Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and characterization of Salmonella spp . from chicken by using enrichment and selective media

The present study was done for characterization and antibiotic sensitivity pattern of the Salmonella spp. found as contaminants in chicken carcass. The overall incidence of Salmonella contamination of poultry carcass was found to be 4.90% with the higher percentage of Salmonella being isolated from chicken meat (8.00%) followed by liver and spleen (6.25%). The isolates were identified as Salmonella Typhimurium , Salmonella Paratyphi B and Salmonella (Rough). Eight Salmonella isolates obtained from poultry were confirmed as Salmonella spp. according to their biochemical profile and their sensitivity to different antimicrobial agents. Amikacin, kanamycin and ciprofloxacin were found to be the most effective antibiotics against Salmonella spp.
Exploration of a Mobile Design for a Privacy Assistant to Help Users in Sharing Content in Online Social Networks

Currently available Online Social Network (OSN) solutions with which users can select the audience with whom they want to share online content have been shown to be inefficient in protecting their privacy. As a result, users may either censure themselves or post information about themselves that they may later regret. To address the shortcomings of existing solutions, we propose to rely on a privacy assistant that will help the users in their audience selection. Among the necessary steps for its design, we focus in this paper on the visible tip of the iceberg, i.e., the interface that will present the suggestion to the users. Our interface addresses our defined criteria and is dedicated to mobile devices. In this first design phase, we have implemented and evaluated our proposed solution with ten participants using System Usability Scale (SUS). We have reached a SUS score of 70.75 and found out that our proposed interface is usable and easy to understand. We finally discuss possible improvements and challenges for designing and evaluating such interfaces.
Overconfidence and Preferences for Competition

We study when preferences for competition are a positive economic trait among high earners and the extent to which this trait can explain the gender gap in income among a master's degree in business administration (MBAs). Consistent with the experimental evidence, preferences for competition are a positive economic trait only for individuals who are not overconfident. Preferences for competition correlate with income only at graduation when bonuses are guaranteed and not a function of performance. Overconfident competition‐loving MBAs observe lower compensation and income growth, and experience greater exit from high‐reward industries and more frequent job interruptions. Preferences for competition do not explain the gender pay gap among MBAs.
Robust speed control algorithm with disturbance observer for uncertain PMSM

ABSTRACT This article suggests a robust cascade speed control algorithm for a permanent magnet synchronous motor (PMSM) combining the classical feedback linearising (FL) method and the disturbance observers (DOBs) without the integrators. The contributions of this method are twofold. The first one is to provide the simple DOBs for not only guaranteeing the closed-loop performance recovery property but also removing the steady-state errors without the integrators with respect to the tracking errors. The second one is to prove that the inner and outer loops are stabilised by the proposed cascade-type controller, simultaneously. The simulation and experimental results reveal that the proposed method maintains the speed tracking performance to be satisfactory for a wide operating region with the fixed control gain despite a plant-model mismatch where a 3-kW interior PMSM is utilised.
Combined Climate Change Stressors on Bivalves

7 Impacts of a range of climate change on marine organisms have been analysed in laboratory and experimental 8 studies. The use of different taxonomic groupings, and assessment of different processes, though, makes 9 identifying overall trends challenging, and may mask phylogenetically different responses. Bivalve molluscs are 10 an ecologically and economically important data-rich clade, allowing for assessment of individual vulnerability 11 and across developmental stages. We use meta-analysis of 203 unique experimental setups to examine how 12 bivalve growth rates respond to increased water temperature, acidity, deoxygenation, changes to salinity, and 13 combinations of these drivers. Results show that anthropogenic climate change will affect different families of 14 bivalves disproportionally but almost unanimously negatively. Almost all drivers and their combinations have 15 significant negative effects on growth. Combined deoxygenation, acidification, and temperature shows the 16 largest negative effect size. Eggs/larval bivalves are more vulnerable overall than either juveniles or adults. 17 Infaunal taxa, including Tellinidae and Veneridae, appear more resistant to warming and oxygen reduction than 18 epifaunal or free-swimming taxa but this assessment is based on a small number of datapoints. The current focus 19 of experimental set-ups on commercially important taxa and families within a small range of habitats creates 20 gaps in understanding of global impacts on these economically important foundation organisms. 21 22 https://doi.org/10.5194/egusphere-2023-287 Preprint. Discussion started: 13 March 2023 c © Author(s) 2023. CC BY 4.0 License.
The Association between Years of Study with Depression among University Students in Borneo, Malaysia

