Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytology Techniques Can Provide Insight into Human Placental Structure Including Syncytiotrophoblast Nuclear Spatial Organisation

The aim of this study was to provide the first systematic description of human placental cytology appearances and to investigate syncytiotrophoblast nuclear organisation patterns using cytology techniques. Term placentas from normal pregnancies were sampled using fine-needle aspiration (FNA) and direct scrapes. Standard histological examination was also performed to exclude pathological changes in the placentas being studied. Both Papanicolaou-stained cytospin preparations and air-dried Giemsa slides from FNA provided high-quality material for cytological assessment with good cellularity. Among the key features of the cytology preparations were villous “microbiopsies” that allowed for the three-dimensional appreciation of villous branching patterns. Cytological appearances, including nuclear characteristics of villous cytotrophoblast and syncytiotrophoblast, were also well demonstrated. In microbiopsies and detached villous trophoblast sheets, complex patterns of syncytiotrophoblast nuclear organisation, not previously described cytologically, were observed, including irregular spacing of nuclei, syncytioplasm windows and linear nuclear arrangements. This study showed that placental cytology (a) provides technically excellent material for cytological evaluation, (b) confirms the presence of complex nuclear organisational patterns in the syncytiotrophoblast by eliminating the possibility of tangential sectioning artefact, (c) provides superior nuclear detail over standard histological sections and (d) may be an untapped research resource for the investigation of normal and pathological processes because of its ability to look at the placenta in a novel way and through its potential for both ex vivo and in vivo placental sampling.
AdaPyramid: Adaptive Pyramid for Accelerating High-resolution Object Detection on Edge Devices

—Deep convolutional neural network (NN)-based object detectors are not appropriate for straightforward inference on high-resolution videos at edge devices, as maintaining high accuracy often brings about prohibitively long latency. Although existing solutions have attempted to reduce on-device inference latency by selecting a cheaper conﬁguration (e.g., choosing a more lightweight NN or scaling a frame to a smaller size before inference) or eliminating a background containing no object, they often ignore various high-resolution features and fail to optimize for those videos. We thus present AdaPyramid, a framework to reduce as much on-device inference latency as possible, especially for high-resolution videos, while achieving the accuracy demand approximately. We observe that the cheapest conﬁguration to achieve the accuracy demand varies signiﬁcantly across both different frames and different regions in a frame. The underlying reason is that object features (e.g., the location, size and category of objects) are more uneven in high-resolution videos, both temporally and spatially. Moreover, we observe that the object size presents a prominent hierarchical distribution in high-resolution frames. AdaPyramid thus partitions each frame hierarchically just like a pyramid and chooses a content-aware conﬁguration for each region, which is adapted online based on the feedback. We evaluate the performance of AdaPyramid on a public dataset and our collected real-world videos. The obtained results show that under comparable accuracy to the state-of-the-art solutions, AdaPyramid can decrease inference latency by 40% on average, with up to 2.5 × speed-up.
Torrefaction of Willow in Batch Reactor and Co-Firing of Torrefied Willow with Coal

The torrefaction process represents a thermal conversion technique conducted at relatively low temperatures ranging between 200 to 300 °C. Its objective is to produce fuel with a higher energy density by decomposing the reactive portion of hemicellulose. In this study, the kinetics of mass loss during torrefaction were investigated for willow. The experiments were carried out under isothermal conditions using thermogravimetric analysis. Batch torrefaction reactor designs were conducted and explained in detail. Co-combustion of willow with hard coal (origin: Katowice mine) in different mass ratios (25% biomass + 75% coal, 50% biomass + 50% coal, and 75% biomass + 25% coal) was conducted in addition to raw biomass torrefaction. TG/MS analysis (a combination of thermogravimetric analysis with mass spectrometry analysis) was performed in the research. The optimal torrefaction conditions for willow were identified as an average temperature of 245 °C and a residence time of 14 min, resulting in the lowest mass loss (30.15%). However, it was noted that the composition of torgas, a by-product of torrefaction, presents challenges in providing a combustible gas with sufficient heat flux to meet the energy needs of the process. Prolonged residence times over 15 min and higher average temperatures above 250 °C lead to excessive energy losses from volatile torrefaction products, making them suboptimal for willow. On the other hand, the co-combustion of torrefied biomass with hard coal offers advantages in reduced sulfur emissions but can lead to increased NOx emissions when biomass with a higher nitrogen content is co-combusted in proportions exceeding 50% biomass. This paper summarizes findings related to optimizing torrefaction conditions, challenges in torgas composition, and the emissions implications of co-combustion.
Modelo de analítica visual para datos educativos

