Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential of GIS Tools for Diagnosing the SFS of Multi-Family Housing towards Friendly Cities—A Case Study of the EU Member State of Poland

Motives: The need for sustainable urban development, including an improvement in residents’ quality of life, requires ongoing urban diagnostics. Assessments of multi-family housing estates play a very important role in this process. Population growth influences the expansion of housing estates in limited urban space. The extent to which spatial and functional structures (SFS) in housing developments meet the residents’ current needs should be evaluated. These needs undergo dynamic change and are influenced by economic, socio-cultural, sanitary, and ecological factors. Aim: The main objective of this study was to develop a methodology for assessing SFS solutions in open spaces in multi-family residential estates (MFREs) based on a complete list of SFS indicators, and to determine the potential of GIS tools and selected open data sources for automating this process. GIS was used to represent data. The intermediate goal was to determine differences in the SFS solutions of two MFREs that were built with different technologies and urban layouts in the last 70 years in the city of Olsztyn (Poland). Methods: An empirical study was conducted with the use of qualitative and quantitative methods based on a review of the literature, the results of a resident survey, and an analysis of spatial data in ArcGIS and QGIS software. Results: The residents’ needs for SFS in MFREs were identified. A list of 26 SFS indicators and their values (on a 3-point scale) was developed to assess multi-family housing. The applicability of GIS software and spatial data from the national spatial data infrastructure (NSDI) and other sources was assessed in the process. The research method was tested to reveal differences in SFS solutions in the compared MFREs.
Performance metric curve analysis framework to assess impact of the decision variable threshold, disease prevalence, and dataset variability in two-class classification

Abstract. Purpose: The aim of this study is to (1) demonstrate a graphical method and interpretation framework to extend performance evaluation beyond receiver operating characteristic curve analysis and (2) assess the impact of disease prevalence and variability in training and testing sets, particularly when a specific operating point is used. Approach: The proposed performance metric curves (PMCs) simultaneously assess sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and the 95% confidence intervals thereof, as a function of the threshold for the decision variable. We investigated the utility of PMCs using six example operating points associated with commonly used methods to select operating points (including the Youden index and maximum mutual information). As an example, we applied PMCs to the task of distinguishing between malignant and benign breast lesions using human-engineered radiomic features extracted from dynamic contrast-enhanced magnetic resonance images. The dataset had 1885 lesions, with the images acquired in 2015 and 2016 serving as the training set (1450 lesions) and those acquired in 2017 as the test set (435 lesions). Our study used this dataset in two ways: (1) the clinical dataset itself and (2) simulated datasets with features based on the clinical set but with five different disease prevalences. The median and 95% CI of the number of type I (false positive) and type II (false negative) errors were determined for each operating point of interest. Results: PMCs from both the clinical and simulated datasets demonstrated that PMCs could support interpretation of the impact of decision threshold choice on type I and type II errors of classification, particularly relevant to prevalence. Conclusion: PMCs allow simultaneous evaluation of the four performance metrics of sensitivity, specificity, PPV, and NPV as a function of the decision threshold. This may create a better understanding of two-class classifier performance in machine learning.
TRANSFER LEARNING WITH LIMITED SAMPLES FOR THE SAME SOURCE HYPERSPECTRAL REMOTE SENSING IMAGES CLASSIFICATION

Abstract. A classification method for hyperspectral datasets with a limited number of samples based on transferred convolutional neural network (CNN) is proposed. For the CNN model, a lot of labeled samples are needed for the classification of hyperspectral images, but it takes plenty of time and labor to annotate images in the experiment. In our work, CNN model and transfer learning are applied to solve this problem. By pre-training the model on the other hyperspectral dataset, the classification results of the target hyperspectral images can be effectively improved, when the number of trainable samples is limited. Three transfer approaches are chosen for classifying hyperspectral images and their performance are compared and analyzed. With the decrease of the number of samples, transfer learning has an increasing impact on the classification results of hyperspectral images. In the three transferred models, freezing the convolutional layer weights and retraining the fully connected layer weights yields the best classification performance, which reaches 77.23% in classification accuracy, when the number of samples per class is set 10. And when the number of training samples is 5, the classification accuracy growth rate reaches a maximum of 33%. The results indicate that a relatively high classification accuracy could be obtained by training only a limited number of samples with the same domain transferred parameters.
Children between 9 and 13 years old as internet users

