Miracle-dz/UniFAR-FTUs huggingface dataset:
Newly Isolated Rhizobacteria as Plant Growth Promoters For Maize Plant (Zea mays) under Water Stress

Plant growth promoting rhizobacteria (PGPR) offers an attractive way to replace chemical fertilizers, pesticides, and supplements. In the current study, nine rhizobacterial strains isolated from the rhizoshere of clover ( Trifolium ) and wheat ( Triticum ) plants were tested for multiple plant growth promoting activities (NH 3 , IAA production, catalase and oxidase activity). Later, they were genetically idendified by 16S RNA sequence analyses as Bacillus subtilis , Bacillus cereus, Pseudomonas mendocina , Pseudomonas putida, Kocuria marina , and Kocuria rhizophila. The rhizobacterial strains were then applied individually, for the evaluation of maize seedling growth promotion activity. Finally, the interaction between the rhizobacterial strains in addition to a previously identified rhizobacterium Bacillus licheniformis , was investigated by applying the Plackett-Burman experimental design under different levels of water stress. The percentages of germination, carbohydrate, and protein production were measured as physiological responses indicating the effect of the bacteria on plant health.
IoT and Cloud Based Automated Pothole Detection Model Using Extreme Gradient Boosting with Texture Descriptors

One of crucial activity related to road monitoring and maintenance is the occurrence of potholes. These potholes are also be major reason of road accidents, damaging of vehicles, discomfort of passenger journey and extensive in terms of time and cost. But, identification of potholes can significantly alleviate the aforementioned issues. Other side, the Internet of Things (IoT) plays a crucial role in different applications, and provides viable and state of art solutions for variety of problems. Hence, the aim of this work is to develop a real time automated pothole detection model to detect the potholes in asphalt roads based on IoT devices.The proposed model comprises of three main components such as collection of pothole data and labeling, image pre-processing and texture feature extraction, and extreme gradient boosting (XGBoost) algorithm. The potholes data on asphalt road is collected by three IoT sensors such as accelerometer, ultrasonic sensor, and GPS and further, the collected data is transmitted on cloud via Wi-Fi module. The texture features are extracted using Gaussian steerable and median filters. The extreme gradient boosting (XGBoost) classifier is adopted for prediction task. The simulation results showed that proposed XGBoost model obtains higher accuracy, recall, precision and F1-score rates as 94.56, 97.41, 96.40, and 96.90 respectively using 10-cross fold validation method.
Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data

Timely and accurate information about land cover is an important and extensively used application of remote sensing data. After successful launch of Landsat 8 is providing a new data source for monitoring land cover, which has the potential to improve the earth surface features characterization. Mapping of Leaf area Index (LAI) in larger area may be impossible when we rely on field measurements. Remote sensing data have been continuing efforts to develop different methods to estimate LAI. In this present study, an attempt has been made to discriminate various land cover features and empirical equation is used for retrieve biophysical parameter (LAI) for satellite NDVI data. Support vector machine classification was performed for Muzaffarnagar district using LANDSAT 8 operational land imager data to separate out major land cover classes (water, fallow, built up, sugarcane, orchard, dense vegetation and other crops). Ground truth data was collected using JUNO GPS which was used in developing the spectral signatures for each classes. The LAI-NDVI existing empirical equation is used to prepare LAI map. It is found that the LAI values in village foloda region maximum LAI pixels in the range 3.10 and above and minimum in the range 1.0 to 1.20. It is also concluded that the LAI values between 1.70 and 3.10 is having most of the sugarcane crop pixels at maximum vegetative growth stage. It shows that the sugarcane crop condition in the study area was very good.
A study of digital transformation and MSMEs performance from a spatial perspective: Evidence from China

