Miracle-dz/UniFAR-FTUs huggingface dataset:
COI gene sequencing reveals genetic population structuring and a separate lineage of the green tiger prawn Penaeus semisulcatus in the Gulf of Suez and the Bitter Lakes, Egypt

– The green tiger prawn Penaeus semisulcatus is one of the major commercial trawl crustaceans in the Red Sea, the Arabian Gulf, and the Indian Ocean. Herein, we aimed to assess its genetic diversity and phylogeographic criteria in the North West (NW) of the Red Sea. Prawn samples were collected from the Bitter Lakes; the North; and the South of the Gulf of Suez in Egypt. The hypervariable, 5 ’ barcode area of the Cytochrome oxidase subunit 1 (COI) gene was PCR-ampli ﬁ ed and sequenced in all samples. The resulting sequences were subjected to several genetic, phylogenetic, and population genetics ’ analyses. Consequently, clear signs of genetic populations ’ diversity, expansion; and possible structuring were identi ﬁ ed in different areas in the NW Red Sea. Furthermore, extremely discordant genetic connectivity was found between the populations around the Arabian Plate (i.e. of the Red Sea and Strait of Hormuz) and the IndoWest Paci ﬁ c, resulting in characterizing the Arabian plate populations as a separate lineage of P. semisulcatus . Based on these ﬁ ndings, it is important to investigate whether there are other discriminative genomic, morphological and/or morphometric differences among the genetically identi ﬁ ed lineages and populations. Consequently, differential conservational strategies of different populations of this species should be applied in the studied areas.
Practical performance of incremental topological sorting and cycle detection algorithms

Algorithms become more advanced and asymptotic time bounds get lower but there is very little data on the actual performance of new algorithms. The aim of this thesis is to do empirical testing of the most recent incremental topological sorting and cycle detection algorithms in order to compare them and to provide an accessible guide to where each algorithm performs best. The algorithms are implemented as the articles describe them and compared on even grounds by measuring their performance by adding edges to graphs. For sparse graphs the HKMST-Sparse [7] algorithm performed best and HKMSTDense [7] for very dense graphs. The Pearce & Kelly [8] algorithm is a strong contender as it is extremely simple and has acceptable performance across all graph densities and performs best in the range 35-80% density.
Recent advances in understanding the roles of whole genome duplications in evolution [version 1; peer review: 2 approved]

Ancient whole-genome duplications (WGDs)— paleo polyploidy events—are key to solving Darwin’s ‘abominable mystery’ of how flowering plants evolved and radiated into a rich variety of species. The vertebrates also emerged from their invertebrate ancestors via two WGDs, and genomes of diverse gymnosperm trees, unicellular eukaryotes, invertebrates, fishes, amphibians and even a rodent carry evidence of lineage-specific WGDs. Modern polyploidy is common in eukaryotes, and it can be induced, enabling mechanisms and short-term cost-benefit assessments of polyploidy to be studied experimentally. However, the ancient WGDs can be reconstructed only by comparative genomics: these studies are difficult because the DNA duplicates have been through tens or hundreds of millions of years of gene losses, mutations, and chromosomal rearrangements that culminate in resolution of the polyploid genomes back into diploid ones (rediploidisation). Intriguing asymmetries in patterns of post-WGD gene loss and retention between duplicated sets of chromosomes have been discovered recently, and elaborations of signal transduction systems are lasting legacies from several WGDs. The data imply that simpler signalling pathways in the pre-WGD ancestors were converted via WGDs into multi-stranded parallelised networks. Genetic and biochemical studies in plants
Scenario-based Question Answering with Interacting Contextual Properties

In the scenario-based Question Answering (QA) task, models are asked to find answers that are appropriate to the user scenarios associated with the question and identify information that is missing from the scenarios but is necessary for the answers to hold. Scenarios commonly include multiple properties of users, such as age, employment status, and income level for the question “How much can I claim from this benefit”. The properties relevant to a potential answer are given in a document, which will state conditions necessary for the answer to hold. Documents also may specify how conditions interact with each other, e.g. with text like “one of the conditions below must apply”. Although understanding the relationship between conditions is crucial for solving this challenging QA task, limited work has been done so far in modeling this. In this paper, we propose the T-Reasoner model, which solves this problem with three jointly learned modules: an entailment module which checks whether a condition has been satisfied by the scenario, a decoding module which locates eligible answers from documents, and a reasoning module which infers the relationship between conditions and performs a reasoning step to determine the logically consistent answers and identify missing conditions. T-Reasoner outperforms strong baselines on a synthetic scenariobased QA dataset and achieves a new state-of-the-art on two scenario-based QA benchmarks, outperforming the prior best models by 3-10 points. 1
Investigating the Correlation Among Personality Traits, Familial Functioning, and Suicidal Thoughts Among University Students: A Descriptive Correlational Study

Objectives: Suicide is a serious public health problem, especially among students as active members of the community. The present study aimed to determine the correlation among personality characteristics, family functioning, and suicidal ideation in students of Shahid Beheshti University of Medical Sciences in Tehran, Iran in 2018. Materials and Methods: The population of this descriptive correlational study included all 675 students of Shahid Beheshti University of Medical Sciences, from whom 244 students were selected using stratified random sampling. The required data was collected using four questionnaires, including demographic characteristics, personality traits, family functioning, and Beck Scale for Suicidal Ideation (BSSI). To analyze data, we used multiple linear regression and the Statistical Package for the Social Sciences (SPSS, version 22). Results: Examination of personality traits suggested that the correlation between psychosis and suicidal thoughts was positive and significant (P<0.05). The correlation coefficients between the components of extraversion-introversion, agreeability, and responsibility and suicidal ideation were negative and significant (P<0.05). There was also a positive and significant relationship between all components of family functioning and suicidal ideation (P<0.05). Conclusions: Effective steps can be taken to prevent suicide among students through providing optimal psychological healthcare and periodic screening of students in terms of suicidal thoughts and mental disorders.
Digital Forensics Investigation Tool using Machine Learning for Person Detection in Images

Large-scale analysis of digital evidence, including photographs, are frequently a part of digital forensics investigations. Image analysis by hand can be laborious and error-prone. In order to solve this, we provide a programme that automates the process of recognizing photographs that contain a specific person of interest by using face detection and machine learning approaches. Our method comprises iterating over a file system, finding faces in the photos using a face detection model that has already been trained, and utilizing machine learning to compare the found faces to the features of the person of interest. In order to facilitate future analysis, the programme records the file names and locations of each image containing the subject of interest. Our method can increase the precision of digital forensics investigations and is effective, scalable and useful for forensic examiners and law enforcement organizations.
Manpower management in construction projects

Construction projects generally involve a long period of construction, huge financial investment and more problems that need to be faced in the development process. Several risk factors have to be encountered, among those one of the major risk factors is Manpower Management. Though the construction sector employs more people than other industries do, its human resource management is still poor and ineffective. This review paper concentrates on a workforce that directly contributes to the building process, labours, site engineers, and engineers or contractors. This study helps engineers or contractors to understand the value of labour resource management in the project to effectively manage them in construction projects. The primary issues in managing labour resources in construction projects have been identified with the help of a literature survey. The major problems identified will be considered for further investigation on manpower management.
A bit significant unsaturated fats uncovered from wedge shellfish Donax cuneatus (Linnaeus)

