Miracle-dz/UniFAR-FTUs huggingface dataset:
Gully erosion susceptibility prediction in Mollisols using machine learning models

In recent years, gully erosion has caused soil loss, land degradation, and a large sediment yield in the Mollisols in northeastern China, threatening agricultural development and national food security. Moreover, the prediction of gully erosion remains a great challenge owing to the difficulty of determining suitable environmental indicators and identifying the best models for predicting gully erosion prone areas. Therefore, the objective of this study was to quantify the contributions of the main factors controlling gully erosion and to identify the best model for predicting areas susceptible to gully erosion in Hailun City, northeastern China. Initially, the spatial distribution of the gully erosion was investigated through visual interpretation of GaoFen-1 satellite images. The analyzed gullies were evenly distributed in the study region, and we selected 70% of the gullies as the training data set and the remaining 30% as the validation data set. Subsequently, 12 variables, including the elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), soil type, land use, normalized difference vegetation index (NDVI), precipitation, distance from rivers, and distance from existing gullies, were selected as the indicators of gully erosion. Then, multicollinearity analysis was conducted to determine the main indicators without linearity. Finally, the contributions of the indicators and the areas susceptible to gully erosion were determined using machine learning models, including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest (RF), and extreme gradient boosting (XGBoost) models. The results revealed that there was no multicollinearity among the 12 indicators, so they were all employed in the machine learning models for the gully erosion susceptibility prediction. The XGBoost model had the highest R2 and lowest root mean square error (RMSE) values in the model validation stage (0.81 and 0.60, respectively), followed by the RF (0.78 and 0.61, respectively), MLPNN (0.65 and 0.70, respectively), and SVM (0.62 and 0.70, respectively). The gully distance had the largest relative importance score (>35%) for gully erosion, followed by the profile curvature, plan curvature, land use, elevation, and soil type, which had relative importance scores of 10% to 15%. The gully erosion susceptibility map revealed that the central part of the study area was more susceptible to gully erosion than the other regions. These results can help managers to identify the regions that are prone to gully erosion and to design soil conservation practices to slow down the soil erosion process.
The Impact of Airborne Radio Occultation Observations on the Simulation of Hurricane Karl (2010)

AbstractThis study evaluates, for the first time, the impact of airborne global positioning system radio occultation (ARO) observations on a hurricane forecast. A case study was conducted of Hurricane Karl during the Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) field campaign in 2010. The assimilation of ARO data was developed for the three-dimensional variational (3DVAR) analysis system of the Weather Research and Forecasting (WRF) Model version 3.2. The impact of ARO data on Karl forecasts was evaluated through data assimilation (DA) experiments of local refractivity and nonlocal excess phase (EPH), in which the latter accounts for the integrated horizontal sampling along the signal ray path. The tangent point positions (closest point of an RO ray path to Earth’s surface) drift horizontally, and the drifting distance of ARO data is about 2 to 3 times that of spaceborne RO, which was taken into account in these simulations.Results indicate that in the absence of other satellite .
Micro Light-Emitting Diode Pixel Circuit Based on Indium–Gallium–Zinc Oxide Thin-Film Transistors Using Pulse Width Modulation

Herein, we proposed a micro light-emitting diode (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>LED) pixel circuit based on indium–gallium–zinc oxide (IGZO) thin-film transistors (TFTs). Pulse-width modulation (PWM) was utilized to overcome the wavelength shift of the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>LEDs. The proposed pixel circuit compensated for the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}{)}$ </tex-math></inline-formula> of the driving TFTs using a source-follower structure and the variation of <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>LED forward voltage (<inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {LED}}{)}$ </tex-math></inline-formula>. The proposed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>LED pixel circuit successfully expressed 256 gray levels using PWM. In addition, the proposed circuit exhibited a normalized luminance error rate lower than 4.8% under <inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula> variation (<inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {TH}}{)}$ </tex-math></inline-formula> of ±1.0 and <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {LED}}$ </tex-math></inline-formula> of ±0.4 V. Consequently, the proposed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>LED pixel circuit based on IGZO TFTs successfully expressed gray levels using PWM and operated stably under <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {TH}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{\text {LED}}$ </tex-math></inline-formula>.
Antibiograms of Commensal Escherichia Coli Isolated from Cattle in Some Selected Commercial Farms in Jos South Plateau State, Nigeria

