Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptomic analysis using RNA sequencing and phenotypic analysis of Salmonella enterica after acid exposure for different time durations using adaptive laboratory evolution

Introduction This study is the final part of a two-part series that delves into the molecular mechanisms driving adaptive laboratory evolution (ALE) of Salmonella enterica in acid stress. The phenotypic and transcriptomic alterations in the acid-evolved lineages (EL) of Salmonella enterica serovar Enteritidis after 70 days of acid stress exposure were analyzed. Materials and methods The stability of phenotypic changes observed after 70 days in acetic acid was explored after stress removal using a newly developed evolutionary lineage EL5. Additionally, the impact of short-term acid stress on the previously adapted lineage EL4 was also examined. Results The results indicate that the elevated antibiotic minimum inhibitory concentration (MIC) observed after exposure to acetic acid for 70 days was lost when acid stress was removed. This phenomenon was observed against human antibiotics such as meropenem, ciprofloxacin, gentamicin, and streptomycin. The MIC of meropenem in EL4 on day 70 was 0.094 mM, which dropped to 0.032 mM when removed from acetic acid stress after day 70. However, after stress reintroduction, the MIC swiftly elevated, and within 4 days, it returned to 0.094 mM. After 20 more days of adaptation in acetic acid, the meropenem MIC increased to 0.125 mM. The other human antibiotics that were tested exhibited a similar trend. The MIC of acetic acid in EL4 on day 70 was observed to be 35 mM, which remained constant even after the removal of acetic acid stress. Readaptation of EL4 in acetic acid for 20 more days caused the acetic acid MIC to increase to 37 mM. Bacterial whole genome sequencing of EL5 revealed base substitutions in several genes involved in pathogenesis, such as the phoQ and wzc genes. Transcriptomic analysis of EL5 revealed upregulation of virulence, drug resistance, toxin-antitoxin, and iron metabolism genes. Unstable Salmonella small colony variants (SSCV) of S. Enteritidis were also observed in EL5 as compared to the wild-type unevolved S. Enteritidis. Discussion This study presents a comprehensive understanding of the evolution of the phenotypic, genomic, and transcriptomic changes in S. Enteritidis due to prolonged acid exposure through ALE.
Packing-Inspired Algorithms for Periodic Scheduling Problems with Harmonic Periods

: We tackle the problem of non-preemptive periodic scheduling with a harmonic set of periods. Problems of this kind arise within domains of periodic manufacturing and maintenance, and also during the design of industrial, automotive, and avionics communication protocols, where efficient scheduling of messages is crucial for the performance of a time-triggered network. We consider the decision variant of the periodic scheduling problem on a single highly-utilized machine. We first prove a bijection between periodic scheduling and a particular (so-called height-divisible) 2D packing of rectangles. We formulate the problem using Constraint Programming and compare it with equivalent state-of-the-art Integer Linear Programming formulation, showing the former’s superiority on difficult instances. Furthermore, we develop a packing-inspired first fit heuristic, which we compare with methods described in the literature. We justify our proposed methods on synthetically generated problem instances inspired by the communication of messages on one channel.
Implementasi Model Atik Dalam Pembelajaran Membedakan Sampah Organik dan Non Organik di Tk Nurul Huda Karawang

Masalah sampah merupakan masalah yang ada hampir diseluruh daerah di Indonesia. Kurangnya pengetahuan anak-anak tentang pengelolaan sampah menjadikan masalah sampah belum dapat teratasi. Kegiatan ini bertujuan untuk mengimplementasikan Model ATIK dalam meningkatkan kesadaran berfikir tentang kebersihan dalam membuang sampah dan memilah sampah pada taman kanak kanak di Kota Karawang. Hal ini dilakukan untuk mempermudah pemilihan sampah organik dan non organik untuk dapat mengurangi pencemaran udara yang diakibatkan oleh penumpukan sampah yang masih tercampur. Metode yang digunakan dalam penelitian ini adalah Deskriptif Kualitatif. Teknik pengumpulan data yang digunakan adalah wawancara, dokumentasi, dan catatan lapangan. Penelitian ini dilaksanakan di lembaga TK Nurul Huda Karawang melalui Model ATIK pada anak usia 5-6 tahun. Hasil penelitian di dapatkan bahwa TK Nurul Huda telah mengimplementasikan Model ATIK dengan cara Amati, Tiru, Kerjakan maka anak dapat dengan mudah membedakan antara sampah organik dan non organik. Penelitian ini dilakukan pada tanggal 1 September hingga 2 November 2023 sampai dengan dapat disimpulkan bahwa Model ATIK anak dapat berhasil membedakan antara sampah organik dan non organic.
The Interaction between Stress and Inflammatory Bowel Disease in Pediatric and Adult Patients