Abstract Aim/purpose – Not only have micro, small, and medium enterprises (MSMEs) been regarded as a driving force of the national economy, but they have also become important in promoting innovation and entrepreneurship. In general, today’s MSMEs face problems, such as high business costs, insufficient innovation, and inadequate management capabilities, that have all forced the digital transformation of MSMEs. However, in existing studies, the impact of digital transformation on performance is controversial, and the subjects are generally listed companies. In this paper, we have decided to use micro business survey data and analyze them from a spatial perspective to explore how MSMEs’ digital transformation plays a role in influencing performance and the path of its effect. Design/methodology/approach – We use a sampling method with a dual directory-regional sampling frame to investigate MSMEs in Ningbo City, Zhejiang Province, China and then delve into the impact of digital transformation on enterprise performance from a spatial perspective by the spatial weighted logit model. Findings – Hierarchical analysis shows that there is a large room for improvement in the Digital transformation of enterprises. The digitalization level of an enterprise has a positive impact on performance. While from a spatial perspective, the digitalization level of neighboring enterprises has a negative impact on the performance of the enterprise. Path analysis shows that the digital transformation of enterprises can increase innovation, reduce costs, and improve the performance of enterprises. Originality/value – We provide an empirical basis for vigorously promoting the digital transformation of enterprises, jointly building digital parks, and improving enterprise performance by reducing costs and improving efficiency. At the same time, it provides relevant suggestions for digital transformation for manufacturing MSMEs that are hesitant to see or are at a loss in the digital transformation and helps manufacturing MSMEs to achieve cost reduction and increase efficiency.
An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.
Mapping soya bean and corn crops in the State of Paraná, Brazil, using EVI images from the MODIS sensor

ABSTRACT This study aimed to map, separate, and estimate soya bean and corn crop areas in Paraná State, Brazil, in the harvest years 2012/13 and 2013/14, using the enhanced vegetation index (EVI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Thus, two methodologies were integrated, the first considering heterogeneity on the dates of crop cycles, the scenes required to generate images of minimum and maximum vegetation indexes, creating a colour composite red, green, and blue (RGB), and identifying two cultures simultaneously. In the second methodology, soya bean and corn were identified and mapped using the selection of pure pixels and the supervised classification algorithm Spectral Angle Mapper (SAM). In order to avoid overlapping areas, we multiplied the results from the first and second methodologies to obtain the final separation. The final validation of the mapping was compared to official data, identifying high correlation to crops. Based on Medium-Resolution Linear Imaging Self-Scanner (LISS-III) and Land Remote Sensing Satellite (Landsat-8) images, the similarity of global accuracy (GA) and kappa accuracy indices was determined, being classified as good and excellent, respectively. It showed that the use of the two consortium methodologies for separation and overlap elimination of these crops in the state of Paraná was efficient.
SIMULATION OF SEISMIC WAVE PROPAGATION, AND AMPLITUDE AND PERIOD CHARACTERISTICS FOR THE SEISMIC MICROZONING OF GUARENAS AND GUATIRE CITIES

We present the results of the simulation of seismic wave propagation in complex geological media through numerical calculation, estimating the 2D/1D seismic soil response by generating synthetic seismograms. This simulation corresponds to the propagation along 2D sections of the basin prepared by using local and regional geological and geophysical information. The hybrid methods technique was applied, that uses analytical (modal summation of surface waves) and numerical (finite differences) methods to obtain synthetic seismograms up to a frequency of 10 Hz, along 8 north-south and south-north profiles, and 3 east-west profiles crossing the sedimentary basin of Guarenas-Guatire. Three scenario earthquake were chosen, located along:San Sebastian, Pichao (system La Victoria) and Tacagua Faults, with magnitudes of 7, 6 and 5 Mw, respectively. The following ground motion parameters were obtained : 2D/1D spectral ratio of the horizontal and vertical components H/V, and 2D/1D acceleration response spectrum ratio for the transverse, radial and vertical components. Response spectra of horizontal acceleration for the ground surface were developed from averages relations of response spectra (considered as transfer functions) and response spectrum at bedrock obtained from a regional seismic
Researching Electromobility in Brazil: Elements for Building a National Policy

Brazil does not present the impulses and motivations that led other countries to establish policies for electromobility, as there are other peculiarities in its energy matrix that disperse efforts towards vehicular electrification. This article presents a documental analysis of the main policy instruments for mobility, the main stakeholders, and some propositions, mainly from the State, in the federal, state and municipal spheres, that can impact the process. This analysis allows to build a framework which was taken as basis for a research protocol under a Q-method, which combines qualitative and quantitative techniques, to describe and analyze subjectivity among Brazilians key specialists regarding their views on opportunities and treats to electromobility in the country. The purpose is to propose a Brazilian electromobility policy model. This article presents the first steps into this effort.
Students' Behavioural Intention towards Adoption of Online Education: A Study of the Extended UTAUT Model