Antimicrobials are used for livestock production in the control and treatment of infections and as growth promoters but has been abusively used over the counter in middle and low income countries where access to these drugs are met with minimal restrictions and prescriptions. Escherichia coli (E. coli) is a normal inhabitant of the gastrointestinal tract of animals and a major reservoir of antibiotic resistant genes. The study aimed to identify antimicrobial resistance (AMR) phenotypes of commensal E. coli isolated from cattle in Jos South Local Government Area (LGA) of Plateau State Nigeria. Forty eight (48) fecal samples were collected from cattle in four randomly selected commercial farms, screened for E. coli, confirmed by conventional PCR and antimicrobial resistant profiles were determined using ten (10) commercial prepared antibiotic discs. Conventional PCR confirmed seven (7) isolates of E. coli equivalent to 14.58 % prevalence. The isolates showed varied phenotypic resistance to six antimicrobials, with a 100 % resistance to Reflacin (10 mm), Ceporex (10 mm) and Nalidix acid (10 mm). Thus, the findings of this study suggests the possibility of an extensive use of cephalosporin among farmers in the study area. The need for a larger implementation of surveillance programme in order to reduce and curb the spread of AMR determinant was recommended.
Intratumoral NKp46+ natural killer cells are spatially distanced from T and MHC-I+ cells with prognostic implications in soft tissue sarcoma

Introduction Soft tissue sarcomas (STS) are rare, heterogenous malignancies with an unmet need for novel immunotherapies. Tumor infiltrating lymphocytes (TILs) have been linked with favorable outcomes in STS patients, though the contribution of natural killer (NK) cells and spatial relationships of TILs with MHC-I expressing cells lacks detailed characterization. Experimental design Using archived and prospectively collected specimens, we evaluated intratumoral NK cells by immunohistochemistry (IHC), flow cytometry, and immunofluorescence (IF). We assessed spatial localization of NK and T cells by multiplex IF, analyzing the effects of MHC-I expression status on NK and T cell clustering. Results Both intratumoral NKp46 and CD56dim expression were associated with significantly improved overall survival (P=0.05), while higher infiltrates of CD56bright NK cells predicted a worse prognosis (P=0.05). The presence of intratumoral NK cells was inversely proportional to CD3+ T cells. Spatial analyses showed NK cells preferentially clustering close to other NK cells with sparse CD3+ T and CD8+ T cells in range (P<0.0001). Additionally, CD3+ T and CD8+ T cells showed significantly greater co-localization with MHC-I+ cells, compared to NK cells (P<0.0001). After neoadjuvant radiotherapy, there was greater CD8 clustering, while after neoadjuvant chemotherapy, there was overall lower TIL clustering. Conclusion Intratumoral NK cells are prognostic in STS and localize closer to MHC-I- cells than T cells. Although both NK and T cells are associated with improved survival in STS, their differential distribution in the TME based on MHC-I expression status may serve as a biomarker for improved immunotherapy treatment selection.
Case Report: Exploration of changes in serum immunoinflammation-related protein complexes of patients with metastatic breast cancer

Patients with advanced breast cancer are difficult to treat and have poor prognosis. At present, the commonly used methods to monitor the disease progression of breast cancer are imaging examinations such as breast ultrasound, mammography and peripheral blood tumor markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 15-3 (CA15-3). However, none of them can detect tumor progression at an early stage. Serum immunoinflammation-related protein complexes (IIRPCs) showed potential to indicate cancer progression. Therefore, we attempted to monitor the level of IIRPCs in peripheral blood of patients with metastatic breast cancer and compare it with patients’ treatment and disease progression, and here we performed case reports of two of them.
An Application of Metric Method of Solving Travelling Salesman Routing Problem to Table Water Company

