Miracle-dz/UniFAR-FTUs huggingface dataset:
Laboratory Evaluation of Soil Amendments To Limit Structural Degradation under a Sequential Irrigation with Coal Seam Gas and Rain Water

Land amendment irrigation (LAI) has become an increasingly useful practice in addressing sodic and alkaline coal seam methane gas (CSG) water for irrigation purposes. However, there is a paucity of information pertaining to rapid dilution of soil solution under LAI management. This study investigated the ability of land amendment to buffer soils against structure degradation under sequential irrigation with CSG water (electrical conductivity [EC], 3 dS/cm; sodium adsorption ratio [SAR], 100 mmolc/L; pH 8.4) and rainwater in a highly smectitic black Vertisol (BV) and kaolinitic Oxisol (OX) soil. Stoichiometric quantities of gypsum and sulfur amendments were applied for the LAI process to reduce sodicity and alkalinity to a target of SAR <20 and HCO3= alkalinity =100 mg/L. Soils were irrigated with 10 ML/ha CSG water and 9 ML/ha rainwater in the sequence of 1 ML/ha and 0.9 ML/ha CSG water and rainwater events, respectively, using a 7.5 mm/h rainfall intensity. Saturated hydraulic conductivity was determined and reported as a reduction from initial conductivity. The collected leachates were used for subsequent measurements of pH, EC, SAR, and alkalinity at each event. Results suggested that LAI was inadequate to protect soil structure from more than three of the intensity–frequency duration events but sufficient for up to three events. This was considered positive evidence for LAI given the annual recurrence of such an event was one for the region. Additional gypsum application had no significant effect on protecting soil from further hydraulic reduction compared with LAI alone. The OX and BV behaved differently in terms of the observed hydraulic reduction, which was determined to be both a function of iron oxides (OX) and the effect of dilution, in contrast to the threshold electrolyte concentration. This highlighted that both the initial hydraulic conductivity and threshold electrolyte conditions are important in implementing LAI.
Functional Group, Biomass, and Climate Change Effects on Ecological Drought in Semiarid Grasslands

Water relations in plant communities are influenced both by contrasting functional groups (grasses and shrubs) and by climate change via complex effects on interception, uptake, and transpiration. We modeled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30 year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally increased biomass (i.e., the effects of invasions that increase community biomass or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought in both current and future climates.
The Removal of Phenol Through Adsorption onto Synthetic Calcium Phosphates – A Study Encompassing Analyses of Kinetics and Thermodynamics

The characteristics and suitability of hydroxyapatite (HAP), tricalcium apatite phosphate (PTCa), and octocalcium apatite phosphate (OCPa), which possess similar attributes to those of an ideal adsorbent, were investigated to determine their efficacy in phenol removal. The aim of this paper is to assess the adsorption behavior of phenol on phosphates powders synthesized by the co-precipitation method at ambient temperature. Furthermore, the im - pact of initial phenol quantities and thermal conditions on the adsorption process was explored. X-ray diffrac - tion analysis revealed the formation of HAP, PTCa, and OCPa structures under room temperature conditions. The sample morphologies were subjected to scrutiny utilizing MEB together with X-ray analysis. Additionally, chemical analysis revealed that Ca/P = 1.6, 1.5, and 1.33 for HAP, PTCa, and OCPa, respectively. The synthesized powders exhibited adsorption abilities of 2.86, 2.74, and 2.52 mg/g for HAP, PTCa, and OCPa, respectively, and reached equilibrium in approximately 80 minutes. The study revealed that the experimental data are appropriately represented by the Langmuir and Freundlich adsorption equations for HAP and PTCa, and Langmuir model in the case of OCPa, as well as by the pseudo-first-order and pseudo-second-order adsorption kinetics. Thermodynamic evaluations, including calculations of ΔG°, ΔH°, and ΔS°, were performed. The results indicated that the adsorp - tion mechanisms exhibited physical characteristics, were thermally absorbing in the case of HAP and exothermic for the other two phosphates, PTCa and OCPa, and occurred spontaneously.
Evidence of rapid production of organic acids in an urban air mass

Gas‐phase acids (nitric, formic, acrylic, methacrylic, propionic, and pyruvic/butryic acid) were measured using negative‐ion proton‐transfer chemical‐ionization mass spectrometry (NI‐PT‐CIMS) in Pasadena, CA as part of the CalNex 2010 (Research at the Nexus of Air Quality and Climate Change) study in May–June 2010. Organic acid concentrations ranged from a few parts per trillion by volume (pptv) to several parts per billion by volume (ppbv), with the largest concentrations observed for formic and propionic acids. Photochemically processed urban emissions transported from Los Angeles were frequently sampled during the day. Analysis of transported emissions demonstrates a strong correlation of organic acid concentrations with both nitric acid and odd oxygen (Ox = O3 + NO2) showing that the organic acids are photochemically and rapidly produced from urban emissions.
Development of Behavior Monitoring System for Honeybees in Hive Using RFID sensors and Image Processing

Recently, a new research field “Computational Ethology” is attracting much attention from not only biologists but also computer scientists because advances in computer technology enabled to automate the measurement and the analysis of animal behavior. Especially, analyzing communications performed by honeybee workers in their hive is one of the most important and interesting issue in ethological research area to reveal a mechanism of honeybee's language. However, these analyses have been usually conducted by manually extracting honeybee's walking trajectories from recorded long-time video data. For a systematic and theoretical analysis of honeybee's communication, we have developed an automatic tracking algorithm of multiple honeybees using image processing and constructed an automatic recording system for long-term tracking of honeybee behaviors with Radio Frequency Identification (RFID) sensors and high-resolution camera modules using multiple small-size single board computers, Raspberry Pi. Using this system, we conducted recording experiments from 6:30 am to 7:30 pm during one month once or twice per a year from 2015 to 2018. In this paper, first we show the overview of a behavior monitoring system of honeybee in hive. Next, we explain the simultaneous tracking algorithm we proposed. Finally, we show the experimental results and confirm the system capabilities.
Structure and Motion from Casual Videos

We provide additional movement map ablations in this section, reporting quantitative depth results separately for the static and dynamic part of the Sintel dataset. First, we ablate our method using a binary movement mask(Tab.1, Ours w. Binary Mask), using the straight-through estimator for differentiability [1]. We then compare with the movement estimation strategy by Zhou et al. [5], which treats the movement map as weights for the L1 loss and adds a sparsity prior to avoid trivial solutions. We empirically find the sparsity prior quite sensitive and fail to generate good results. Here we report the result using the default value (0.5) from Zhou et al. [5] (Tab.1, Ours w. Zhou et al. [5] Mask).
Éléments pour une représentation (carto)graphique des matrices de flux

La cartographie des matrices de flux denses, formee de nombreux lieux et a fortiori de liens, est caracterisee par une complexite graphique intrinseque, l'effet spaghetti. Il s'agit de la manifestation conjointe d'une surcharge graphique et d'une couverture spatiale trop importante. Les principaux modeles de representation (carto)graphique des matrices apportent des solutions complementaires qui se distinguent avant tout par la maniere dont l'espace des flux est considere.
Research to Advance Decontamination, Sampling, and Analytical Approaches Following Biological Terror Incidents

In the fall of 2001, several envelopes containing spores of Bacillus anthracis (the causative agent of anthrax) were sent through the U.S. mail to locations in New York, New Jersey, Florida, and Washington D.C. As a result, numerous government and private buildings were contaminated to varying degrees [1]. Full remediation of these facilities cost over $650 million, and took more than three years to complete [1,2]. This relatively small incident underscores the need for bioterror preparedness, as a biological attack on a much larger spatial scale which would require significantly more time and resources for recovery [3-5].
Users' Emotional Responses and Behavioural Intentions to Augmented Reality Ad Content Designs

This article specifically examines the emotional implications of using augmented reality (AR) technology in advertising. It primarily focuses on how users' emotional reactions and subsequent actions are influenced by the design of AR advertisements. The text examines the multifaceted impact of emotion in commercial communication, encompassing diverse areas such as political advertising and online advertising that employs hilarious storytelling. The study subsequently examines the impact of emotional components of augmented reality (AR) advertisements on users, emphasizing the crucial roles played by immersive and interactive experiences, creative design, user engagement, and other aspects in guiding emotions and shaping behavioral intentions. Ultimately, the utilization of augmented reality (AR) technology is showcased in the domains of electronic commerce, internet-based food delivery, and educational gaming. In order to gain a comprehensive understanding of emotion in AR advertising, it is necessary to conduct thorough study on the particular application and implementation methods. Additionally, it is important to examine how technological, cultural, and societal factors impact user responses. This paper seeks to analyze and combine existing studies in order to uncover the potential of emotional applications in augmented reality (AR) marketing. Additionally, it attempts to offer guidelines for future research and practical implementation.
INFINITELY MANY NONTRIVIAL SOLUTIONS FOR NONLINEAR PROBLEMS INVOLVING THE

In this paper, by using the mountain pass theorem and the Ekeland variational principle, we show the existence of infinitely many nontrivial weak solutions of a Dirichlet problem involving the (p1(x), p2(x))-Laplace problem. 2000 Mathematics Subject Classification: 35B38, 35D05, 35J60.
BIOINFORMATICS ORIGINAL PAPER

Motivation: Protein–protein interactions play vital functional roles in various biological phenomena. Physical contacts between proteins have been revealed using experimental approaches that have solved the structures of protein complexes at atomic resolution. To examine the huge number of protein complexes available in the Protein Data Bank, an efﬁcient automated method that compares protein complexes is required. Results: We have developed Structural Comparison of Protein Complexes (SCPC), a novel method to structurally compare protein complexes. SCPC compares the spatial arrangements of subunits in a complex with those in another complex using secondary structure elements. Similar substructures are detected in two protein complexes and the similarity is scored. SCPC was applied to dimers, homo-oligomers and haemoglobins. SCPC properly estimated structural similarities between the dimers examined as well as an existing method, MM-align. Conserved substructures were detected in a homo-tetramer and a homo-hexamer composed of homologous proteins. Classiﬁcation of quaternary structures of haemoglobins using SCPC was consistent with the conventional classiﬁcation. The results demonstrate that SCPC is a valuable tool to investigate the structures of protein complexes. Availability: SCPC is available at http://idp1.force. cs.is. nagoya-u . ac.jp/scpc/.
Robust Feature Based Image Watermarking Process

A digital image watermarking scheme must be robust against a variety of possible attacks. In the proposed approach, a new rotation and scaling invariant image watermarking scheme is proposed based on rotation invariant feature and image normalization. The rotation invariant features are extracted from the segmented areas and are selected as reference points. Sub-regions centered at the feature points are used for watermark embedding and extraction. Image normalization is applied to the sub-regions to achieve scaling invariance. In the scheme, first, the image is segmented into a number of homogeneous regions and the feature points are extracted. Then the circular regions for watermark embedding or extraction are defined. Based on the image normalization and orientation assignment, the rotation, scaling, and translation invariant regions can be used for watermark embedding and extraction. The segmented image is modeled as mixture generalized Gaussian distribution and this model is the basis of mathematical analysis of various aspects of the watermarking processes such as probability of error, embedding strength adjustment. The watermark embedding strength is adjusted adaptively using the noise visibility function.The original image is not needed for the watermark detection. The effectiveness and accuracy of the proposed scheme is established through experimental results.
Real-time Cooperative Behavior for Tactical Mobile Robot Teams Georgia Institute of Technology Honeywell Technology Center

Georgia Institute of Technology Technical Point of Contact: Administrative Point of Contact: Prof. Ronald C. Arkin Mr. Christoper D'Urbano College of Computing O ce of Contract Administration Georgia Institute of Technology Georgia Institute of Technology Atlanta, Georgia 30332 Atlanta, Georgia 30332 email: arkin@cc.gatech. edu email: Christoper.DUrbano@oca.gatech. edu Fax: (404) 894-9846 Fax: (404) 894-6956 Phone: (404) 894-1634 Phone: (404) 894-4817
Dissecting the genomic heterogeneity of cancer hallmarks’ acquisition with SLAPenrich

We present a computational method, implemented in an open source R package, to explore how cancers from diﬀerent tissue types might have acquired the same cancer hallmark (evolutionary successful trait) via preferential genomically altering diﬀerent biological pathways. To this aim, we have curated a collection of 374 orthogonal pathway gene-sets encompassing thousands of genes (from public available resources) mapped to 10 canonical cancer hallmarks. Using this curated data resource, we have characterised the landscape of pathway alterations putatively contributing to the acquisition of diﬀerent cancer hallmarks via systematic analysis of somatic mutations in large cohorts of patients across 10 cancer types, from the Cancer Genome Atlas. We assume that the heterogeneity of each hallmark in terms of number of corresponding enriched pathway alterations is reﬂective of its evolutionary ﬁtness to the cancer type under consideration. A systematic evaluation of this heterogeneity across hallmarks and cancer types has resulted into a set of cancer hallmark heterogeneity signatures. These signatures quantitatively conﬁrm the established predominance of certain hallmarks in determined cancer types and their clinical relevance, and they allow an easy data-driven comparison of cancer hallmark heterogeneity across diﬀerent lineages. We have found, as expected, that most of the pathway alteration enrichments and large hallmark heterogeneities are guided by somatic mutations in established, and highly frequently mutated, high-conﬁdence cancer driver genes. However and most importantly, when excluding these variants from the analyses, we observe that the hallmark heterogeneity signatures, thus the level of predominance of the considered hallmarks, are strikingly preserved across cancer types. Therefore we propose to use the hallmark heterogeneity signatures as a ground truth to characterise long tails of infrequent genomic alterations, across cancer types, and we highlight a number of potential novel cancer driver genes and networks.
Lecture 2 Sequence Similarity January 4 & 9 , 1996