Online education (o-education) is gaining significant attention from education providers, students, and policy makers. This research explored students’ behaviours and intentions towards online education. The present research utilised the Unified Theory of Acceptance and Use of Technology (UTAUT) as a model to investigate these intentions and behaviours. The study incorporated UTAUT predictors along with three additional independent variables. This study adopted a cross-sectional design and used a closed-end questionnaire to collect data from 403 respondents. The data were analysed with SPSS and AMOS by utilising structural equation modeling (SEM) techniques. The results of the SEM analysis indicate that perceived value (PV) and convenience (Cnv) have positive influences on behavioural intention (BI), while perceived risk has a negative impact on behavioural intention (BI). Additionally, facilitating conditions (FC) and behavioural intention (BI) are found to positively influence use behavior (UB). The research findings suggest that demographics, including age, education and gender, influence behavioural intention (BI). The findings of the study have theoretical and practical implications, which are elaborated in the last section of this article.
Conservation of paleontological heritage in Serbia: From philosophy to practice

Serbia has a rich paleontological heritage that includes both, fossiliferous sites and fossilized remains and traces of once living plants and animals. Their study allows paleontologists and geologists to understand and interpret the evolution of life on Earth, ancient environments and past climates, to establish the age of rocks and to correlate rock units in different part of the world. Fossiliferous sites and fossils are not only an integral part of geodiversity and geoheritage but also important component of our national natural and cultural patrimony. Criteria for evaluation of potential paleontological heritage are suggested along with required conservation strategy in order to ensure that these phenomena, inherited from the past will still be with us in the future.
Efficacy and Safety of Programmed Death-1/Programmed Death-Ligand 1 Inhibitor for Metastatic Urothelial Carcinoma: A Systematic Review and Meta-Analysis

Objective: The purpose of this study was to evaluate the efficacy and safety of programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for the treatment of metastatic urothelial carcinoma (mUC). Methods: A literature search was conducted of PubMed, EMBASE, and the Cochrane Library and was limited to the English literature. Randomized controlled trials (RCTs) published up to July 2022 were considered for inclusion. The outcomes were progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and grade ≥ 3 treatment-related AEs (TRAE). Subgroup analysis was performed based on the PD-L1 expression status, and the differences between first- and second-line PD-1/PD-L1 inhibitors were estimated. Results: We included five RCTs comprising 3584 patients in the analysis. Compared with chemotherapy alone, the use of PD-1/PD-L1 inhibitors as monotherapy did not significantly prolong OS [hazard ratios (HR), 0.90; 95% CI, 0.81–1.00] or PFS (HR, 1.12; 95% CI, 0.95–1.32). However, the PD-1/PD-L1 inhibitor combined with chemotherapy significantly improved both OS (HR, 0.85; 95% CI, 0.74–0.96) and PFS (HR, 0.80; 95% CI, 0.71–0.90). Additionally, subgroup analysis showed that in mUC with PD-L1 expression ≥ 5%, treatment with the PD-1/PD-L1 inhibitor alone did not reduce the risk of death. Safety analysis showed that the PD-1/PD-L1 inhibitor alone did not significantly increase the incidence rates of grade ≥ 3 TRAEs. Conclusions: The results show that use of the PD-1/PD-L1 inhibitor alone as first-line treatment is similar to chemotherapy in terms of both survival and response rates. However, the PD-1/PD-L1 inhibitor plus chemotherapy has a significant benefit in terms of PFS or OS. Nonetheless, more RCTs are warranted to evaluate efficiency and safety in the combination regimen of chemotherapy and PD-1/PD-L1 inhibitors.
First production application of high-density vibroseis acquisition on Alaska's North Slope

Abstract A case study of the acquisition of a high-density, broad-bandwidth vibroseis survey on Alaska's North Slope, using a high-productivity slip-sweep technique, shows the merits of this technique for acquisition of high-density data in a short time frame in extreme conditions as well as the benefits of high-density broadband data for subsequent processing, imaging, and seismic inversion.
Daily Minimum and Maximum Air Temperature in Poland in the Years 1951–2005