Current technologies used in learning processes imply the logging of all the performed activities. These data can be exploited to gain insight into the learning process and can be used for the assessment of students, professors and the processes themselves. However, although this wealth of data exists, it is still difficult for the teachers (and interested stakeholders) to verify hypothesis, extract conclusions, or make decisions based on discovered facts or situations. This paper introduces an educational data analysis model based on visual analytics, learning analytics and academic analytics, by means of a software tool that allows performing confirmatory and exploratory data analysis through the interacting with the gathered information from a typical Learning Management System. The main goal thus is to define a model which enable the discovery of knowledge on the specific learning process that, in turn, will permit to improve it.
Loss function of NtGA3ox1 delays flowering through impairing gibberellins metabolite synthesis in Nicotiana tabacum

Flowering time, plays a crucial role in tobacco ecological adaptation besides its substantial influence on tobacco production and leaf quality. Meanwhile, it is sensitive to biotic or abiotic challenges. The plant hormones Gibberellins (GAs), controlling a number of metabolic processes, govern plants growth and development. In this study, we created a late flowering mutant HG14 through knocking out NtGA3ox1 by CRISPR/Cas9. It took around 13.0 and 12.1 days longer to budding and flowering compared to wild type Honghuadajinyuan. Nearly all of the evaluated agronomic characters deteriorated in HG14, showing slower growth and noticeably shorter and narrower leaves. We found that NtGA3ox was more prevalent in flowers through quantitative reverse transcription PCR analysis. Transcriptome profiling detected 4449, 2147, and 4567 differently expressed genes at the budding, flowering, and mature stages, respectively. The KEGG pathway enrichment analysis identified the plant-pathogen interaction, plant hormone signal transduction pathway, and MAPK signaling pathway are the major clusters controlled by NtGA3ox1 throughout the budding and flowering stages. Together with the abovementioned signaling pathway, biosynthesis of monobactam, metabolism of carbon, pentose, starch, and sucrose were enriched at the mature stage. Interestingly, 108 up- and 73 down- regulated DEGs, impairing sugar metabolism, diterpenoid biosynthesis, linoleic and alpha-linolenic acid metabolism pathway, were continuously detected accompanied with the development of HG14. This was further evidenced by the decreasing content of GA metabolites such as GA4 and GA7, routine chemicals, alkaloids, amino acids, and organic acids Therefore, we discovered a novel tobacco flowering time gene NtGA3ox1 and resolved its regulatory network, which will be beneficial to the improvement of tobacco varieties.
AUGUST: An Interpretable, Resolution-based Two-sample Test

Two-sample testing is a fundamental problem in statistics. While many powerful nonparametric methods exist for both the univariate and multivariate context, it is comparatively less common to see a framework for determining which data features lead to rejection of the null. In this paper, we propose a new nonparametric two-sample test named AUGUST, which incorporates a framework for interpretation while maintaining power comparable to existing methods. AUGUST tests for inequality in distribution up to a predetermined resolution using symmetry statistics from binary expansion. Designed for univariate and low to moderate-dimensional multivariate data, this construction allows us to understand distributional differences as a combination of fundamental orthogonal signals. Asymptotic theory for the test statistic facilitates p-value computation and power analysis, and an efficient algorithm enables computation on large data sets. In empirical studies, we show that our test has power comparable to that of popular existing methods, as well as greater power in some circumstances. We illustrate the interpretability of our method using NBA shooting data.
Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.
Algorithms for Relative Radiometric Correction in Earth Observing Systems Resource-P and Canopus-V

Abstract. The present paper has considered two algorithms of the relative radiometric correction of information obtained from a multimatrix imagery instrument of the spacecraft “Resource-P” and frame imagery systems of the spacecraft “Canopus-V”. The first algorithm is intended for elimination of vertical stripes on the image that are caused by difference in transfer characteristics of CCD matrices and CCD detectors. Correction coefficients are determined on the basis of analysis of images that are homogeneous by brightness. The second algorithm ensures an acquisition of microframes homogeneous by brightness from which seamless images of the Earth surface are synthesized. Examples of practical usage of the developed algorithms are mentioned.
Cadaverine as a Potential Spoilage Indicator in Skin-Packed Beef and Modified-Atmosphere-Packed Beef

