Miracle-dz/UniFAR-FTUs huggingface dataset:
Students’ Assessment of the Online Enrollment System of Nueva Ecija University of Science and Technology: An Experienced Based

:- The pandemic crisis brought a multitude of challenges in every higher educational institution for the reason that the national government imposed different restrictions and health protocols. Hence, this study aimed to determine the predicaments, situations, and experiences of students specifically in terms of the online enrollment process of Nueva Ecija University of Science and Technology for the academic year 2020–2021. The researchers of this study used a descriptive method of research to assess the feedback of 1,078 students in terms of the system’s functionality, reliability, usability, efficiency, maintainability, and portability. At the same time, this study was conducted to identify the level of qualities the system can provide to the university’s stakeholders and to determine the technical loopholes and attribute that requires updating to satisfy the university’s clientele–the students. The results have shown that the students found the system very good in terms of its functionality, reliability, usability, efficiency, maintainability, and portability and only good concerning its efficiency. This means that the system provides a high level of quality and requires minimal rectifications for improvement. In that case, the researchers suggested improving the system in terms of efficiency which is about the use of possible input to produce a quality and highest possible amount of output. Finally, the researchers suggested conducting future studies wherein the system will be assessed by Technical Experts in terms of technical aspects of the system which is based on ISO/IEC 25010:2011 or software quality standards.
A network model for prediction and diagnosis of sediment dynamics at the watershed scale

[1] We present a semi-distributed model that simulates suspended sediment export from a watershed in two stages: (1) delivery of sediments from hillslope and bank erosion into the river channel, and (2) propagation of the channel sediments through the river network toward the watershed outlet. The model conceptualizes a watershed as the collection of reaches, or representative elementary watersheds (REW), that are connected to each other through the river network, and each REW comprises a lumped representation of a hillslope and channel component. The flow of water along the stream network is modeled through coupled mass and momentum balance equations applied in all REWs and sediment transport within each REW is simulated through the sediment balance equations. Every reach receives sediment inputs from upstream REWs (if present) and from the erosion of adjacent hillslopes, banks and channel bed. We tested this model using 12 years (1982–1993) of high temporal resolution data from Goodwin Creek, a 21.3 km2 watershed in Mississippi, USA. The model yields good estimates of sediment export patterns at the watershed outlet, with Pearson correlation coefficient (R value) of 0.85, 0.87, and 0.95 at daily, monthly, and annual resolution, respectively. Furthermore, the model shows that the dynamics of sediment transport are controlled to a large extent by the differences in the behavior of coarse and fine sediment particles, temporary channel storage, and the spatial variability in climatic forcing. These processes have a bearing on the patterns of sediment delivery with increasing scale.
The effect of streamflow reductions on aquatic habitat availability and fish and macroinvertebrate assemblages in coastal plain streams

The purpose of this study was to estimate changes to aquatic habitat availability and fish and aquatic macroinvertebrate assemblages in coastal plain streams that may result from groundwater withdrawals. Hypothetical streamflow reductions of 5, 10, 20, and 30% of average annual streamflow and the 7‐day low flow were calculated for 14 study sites. Using study‐site streamflow and channel morphology data, relationships were developed to estimate five physical habitat metrics including average stream width, stream depth, stream cross‐sectional area, stream‐reach volume, and stream velocity. A second set of relationships was then developed to estimate the percentage reduction to each habitat metric under the hypothetical streamflow reduction scenarios. The average percentage decrease and variability of each habitat metric increased with successive streamflow reductions. Average percentage decreases in stream width, stream depth, stream cross‐sectional area, and stream‐reach volume were more pronounced for the reductions of average flow, whereas the average percentage decreases in stream velocity were more pronounced for the reductions of low flow. Models derived from a separate fish and macroinvertebrate study were used to estimate assemblage structure responses based on hypothetical reductions in average annual streamflow. The results of this study can be used to estimate the potential impact of groundwater withdrawals and subsequent streamflow reductions on available aquatic habitat and changes to fish and macroinvertebrate assemblages in coastal plain streams. Copyright © 2011 John Wiley & Sons, Ltd.
Heptanoate Improves Compensatory Mechanism of Glucose Homeostasis in Mitochondrial Long-Chain Fatty Acid Oxidation Defect

Defects in mitochondrial fatty acid β-oxidation (FAO) impair metabolic flexibility, which is an essential process for energy homeostasis. Very-long-chain acyl-CoA dehydrogenase (VLCADD; OMIM 609575) deficiency is the most common long-chain mitochondrial FAO disorder presenting with hypoglycemia as a common clinical manifestation. To prevent hypoglycemia, triheptanoin—a triglyceride composed of three heptanoates (C7) esterified with a glycerol backbone—can be used as a dietary treatment, since it is metabolized into precursors for gluconeogenesis. However, studies investigating the effect of triheptanoin on glucose homeostasis are limited. To understand the role of gluconeogenesis in the pathophysiology of long-chain mitochondrial FAO defects, we injected VLCAD-deficient (VLCAD−/−) mice with 13C3-glycerol in the presence and absence of heptanoate (C7). The incorporation of 13C3-glycerol into blood glucose was higher in VLCAD−/− mice than in WT mice, whereas the difference disappeared in the presence of C7. The result correlates with 13C enrichment of liver metabolites in VLCAD−/− mice. In contrast, the C7 bolus significantly decreased the 13C enrichment. These data suggest that the increased contribution of gluconeogenesis to the overall glucose production in VLCAD−/− mice increases the need for gluconeogenesis substrate, thereby avoiding hypoglycemia. Heptanoate is a suitable substrate to induce glucose production in mitochondrial FAO defect.
Corrosion by Polythionic Acid in the Oil and Gas Sector: A Brief Overview

Polythionic acid (PTA) corrosion is a significant challenge in the refinery industry, leading to equipment degradation, safety risks, and costly maintenance. This paper comprehensively investigates the origin, progression, mechanism, and impact of PTA corrosion on various components within refinery operations. Special attention is afforded to the susceptibility of austenitic stainless steels and nickel-based alloys to PTA corrosion and the key factors influencing its occurrence. Practical strategies and methods for mitigating and preventing PTA corrosion are also explored. This paper underscores the importance of understanding PTA corrosion and implementing proactive measures to safeguard the integrity and efficiency of refinery infrastructure.
An Analysis of Family Budgets of Cultivators in Haryana

The purpose of this study is to investigate the impact of increased farm income and income distribution on expenditure pattern of cultivators in Haryana and also to estimate the effect of price and family size on the expenditure pattern of the cultivators, for the period of 1968 to 1992. The major source of data for the study has been the ''Family budgets of cultivators in Haryana'' published annually by Economic and Statistical Organization, Planning Dept., Government of Haryana. In order to estimate the relationships Almost Ideal Demand System proposed by Deaton and Mullebauer (1980) has been used. It has been shown that the fragmentation of families leads to wastage of resources.
PALEOBIOLOGY AND PALEOECOLOGY OF THE EARLY TURONIAN (LATE CRETACEOUS) AMMONITE PSEUDASPIDOCERAS FLEXUOSUM

Abstract During the early Turonian, Pseudaspidoceras flexuosum was a common ammonite in low and intermediate latitudes. At Vallecillo section, northeastern Mexico, 160 specimens were recovered from the section, allowing for quantitative analysis. The combination of quantitative data with sedimentology and geochemistry allow for erection of a differentiated model for the mode of life of P. flexuosum. This model provides insight into the paleobiology and paleoecology of this ammonite, and ammonites in general. The Vallecillo section contains the longest record of this species worldwide. Changes in abundance and diameter of this ammonite appear to be related to changes of oxygen content in the water column. P. flexuosum dwelled in the well-oxygenated upper surface water and formed part of the open marine pelagic ecosystem; a demersal mode of life is excluded here. Dimorphism is expressed in size and less in ornamentation, but both dimorphs bear long spines, first recorded for this species. The spines were likely used for stabilization in the water column and protection against suction-feeding fishes and comparable predators, but not against pycnodont sharks. A sensory function is also plausible for the last one to three pairs of spines. This differentiated model of the mode of life of P. flexuosum contrasts with the paleoecology reconstructed for other ammonites. Ammonites covered a wide range of ecological niches, and their complex paleoecology will only be solved with more species-based analyses.
Selected Fungicides as Potential EDC Estrogenic Micropollutants in the Environment

An increasing level of pesticide exposition is being observed as a result of the consumption of large amounts of fruits, vegetables and grain products, which are key components of the vegetarian diet. Fungicides have been classified as endocrine-disrupting compounds, but their mechanisms of action have not yet been clarified. The effect of boscalid (B), cyprodinil (C) and iprodione (I) combined with Tamoxifen (T) and 17β-estradiol (E2) on cell viability, cell proliferation, reporter gene expression, ROS content, the cell membrane’s function, cell morphology and antioxidant enzymes gene expression in MCF-7 and T47D-KBluc cell lines were investigated. The cell lines were chosen due to their response to 17β -estradiol. The selected fungicides are commonly used in Poland to protect crops against fungi. Our results revealed that the studied fungicides caused significant increases in cell viability and proliferation, and estrogenic activity was present in all studied compounds depending on their concentrations. Oxidative stress activated uncontrolled cancer cell proliferation by inducing ROS production and by inhibiting antioxidant defense. Our findings verify that the studied fungicides could possibly exhibit endocrine-disrupting properties and exposure should be avoided.
Weather types at Brisbane, Queensland: An example of the use of principal components and cluster analysis

Principal components analysis was conducted on 15 years data of surface weather observations at Brisbane, Queensland. Component scores from each year's analysis were grouped using an hierarchical clustering method. Each of the 10,920 observations used in the study was able to be placed into a distinctive class so that the weather in Brisbane from 1967 to 1981 may now be described in terms of nine broad weather complexes, or weather yatypes’, and 63 hybrid types at a finer level of refinement. The nine broad weather types uniquely describe variation in Brisbane's climate during the period studied and hint at an ability to identify important variations in controlling mechanisms—in this case the subtropical ridge. Copyright
Knowledge, Attitude and Perception Regarding Artificial Intelligence in Periodontology: A Questionnaire Study

Introduction: The utilization of artificial intelligence (AI) and machine learning (ML) models has brought about a significant transformation in the manner in which periodontists gather information, evaluate associated risks, develop diverse treatment alternatives, anticipate and diagnose dental conditions that compromise periodontal health. The principal objective of this prospective study was to examine periodontists’ understanding and acceptance of the application of AI in the realm of periodontology. Materials and methods: This observational study was conducted on 275 participants based on questionnaire using Google Forms. These forms were pre-validated and subsequently circulated among periodontists in Maharashtra via various social media platforms. The study, in its entirety, comprised four open-ended questions on participants’ demographics and 14 closed-ended questions, all of which were presented to the participants in English. These questions aimed to elicit participants' awareness, knowledge, attitudes, and perspectives regarding emerging applications of AI in the field of periodontology. To analyze the collected data, researchers employed the widely utilized Statistical Package for Social Sciences (SPSS) version 22.0. Result: A 75% response rate was achieved and 68% of the respondents were female. 62% periodontists were aware of AI; however, only 24% were aware of its working principles. Most respondents agreed with the use of AI in periodontal diagnosis; however, they disagreed with the use of AI in predicting clinical attachment loss (69%). 80-82% respondents felt that AI should be a part of postgraduate training and should be implemented in clinical practice. However, most periodontists do not use AI for diagnostic or research purposes. 49% periodontists felt that AI does not have better diagnostic accuracy than periodontists, and therefore cannot replace them in the future. Conclusion: Most periodontists possessed a reasonable level of understanding regarding the utilization of AI in the domain of periodontology and expressed a desire to incorporate it into their diagnostic and treatment planning processes for periodontal conditions. Additional endeavors must be undertaken to enhance periodontists’ awareness concerning the effective implementation of AI within their professional practice, with the aim of facilitating personalized treatment planning for their respective patients. It is postulated that the integration of AI will augment the likelihood of achieving favorable outcomes within the realm of periodontology.
AREND: A Sensor Aircraft to support Wildlife Rangers

An international student team AREND (Aircraft for Rhino and ENvironmental Defense) is collaborating to design, build and fly an electric unmanned aerial system (UAS) in support of anti-poaching operations conducted by rangers in South Africa. The solution shall constitute, but not be limited to, an unmanned fixed-wing aircraft capable of conducting remote surveillance of large park areas with diverse intelligence gathering sensors, along with a network of ground sensors. The primary challenge in the poaching problem is to develop a proactive technological solution to prevent the poachers and rhinos from ever meeting. Multiple requirements and challenges are derived from this strategy in order to develop a comprehensive design. The AREND electric aircraft system is capable of quickly delivering selected payloads to any location within a 30 km radius, silently performing a search pattern, returning to a landing area and landing safely within the park or reserve. The aircraft system has been designed from ground up around selected sensor requirements. The aircraft structure and volume are designed to accept a variety of payload modules and particularly sized to support the largest expected payload. It is made of advanced composite materials and has a highly efficient aerodynamic shape to remain airborne for at least 90 minutes. The standard payload includes a gimbalstabilized visual camera system, capable of capturing high-resolution image data throughout the search pattern of the flight mission. Visual cameras help with surveillance of the area for poachers and rhinos during the day. The GPS location data informs the rangers about the exact location of observed rhinos and poachers so they can pursue and intercept an encounter between them. A ground sensor network capable of gathering data relevant to the anti-poaching effort and remotely transmitting data to other ground systems, air systems and a central command center is included. Several subsystems have been tested individually and integrated into the prototype airframe which successfully passed initial structural and flight mechanical testing. Making use of the different Universities complimentary technical skills, subsystems were divided amongst the students internationally and weekly meetings ensure alignment between designs. Independent global team organization requires a global project manager with excellent personal skills and understanding of the system engineering process to develop a successful collaborative work environment. Experience gained by students was not limited to systems engineering design of a small aircraft, but also provided an opportunity for the students to learn skills in global research and development collaboration, which is of interest to global technology corporations.
UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS

The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an understanding of the rules by which the world changes. Here, we explore the internal models developed by deep neural networks trained using a loss based on predicting future frames in synthetic video sequences, using an Encoder-Recurrent-Decoder framework (Fragkiadaki et al., 2015). We first show that this architecture can achieve excellent performance in visual sequence prediction tasks, including state-of-the-art performance in a standard “bouncing balls” dataset (Sutskever et al., 2009). We then train on clips of out-of-the-plane rotations of computer-generated faces, using both mean-squared error and a generative adversarial loss (Goodfellow et al., 2014), extending the latter to a recurrent, conditional setting. Despite being trained end-to-end to predict only pixel-level information, our Predictive Generative Networks learn a representation of the latent variables of the underlying generative process. Importantly, we find that this representation is naturally tolerant to object transformations, and generalizes well to new tasks, such as classification of static images. Similar models trained solely with a reconstruction loss fail to generalize as effectively. We argue that prediction can serve as a powerful unsupervised loss for learning rich internal representations of high-level object features. Under review as a conference paper at ICLR 2016 UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS William Lotter, Gabriel Kreiman & David Cox Harvard University Cambridge, MA 02138, USA {lotter,davidcox}@fas.harvard. edu gabriel.kreiman@tch.harvard. edu
Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes

Observations indicate that the height of the tropopause—the boundary between the stratosphere and troposphere—has increased by several hundred meters since 1979. Comparable increases are evident in climate model experiments. The latter show that human-induced changes in ozone and well-mixed greenhouse gases account for ∼80% of the simulated rise in tropopause height over 1979–1999. Their primary contributions are through cooling of the stratosphere (caused by ozone) and warming of the troposphere (caused by well-mixed greenhouse gases). A model-predicted fingerprint of tropopause height changes is statistically detectable in two different observational (“reanalysis”) data sets. This positive detection result allows us to attribute overall tropopause height changes to a combination of anthropogenic and natural external forcings, with the anthropogenic component predominating.
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework

Enforcing orthogonality in convolutional neural networks is a remedy for gradient vanishing/exploding problems and sensitivity to perturbation. Many previous approaches for orthogonal convolutions enforce orthogonality on its ﬂattened kernel, which, however, do not lead to the orthogonality of the operation. Some recent approaches consider orthogonality for standard convolutional layers and propose speciﬁc classes of their realizations. In this work, we propose a theoretical framework that establishes the equivalence between diverse orthogonal convolutional layers in the spatial domain and the paraunitary systems in the spectral domain. Since 1D parau-nitary systems admit a complete factorization, we can parameterize any separable orthogonal convolution as a composition of spatial ﬁlters. As a result, our framework endows high expressive power to various convolutional layers while maintaining their exact orthogonality. Furthermore, our layers are memory and computationally ef-ﬁcient for deep networks compared to previous designs. Our versatile framework, for the ﬁrst time, enables the study of architectural designs for deep orthogonal networks, such as choices of skip connection, initialization, stride, and dilation. Consequently, we scale up orthogonal networks to deep architectures, including ResNet and ShufﬂeNet, substantially outperforming their shallower counterparts. Finally, we show how to construct residual ﬂows, a ﬂow-based generative model that requires strict Lipschitzness, us-ing our orthogonal networks. Our code will be publicly available at https://github.com/ umd-huang-lab/ortho-conv .