The paper presents the new new media as a contemporary and dominant space in children's lives. Most children begin to use the Internet when they are a few years old, for example, while watching cartoons. The online world can be both an opportunity and a threat to young users. The aim of the research was to determine whether children's growing interest in the Internet has a limiting impact on traditional ways of communication with peers and whether the increasing use of social networking sites by children is determined by the fear of exclusion from their circle of peers. The study was conducted with the use of an online survey distributed among children from the third, fifth, and seventh years of two Polish primary schools. The results include several conclusions regarding children's contact with the new media and their peers. The study showed that the majority of primary school pupils are regular users of the new new media, and their interest in new technologies increases with age.
Naive and memory T cells TCR–HLA-binding prediction

Abstract T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR–MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR–pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4–CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac. il/Home.
Nutritional, Physicochemical, and Endogenous Enzyme Assessment of Raw Milk Preserved under Hyperbaric Storage at Variable Room Temperature

Raw milk (a highly perishable food) was preserved at variable room temperature (RT) under hyperbaric storage (HS) (50–100 MPa) for 60 days and compared with refrigeration (RF) under atmospheric pressure (AP) on quality, nutritional, and endogenous enzyme activity parameters. Overall, a comparable raw milk preservation outcome was observed between storage under AP/RF and 50/RT after 14 days, with similar variations in the parameters studied indicating milk degradation. Differently, even after 60 days (the maximum period studied) under 75–100/RT, a slower milk degradation was achieved, keeping most of the parameters similar to those of milk prior to storage, including pH, titratable acidity, total solid content, density, color, viscosity, and volatile organic and fatty acid profiles, but with higher free amino acid content, signs of an overall better preservation. These results indicate an improved preservation and enhanced shelf life of raw milk by HS/RT versus RF, showing HS potential for milk and highly perishable food preservation in general.
Problemy etyczne niejawnej obserwacji uczestniczącej. Perspektywa antropologów społeczno-kulturowych i socjologów

W niejawnej obserwacji uczestniczącej badacz/ka celowo zataja prowadzenie badań i/lub swoją badawczą tożsamość przed jej uczestnikami. Wywołuje to kontrowersje etyczne w środowisku naukowym, między innymi z powodu braku świadomej i dobrowolnej zgody na udział w badaniu. Zapisy zawarte w anglosaskich kodeksach etyki antropologa sugerują nieetyczność takiego sposobu pozyskiwania danych, chyba że badania odbywają się w sferze publicznej lub gdy ujawnienie się badacza/ki zagrażałoby czyjemuś życiu. Z kolei Kodeks etyki socjologa Polskiego Towarzystwa Socjologicznego (2012) przyzwala na badania ukryte na zasadzie wyjątku, gdy istnieją ku temu przesłanki metodologiczne. Niewiele jednak wiadomo o tym, jak badacze i badaczki oceniają taką praktykę pod względem etycznym. Na podstawie wywiadów pogłębionych z 56 antropologami i antropolożkami oraz socjologami i socjolożkami na temat etyki badań w praktyce badawczej analizuję, pod jakimi warunkami można usprawiedliwić przeprowadzenie niejawnej obserwacji uczestniczącej. Odpowiadam także na pytanie, czy opinie socjologów i socjolożek różnią się od opinii antropologów i antropolożek w tej sprawie.
Genetic parameters, productivity indices and breeding plans for designing community-based goat breeding programs in Ethiopia

