Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Water Quantity and Quality Nonlinearities for Watershed Adaptability to Hydroclimate Extremes in Agricultural Landscapes

Changing water supplies and demands, inherent to climate fluctuations and human activities, are pushing for a paradigm shift in water management worldwide. The occurrence of extreme hydrometeorological and climate events such as extended wet periods and droughts, compounded with contaminants, impair the access to water resources, demanding novel designs, construction, and management across multiple hydrologic scales and biogeochemical processes. A constraint to studying hydrologic and biogeochemical disturbances and improving best management practices for water quantity and quality at the watershed scale resides in the suitable monitoring, data availability, and the creation of frameworks. We hypothesize that streamflow and contaminants, simulated by the hydrologic model Soil and Water Assessment Tool (SWAT) and evaluated during drought and extended wet periods, capture the nonlinearities of contaminants of multiple biogeochemical complexities, indicating the adaptive abilities of watersheds. Our objectives are to (1) use rain gauge and radar data and linear regression to consolidate long-term precipitation data to simulate streamflow and water quality using the SWAT model in the Shell Creek (SC) watershed, Nebraska, U.S.; (2) use drought and extended wet events analytics on observed and simulated hydroclimate and water quality variables to identify SWAT’s performance; and (3) identify the temporal attributions of streamflow and water quality to complex biogeochemical patterns of variability. We implement a watershed modeling approach using the SWAT model forced with rain gauge and radar to simulate the intraseasonal and interannual variability streamflow, sediments, nutrients, and atrazine loads in the SC watershed. SWAT performance uses a calibration period between 2000 and 2005 and a validation period between 2005 and 2007. We examine the model’s ability to simulate hydrologic and biogeochemical variables in response to dry and extended wet flow regimes. The hydrologic model forced by either radar or rain gages performs similarly in the calibration (NSE = 0.6) and validation (NSE = 0.92) periods. It reproduces medium flows closer to the observations, although it overestimates low–flows up to 0.1 m3/s while underestimates high flows by 1 m3/s. The water quality model shows higher NSE for streamflow and sediments followed by nutrients, whereas it poorly reproduces atrazine. We conclude that seasonal changes and hydroclimate conditions led to the emergence of patterns of variability associated to the nonlinearities and coupling between processes of natural and human-origin sources. As climate change propels the occurrence of hydroclimate extremes, the simulation of water quantity and quality nonlinearities—as properties of complex adaptive hydrologic systems—can contribute to improve the predictability of climate-resilient water resources.
Detecting Turistic Places with Convolutional Neural Networks

: A mobile application was developed for the recognition of places from a photo using the technique “content based photo geolocation as spatial database queries”. For this purpose, an investigation and analysis of the different existing methods that allow us to recognize images from a photo was carried out in order to select the best possible model and then improve it. Performance comparisons, comparison of number of parameters, Error: imagenet and the Brain-Score were made; once the best model was obtained, the algorithm was implemented and with the results the expected information of the place in the photo was shown. The purpose of this information is to recommend nearby places of interest. In the development stage, ﬁrst, we implement an architecture with convolutional neural networks VGG16, for the recognition of places, the model was trained, after obtaining a trained model with successful results, the construction phase of the application continued. mobile in order to test the operation of the model. Users will use the app by submitting a photo which will query the trained model, and results will be obtained in seconds, information that will provide a better experience when visiting unknown places.
Research on Operation Management under the Environment of Cloud Computing Data Center

In recent years, with the rapidly developing of technical and commercial related with cloud computing, many countries have issued the cloud computing development strategic plan. Characteristics of cloud computing technology determine the cloud computing platform operational mode cannot copy the existing business platform of operational mode. So exploring the data center operations in cloud computing environment system, to ensure the cloud platform and business platform of the bearing can be efficient, safe and stable operation becomes important issues in operations management. Cloud computing operations management should be emphatically from daily operation monitoring, security management and automated processing. Introducing ITIL V3 operations management, strategy, design, operation and continuous improvement to cover every stage, will push the success of enterprise cloud computing strategy.
Sistem Informasi Geografis Pengelolaan Praktek Kerja Lapangan di Sekolah Menengah Kejuruan Berbasis Web

Praktek Kerja Lapangan adalah kegiatan yang dilaksanakan di dunia kerja (industri/usaha) sebagai pembelajaran, pendidikan, atau pelatihan yang disesuaikan dengan kompetensi (keahlian) dibidangnya. Permasalahan yang dihadapinya yaitu kurangnya pemetaaan lokasi industri secara geografis, kurangnya mengatur pengelolaan Praktek Kerja Lapangan di industri dan kurangnya pengelolaan Praktek Kerja Lapangan secara daring. Dan dikarenakan fenomena penyebaran Coronavirus Disease 19 menyebabkan Praktek Kerja Lapangan tidak bisa dilaksanakan secara tatap muka, kemudian Pemerintah memperbolehkan Praktek Kerja Lapangan secara daring sesuai dengan ketetapan yang diberikan. Penelitian ini akan mengembangkan sebuah sistem yang dapat menyediakan pengelolaan Praktek Kerja Lapangan secara daring. Metode yang digunakan pada penelitian ini adalah Rational Unified Process dan menggunakan 3 tahapannya yaitu Inception, Elaboration dan Construction. Berdasarkan hasil sistem yang dibangun, sistem telah menerapkan pemetaan secara geografis pada map, menyediakan pengelolaan di pihak Industri dan menyediakan pengelolaan Praktek Kerja Lapangan secara daring.
Carotid artery injury in endoscopic endonasal surgery: Risk factors, prevention, and management

Abstract Objective Endoscopic approaches for sinus and skull base surgery are increasing in popularity. The objective of this narrative review is to characterize risk factors for internal carotid artery injury in endoscopic endonasal surgery (EES), highlight preventative measures, and illustrate key management principles. Data Sources Comprehensive literature review. Methods Relevant literature was reviewed using PubMed/MEDLINE. Results Carotid artery injury in EES is rare, with most studies reporting an incidence below 0.1%. Anatomic aberrancies, wide dissection margins, as well as specific provider and hospital factors, may increase the risk of injury. Multidisciplinary teams, comprehensive preoperative imaging, patient risk assessment, and formal training in vascular emergencies may reduce the risk. Management protocols should emphasize proper visualization of the injury site, fluid replacement, rapid packing, angiography, and endovascular techniques to achieve hemostasis. Conclusions While EES is a relatively safe procedure, carotid artery injury is a devastating complication that warrants full consideration in surgical planning. Important preventative measures include identifying patients with notable risk factors and obtaining preoperative imaging. Multidisciplinary teams and management protocols are ultimately necessary to reduce morbidity and mortality.
Zones of Nonbeing: Abjection, White Accumulation, and Neoliberal School Reform

Background/Context: Jean Anyon’s work provides a powerful intervention in the study of education with her attention to political economy and the social contexts of education. Mainstream neoliberal charter reform arguments often counter Anyon’s work by suggesting a “no excuse” ideology, which often ignores structural realities facing youth. Over the decades, charter schools have garnered bipartisan interest and expanded significantly. Following Hurricane Katrina, New Orleans was one such site where the expansion and experimentation with charter schools has taken place. Utilizing the restructuring of New Orleans after Katrina as a site for examination, this article builds from a larger yearlong qualitative critical race case study on the reproduction of White dominance via the charter school authorization and application process. Purpose/Objective/Research Question/Focus of Study: Utilizing the educational restructuring of New Orleans post-Katrina, this article aims to illustrate the interconnectedness of political economy and race. Extending Anyon’s analysis of political economy, the article focuses on the neoliberal restructuring of New Orleans education after Hurricane Katrina to illustrate how the notions of abjection and zones of nonbeing form a guiding constellation for the accumulation and solidification of White power and capital. Research Design/Data Collection and Analysis: This article builds off a larger yearlong qualitative critical race case study on the reproduction of White racial educational dominance in post-Katrina New Orleans. Interviews and student artifacts are the central data. Conclusion/Recommendations: The author recommends the grounding of youth voice in educational policy analysis, conceptualization, and implementation; the consideration of how race impacts education; and continued research on the inter-imbrication of race and political economy.
Design, Manufacture, Test and Experiment of Six-Axis Force Torque Sensor for Chinese Experimental Module Manipulator

A novel six-axis force/torque sensor (F/T sensor) for an Experimental Module Manipulator (EMM) in the Chinese Space Station (CSS) is developed in this paper. First, we designed the elastomer structure of the F/T sensor and used the analytical method and the finite element method to analyze the strain, in order to accomplish the strain gauges’ layout. Then, the electrical system was designed, which mainly realizes the acquisition of force/torque information, temperature and serial communication with the end effector (EE). Following this, we analyzed and designed the adaptability of the F/T sensor to the space environment. After this, the manufacturing process of the sensor was introduced in detail, and the F/T sensor was calibrated by a pulley weight system. Finally, the sensor was tested on the space environment adaptability of mechanical vibration and thermal vacuum on the ground. The test results show that the developed sensor has the ability to accurately measure three-dimensional force and three-dimensional moment information on orbit, which provides necessary conditions for the on-orbit fine operation of EMM.
A new method for operating point free power system transient stability situational awareness based on single machine equivalent approach

Transient stability assessment is an essential element for situational awareness and decision making process of a power system operator in maintaining the secure operation. Continuous efforts have been made for devising methods to assess the transient stability. However, the existing methods have dependency on pre‐fault operating condition. This article aims to report a new method, towards deriving contingency wise operating point free transient stability assessment. The method is based on single machine equivalent (SIME) approach and has two phases (a) off line tuning (b) online application. In offline tuning phase each plausible contingency is characterized by an operating point free parameter called mean critical return angle over the specified operating region. In online application phase, transient stability for a given plausible contingency is predicted using SIME approach. Two new severity indices namely stable severity index and unstable severity index are proposed and evaluated for assessing the degree of stability and instability. Further, these indices are used in contingency screening. The proposed method is tested on 3‐machine 9‐bus system and, 10‐machine 39‐bus system. Results shows that the proposed method is simple and can aid the existing methods in providing reliable transient stability situational awareness to the system operator.
In-Situ Imaging of a Light-Induced Modification Process in Organo-Silica Films via Time-Domain Brillouin Scattering

We applied time-domain Brillouin scattering (TDBS) for the characterization of porogen-based organosilicate glass (OGS) films deposited by spin-on-glass technology and cured under different conditions. Although the chemical composition and porosity measured by Fourier-transform infrared (FTIR) spectroscopy and ellipsometric porosimetry (EP) did not show significant differences between the films, remarkable differences between them were revealed by the temporal evolution of the Brillouin frequency (BF) shift of the probe light in the TDBS. The observed modification of the BF was a signature of the light-induced modification of the films in the process of the TDBS experiments. It correlated to the different amount of carbon residue in the samples, the use of ultraviolet (UV) femtosecond probe laser pulses in our optical setup, and their intensity. In fact, probe radiation with an optical wavelength of 356 nm appeared to be effective in removing carbon residue through single-photon absorption processes, while its two-photon absorption might have led to the breaking of Si-CH3 bonds in the OSG matrix. The quantum chemical calculations confirmed the latter possibility. This discovery demonstrates the possibility of local modifications of OSG films with a nanometric resolution via nonlinear optical processes, which could be important, among other applications, for the creation of active surface sites in the area-selective deposition of atomic layers.
Subclinical bovine mastitis associated with Staphylococcus spp. in eleven Uruguayan dairy farms.

INTRODUCTION Bovine mastitis is the most common disease affecting the dairy industry, with staphylococci being considered as one of the most significant and prevalent causes. This study aimed to assess the presence of staphylococcal subclinical mastitis (SCM) in Uruguayan dairy farms and to identify Staphylococcus aureus (SA) and non-aureus staphylococci (NAS) in milking cows. In addition, the antibiotic susceptibility of isolated staphylococci was evaluated. METHODOLOGY We tested 546 apparently healthy milking cows from 11 farms for detecting SCM using the California Mastitis Test (CMT). The cows were not treated with antibiotics. CMT-positive samples were cultured, and colonies compatible with Staphylococcus spp. were further identified through molecular techniques. The susceptibility of the Staphylococcus spp. isolates against thirteen antibiotics was determined using the disk diffusion method. RESULTS Subclinical staphylococcal mastitis was present in almost all (82%) farms. SA (n = 39) was more common than NAS (n = 9) in the 48 samples tested. Isolates exhibited resistance to one, two, and even three different antibiotics. Resistance to penicillin was the most frequent among SA (23/39) and NAS (4/9). No staphylococci isolates exhibited resistance to cefoxitin, vancomycin, trimethoprim-sulfamethoxazole, erythromycin, or clindamycin. CONCLUSIONS Staphylococcal SCM is one of the most common diseases in Uruguayan dairy farms. SA was the prevalent pathogen, however SA and NAS mastitis coexisted in many farms. NAS were identified and its distribution was similar to other countries. Penicillin had the highest and most frequent percentage of resistance.
Unbalanced Risk of Pulmonary Tuberculosis in China at the Subnational Scale: Spatiotemporal Analysis

Background China has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear. Objective We aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China. Methods County-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties. Results From 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; P<.001), followed by Guizhou (117/100,000; RR 1.8, 95% CI 1.3-1.9; P<.001) and Tibet (108/100,000; RR 1.7, 95% CI 1.3-2.1; P<.001). The RR for PTB showed a steady downward trend. Gansu (local trend [LT] 0.95, 95% CI 0.93-0.96; P<.001) and Shanxi (LT 0.94, 95% CI 0.92-0.96; P<.001) experienced the fastest declines. However, the RRs for PTB in the western region (such as counties in Xinjiang, Guizhou, and Tibet) were significantly higher than those in the eastern and central regions (P<.001), and the decline rate of the RR for PTB was lower than the overall level (P<.001). Conclusions PTB risk showed significant regional inequality among counties in China, and western China presented a high plateau of disease burden. Improvements in economic and medical service levels are required to boost PTB case detection and eventually reduce PTB risk in the whole country.
Automatic Detection of DGA-Enabled Malware Using SDN and Traffic Behavioral Modeling

Enterprise networks are under enormous threats from sophisticated cyber-attacks. Various kinds of malware are installed by attackers on compromised hosts, acting as bots that typically use Domain Generation Algorithms (DGAs) to communicate with their Command and Control (C&C) servers. It is computationally expensive to inspect all network packets of every host connected to a large enterprise network in “real-time” at scale with hundreds of gigabits per second data rates. This paper combines Software Defined Networking (SDN) and machine learning to develop an accurate, cost-effective, and scalable system for detecting infected hosts communicating with external C&C servers, subsequent to the resolution of DGA query names. Our solution dynamically selects network flows for diagnosis by trained models in real-time, and relies more on the behavioral traffic profile, rather than packet content. Our first contribution highlights the prevalence and activity pattern of DGA-enabled malware across internal hosts. We draw insights into the behavioral profile of DGA-enabled malware flows when communicating with C&C servers. For our second contribution, we identify malware traffic attributes and train three specialized one-class classifier models using behavioral attributes of malware HTTP, HTTPS and UDP flows. We develop an SDN-based monitoring system to automatically mirror TCP/UDP flows pertinent to DGA queries for diagnosis by the trained models. Finally, we evaluate the efficacy of our approach by testing suspicious traffic flows (selectively recorded by SDN reactive rules), identifying infected hosts, and verifying our detection with an off-the-shelf Intrusion Detection System (IDS) software tool.
An Improved Location Algorithm Based on TDOA for Wireless Sensor Networks

An Improved TDOA-based location algorithm (TBL) is presented for the wireless sensor networks of large scale and high density nodes. The algorithm is analyzed from three processes, including measuring distance, computing position, circulating location. Capabilities of the algorithm affected by the parameters are analyzed, and it is compared with the Euclidean algorithm in simulations. Results show that TBL is an efficient location algorithm based on different rapid of waves in different medium, and it shows an induction for location of IoT.
Systematic Mapping: Artificial Intelligence Techniques in Software Engineering

Artificial Intelligence (AI) has become a core feature of today’s real-world applications, making it a trending topic within the software engineering (SE) community. The rise in the availability of AI techniques encompasses the capability to make rapid, automated, impactful decisions and predictions, leading to the adoption of AI techniques in SE. With industry revolution 4.0, the role of software engineering has become critical for developing productive, efficient, and quality software. Thus, there is a major need for AI techniques to be applied to enhance and improve the critical activities within the software engineering phases. Software is developed through intelligent software engineering phases. This paper concerns a systematic mapping study that aimed to characterize the publication landscape of AI techniques in software engineering. Gaps are identified and discussed by mapping these AI techniques against the SE phases to which they contributed. Many systematic mapping review papers have been produced only for a specific AI technique or a specific SE phase or activity. Hence, to our best of knowledge within the last decade, there is no systematic mapping review that has fully explored the overall trends in AI techniques and their application to all SE phases.
Therapeutic potential of anti-miR29a in breast cancer patients with type 2 diabetes: an in vitro and xenograft mouse-model study

Background MicroRNAs (miRNAs) acting as tumour suppressors or oncogenes, known as oncomiRs, are a promising new focus in targeted therapies for cancer. Approximately 16% of breast cancer patients have pre-existing diabetes. Breast cancer with type 2 diabetes mellitus (BDM) is provided with its unique biological characteristics and clinical characteristics. This study primarily investigated the therapeutic potential and regulatory mechanism of miR-29a in patients with BDM. Methods The significance of miR-29a in BDM was analyzed by real-time reverse transcriptase polymerase chain reaction (qRT-PCR) in breast tissues. A cell model for BDM was established by using MDA-MB-231 cells cultured in 3T3-L1 adipocytes cultured with high levels of glucose and insulin. A type 2 diabetes mellitus (T2DM) mouse model was induced in female BALB/c mice through a high-fat diet plus low doses of streptozotocin (STZ). The xenograft mouse-model for BDM was established on these T2DM mouse by using MDA-MB-231 cells. Then the biological effects of miR-29a knockdown mediated by lentivirus-shRNAs on cell proliferation, apoptosis, cell cycle, and migration were investigated. Results Our results indicated that miR-29a was upregulated in patients with BDM, which correlated with a worse prognosis. In human breast cancer cells, miR-29a knockdown reduced cell proliferation and cell migration and invasion in BDM. In the T2DM xenograft, miR-29a knockdown suppressed MDA-MB-231 cells tumorigenesis and metastasis. We also demonstrated that miR-29a promoted BDM cell growth and metastasis by targeting Sirtuin 1 (SIRT1). Conclusions Our findings indicated that anti-miR-29a inhibited cell proliferation and invasion in BDM by targeting SIRT1. We believe anti-miR-29a may represent a novel therapeutic approach for the management of patients with BDM.