Abstract In this study grid data of daily maximum and minimum air temperatures taken from the NCEP/NCAR reanalysis for the territory of Poland for the years 1951-2005 have been used as a basis for an analysis of the spatial distribution of daily maximum and minimum air temperature, the frequency of characteristic days and the variability of these parameters in the period analysed. The results obtained were then compared to the variability in atmospheric circulation in Europe, described by the North Atlantic Oscillation (NAO) index.
A Linear Model of Wintertime Low-Frequency Variability. Part I: Formulation and Forecast Skill

A linear inverse model (LIM) suitable for studies of atmospheric extratropical variability on longer than weekly timescales is constructed using observations of the past 30 years. Notably, it includes tropical diabatic heating as an evolving model variable rather than as a forcing, and also includes, in effect, the feedback of the extratropical weather systems on the more slowly varying circulation. Both of these features are shown to be important contributors to the model’s realism. Forecast skill is an important test of any model’s usefulness as a diagnostic tool. The LIM is better at forecasting week 2 anomalies than a dynamical model based on the linearized baroclinic equations of motion (with many more than the LIM’s 37 degrees of freedom) that is forced with observed (as opposed to the LIM’s predicted) tropical heating throughout the forecast. Indeed, at week 2 the LIM’s skill is competitive with that of the global nonlinear medium-range forecast (MRF) model with nominally O(106) degrees of freedom in use at the National Centers for Environmental Prediction (NCEP). Importantly, this encouraging model performance is not limited
The Year 2023 in Retrospect: Celebrating the Best of Community College Research

ABSTRACT The year 2023 has afforded the Community College Journal of Research and Practice opportunities to publish 12 issues featuring impactful research and scholarship about community colleges. This article describes the journal’s achievements while showcasing published works that address issues of policy, research and practice. The article aims to share useful information about the journal’s reach, metrics and impact. Opportunities for publishing and volunteering are described.
Pest recognition based on multi-image feature localization and adaptive filtering fusion

Accurate recognition of pest categories is crucial for effective pest control. Due to issues such as the large variation in pest appearance, low data quality, and complex real-world environments, pest recognition poses challenges in practical applications. At present, many models have made great efforts on the real scene dataset IP102, but the highest recognition accuracy is only 75%. To improve pest recognition in practice, this paper proposes a multi-image fusion recognition method. Considering that farmers have easy access to data, the method performs fusion recognition on multiple images of the same pest instead of the conventional single image. Specifically, the method first uses convolutional neural network (CNN) to extract feature maps from these images. Then, an effective feature localization module (EFLM) captures the feature maps outputted by all blocks of the last convolutional stage of the CNN, marks the regions with large activation values as pest locations, and then integrates and crops them to obtain the localized features. Next, the adaptive filtering fusion module (AFFM) learns gate masks and selection masks for these features to eliminate interference from useless information, and uses the attention mechanism to select beneficial features for fusion. Finally, the classifier categorizes the fused features and the soft voting (SV) module integrates these results to obtain the final pest category. The principle of the model is activation value localization, feature filtering and fusion, and voting integration. The experimental results indicate that the proposed method can train high-performance feature extractors and classifiers, achieving recognition accuracy of 73.9%, 99.8%, and 99.7% on IP102, D0, and ETP, respectively, surpassing most single models. The results also show that thanks to the positive role of each module, the accuracy of multi-image fusion recognition reaches the state-of-the-art level of 96.1%, 100%, and 100% on IP102, D0, and ETP using 5, 2, and 2 images, respectively, which meets the requirements of practical applications. Additionally, we have developed a web application that applies our research findings in practice to assist farmers in reliable pest identification and drive the advancement of smart agriculture.
Enabling Accurate Tuberculosis Diagnosis through Deep Learning on Patient CXR Images

Tuberculosis stands out as a severe and deadly infectious disease globally, characterized by a high incidence rate and mortality. While common tuberculosis screening methods involve diagnosing through CXR images, this approach carries a significant risk of misdiagnosis. The recent emergence of deep learning models has opened new avenues to tackle this issue. This paper is dedicated to exploring the utilization of deep learning models for tuberculosis screening using CXR images. The primary objective of this article is to leverage the latest advancements in deep learning to craft DNN network models and facilitate feature integration. This integration is based on the image features extracted by the DNN models. Furthermore, the article includes performance testing on the publicly accessible Shenzhen dataset. The experimental findings underscore the accuracy and reliability of our method in diagnosing tuberculosis with precision through CXR images. This conclusion holds immense significance for potential clinical applications.