection, reducing operator burden, and improving network maintenance

Working Conditions of Indonesian Remote Elementary School Teachers: A Qualitative Case Study in Southern Papua

The quality of the teaching-learning process is determined not only by the qualifications and competence of teachers, but also by the school conditions in which they work. Teachers are clear about the working conditions that they need for them to be successful with students. A better working condition improves mood and concentration and provides an excellent working approach for teachers. This case study was intended to learn what working conditions teachers note as challenging while teaching in the remote elementary schools of Southern Papua. Seventeen teachers were willing to be interviewed to assist in answering the main inquiry question: “What are the most concerning aspects of working conditions that the remote elementary school teachers in Southern Papua have to deal with?” The study found four aspects of working conditions facing the remote elementary school teachers of Southern Papua, Indonesia, as the following: (a) school physical facilities and resources, (b) school principal, (c) salary and allowance, and (d) parents’ support and involvement. From the results, we recommend the need for the regional government of Southern Papua, Indonesia, to create and maintain good conditions for teachers to remain teaching the youth of nations in a quality manner

{4,4'-Dimethyl-2,2'-[2,2-dimethylpropane-1,3-diylbis(nitrilomethanylylidene)]diphenolato}copper(II) monohydrate.

The asymmetric unit of the title compound, [Cu(C21H24N2O2)]·H2O, comprises half of a Schiff base complex and half of a water mol­ecule. The whole compound is generated by crystallographic twofold rotation symmetry. The geometry around the CuII atom, located on a twofold axis, is distorted square-planar, which is supported by the N2O2 donor atoms of the coordinating Schiff base ligand. The dihedral angle between the symmetry-related benzene rings is 47.5 (4)°. In the crystal, the water mol­ecule that is hydrogen bonded to the coordinated O atoms links the mol­ecules via O—H⋯O inter­actions into chains parallel to [001]. The crystal structure is further stabilized by C—H⋯π inter­actions, and by π–π inter­actions involving inversion-related chelate rings [centroid–centroid distance = 3.480 (4) Å]

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi