Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Clostridium butyricum on Growth Performance, Gut Microbiota and Intestinal Barrier Function of Broilers

This study was conducted to evaluate the effects of Clostridium butyricum dietary supplementation on the growth, antioxidant, immune response, gut microbiota, and intestinal barrier function of broilers under high stocking density (HSD) stress. A total of 324 1-day-old Arbor Acres male broilers were randomly assigned to three treatments with six replicates, each replicate including 18 chickens (18 birds/m2). The experiment lasted 6 weeks. The three treatments were basal diet (control, CON), basal diet supplemented with 1 × 109 colony forming units (cfu)/kg C. butyricum (CB), and basal diet supplemented with 10 mg/kg virginiamycin (antibiotic, ANT). The results showed that the body weight (BW) and average daily gain (ADG) of broilers in the CB group were significantly higher than those in the CON group in three periods (p < 0.05). The total antioxidant capacity (T-AOC) and the superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activity in serum of the CB group were significantly increased compared with those in the CON and ANT groups at 42 days (p < 0.05). At 42 days, the serum immunoglobulin M (IgM) and immunoglobulin G (IgG) levels of the CB group were significantly higher than those of the CON group. Compared with the CON group, interleukin-1β (IL-1β) in the CB group was significantly decreased in the starter and grower stages (p < 0.05), but there was no significant difference between the two treatment groups (p > 0.05). C. butyricum significantly decreased the high stocking density-induced expression levels of IL-1β and tumor necrosis factor-α (TNF-α) in the ileum of broilers at different stages. Additionally, C. butyricum could increase the expressions of claudin-1 and zonula occludens-1 (ZO-1) in intestinal tissue. Moreover, C. butyricum significantly increased the Sobs and Shannon indices in the CB group compared with the ANT group (p < 0.05), while the Ace index in the CB group was significantly higher than that of the CON group (p < 0.05). Furthermore, by using 16S rRNA gene sequencing, the proportion of Bacteroides in the CB group was increased compared to those in the CON and ANT groups at the genus level. In conclusion, C. butyricum supplemented into feed could improve the growth performance and feed utilization of broilers by promoting immune and intestinal barrier function and benefiting the cecal microflora.
Levels of Disaggregation and Degrees of Aggregate Constraint in Transportation System Modeling

This paper compares modeling approaches used in transportation system modeling and in system modeling more generally. It considers two dimensions: (a) the level of disaggregation in the representation of system elements and (b) the degree of aggregate constraint on the system. Furthermore, it incorporate both the equilibrium and the process simulation approaches and thereby enters the debate concerning the relative merits and (perceived? ) flaws of these two approaches. The intentions of this paper are (a) to engender a greater appreciation for the advantages and disadvantages within the range of available techniques and the potential for matching technique with context in a given instance and (b) to present a more complete view of the linkages among techniques and the scope for hybrid approaches. Coverage includes some new, emerging approaches, including the combination of an activity- based model with equilibrium treatments for both land use and network assignment. Therefore, the paper provides the framework for a discussion of the opportunities and challenges arising with the implementation of activity- based models and transportation system models more generally, helping progress beyond the standard positions taken in the debate about equilibrium versus process simulation and aiding the consideration of appropriate directions for further research and development work.
Single-Channel Speech Dereverberation using Subband Network with A Reverberation Time Shortening Target

This work proposes a subband network for single-channel speech dereverberation, and also a new learning target based on reverberation time shortening (RTS). In the time-frequency domain, we propose to use a subband network to perform dere-verberation for different frequency bands independently. The time-domain convolution can be well decomposed to subband convolutions, thence it is reasonable to train the subband network to perform subband deconvolution. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reﬂections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training, and leads to a large prediction error. In this work, we propose a RTS learning target to suppress reverberation and meanwhile maintain the exponential decaying property of reverberation, which will ease the network training, and thus reduce the prediction error and signal distortions. Experiments show that the subband network can achieve outstanding dere-verberation performance, and the proposed target has a smaller prediction error than the target of direct-path speech and early reﬂections.
RECENT DETECTION AND PREDICTION METHODS FOR URINARY TRACT INFECTION IN HUMANS: A DETAILED REVIEW

Urinary tract infection (UTI) is common infection in humans due to several reasons. It occurs with the incursion and growth of wide range microorganisms, consists of bacteria such as Gram-negative, Gram-positive and fungi. The urinary system consists of kidneys, urethra, ureters and bladder which get affected by urinary tract infection. Primarily the infection occurs in the lower tract of the urinary system i.e. bladder and urethra. UTI get severe, if it appears in upper tract which includes kidneys and ureters. UTI is among a most prevalent diagnosed infection that affects both men and women and it is more common in women because of their physiology. UTI give rise to several diseases related to liver, kidneys and bladder if it left untreated. In the last few years, numerous healthcare devices have emerged which permits people to detect and manage their health conditions easily at home. The aim of this paper is to review and assess the causes, impacts, requirements for the prediction of urinary illness. Urine infection (UI) is the highly common disease in the world's population. UI monitoring has been an issue of concern for healthcare industry. Outcomes are equated by several state-of-the-art forecast methods that shows the reviewed method attain major improvement and high efficacy. Internet of Things enhanced responses in terms of efficiency classification, forecast productivity, temporal delay and stability. (RNN).
A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

—Social media platforms generate a huge amount of data every day. However, liberty of speech through these networks could easily help in spreading hatred. Hate speech is a severe concern endangering the cohesion and structure of civil societies. With the increase in hate and sarcasm among the people who contact others over the internet in this era, there is a dire need for utilizing artificial intelligence (AI) technology innovation that would face this problem. The rampant spread of hate can dangerously break society and severely damage marginalized people or groups. Thus, the identification of hate speech is essential and becoming more challenging, where the recognition of hate speech on time is crucial in stopping its dissemination. The capacity of the Arabic morphology and the scarcity of resources for the Arabic language makes the task of distinguishing hate speech even more demanding. For fast identification of Arabic hate speech in social network comments, this work presents a comprehensive framework with eight machine learning (ML) and deep learning (DL) algorithms, namely Gradient Boosting (GB), K-Nearest Neighbor (K-NN), Logistic Regression (LR), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Support Vector Machine (SVM), Ara-BERT, and BERT-AJGT are implemented. Two representation techniques have been used in the proposed framework in order to extract features: a bag of words followed by BERT-based context text representations. Based on the result and discussion part, context text representation techniques with Ara-BERT and BERT-AJGT outperform all other ML models and related work with accuracy equal to 79% for both models.
Prevalence of depressive disorders in a primary care setting in Ho Chi Minh City, Vietnam: A cross-sectional epidemiological study

Objective This research was conducted to identify the prevalence and associated factors of depressive disorders, as well as evaluate the recognition rate of general practitioners in detecting these mental health issues in primary care. Method Five hundred and twelve participants (55.3% female, mean age = 46.35 years) were assessed by psychiatrists based on the DSM-5 clinical procedures over a two-month survey in a primary care facility in Ho Chi Minh City, Vietnam. Results There were 15.8% (95% confidence interval [CI] 12.9-19.2) of the population having depressive disorders, with major depressive disorder being the most prevalent subtype at 8% (95% CI 5.9-10.6). General practitioners could detect depressive disorders in 2.5% of all cases (95% CI .5-7.7). Significantly linked with depressive disorders in multivariable analysis were Chinese ethnic or other minority races (adjusted odds ratios [aOR] = 4.10, 95% CI 1.04-16.12), and low economic status (aOR = 5.41, 95% CI 1.29-22.59). Conclusions The high prevalence of depressive disorders in outpatients of primary care clinics may raise the awareness of the practitioners about screening and other appropriate actions to tackle the issue.
The Shadow Values of Soil Hydrological Properties in the Production Potential of Climatic Regionalization of the Czech Republic

The Czech Republic uses a dual system of agricultural land prices, namely the formal/official price—for tax purposes—and the market price in the case of a standard sale or purchase of farmland. This paper focuses on the construction of an econometric model to quantify the influence of soil hydrological characteristics on the production potential in a given climatic region. It also focuses on the use of this model for the determination of the official price of agricultural land, which is expressed on the basis of the code of the evaluated soil-ecological unit (ESEU) and based on defined soil characteristics. The pricing itself is based on the production potential of the land, which in practice is very important for spatial planning, as it determines the classes of agricultural land protection with regard to the possibility of setting aside agricultural land for non-productive purposes or for drawing subsidies for less favourable areas. In this context, the non-productive functions of agricultural land are also frequently discussed, especially its retention, which plays a very important role in the currently changing climatic conditions. There are a number of studies on soil retention, and numerous approaches to measuring it, but none of them address its impact on production potential and thus on the price of land. For this reason, this paper focuses on defining the influence of the retention of the main soil units (defined for the conditions of the Czech Republic) on production potential. For this definition, SUR models are used, where the endogenous variable is expressed as production potential and the exogenous variables include the basic soil characteristics such as grain size, porosity, hydrological component of the soil, and retention. The obtained outputs show both the high explanatory power of the model and the adequate parametric significance of most variables, which provides sufficient support for the use of the results in practice. In addition, the estimated models across all climatic regions are consistent with substantively logical assumptions about the link between production potential and soil hydrological properties, which secondarily demonstrates their applicability in practice, especially for state administration, but also for local government in the sense of municipalities, cities, and other organizational units.
Coyote: A Compiler for Vectorizing Encrypted Arithmetic Circuits