Introduction: Depression is currently one of the significant mental health problems worldwide. Depression can lead to multiple emotional and physical issues and decrease a person's ability to function. It affects someone's feelings, thoughts, and actions. Evidence shows that the prevalence of depression is higher among university students than the average population, with those in Asian countries being more severely affected. This study aims to determine the prevalence and levels of depression among students in a public university in Sabah and to measure the various associated factors with depression which include the year of study they were in. Methods: This is a cross-sectional study with 260 respondents from a public university in Borneo, Malaysia. The Patient Health Questionnaire (PHQ-9) was used to measure the level of depression. The sociodemographic questionnaire was included for sociodemographic profiling, particularly the year of study they were in at the time of the study. The International Physical Activity Questionnaire (IPAQ-M) was used to assess physical activity. Participants answered the given questionnaires through Google form. Binary logistic regression was used to test the associations between depression and variables of interest while adjusting for confounders. Results: Findings showed the prevalence of depression among university students in a public university in Borneo was 82%, with 46% having mild depression, 30.0% having moderate depression, 17.9% having moderate-severe depression, and 6.1% having severe depression. The year of study was significantly associated with depression. Students in Year 1 and Year 2 were two to three times more likely to experience a higher level of depression than those in Year 3 and Year 4. (OR: 2.55, 95% CI: 1.25, 4.06). Conclusion: Given that junior students were more prone to a higher level of depression due to adjustment factors, the university and health care providers should target this group while implementing programs and interventions.
D4AM: A General Denoising Framework for Downstream Acoustic Models

The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noisy-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM.
Development of technology for obtaining polymer cement glue (PCG) with use of chemical additives and industrial waste

The article presents the results of a study on the production of polymer cement glue with the use of chemical additives and industrial waste, as well as the influence of the amount and fineness of the filler grinding on the properties of polymer cement glue and the possibility of obtaining polymer cement glue PCG using water-soluble POLIMIX technology dry construction mix (DBM).
Supplementary Material for L INE EX: Data Extraction from Scientific Line Charts

This supplementary material provides additional details for the L INE EX system proposed to extract data from scientific line charts. Specifically, here we provide details and results obtained with the background detection algorithm mentioned in the paper. We provide training specifications for the chart element detection and the keypoint extraction modules. Finally, we give details on the dataset used for the legend mapping module.
Orthography-based Pronunciation Scoring for Better CAPT Feedback

We establish the viability of a streamlined architecture for pedagogically appropriate computer assisted pronunciation training (CAPT), to give second language learners automatic feedback about their mispronunciations. This takes advantage of end-to-end speech recognition models to detect mispronunciation in audio segments that correspond directly to orthographic letters, in contrast to standard mispronunciation detection using phone representations. Results in a classification task show the potential for similar sensitivity to non-nativelike phonetic errors in grapheme-aligned segments as in phone-aligned segments. Advantages of this approach over phone-based pronunciation scoring can include providing naturally comprehensible (ortho-graphic, not phonemic) feedback to learners, being inherently open-vocabulary in the target language, and evaluating pronun-ciations with reference to a full range of target-language acoustic variants rather than a prespecified canonical phone sequence.
Scientific and methodological substantiation of measures for environmental management of upstream area Tupolangsky waterworks

For the 8.2 hectares of the Tupolang hydroscheme near the dam, the measures for its landscaping with the help of a drip irrigation system have been developed. The source of irrigation is infiltration water from the old riverbed. After natural filtration, the water is clear, without mechanical impurities, with a salinity of 0.2 g/l and a flow rate of about 50 l/s. At the end of this pipe, the head is 4 MPa, which eliminates the traditional device of the pumping station to create the required head in a closed irrigation network. This allows the application of these irrigation systems without a pumping station and water treatment. The article presents the initial data for the calculation of drip irrigation: 1. Soils are typical gray soils, non-saline, medium loam by mechanical composition; 2. Groundwaters are at a depth of > 3 m; 3. Mass volume of soils is 1.4 t/m3; 4. Marginal field moisture capacity (MWC) - 28 %; 5. Ultimate moisture capacity is accepted at 85 % of FWP and makes 23.8 %. And most importantly, such irrigation saves water resources.
Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm

The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.
Towards a long-term data record

We report the development of an algorithm for the retrieval of Total Column Water Vapor (TCWV) from blue 12 spectra obtained by satellite instruments such as the Ozone Monitoring Instrument (OMI). The algorithm is implemented in 13 an automatic processing pipeline and will be used to generate a long-term data record as part of a MEaSUREs project. 14 TCWV is calculated as the ratio between the Slant Column Density (SCD) and Air Mass Factor (AMF). Both these factors 15 are improved upon previous work by incorporating more constraints or physical processes. For the SCD, we have 16 optimized the retrieval window to 432 – 466 nm, performed a temperature correction, and employed a new stripe-removal 17 post-processing routine. The use of OMI collection 4 spectra reduces the SCD fitting uncertainty by ~9% with respect to 18 collection 3. For the AMF, we perform on-line radiative transfer using VLIDORT. Over land surfaces, we use bi19 directional reflectances based on MODIS products. Over the oceans, we consider surface roughness and water-leaving 20 radiance, and we find that water-leaving radiance is important for avoiding large TCWV biases over the oceans. 21 Under relatively clear conditions, the MEaSUREs OMI data are well correlated with the reference datasets, having 22 correlation coefficients of r ~0.9. Over the oceans, MEaSUREs-AMSR_E has an overall mean (median) of ~ 1 mm (0.6 23 mm) with a standard deviation of σ ~6.5 mm, though large systematic differences in certain regions are also found. Over 24 land surfaces, MEaSUREs-GPS has an overall mean (median) of -0.7 mm (-0.8 mm) with σ ~5.7 mm. Even a small amount 25 of cloud can introduce large bias and scatter; thus, without further correction, strict data filtering criteria are required. 26 However, the MEaSUREs TCWV data can be corrected through machine learning when accurate measurements are 27 abundant. In this regard, under all-sky conditions, the mean bias of MEaSUREs over the oceans reduces from 4.5 mm 28 (without correction) to -0.3 mm (with correction using LightGBM models), and the standard deviation decreases from 11.8 29 mm to 3.8 mm. We also examined the representation error of the GPS stations using the dense GEONET data. The within30 pixel variance of TCWV varies with grid size following a power law dependence. At 0.25°×0.25° resolution, the derived 31 representation error is about 1.4 mm. 32
AERODYNAMIC EFFECTS OF PHASE OFFSET BETWEEN SYNCHRONIZED PROPELLERS IN HOVER

Emerging advances in electric-propulsion technology are enabling aircraft to use distributed electric propulsion (DEP) to increase efficiency and maneuverability. The implementation of DEP effectively decreases the spacing between propellers, introducing complex aerodynamic interactions that are not well understood. This study presents the use of stereo-scopic particle image velocimetry (SPIV) to obtain 3D velocity measurements of the flow fields of phase locked and synchronized propellers at close proxim-ity in a side-by-side configuration. These experiments were performed to explore the aerodynamic effect of phase offset between the propellers. It was found that while the propeller interactions cause an induced upwash and a larger wake interaction region, phase offset was seen to have little influence on the averaged wake flow features. The tip vortex trajectories and circulation strengths were altered due to the interacting propellers. The out of phase tip vortex circulation strength is higher than for the in phase, representing a 21% drop when compared to the single case as opposed to the 32% drop for the in phase test.
MULTI FACE DETECTION AND RECOGNITION SYSTEM WITH VIDEO STREAMING

