Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth, reproductive performance and survival rate of Bonga sheep and their crossbreds in Southern Ethiopia

The dissemination of improved Bonga sires from Bonga sheep community-based breeding for the purpose of genetic improvement have been start in different parts of Ethiopia since 2012. Four districts from southern Ethiopia were selected for this study purposively. Monitoring and survey tools were used to evaluate the growth and reproductive performance and survival rate of Bonga crosses in the areas. Lambs obtained from Bonga sire show better and fast growth rate than lambs obtained from local sire. The obtained body weights for Bonga cross lambs at their birth, three and six-month heavier by 0.56, 1.92 and 4.4kg than local lambs under the same management practices of farmers. Based on survey result, 0.29 more lambs were obtained from ewes mated by Bonga ram than ewes mated by local ram. The findings indicated that, lambing interval were minimized by one month and age at first lambing of Bonga cross ewe earlier by 2.4 months than local ewe lambs. We observed that, the pre and post weaning mortality rate for Bonga cross lambs was significantly lower than local sheep in the present study areas. The fast growth rate of Bonga crosses over local sheep attracts farmers in the study areas to use Bonga rams as a breeding sire.
Undrained uplift resistance of strip plate anchors in clayey slopes

Applying lower bound finite element limit analysis in combination with second-order conic programming, the vertical uplift resistance of strip anchors buried in a clayey sloping ground has been determined under undrained condition. The effect of slope angle, height of slope, normalised unit weight of soil, depth of embedment of anchor and the edge distance of the anchor from the toe as well as crest of the slope on the uplift resistance has been examined. The results are expressed in terms of a non-dimensional uplift factor defined as Fcγ. For a given embedment ratio, the magnitude of Fcγ increases linearly with an increase in the normalised unit weight of soil up to a certain limit, thereafter with further increase in the normalised unit weight of soil, the value of Fcγ either remains constant or the numerical solution becomes infeasible. This constant magnitude of Fcγ is attributed to the change of shallow anchor to deep anchor behaviour, and the infeasibility in solutions may be due to overall slope failure. Empirical equations have also been proposed to obtain the uplift capacity of strip anchors buried in a sloped clayey ground.
Active Participatory Regional Surveillance for Notifiable Swine Pathogens

Simple Summary Effective, sustainable regional surveillance for the early detection of notifiable swine pathogens has been difficult to achieve. Regional surveillance based on clinical signs (syndromic surveillance) is not diagnostically sensitive and specific. Surveillance based on farm-by-farm testing is burdensome and costly. Borrowing the strengths of each approach, we evaluated an active participatory surveillance design in which regional status was determined by targeted sampling of 10 poor-doing pigs in each participating farm followed by screening in credentialed laboratories. The analysis showed that at 0.1% prevalence (18 infected farms among 17,521 farms) and a farm-level detection probability of 30%, active participatory surveillance would detect ≥ 1 positive farms with 67%, 90%, and 97% probability when producer participation was 20%, 40%, and 60%, respectively. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.016 to EUR 0.032 (USD 0.017 to USD 0.034 USD) per pig in the region. The techniques and technologies required for active participatory surveillance are widely available and in common use. Implementation would require coordination among producers, industry groups, and animal health authorities. Abstract We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.
Optimal dsRNA Concentration for RNA Interference in Asian Citrus Psyllid

Simple Summary The Asian citrus psyllid is an insect pest of citrus trees that transmits the causal agent of huanglongbing disease. Double-stranded RNA molecules trigger RNA interference (RNAi), a naturally occurring process in eukaryotes that modulates the expression of genes involved in host innate immunity. Use of dsRNA biopesticides offers an alternative to traditional insecticides. In this study, the concentration of dsRNA required to achieve optimal knockdown and mortality for screening exogenous dsRNA by oral delivery was 200 ng/μL. Implementing this pre-established dsRNA regime for laboratory screening of ACP RNAi will enable high-throughput discovery of dsRNA targets and aid in evaluating RNAi biopesticide formulations. Establishing a working threshold can also prevent the use of excessive amounts of dsRNA, which is expensive, can negatively influence RNAi efficiency, and potentially trigger off-target effects. Abstract The Asian citrus psyllid (ACP) is a citrus pest and insect vector of “Candidatus Liberibacter asiaticus”, the causal agent of citrus greening disease. Double-stranded RNA (dsRNA) biopesticides that trigger RNA interference (RNAi) offer an alternative to traditional insecticides. Standardized laboratory screening of dsRNA requires establishing the minimal effective concentration(s) that result in effective RNAi “penetrance” and trigger RNAi, resulting in one or more measurable phenotypes, herein, significant gene knockdown and the potential for mortality. In this study, knockdown was evaluated for a range of dsRNA concentrations of three ACP candidate genes, clathrin heavy chain (CHC), vacuolar ATPase subunit A (vATPase-A), and sucrose non-fermenting protein 7 (Snf7). Gene knockdown was quantified for ACP teneral adults and 3rd instar nymphs allowed a 48 h ingestion-access period (IAP) on 10, 50,100, 200, and 500 ng/µL dsRNA dissolved in 20% sucrose followed by a 5-day post-IAP on orange jasmine shoots. Significant gene knockdown (p < 0.05) in ACP third instar nymphs and adults ranged from 12–34% and 18–39%, 5 days post-IAP on dsRNA at 10–500 and 100–500 ng/µL, respectively. The threshold concentration beyond which no significant gene knockdown and adult mortality was observed post-48 h IAP and 10-day IAP, respectively, was determined as 200 ng/µL, a concentration indicative of optimal RNAi penetrance.
Historic channel changes in the Muskegon River, north-central Michigan, USA