Background: Inflammatory bowel diseases (IBDs) have seen an exponential increase in incidence, particularly among pediatric patients. Psychological stress is a significant risk factor influencing the disease course. This review assesses the interaction between stress and disease progression, focusing on articles that quantified inflammatory markers in IBD patients exposed to varying degrees of psychological stress. Methods: A systematic narrative literature review was conducted, focusing on the interaction between IBD and stress among adult and pediatric patients, as well as animal subjects. The research involved searching PubMed, Scopus, Medline, and Cochrane Library databases from 2000 to December 2023. Results: The interplay between the intestinal immunity response, the nervous system, and psychological disorders, known as the gut–brain axis, plays a major role in IBD pathophysiology. Various types of stressors alter gut mucosal integrity through different pathways, increasing gut mucosa permeability and promoting bacterial translocation. A denser microbial load in the gut wall emphasizes cytokine production, worsening the disease course. The risk of developing depression and anxiety is higher in IBD patients compared with the general population, and stress is a significant trigger for inducing acute flares of the disease. Conclusions: Further large studies should be conducted to assess the relationship between stressors, psychological disorders, and their impact on the course of IBD. Clinicians involved in the medical care of IBD patients should aim to implement stress reduction practices in addition to pharmacological therapies.
Efficiency Evaluation of Water Consumption in a Chinese Province-Level Region Based on Data Envelopment Analysis

Due to the large volume of sewage in China, the efficiency of water consumption evaluated by the traditional model may be inaccurate. This paper evaluates the water consumption efficiency more scientifically. First, this paper uses the CCR model to evaluate the resource efficiency and environmental efficiency separately. The latter is generally lower than the former, which means the issue of water pollution is more serious than the problem of water resource consumption. Then, the water consumption efficiency is integrally evaluated by an eco-inefficiency model which focuses on undesirable outputs. The results are in good agreement with the results of the CCR model: (1) Only Beijing, Tianjin, and Shanghai are eco-efficient in terms of water consumption, water consumption efficiency in the southeastern coastal areas is higher than in the Midwest, and the overall water environment is bad; (2) China needs to focus on reducing industrial wastewater; (3) the output of water consumption has a lot of room for improvement; and (4) the output improvement schemes of all provinces have some similarities and are related to many features. So, this paper has made a clustering analysis of the improvement schemes and given detailed suggestions for improving the eco-efficiency of water consumption in China according to the clustering result.
Communities of woody vegetation and wood destroying fungi in natural and semi-natural forests of kyiv city, Ukraine

Abstract Selected forestry parameters were investigated in the system of trees and wood-destroying fungi in the natural forests of the Kyiv city on a gradient of recreational transformation. We investigated the vitality, age compositions, and health condition of woody plants (11 species), and species, systematic, trophic and spatial compositions of xylotrophic fungi (51 species, 224 findings of xylotrophs representing 34 genera, 20 families, 7 orders of divisions Basidiomycota; class Agaricomycetes). The results showed that communities of woody vegetation and xylotrophic fungi in forests depend on the degree of recreational transformation of the environment. Vitality, age compositions and health condition of trees altered species composition of xylotrophs.
Data-free Universal Adversarial Perturbation and Black-box Attack Supplementary Material

We report results of our Simple-UAP algorithm trained on images sampled from a single class in Table 1. The performance is close to the case of using images of all classes. Additionally, training on different single classes leads to different dominant labels, and the results are summarized in Table 2. When the single training class is fixed, the resulting dominant label with different runs is the same in most cases. However, when the single training class is changed, the corresponding dominant label also changes. This constitutes another empirical evidence that the dominant label does not necessarily occupy large regions in the image space as hypothesized by [3]. If the dominant label phenomenon is caused by the fact that the dominant label occupies large regions in the image space, the resulting dominant label is not supposed to change with the choice of a single training class.
Quantization-based Language Model Compression