Fully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to operate on encrypted data and produce a result that, when decrypted, yields the result of the unencrypted computation. While FHE enables privacy-preserving computation, it is extremely slow. However, the mathematical formulation of FHE supports a SIMD-like execution style, so recent work has turned to vectorization to recover some of the missing performance. Unfortunately, these approaches do not work well for arbitrary computations: they do not account for the high cost of rotating vector operands to allow data to be used in multiple operations. Hence, the cost of rotation can outweigh the benefits of vectorization. This paper presents Coyote, a new approach to vectorizing encrypted circuits that specifically aims to optimize the use of rotations. It tackles the scheduling and data layout problems simultaneously, operating at the level of subcircuits that can be vectorized without incurring excessive data movement overhead. By jointly searching for good vectorization and lane placement, Coyote finds schedules that avoid sacrificing one for the other. This paper shows that Coyote is effective at vectorizing computational kernels while minimizing rotations, thus finding efficient vector schedules and smart rotation schemes to achieve substantial speedups.
Winding Type Alternation of a Refurbished Old Generator

Refurbishment of hydro-generators promises an increased lifespan and improved efficiency. Old generators are not only less efficient but also less reliable; their fault occurrence is more common. The economic impact of out-service time can be astronomical. Thus, refurbishment is a critical task; but it is challenging too. Most of the conducted works focus on the material update, enhancing the cooling network, and obtaining better efficiencies. Modifying winding type, i.e. conversion from lap winding to wave winding is rare since it often requires a change in the slot number. Such a move can be laborious, size change can become mandatory. In this study, a refurbishment design strategy is proposed which includes a winding type conversion. The task is to implement a wave winding since it has less out-service time and is easily replaced by field workers. In this paper, an old generator that started operation in 1956 is used for a refurbishment. This study promises a guideline to hydro-generator designers who work on a refurbishment study on old hydro-generators that have diamond coils.
Panda : Performance Debugging for Databases using LLM Agents

Debugging a performance issue in databases is notoriously hard. Wouldn’t it be convenient if there exists an oracle or a co-pilot for every database system which users can query in natural language (NL) — ‘ what’s wrong? ’, or even better— ‘ How to fix it? ’. Large Language Models (LLMs), like ChatGPT, seem to be a natural surrogate to this oracle given their ability to answer a wide range of questions by efficiently encoding vast amount of knowledge for e.g., a major chunk of the internet. However, prompting ChatGPT with database performance queries often results in ‘technically correct’ but highly ‘vague’ or ‘generic’ recommendations typically rendered useless and untrustworthy by experienced Database Engineers (DBEs). In this work we propose Panda , a framework to provide context grounding to pre-trained LLMs in order to generate more ‘useful’ and ‘in-context’ troubleshooting recommendations. Panda draws inspiration from the way experienced DBEs perform debugging, and puts a system in place with necessary components required to robustly deploy pre-trained LLMs in production for debugging. The 4 key components of Panda are: (1) Grounding ; (2) Verification ; (3) Affordance ; and (4) Feedback . We describe the necessity and usefulness of each component and how they communicate internally to transform a given pre-trained LLM into generating in-context, actionable, useful and accurate recommendation for debugging a given database system.
Correlation of Radiation-Induced Interface Traps With Band Edge Energy Through Band Structure-Based Analysis of Electrostatics of UTB SOI Devices

The eﬀect of Radiation on the semiconductor-oxide interface, inducing interface trap states, has generally only been experimentally measured, which makes it diﬃcult to quantify the impact of this radiation on device electrostatics. For an Ultra-Thin-Body (UTB) MOS device, the 1-D Band structure along the direction of conﬁnement, if solved selfconsistently with the 1-D Poisson’s equation, while varying the band edge energy ([? ]Eedge) at the Si-SiO2 interface, can enable the quantiﬁcation of the eﬀect of interface trap states on channel electrostatics. In this work, we present an approach to correlate the radiation dose to the band edge energy ([? ]Eedge), thus enabling the band structure-based approach to be used to quantify the eﬀect of these radiation-induced traps on the device electrostatics. We show a methodology that co-relates the interface charge induced by [? ]Eedge and the charge yield, due to diﬀerent radiating particles, on the Si - SiO2 interface. After identifying appropriate values of [? ]Eedge for diﬀerent particles and doses, the degradation due to radiation on the channel electrostatics can be accurately simulated with the atomistic band structure-based methodology. We also show an approach to extend this methodology to lower device temperatures, thus eﬀectively quantifying the eﬀect of radiation dose on UTB device electrostatics for a wide range of device temperatures.
Integration of inter-personal space and shared workspace: ClearBoard design and experiments