Background:This research work deals with the travelling salesman problem, the aim of this research is to obtain the cheapest possible route to be taken during the distribution of product (water) in some districts in Obio-Apkor Port Harcourt by Junac Table Water Company in other to maximize the company’s profit. MaterialsandMethods: The metric method is adopted in solving the problem analytically, and the result was also confirmed and compared with aid of a statistical software (TSP solver and Generator v0.1). Results: The route with the minimum cost from both the matrix method and the statistical software is N1010 (one thousand and ten naira only). Conclusions: The cheapest route to be a taken is Choba→Mbuogba→Location→MileIII→MileI→Rumuola→ Artillery→Rumuokurusi→ Tank→Eneka→Eliozu→Rumuodara→Rumuokwuta→Rumuigbo→Rumuokoro→Rumuosi→Choba.
Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

Background Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute. Objective We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size. Methods We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records. Results The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist. Conclusions Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.
Data-Based Predictive Control for Power Congestion Management in Subtransmission Grids Under Uncertainty

The energy transition of power grids has spawned a large spectrum of new technical challenges at the design, deployment, and operation levels. From a control standpoint, the integration of renewable-energy-based power generation sources into the power grid translates into emerging uncertainties which compromise the system’s safety, stability, and performance. This article proposes a model-based predictive controller (MPC) that incorporates the stochastic nature of these sources into its feedback decision-making policy. The overarching objective is to balance upholding operational constraints of power lines with smart power generation curtailment and energy storage strategies. The proposed method introduces a novel characterization of disturbance trajectory scenarios, and their incorporation into the optimization problem is detailed leading to a robust congestion management strategy. Simulation results are discussed with respect to a baseline of a trend-based disturbance estimation.
An Improved 3D Shape Recognition Method Based on Panoramic View

Recognition of three-dimensional (3D) shape is a remarkable subject in computer vision systems, because of the lack of excellent shape representations. With the development of 2.5D depth sensors, shape recognition is becoming more important in practical applications. Many methods have been proposed to preprocess 3D shapes, in order to get available input data. A common approach employs convolutional neural networks (CNNs), which have become a powerful tool to solve many problems in the field of computer vision. DeepPano, a variant of CNN, converts each 3D shape into a panoramic view and shows excellent performance. It is worth paying attention to the fact that both serious information loss and redundancy exist in the processing of DeepPano, which limits further improvement of its performance. In this work, we propose a more effective method to preprocess 3D shapes also based on a panoramic view, similar to DeepPano. We introduce a novel method to expand the training set and optimize the architecture of the network. The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation.
Multi‐label feature selection based on relative entropy and fuzzy neighborhood mutual discrimination index

Multi‐label feature selection eliminates irrelevant and redundant features, and then improves the performance of multi‐label classification models. Most multi‐label feature selection algorithms assume that the training set contains logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances with respect to labels. To solve the problem, a multi‐label feature selection method based on relative entropy and fuzzy neighborhood mutual discriminant index is proposed. Firstly, logical labels are converted to label distribution through label enhancement. Secondly, the neighborhood and relative entropy are introduced into the label distribution, the label neighborhood similarity matrix is constructed to describe the similarity of samples under label space. Finally, the fuzzy neighborhood mutual discrimination index is used to combine the candidate features with the label neighborhood similarity matrix, which is used to judge the distinguishing ability of the candidate features. Comprehensive experiment of eight multi‐label datasets shows that the proposed algorithm has better classification performance than other compared algorithms.
Mukukai: Kaitiaki o te kauhoe: The influence of swimming on whānau engagement with the water