This study investigated cadaverine as a spoilage indicator in commercial beef products stored under conditions favourable for the growth of lactic acid bacteria. Samples included vacuum-skin-packed entrecotes (EB) aged up to 42 days and modified-atmosphere-packed (70% O2 + 30% CO2) minced beef (MB) stored at 5 °C. Two MB product lines were analysed: one stored aerobically two days post-slaughter before mincing and another stored for 14 days in vacuum packaging prior to mincing. Sensory assessment/evaluation and microbial analysis were performed throughout the shelf life of the products and compared to cadaverine levels measured using LC-MS/MS. Cadaverine concentrations in EB reached approximately 40,000 µg/kg on the “best before” date, while remaining below 50 µg/kg in both MB products on the corresponding date. While cadaverine concentrations in EB displayed a consistent increase, suggesting its potential as a spoilage indicator post-ageing, the low concentrations in MB, did not correlate with sensory assessments, revealing its limitations as a universal spoilage marker. In conclusion, it is necessary to conduct product-specific studies to evaluate the applicability of cadaverine as a spoilage indicator for beef products.
Examining Training and Development Implementation in Private Sector: A Case Study of Alliance One Tobacco Tanzania Limited in Morogoro Municipality

: The purpose of this study was to examine the extent to which training and development programs are being implemented in private sector organizations, a case study of Alliance One Tobacco Tanzania Limited (AOTTL). The target population of this study was 207 respondents. A sample size of 88 respondents that include 13 top and middle management personnel and 75 laborers was used. Purposive sampling techniques and convenience sampling techniques were used in selection of respondents. Data were collected by using questionnaire, interviews and documentary reviews. Quantitative data were analyzed by using descriptive statistics while qualitative data were analyzed by using content analysis. The study found that, training and development programs align with the needs of the organizations, training and development program objectives are clearly defined, sufficient resources are not allocated to support the implementation of training and development programs, training and development programs are not always effectively communicated to employees in organizations, and training and development programs are not regularly evaluated. The study recommends that, it is essential to allocate adequate resources, including budget, time, and personnel, to support the implementation of training and development programs. Furthermore, effective communication is crucial to ensure that employees are aware of and understand the training and development programs available to them.
A New Dilemma of Social Democracy? The British Labour Party, the White Working Class and Ethnic Minority Representation

Like much of the European centre-left, Britain's Labour Party has struggled to appeal to its former core working class support base in recent years. However, this is largely a failure to connect with the ‘white working class’ (WWC) specifically, whereas support among ethnic minorities remains robust. We hypothesise that Labour could be experiencing a ‘trade-off’, whereby efforts to cater to minorities harm its perceived ability to represent WWC interests. We test this thesis by examining whether WWC voters are more likely to view minority and working class representation in zero-sum terms and shun Labour when they associate the party with minority interests. We show that the WWC are somewhat less likely to view working class and ethnic minority representation as strongly correlated, and Labour's perceived ability to represent minorities is negatively associated with WWC support. This is not (primarily) about ethnocentrism. Instead, we suggest that ‘relative political deprivation’ is crucial.
Reducing Sediment Connectivity Through man‐Made and Natural Sediment Sinks in the Minizr Catchment, Northwest Ethiopia

Man‐made and natural sediment sinks provide a practical means for reducing downstream reservoir sedimentation by decreasing soil erosion and enhancing the rate of sedimentation within a catchment. The Minizr catchment (20 km2) in the northwest Ethiopian highlands contains numerous man‐made soil and water conservation (SWC) structures such as soil bunds (Erken), fanya juu ridge (Cab) and micro‐trenches and natural sediment sinks such as wetlands, floodplains and grassed waterways. These sediment sinks reduce downstream sedimentation into the Koga reservoir, located at the catchment outlet, however, a large quantity of sediment is still reaching the reservoir. This study evaluates the function and effectiveness of both man‐made SWC structures and natural sediment sinks in reducing sediment export from the Minizr catchment. SWC structures and natural sediment sinks were digitized using Google Earth Imagery. Sediment pins and vertical sampling through the deposit were used to quantify the amount of deposited sediment. In addition, inflow and outflow of suspended sediment data were used to calculate the sediment‐trapping efficacies (STE) of man‐made SWC structures (soil bunds and fanya juu ridges) and natural sediment sinks. Results reveal that 144 km soil bunds and fanya juu ridges trapped 7,920 Mg y−1 (55 kg m−1 y−1) and micro‐trenches trapped 13·26 Mg y−1, each micro‐trench on average trapped 23 kg y−1. The 17 ha floodplain located in the centre of the catchment trapped 9,970 Mg y−1 (59 kg m−2 y−1), while a wetland with a surface area of 24 ha, located near the outlet of the catchment, trapped 8,715 Mg y−1 (36 kg m−2 y−1). The STEs of soil bunds and fanya juu ridges, wetlands and floodplains were 54%, 85% and 77%, respectively. Substantial differences were observed between the STE of grassed and un‐grassed waterways at 75% and 21%, respectively. Existing man‐made and natural sediment sinks played an important role in trapping sediment, with 38% (26,600 Mg y−1) of transported sediment being trapped, while 62% (43,000 Mg y−1) is exported from the catchment and thus enters the Koga reservoir. Therefore, additional catchment treatment measures are required as an integrated catchment scale sediment trapping approach to help reduce sediment loads entering Koga reservoir. Moreover, to maximize the effectiveness of sediment trapping measures, avoid structural failure and ensure their sustainability, regular maintenance is needed. Copyright © 2016 John Wiley & Sons, Ltd.
The Impact of Follicular Fluid Oxidative Stress Levels on the Outcomes of Assisted Reproductive Therapy