HISTORIC CHANNEL CHANGES IN THE MUSKEGON RIVER, NORTH-CENTRAL MICHIGAN, USA By Michael J Michalek This study assesses channel patterns over the Muskegon Rivers entire length during the historic record by comparing stream patterns from the initial land survey taken between the 1830s and 1850s with aerial photos acquired in 1938 and 2012. The research focuses on the following questions: (1) What is the nature of changes in channel position between the three sample times? (2) What is the extent of change in channel width throughout the system from the initial survey to the present? (3) What are the key variables that may have caused channel changes? To assess these questions a combination of field and laboratory analyses were conducted. River positions at each time period were georectified in ArcGIS10 for comparative purposes. The stream was surveyed by kayak during the summer of 2013 in order to collect modern width measurements along section lines and to analyze channel changes since the initial
Spatial Variability of Regional Water Requirement and Water Deficient Ratio

The spatial variability of regional water requirement(WR) and water deficient ratio(WRD) are studied with geostatistics.Taking the WR and WDR in 2000 and 2010 level year as examples,which are from Dianzhong district,Yunnan Province,in China,the suitability with geostatistics is analyzed theoretically.The WR is close to the lognormal distribution,while the WDR is close to the normal distribution.Both have great spatial variability and they are mainly influenced by structure factors.Their isotropic variograms can be described with sphere models and exponential models.The variables show complicated anisotropic properties combining geometric anisotropy and zonal anisotropy.The relativity of WR between 2000 and 2010 keeps constant at different scales,but the relativity of WDR varies with the distance.The spatial distributions of the variables are interpolated with cokriging and their factors are analyzed.
Correlation of Histopathology and Radiological Findings Among the Diverse Breast Lesions in a Tertiary Care Centre

Introduction Breast lumps in any age group are addressed cautiously to exclude the possibility of breast cancer. The clinical approach to breast lumps involves the "triple test" for cancer screening. The triple test includes clinical examination, imaging (mammogram or ultrasonogram), and tissue sampling (fine needle aspiration cytology (FNAC) and core needle biopsy). These tests happen in a sequential process, and it is important that their findings support the final diagnosis for accurate management of the patient. Aims and objectives This study aims to determine the correlation between the histopathological and radiological findings among the various breast lesions and describe the spectrum of breast lesions received in our center. Methods This is a retrospective observational study for a period of three years, from January 2020 to December 2022. The study included 400 patients who had undergone ultrasonography or mammograms for breast lumps, FNAC, core needle biopsy, or surgical resection. The data collected was analyzed for concordance and discordance status. Results A total of 400 cases were reviewed. There were 238 (59.5%) histologically confirmed benign breast lesions and 162 (40.5%) malignant lesions with their corresponding BI-RADS (Breast Imaging Reporting and Data System) scores. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the imaging modalities (ultrasonogram and mammogram) in diagnosing breast lesions were 95.06%, 94.96%, 92.77%, 96.58%, and 95%, respectively, which were comparable with other similar studies. The biological and immunohistochemical factors of all the invasive carcinomas were studied in detail. Conclusions Imaging modalities (ultrasonogram or mammogram) have good sensitivity and specificity in diagnosing breast lesions and can be reliably used as a preliminary test in breast lump evaluation. The BI-RADS score is a reliable indicator and can be considered for the effective follow-up or intervention of the breast lesion. In discordant cases, a repeated core needle biopsy or excision has to be recommended, as pathological diagnosis is the cornerstone of effective management. A good rapport between the surgeon, radiologist, and pathologist aids in effective feedback and learning for achieving diagnostic accuracy.
An Automated Instance Segmentation Method for Crack Detection Integrated with CrackMover Data Augmentation

Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress.
Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke

Introduction Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models. Methods The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score. Experiments Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters. Conclusion The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model.
Performance of Ultrafast, Nanoantenna-Based, Long-Wave Infrared Detectors in Vacuum