— Now a day’s face acts as a major part in which the identity of each human classification is done mainly using face. Thus on a developing Artificial Intelligent environment detection of the face on multiple person and recognition is the main thing employed. Thus the proposed system focuses on the detection of multi face and recognition over live video streaming on public places. The classification focus on the Dense Bi-Directional Back Propagation Algorithm (DBPNN) which gives a exact classification of the user side identity. Thus the developmental feature classified fields with HAAR cascades and Gaussian functionality helps in the identification of the face matching and classification system. Here the face features are extracted and stored in Viola Jones method which make exact texture pattern identity. The detection and the recognition can be made out with the normal realization point plotting. Thus the classification of DBPNN will plans to give out 99.83% of accuracy on a detection and recognition over multi face in a single video streaming system. The developed system helps in many application system like biometric, public place access, police verifications etc. The term level of the extraction ensures the high probability of the developmental extraction.
Response to the ASA’s Statement on p-Values: Context, Process, and Purpose

ISSN: 0003-1305 (Print) 1537-2731 (Online) Journal homepage: https://www.tandfonline. com/loi/utas20 Response to the ASA’s Statement on p-Values: Context, Process, and Purpose Edward L. Ionides, Alexander Giessing, Yaacov Ritov & Scott E. Page To cite this article: Edward L. Ionides, Alexander Giessing, Yaacov Ritov & Scott E. Page (2017) Response to the ASA’s Statement on p-Values: Context, Process, and Purpose, The American Statistician, 71:1, 88-89, DOI: 10.1080/00031305.2016. 1234977 To link to this article: https://doi.org/10.1080/00031305.2016. 1234977
Anatomy of the aerial vegetative organs of Phoradendron strongyloclados Eichler (Viscaceae)

Phoradendron strongyloclados Eichler is a hemiparasite commonly known as mistletoe. Representatives of the genus Phoradendron are used in folk medicine, but little is known about P. strongyloclados, including its anatomical structure. The present work aimed to describe the anatomy of the aerial vegetative organs of P. strongyloclados. For this purpose, the usual methodology in plant anatomy was used. The leaves have thick cuticle, unistratified epidermis, paracytic stomata and isobilateral mesophyll. The cataphyll is bilobed, with homogeneous mesophyll and vascularization. The stem has an elliptical cross section in primary structure. In a secondary structure, it maintains the photosynthetic tissues and the epidermis. The pith is sclerified. The wood is developed, rigid, fibrous, with short vessel elements and simple perforation plate. Rays are heterocellular and their cells may contain prismatic crystals. The compact arrangement of parenchyma cells, thick cuticle, high lignification and low surface/volume ratio; small, fleshy, closely spaced leaves characterize xeromorphy.
Efficient Detection of Human Misbehaviour in CCTV Real Time Footage

—In this paper, we propose real-time image-based recognition of human activities from series of images considering different human actions performed in an indoor environment. The proposed image-based human activity recognition (IHAR)system can be utilized for assisting the life of disabled persons, surveillance and human tracking, human computer interaction, and efficient resource utilization. The proposed IHAR system consists of closed-circuit television (CCTV) camera-based image acquisitioning, various filtering-based image enhancement, principle component analysis (PCA) based features extraction, and various machine learning algorithms for recognition accuracy performance comparison. We collected dataset of 10 different activities such as walking, sitting down and standing up consists of 35,530 images. The dataset is divided into (90%,10%), (80%,20%), and (70%,30%) training and testing respectively and evaluated three classifier K-nearest neighbors (KNN), Random Forest (RF), and Decision Tree (DT). The experimental results show the accuracy of 95%, 97%, and 90% by KNN, RF, and DT respectively.
IMAGE TO AUDIO CONVERSION USING DIGITAL IMAGE PROCESSING

: In order to make digital pictures accessible to those with visual impairments, a process known as "image to audio conversion" converts them into audible files. In this research, we present a novel approach to converting visuals to sound with the use of digital image processing. The suggested approach for turning digital photos into audio files utilises segmentation, feature extraction, and audio synthesis methods. Using a publically accessible dataset, the system was built and tested to demonstrate that the suggested technique can successfully convert photos to audio files with a high degree of precision. This device provides a low-cost, non-invasive option for people with visual impairments to see and interact with digital pictures, which may have a profound impact on their independence and happiness. This study's results show the promise of applying digital image processing methods to the age-old problem of converting visuals to audible forms, with applications ranging from the pedagogical to the artistic.