Oocyte quality is a pivotal determinant of assisted reproductive outcomes. The quality of oocytes is intricately linked to their developmental microenvironment, particularly the levels of oxidative stress within the follicular fluid. Oxidative stress levels in follicular fluid may have a substantial influence on oocyte health, thereby impacting the outcomes of ART procedures. This review meticulously explores the intricate relationship between oxidative stress in follicular fluid and ART outcomes. Furthermore, it delves into strategies aimed at ameliorating the oxidative stress status of follicular fluid, with the overarching goal of enhancing the overall efficacy of ART. This research endeavors to establish a robust foundation and provide valuable guidance for clinical treatment approaches, particularly in the context of infertile women, including those of advanced maternal age.
Digital Transformation in Hisab Rukyat: A Sociological Perspective on Integrating Information Technology in Islamic Astronomical Tradition

This research investigates how the integration of Information Technology (IT) in the practice of Hisab Rukyat, a traditional method in Islamic astronomy for determining prayer times, is transforming the social and cultural landscape in Muslim communities. Employing a qualitative approach, the study collected data through in-depth interviews, participatory observations, and analysis of documents related to the Hisab Rukyat bodies in various countries. Findings indicate that the adoption of IT not only enhances accuracy and efficiency in astronomical calculations but also sparks sociological debates over balancing technological innovation with tradition. Furthermore, the study identifies shifts in community dynamics, where younger generations are more receptive to technological use, while older ones tend to adhere to traditional methods. These findings suggest that while IT provides a more efficient means for calculations, an intergenerational dialogue is also necessary to maintain cultural continuity in this digital transition. The implications of this study are significant for policymakers, religious communities, and academics interested in the interplay between technology, society, and religious traditions.
Deep Q Network Based on a Fractional Political–Smart Flower Optimization Algorithm for Real-World Object Recognition in Federated Learning

An imperative application of artificial intelligence (AI) techniques is visual object detection, and the methods of visual object detection available currently need highly equipped datasets preserved in a centralized unit. This usually results in high transmission and large storage overheads. Federated learning (FL) is an eminent machine learning technique to overcome such limitations, and this enables users to train a model together by processing the data in the local devices. In each round, each local device performs processing independently and updates the weights to the global model, which is the server. After that, the weights are aggregated and updated to the local model. In this research, an innovative framework is designed for real-world object recognition in FL using a proposed Deep Q Network (DQN) based on a Fractional Political–Smart Flower Optimization Algorithm (FP-SFOA). In the training model, object detection is performed by employing SegNet, and this classifier is effectively tuned based on the Political–Smart Flower Optimization Algorithm (PSFOA). Moreover, object recognition is performed based on the DQN, and the biases of the classifier are finely optimized based on the FP-SFOA, which is a hybridization of the Fractional Calculus (FC) concept with a Political Optimizer (PO) and a Smart Flower Optimization Algorithm (SFOA). Finally, the aggregation at the global model is accomplished using the Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaRs) model. The designed FP-SFOA obtained a maximum accuracy of 0.950, minimum loss function of 0.104, minimum MSE of 0.122, minimum RMSE of 0.035, minimum FPR of 0.140, maximum average precision of 0.909, and minimum communication cost of 0.078. The proposed model obtained the highest accuracy of 0.950, which is a 14.11%, 6.42%, 7.37%, and 5.68% improvement compared to the existing methods.