Thispaperdescribestwo techniquesfor reducingthesizeof statisticalback-of -gramlanguagemodelsin computermemory. Languagemodelcompressionis achievedthroughacombinationof quantizinglanguagemodelprobabilitiesandback-of weightsandthepruningof parametersthataredeterminedto be unnecessaryafter quantization. The recognitionperformance of the original and compressedlanguagemodelsis evaluated acrossthreedifferentlanguagemodelsandtwo differentrecognition tasks.Theresultsshow that the languagemodelscanbe compressedby up to 60%of their original sizewith no significantlossin recognitionperformance. Moreover, thetechniques that aredescribedprovide a principledmethodwith which to compresslanguagemodelsfurther while minimising degradation in recognitionperformance.
fine-GRAPE : fine-Grained APi usage Extractor An Approach and Dataset to Investigate API Usage Version of November 16 , 2015

An Application Programming Interface (API) provides a specific set of functionalities to a developer, with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution, to get a better understanding of their various characteristics. For such studies software repositories are mined for API usage examples. However, the mining algorithms used for such purposes do not take type information into account, thus making the results imprecise. In this thesis, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotations. fine-GRAPE establishes a connection between a method invocation and the class of the API to which the method belongs. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We use the resulting dataset to perform four separate analyses. The first measures the lag time of each client by leveraging the version information that has been collected. We see that in most cases clients do not upgrade the version of the API that they are using to the latest version. The consequence of this is that the lag time that each client displays is quite high. The second study investigates the percentage of API features that are used by using the type information in the dataset. The results of this study show that a very small percentage of an API is actually used by clients in the real world. Our third study aims to show the relation between popular features and software quality. Finally, the fourth study analyzes the reaction of clients to the deprecation of API artifacts. Our deprecation study shows that most clients do not really react to deprecated entities.
nxs: a program library for neutron cross section calculations

A collection of routines for calculating neutron scattering and absorption cross sections on the basis of crystal structure descriptions is presented and implemented in the new and reusable nxs program library. An example program providing a graphical user interface to the nxs functions is created to demonstrate their usage. The flexibility of the library and the possibilities for multiple areas of application are shown by further examples involving Monte Carlo neutron simulations concerned with imaging experiment validation and neutron instrument development.
The International SAT Solver Competitions

is satisfiable is one of the most fundamental problems in computer science, known as the canonical NP-complete Boolean satisfiability (SAT) problem (Biere et al. 2009). In addition to its theoretical importance, major advances in the development of robust implementations of decision procedures for SAT, SAT solvers, have established SAT as an important declarative approach for attacking various complex search and optimization problems. Modern SAT solvers are routinely used as core solving engines in vast numbers of different AI and industrial applications. The International SAT Solver Competition1 is today an established series of competitive events aiming at objectively evaluating the progress in state-of-the-art SAT solving techniques. In this short article, we will provide an overview of the SAT solver competitions. Competition Reports
Intelligent Neural Network Based STLF

important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Application of Artiﬁcial Neural Networks to Forecast River Discharge Rates: A Case Study of the Grootfontein Aquifer

—In this paper, we attempt to provide a data driven approach for modelling discharge rates in a groundwater-fed spring emanating from the Grootfontein dolomite aquifer, within the North West province. While application of neural networks for discharge prediction in general has been investigated by many authors, in this paper, we employ several neural network architectures using different feature sets to select the most prominent feature set and neural network model in the prediction of discharge rates from the Grootfontein aquifer. Three different neural networks were tested including Long-short Term Memory, Gated Recurrent Unit and Feed Forward Neural network. The results show that by including groundwater levels, all networks signiﬁcantly improved in their performance as compared to without, recording lowest MAE score of 0.046 (m3/s) and 0.219 (m3/s) respectively. The results from this paper infer that recurrent neural networks are promising tools for predicting discharge rates in the study area. Furthermore, the paper also infers that groundwater levels can be successfully applied to predict the discharge rates of groundwater-fed springs, generate discharge data or patch missing discharge data in the compartment as long as we have key groundwater levels and a weather station nearby. Due to the lack of spring discharge monitoring in many areas of South Africa, this kind of methodology could become a useful tool in water resource management and also supporting water agencies to face drought issues speciﬁcally in karst regions.
Entropy and Algorithmic Complexity in Quantum Information Theory: a Quantum Brudno's Theorem