Rotating notetaking: Notes are to be written up in L aTEX if you know it, or plain text if not, and sent by electronic mail tokarp@cs, ruzzo@cs, ortompa@cs, as appropriate. (If in L aTEX, see the instructions in /homes/june/ruzzo/SHARED/590bi/lectures/DIRECTIONS. If in plain text, don’t worry much about mathematical formulas, figures, bibliography ci tations, etc.) Notes are due at noon the day following lecture, so that they may be distributed to the cla ss s soon as possible.
A multiscale time-space approach to analyze and categorize the precipitation fluctuation based on the wavelet transform and information theory concept

The present study proposed a time-space framework using discrete wavelet transform-based multiscale entropy (DWE) approach to analyze and spatially categories the precipitation variation in Iran. To this end, historical monthly precipitation time series during 1960–2010 from 31 rain gauges were used in this study. First, wavelet-based de-noising approach was applied to diminish the effect of noise in precipitation time series which may affect the entropy values. Next, Daubechies (db) mother wavelets (db5–db10) were used to decompose the precipitation time series. Subsequently, entropy concept was applied to the sub-series to measure the uncertainty and disorderliness at multiple scales. According to the pattern of entropy across scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation in each cluster. Spatial categorization of rain gauges was performed using DWE values as input data to k-means and self-organizing map (SOM) clustering techniques. According to evaluation criteria, it was proved that k-means with clustering number equal to 5 with Silhouette coefficient = 0.33 , Davis–Bouldin = 1.18 and Dunn index = 1.52 performed better in determining homogenous areas. Finally, investigating spatial structure of precipitation variation revealed that the DWE had a decreasing and increasing relationship with longitude and latitude, respectively, in Iran.
Industrial Management & Data Systems Knowledge prioritisation for ERP implementation success : perspectives of clients and implementation partners in UK industries

Knowledge prioritisation for ERP implementation success: perspectives of clients and implementation partners in UK industries Uchitha Jayawickrama, Shaofeng Liu, Melanie Hudson Smith, Article information: To cite this document: Uchitha Jayawickrama, Shaofeng Liu, Melanie Hudson Smith, "Knowledge prioritisation for ERP implementation success: perspectives of clients and implementation partners in UK industries", Industrial Management & Data Systems, https:// doi.org/10.1108/IMDS-09-2016-0390 Permanent link to this document: https://doi.org/10.1108/IMDS-09-2016-0390
A Multi-Purpose Ontology-Based Approach for Personalized Content Filtering and Retrieval

Personalized multimedia access aims at enhancing the retrieval process by complementing explicit user requests with implicit user preferences. We propose and discuss the benefits of the introduction of ontologies for an enhanced representation of the relevant knowledge about the user, the context, and the domain of discourse, as a means to enable improvements in the retrieval process and the performance of adaptive capabilities. We develop our proposal by describing techniques in several areas that exemplify the explotation of the richness and power of formal and explicit semantics descriptions, and the improvements therein.
Computer Science & Systems Biology

Machine Learning is the backbone and most essential ingredient for working in the field of Artificial Intelligence as well as working in advance technologies. Some of the models which we already using are RNN, SPPNET. In this research paper we will be getting to know about the New Neural Model called Renshaw Model with its advantages and the web platform which makes the life easier for the user to work and understand the neural networks through machine learning. Usage of Cloud services and as well the usage of API will also be defined. The web platform is developed to tackle the issue of complexity storage, and security issue. This platform is basically running in three different formats and according to the user professionality the execution takes place. Researchers in the field of artificial intelligence can contribute and change the dimensions of the execution by designing and executing their code in swagger.api.
Computers and Electrical Engineering

A comprehensive evaluation of supervised machine learning models for COVID-19 related domain name detection is presented. One representative conventional machine learning implementation and nineteen state-of-the-art deep learning implementations are evaluated. The deep learning implementation architectures evaluated include the recurrent, convolutional, and hybrid models. The detection rate metrics and the computing time metrics are considered in the evaluation. The result reveals that advanced deep learning models outperform conventional machine learning models in terms of detection rate. The results also show evidence of a tradeoff between detection rate and computing speed for the selection of machine learning models/architectures. High-frequency lexical analysis is provided for a better understanding of the COVID-19 related domain names. The limitations, implications, and considerations of the use of supervised machine learning to detect abuse of COVID-19 related domain names are discussed.
A Deterministic Polynomial-time Algorithm for the Clique Problem and the Equality of P and NP Complexity Classes

In this paper a deterministic polynomial-time algorithm is presented for the Clique problem. The case is considered as the problem of omitting the minimum number of vertices from the input graph so that none of the zeroes on the graph’s adjacency matrix (except the main diagonal entries) would remain on the adjacency matrix of the resulting subgraph. The existence of a deterministic polynomial-time algorithm for the Clique problem, as an NP-complete problem will prove the equality of P and NP complexity classes. Keywords—Clique problem, Deterministic Polynomial-time Algorithm, Equality of P and NP Complexity Classes.