A meta-analysis of 84 published reports on goats was conducted to calculate weighted and unweighted average direct heritability (ha 2 ), maternal genetic effect (hm 2 ), ratio of common environmental effect (c 2 ), repeatability (R), genetic (rg) and phenotypic (rp) correlations for growth, reproduction and milk production traits. Weighted average ha 2 , hm 2 , and c 2 for growth traits ranged from 0.03 to 0.45, 0.05 to 0.27, and 0.02 to 0.10, respectively. Corresponding weighted average ha 2 for reproduction and milk production traits ranged from 0.00 to 0.17 and 0.15 to 0.22, respectively. Weighted R for the growth, reproduction, and milk traits ranged from 0.06 to 0.56, 0.06 to 0.13, and 0.50 to 0.61, respectively. Weighted averages of rp and rg among growth traits ranged from -0.06 to 0.84 and 0.01 to 0.98, respectively. Weighted average rp among milk traits ranged from 0.18 to 0.94. In most cases average ha 2 and rg had higher observed standard deviations compared to the theoretical standard error. Based on the present findings, it seems that weighted average ha 2 , hm 2 , c 2 , R, and rg are more reliable for two reasons: estimates of ha 2 for some growth traits were more conservative than values based on relatively higher number of records and the absence of significant effects of the tested fixed factors on some parameter estimates. More studies on genetic parameter estimations are required for growth, reproduction, and milk traits in goats.
Gain-switched all-fiber lasers and quasi-continuous wave supercontinuum generation

The extreme broadening phenomenon of supercontinuum (SC) generation in optical fibers is the basis of SC laser sources. These sources have numerous applications in areas, such as spectroscopy and microscopy due to the unique combination of extremely broad spectral bandwidths, high spectral power densities, and high spatial coherence. In this work the feasibility of applying gain-switched all-fiber lasers to SC generation is investigated. It is motivated by the simplicity of the architecture and the ability to scale the optical output power of such fiber lasers. The physics of fiber lasers are reviewed to understand the mechanisms involved in gain-switching. A detailed numerical model is provided to give deep insight into the different stages of pulse generation. A simplified model is also developed to derive an analytic expression for the pulse duration. Extensive experiments with gain-switching of fiber lasers with a variety of different configurations are carried out. The peak power, pulse duration, bandwidth, and scaling with repetition rate are thoroughly described. General guidelines are submitted to enable designing of gainswitched fiber lasers with specifically tailored properties. The physics of SC generation are reviewed with focus on the quasicontinuous wave pumping regime. Experiments with a gain-switched fiber laser as a pump for SC generation in photonic crystal fibers are carried out. Requirements for efficient SC generation are analyzed. Finally, an optimization of photonic crystal fibers for gain-switched fiber laser-pumped SC generation is presented.
Avlistoria: uma proposta narrativa para ensinar sobre criptomoedas

Os jogos eletrônicos do gênero plataforma têm como característica popular serem simples e divertidos e são uma escolha natural para aventuras com planos de fundo menos complexos. Neste artigo, é apresentado o desenvolvimento do jogo ``Avlistoria", que faz uso dessas características para ensinar sobre o tema atual das criptomoedas, de forma pouco complexa, lúdica e com narrativa cativante. Um protótipo do jogo foi avaliado por 10 pessoas, que consideraram a abordagem adequada e com alto potencial de atingir o objetivo proposto.
Anode‐Free Solid‐State Lithium Batteries: A Review

Anode‐free solid‐state lithium batteries are promising for next‐generation energy storage systems, especially the mobile sectors, due to their enhanced energy density, improved safety, and extended calendar life. However, the inefficiency of lithium plating and stripping leads to rapid capacity degradation due to the absence of excess lithium inventory. Therefore, dissecting the difficulties and challenges faced by anode‐free solid‐state lithium batteries can pave the way to improving the cycle life of many lithium batteries. In this review, the key issues affecting capacity degradation are elaborated step‐by‐step based on the current understanding of anode‐free solid‐state lithium batteries. Furthermore, various strategies for optimizing performance are targeted. Finally, future opportunities and possible directions for anode‐free solid‐state lithium batteries are evaluated, aiming to stimulate the exploration of this emerging field.
A climate-smart world and the rise of Green Extractivism