In classical information theory, entropy rate and algorithmic complexity per symbol are related by a theorem of Brudno. In this paper, we prove a quantum version of this theorem, connecting the von Neumann entropy rate and two notions of quantum algorithmic complexity, both based on the shortest qubit descriptions of qubit strings that, run by a universal quantum Turing machine, reproduce them as outputs.
Robustness in Salient Point Detection

Salient point algorithms are one of the most prominent methods used in computer vision. Many salient point detection algorithms exist today, two of which are SIFT and SURF. The aim of this paper is to test and compare these two algorithms, trying to measure the repeatability, stability and scale and rotation invariance. This is done by doing experiments with both algorithms and comparing the sets of salient points found
Mobile and more productive? Firm-level evidence on the productivity effects of mobile internet use at the early stage of diffusion

Mobile internet access allows for flexibility with respect to working time and working place. We analyse whether employees' use of mobile internet access improves firms' labour productivity. Our data set comprises 2460 German firms and refers to the year 2010, when mobile internet started its diffusion process to firms on a large scale. The econometric analysis shows that firms' labour productivity significantly increases with the share of employees with mobile internet access. However, an instrumental variable approach reveals that mobile internet use does not cause higher labour productivity.
Knowledge-Based Systems

The neural-based interactive dialogue system focuses on engaging and retaining humans in long-lasting conversations. This has been explored for a variety of goal-oriented dialogue domains, such as education, health care, entertainment, sports, and politics. To develop an understanding and awareness of social and cultural norms, and to address specific social skills, we need to invent strategies for building interactive systems that take into account the user’s emotions and relevant-facts in a multi-turn conversation. In this paper, we propose a new neural generative model that combines step-wise co-attention with a self-attention-based transformer network along with an emotion classifier to jointly control emotion and knowledge transfer during response generation. Quantitative, qualitative, and human evaluation results on the benchmark Topical Chat and the CMU_DoG dataset show that the proposed models can generate natural and coherent sentences, capturing essential facts with considerable improvement over emotional content.
Sensitivity of WRF/Urban simulations to urban morphology parameters: a case study in the city of Bolzano

The recent progress in numerical weather prediction modelling, and in particular the possibility to reach increasingly finer spatial resolutions, allowed researchers to reproduce building-atmosphere interactions in a more accurate and realistic way, especially in urban areas. The present work aims at evaluating the impact of highresolution gridded datasets of urban morphology parameters on the results of numerical simulations of atmospheric processes performed with the WRF/Urban suite in the city of Bolzano (Italy), and to analyze how they affect near-ground temperature fields. A sensitivity test was carried out, combining the WRF model with the Building Effect Parameterization (BEP) scheme to simulate two typical clear-sky summer days, respectively with and without the input gridded data of urban morphology. The structure and the morphology of the city of Bolzano were carefully reproduced through several fine-scale morphometric parameters from surface and terrain models (0.5 m resolution). The results highlight that urban morphological parameters display a high spatial variability, moderately affecting the distribution of the temperature field near the ground. High-resolution meteorological fields inside urban areas can be valuable information for building energy simulations. Accordingly, a scheme of model chain coupling WRF and TRNSYS codes is proposed, in order to enhance the future assessment of urbanization effects and in the same way to provide more realistic and accurate building
Recent Advances on Computational Proteomics

The application of computational methods in the study of problems related to proteins and enzymes and to their interaction with other molecules is a powerful and vast field of research, which encompasses several different disciplines, normally grouped under the generic designation of Computational Proteomics. In this work, we trace and up-todate analysis of this rich field of research, by presenting a brief
Study on biodiversity of yeasts isolated in samples, collected in Phongnha- Kebang National Park

From 53 samples (13 of leaves, 18 of litters and 22 of soils), which were collected in PhongnhaKebang National Park, 57 yeast strains were isolated. Those yeast strains were identified based on the morphological characteristics and the sequence analysis of D1/D2 domain of 26S rDNA. They belonged to 13 genera and 26 species, of which 5 were suspected as new species. A mong 13 genera, Basidiomycetous yeasts were dominant from isolated samples. Thirty-four strains out of 57 were mainly grouped into 3 genera of Rhodotorula, Sporobolomyces, Candida and 11 species. The other 18 strains belonged to the genera of Cryptococcus, Hannaella, Rhodosporidium, Pseudozyma and Trichosporon and 10 species. The rest were identified as Jaminaea, Pichia, Quambalaria, Sporisorium, Saccharomycopsis and Trichosporon (5 strains, 5 species).