We describe the evolution of the novel shared drawing medium ClearBoard which was designed to seamlessly integrate an interpersonal space and a shared workspace. ClearBoard permits coworkers in two locations to draw with color markers or with electronic pens and software tools while maintaining direct eye contact and the ability to employ natural gestures. The ClearBoard design is based on the key metaphor of “talking through and drawing on a transparent glass window. ” We describe the evolution from ClearBoard- 1 (which enables shared video drawing) to ClearBoard-2 (which incorporates TeamPaint, a multiuser paint editor). Initial observations and findings gained through the experimental use of the prototype, including the feature of “gaze awareness,” are discussed. Further experiments are conducted with ClearBoard-O (a simple mockup), ClearBoard- 1, and an actual desktop as a control. In the settings we examined, the ClearBoard environment led to more eye contact and potential awareness of collaborator’s gaze direction over the traditional desktop environment.
Visually Summarizing the Web using Internal Images and Keyphrases

Visual summarization of web pages helps to achieve a friendlier user experience in search and refinding tasks. With the use of this method users quickly get an idea of what the web page is all about and help in recalling the visited web page. The internal images of web page are usually used to describe the content of a webpage. Internal images in web pages are generally worthy for this purpose. Nevertheless, in many web pages dominant internal images are unavailable. So, we move towards a new approach i.e. external image summarization to summarize web pages without any dominant internal images. But, external images are sometimes unreliable because of the way they are retrieved from internet. By taking advantage of internal and external images we propose a scheme for visual summarization. To improve the reliability of external images, relevant external images are retrieved from the Internet by considering textual and visual importance between target webpage and hosting webpage. Relevance, Dominance and typicality of the image with respect to web page are the features of images. We propose an Affinity Propagation Clustering based algorithm to select the best of internal and external images as summarization.
Fabrication of Metallized Nanowires from Self‐Assembled Diblock Copolymer Templates

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Les processus de contrôle dans la structuration de l'organisation

Dans un contexte de mutation, lie a l'ouverture des marches de l'electricite a la concurrence, l'entreprise EDF nous a sollicites pour mener une reflexion a propos de ses methodes de controle en occupant un poste d'auditeur. Dans un contexte de recherche-action et par une methodologie qualitative, la recherche s'est attache a repondre a la problematique suivante: « Comment un processus de controle participe a l'emergence de la structuration sociale de l'organisation? » Pour repondre a cette question, la recherche a mis a l'epreuve des donnees empiriques la theorie de la structuration sociale de Giddens.
Accurate Depth and Normal Maps from Occlusion-Aware Focal Stack Symmetry

We introduce a novel approach to jointly estimate consistent depth and normal maps from 4D light fields, with two main contributions. First, we build a cost volume from focal stack symmetry. However, in contrast to previous approaches, we introduce partial focal stacks in order to be able to robustly deal with occlusions. This idea already yields significanly better disparity maps. Second, even recent sublabel-accurate methods for multi-label optimization recover only a piecewise flat disparity map from the cost volume, with normals pointing mostly towards the image plane. This renders normal maps recovered from these approaches unsuitable for potential subsequent applications. We therefore propose regularization with a novel prior linking depth to normals, and imposing smoothness of the resulting normal field. We then jointly optimize over depth and normals to achieve estimates for both which surpass previous work in accuracy on a recent benchmark.
From High School to College: Improving Opportunities for Success in Postsecondary Education.

Acknowledgments.1. Bridging the Great Divide: How the K-12 and Postsecondary Split Hurts Students, and What Can Be Done About It (Michael W. Kirst, Kathy Reeves Bracco). 2. Working Toward K-16 Coherence in California (Anthony Lising Antonio, Samuel H. Bersola). 3. K-16 Turmoil in Texas (Andrea Venezia). 4. Roadblocks to Effective K-16 Reform in Illinois (Betty Merchant). 5. Oregon's K-16 Reforms: A Blueprint for Change? (Andrea Conklin Bueschel, Andrea Venezia). 6. Georgia's P-16 Reforms and the Promise of a Seamless System (Caroline Sotello Viernes Turner, Lisa M. Jones, James C. Hearn). 7. K-16 Reform in Maryland: The First Steps (Heinrich Mintrop, Toby H. Milton, Frank A. Schmidtlein, Ann Merck MacLellan). 8. The Missing Link: The Role of Community Colleges in the Transition Between High School and College (Andrea Conklin Bueschel). 9. What Have We Learned, and Where Do We Go Next? (Michael W. Kirst, Andrea Venezia, Anthony Lising Antonio). Appendix A: Research Design and Methodology.Appendix B: RAND Data.The Authors.Index.