ABSTRACT Climate change policies' implications for the capitalist system call for us to go beyond efficiency-driven extractivism and further analyse the outcomes of green policies. The implementation of Mozambique's climate change policy resulted in the emergence of green extractivism, a variation of extractivism that is based on the extraction, expropriation and transfer of emissions rights from rural poor, in favor of external accumulation. Emission rights are one's ability to rightfully use and benefit from ecological assets. Thus, under green extractivism, rural households are not only being deprived of resources determinant for their social reproduction, but also of their right to emit.
Comparative evaluation of antioxidant enzymes, lipid peroxidation, nitric oxide, and non – microsomal oxidases in Galatea paradoxa exposed to varying concentrations of ‘uproot’ a glyphosate-based herbicide

Herbicides are employed in farmlands to increase agricultural yields by controlling the growth of unwanted weeds. Glyphosate, (N(phosphonomethyl) glycine (an isopropylanine salt of glycine), is a non – selective herbicide currently among the most widely used agricultural chemicals globally for the control of weeds in cultivars. It was ARTICLE INFO ABSTRACT Article History: Received:April 13, 2022 Accepted:April 30, 2022 Online: May 25, 2022 _______________
Evaluation of red-edge features for identifying subtropical tree species based on Sentinel-2 and Gaofen-6 time series

ABSTRACT Fast and accurate tree species mapping using remote sensing technology is invaluable to forest sustainable management. However, due to the limitation of ecosystem complexity and the frequent cloudy weather, it remains challenging to obtain detailed knowledge about the varieties in subtropical areas. In this study, 18 scenes of Gaofen-6 (GF6) and 13 scenes of Sentinel-2 (S2) images were harmonized using linear regression models, and the identification abilities of 23 red-edge vegetation index (REVI) time-series and the normalized difference vegetation index (NDVI) time-series were evaluated using the random forest classifier (RF). The feature reduction methods were employed to boost the effectiveness of the input features, and the optimal red-edge combination consisting of spectral and textural features was built to map the dominant tree species precisely in Southeast China. Our results showed as follows: (1) GF6 and S2 could be harmonized with a mean coefficient of determination (R2) greater than 0.94 and the root means squared error (RMSE) less than 0.02, which was helpful to improve the consistency between multi-source remote sensing data and subsequently derived products. (2) Most of the REVI time-series performed better than NDVI time-series, and the transformed chlorophyll absorption ratio indexes (TCARI2) got the highest overall accuracy of 86.28%, which could be further improved to 88.37% by adding 26 textural features. (3) The phenological characteristics from different seasons were conducive to tree species classification compared to the effect of different sensors, and the contribution of spring images played the most important role in RF, followed by autumn and winter, while the mid-summer images seemed to be negligible in the classifier. Overall, this study proposed an accurate and cost-efficient approach for tree species identification, which demonstrated good potential in providing a profound reference for subtropical forest landscape studying.
Enhancing the Classroom Experience with Learning Technology Teams.

19 The driving force behind adoption of educational technologies in universities is the belief that they improve the quality of teaching.1 Despite this assumption, faculty experimentation with technologies in the classroom is slow and focuses on a narrow range of tools such as e-mail, presentation handouts, Web pages, and Internet resources.2,3 This pattern suggests that weaving technologies into the learning experience poses challenges that go beyond mere adoption. The use of new tools in the classroom, however, does not ensure that teaching will improve or that students will learn. Rather, thoughtful pedagogical strategy matters most if educational technology is to succeed in building invigorating learning environments.4 How are faculty best supported in efforts to integrate technology in their courses? This question identifies the single most important technology issue for the next few years in U.S. public universities, according to the 1999 National Survey of Information Technology in U.S. Higher Education.5 In response to the need for faculty support, some campuses have developed comprehensive programs to reach this goal.6,7 Queen’s University, a midsize research university in Canada, provides a selection of activities to engage faculty in thinking about educational technologies. The Learning Technology Unit offers regular workshops on both the technical and pedagogical aspects of frequently used tools such as WebCT, PowerPoint, and HTML. Educational Technology Days showcase best practices Enhancing the Classroom Experience with
A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.
Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit

Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.