Māori consider water to be the foundation of all life; it is a valued taonga gifted by our ancestors that provides sustenance and nourishment to communities and enhances hauora Māori (Royal, 2010). For generations, Māori have participated in water-related activities such as fishing, gathering kai, diving, waka and swimming (Karapu et al., 2007). It is through these activities in and around the water that hauora Māori can be enhanced. Despite this positive relationship with water, Water Safety New Zealand (2022) statistics demonstrate high drowning rates for Māori, with the 2021 drowning toll being the highest since 2001. In that year, Māori accounted for 31% of all drownings despite only comprising 17.4% of the population (Stats NZ, 2022; Water Safety New Zealand, 2022). Most of these drownings of Māori occurred while swimming (Water Safety New Zealand, 2022). With this in mind, this article will examine the significance of swimming on Māori engagement with water and therefore hauora Māori. This examination will be done using a whānau case study that was undertaken for the purpose of the lead author’s master’s research. In bringing together the key findings, a framework named Mukukai: Kaitiaki o Te Ao Kauhoe draws on five main values to describe how swimming can enhance hauora Māori. These values include kaitiakitanga, ūkaipōtanga, whakatinanatanga, whanaungatanga and whakapapa. The values are symbolised by elements of pepeha in the model to demonstrate the significance for Māori of swimming for connection to whakapapa and therefore its influence on hauora Māori.
The Impact of the Use of Soft Power in Social Media on Public Diplomacy for International Co-existence

Social media has become a game changer fundamentally in the way that it connects citizens, governments, and their ideas and voices across geographical divides. The policy process today requires decision making, feedback mechanisms, and quality input to be successful. Thus, social media presents itself as a quality instrument for incorporating a multitude of ideas, opinions, and voices for different stages of policy processes. This means that citizen engagement, which is crucial in policy making becomes easier via social media. However, social media also presents new challenges for soft power. The fast-paced nature of social media means that misinformation and fake news can quickly spread, which can undermine efforts to build relationships and promote understanding between countries. In addition, some countries use social media to spread propaganda, fake news, and extremist ideologies, which can also damage their soft power. This study examines the impact of the use of soft power in social media on public diplomacy for international co-existence in the context of the Middle East and Africa. The alternate methods of soft power include information and gathering data. For instance, social media is a great source of data and gathering information. Countries can achieve their objectives by attracting and persuading others to adopt their values and interests, rather than through coercion or force. Soft power is the ability to influence others through attraction and persuasion, rather than through threats or payment. The proliferation of social media such as YouTube, Facebook, and Twitter present powerful and new communication tools that are capable of influencing policy decisions by channelizing the political opinions of the masses. The upstart of social media and its engagement with the masses have influenced policy as well as politics which can be demonstrated in the Middle East. The Middle East, for instance, has been rife with regimes that have taken advantage of the region’s cultural, linguistic, and religious connections to shape the political environment. In addition, social media has also given voice to previously marginalized groups, particularly youth, women, and minorities, who have used these platforms to advocate for their rights and amplify their voices. This has created new opportunities for public diplomacy, as governments and civil society organizations can engage with these groups and build relationships with them. This study aims to look at the relationship between social media and public diplomacy and identify opportunities and challenges social media networks pose for international coexistence in the context of past and present events from the perspective of both policymakers and scholars. Furthermore, the public diplomacy process and the use of social media as a soft power have been analyzed through a framework and discussed further for a better understanding of the variables. involved.
Nutrient Intake among Pregnant Women in Spain and Poland: A Comparative Analysis

Prenatal nutrition plays a crucial role in maternal and child health. This study aims to compare nutrient intake and its adequacy to recommendations among pregnant women in Spain and Poland. The ECLIPSES study in Spain utilized a self-administered food frequency questionnaire, while the PREDISH study in Poland employed a 3-day interview method. We assessed energy and nutrient intake against recommended dietary allowances. The analysis included 583 participants in the first trimester and 465 participants in the third trimester from both countries. Our findings revealed insufficient intake of iron, vitamin D, and vitamin B9 among pregnant women in both Spain and Poland. Significant differences were observed in the intake of energy, carbohydrates, fiber, calcium, iron, and vitamins D, E, C, B6, B9, and B12. Notably, 81.6% and 21.5% of participants did not meet the recommended minimum carbohydrate intake, while 99.8% and 43.8% exceeded the limit for total fat, particularly monounsaturated fatty acids (MUFAs). Tailored dietary guidance based on regional differences is crucial for pregnant women. Although variations in dietary intake were observed, both Spain and Poland faced similar risks of nutritional deficiencies, particularly for iron, vitamin D, and vitamin B9. These findings emphasize the need for enhanced efforts in preventing these deficiencies and promoting optimal prenatal nutrition.