This work investigates the speed and responsivity of thermoelectrically coupled nanoantennas (TECNAs) in a vacuum. TECNAs are long-wave infrared (IR) detectors that absorb electromagnetic (EM) radiation via a resonant dipole antenna. Joule heating within the antenna is converted to a voltage via a nanoscale thermocouple (NTC). Heat loss due to air is reduced under vacuum, allowing for improved device responsivity, but at the expense of speed. A finite difference model is introduced that calculates the steady-state and transient thermal transport within various TECNA designs across a range of ambient pressures. The modeled pressure dependence was derived from nanoscale convection coefficients found in the literature. Models were experimentally verified by studying the pressure-dependent and frequency-dependent voltage response of TECNAs exposed to modulated <inline-formula> <tex-math notation="LaTeX">$10.6 \, \mu \text{m}$ </tex-math></inline-formula> IR radiation. Improvements in responsivity ranging from <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$4.5\times $ </tex-math></inline-formula> were demonstrated between atmosphere and vacuum, with 90% of improvement occurring between atmospheric pressure and 1 torr (rough vacuum) in each design. Devices exhibited bandwidths of at least 10 kHz (roughly <inline-formula> <tex-math notation="LaTeX">$100\times $ </tex-math></inline-formula> faster than common microbolometers) under vacuum with negligible signal attenuation, though future work is needed to fully characterize device frequency response.
Preparation of solids balances for municipal wastewater treatment facilities

Mass balance is a well-known method in many engineering applications including reactor design, process evaluation, and benchmarking. This method assumes and calculates the remaining stable the outputs and inputs of substances in a mass flow system. In environmental engineering field, preparation of mass balances is considered as a very important tool to compute the fluxes of substances compare operational conditions and check the general validity in wastewater treatment facilities (WWTFs). It is a very current way to compute the influent and effluent flows and their characteristics at wastewater treatment plants. However, application of mass balances on WWTP data is mainly difficult since the treatment processes are dynamic systems and the variability of the influent loading is unknown (Puig et al., 2008). Although mass balance calculations have been preferred to improve the quality of WWTF information (Meijer et al. 2002), getting reliable information from raw WWTF data is mainly not possible. For example, to establish a mass balance in the biological WWTFs having activated sludge units for process integrity, all inand outgoing flows including the activated sludge composition and sludge production should be known (Puig et al., 2008). For process design including mass balances, the use of mathematical modeling of wastewater treatment processes has taken great attention based on engineering scale applications since 1990s and early 2000s. To evaluate and refine process configurations not only in the design of a particular unit process, but also in terms of plant-wide effects, some companies have developed simulation software programs such as BioWin (EnviroSim Associates Ltd., Flam borough, Ontario, Canada); GPS-X (Hydromantis Inc., Hamilton, Ontario, Canada); ATV-131 E (DWA, Germany) (WERF, 2010). The models are very useful to develop the steady-state mass balances of the integrated plant processes regarding the
A lightweight mixup-based short texts clustering for contrastive learning

Traditional text clustering based on distance struggles to distinguish between overlapping representations in medical data. By incorporating contrastive learning, the feature space can be optimized and applies mixup implicitly during the data augmentation phase to reduce computational burden. Medical case text is prevalent in everyday life, and clustering is a fundamental method of identifying major categories of conditions within vast amounts of unlabeled text. Learning meaningful clustering scores in data relating to rare diseases is difficult due to their unique sparsity. To address this issue, we propose a contrastive clustering method based on mixup, which involves selecting a small batch of data to simulate the experimental environment of rare diseases. The contrastive learning module optimizes the feature space based on the fact that positive pairs share negative samples, and clustering is employed to group data with comparable semantic features. The module mitigates the issue of overlap in data, whilst mixup generates cost-effective virtual features, resulting in superior experiment scores even when using small batch data and reducing resource usage and time overhead. Our suggested technique has acquired cutting-edge outcomes and embodies a favorable strategy for unmonitored text clustering.
COMBINING TYPE CLASSES At'fD EXISTENTIAL TYPES

This paper demonstrates that the novel combination of type classes and existential types adds sig nificant expressive power toa language, requiring only a minor syntactic change. We explore this combination in the context of higher-order functional languages with static typing, parametric polymorphism, algebraic data types, and Hindley-Milner type inference. Since we have examined the underlying type-theoretic issues already, this paper focuses on the practica! aspects of our extension. We first examine limitations of existing functional and opject-oriented languages. We then give examples to dernonstrate how our first-class abstract types with user-defined interfaces address those limítations. Finally, we give an informal description of the translation from our language to a target language without type classes. Our extension equally applies to other languages with similar type systems and is independent of strictness considerations. It has been implemented in the Chalmers Haskell B. system, and all examples from this paper have been developed using this system.
Cumulus Clouds and Reflected Sunlight from Landsat ETM

Cumulus clouds attenuate solar radiation casting shows on the ground. Cumulus clouds can also enhance solar radiation in the clear region nearby. The enhancement of down-welling solar radiation has been observed at the ground level in the clear region near cumulus clouds (Mims and Frederick 1994). The additional diffuse radiation source from cumulus clouds makes the clear gaps appear to be brighter from the space. If this effect is not accounted for, large errors may be introduced in aerosol optical thickness retrieval in the clear gaps in cumulus cloud field.
International Journal of Public Health Science (IJPHS)

Marwa Rashad Salem, Eman F. Ataya, Ahmed Taher Mahmoud, Mohamed Fathy, Ahmed Tammam Marwa M. Zein Department of Public Health and Community Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt Department of Critical Care Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt Ministry of Health and Population, Cairo, Egypt Department of Psychiatry, Psychological Medicine Hospital, Cairo, Egypt