The fatty acid composition of the wedge clam Donax cuneatus collected from the sandy beach of Cuddalore (southeast coast of India) was determined. In the analysis, the fatty acid profile by gas chromatography revealed the presence of higher amount of saturated fatty acids (35.28%) than mono (12.71%) and polyunsaturated (11.72%) fatty acids. Among the saturated fatty acids, the stearic acid contributed the maximum amount of 15.68%. Whereas the monounsaturated fatty acids (MUFA) were accounted for 26.57% with mostly 16:1  7c (12.71%), fatty acid. Among the PUFA 20:4  6c (6.75%) and 18:2  6c (2.41%) acid contents were high. Omega – 6 fatty acids accounted for 10.74% of total PUFA and omega – 3 fatty acids were accounted for 0.48%. Thus the present study enlightens the possible role of this clam in the field of human nutrition.
Further validation of the McClear estimates of the downwelling solar radiation at ground level in cloud-free conditions provided by the McClear service: The case of the Sub-Saharan Africa and Maldives Archipelago

Being itself part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. 15 McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in the Sub-Saharan Africa and Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surfaces (G) and its direct component received at normal incidence (BN) provided byfrom the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The 20 correlation coefficient is greater than 0.96 for G whereas it is greater than 0.70 at all stations but five for BN. The mean of G is correctly accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without dry season and hot or warm summer (Cfa, Cfb) or with dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climate of monsoon type (Am) and overestimated in tropical climate of savannah type 25 (Aw) and temperate climates with dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the means of BN. The standard deviation of errors for G ranges between 13 W m (1.3 %) and 31 W m (3.7 %) and that for BN ranges between 31 W m (3.0 %) and 70 W m (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service. 30
Synthesizing Skolem functions: A view from theory and 1 practice 2

Skolem functions play a central role in logic, from helping eliminate 6 quantiﬁers in ﬁrst order logic formulas to providing functional implementations of 7 relational speciﬁcations. While their existence follows from classical results in logic, 8 less is known about how to compute them eﬀectively and eﬃciently (whenever 9 such computation is possible). The problem of computing or synthesizing Skolem 10 functions from relational speciﬁcations, however, continues to show up in many 11 interesting applications. Recently, a rich line of work has considered theoretical 12 and practical aspects of the problem in a restricted setting, namely synthesis of 13 Boolean Skolem functions from Boolean relational speciﬁcations. In this article we 14 take an indepth look into this fascinating problem and its various implications, 15 from general theoretical and complexity results to practical algorithms, and also 16 draw interesting connections to the knowledge representation literature. 17
Deﬁning Medication Exposure When Analyzing Real-World Data

1. Alecia Clary , Ph.D., MSW (AC) (Corresponding author)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: aclary@reaganudall.org Phone: 202-849-2075 ORCID ID: 00000002-7774-9808 2. Nancy D Lin , (NDL) Affiliation: IQVIA Real World Solutions, Bridgewater, NJ, USA Address: 77 Corporate Drive, Bridgewater, NJ 08807 Email: Nancy.lin@iqvia.com 3. Tamar Lasky , (TL) Affiliation: The United States Food and Drug Administration, Office of Data, Analytics, & Research, Office of the Commissioner, Washington DC, USA Address: 10903 New Hampshire Avenue, Silver Spring, MD, USA. Email: Tamar.lasky@fda.hhs. gov 4. Matthew W Reynolds , (MR) Affiliation: IQVIA Real-World Solutions, Washington, DC, USA Address: 201 Broadway, Cambridge, MA, USA. Email: Matthew.Reynolds@IQVIA.com 5. Anand Chokkalingam , (ACH) Affiliation: Gilead Sciences, Foster City, California, USA Address: 333 Lakeside Drive Email: Anand.Chokkalingam@gilead.com 6. Carla Rodriguez-Watson, (CRW)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: crodriguezwatson@reaganudall.org
Persistent La Niña’s favor drive joint soybean harvest failures in North and South America

Around 80% of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean yield variability, with soybean especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas presenting potential risks for spatially 5 compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on crop soybean yields via mediating variables like local weather conditions and extratropical sea-surface temperatures (SST). We show that soybean yields are predominately driven by soil moisture conditions in summer explaining ~50%, 18% and 40% of yield variability in SESA, CB and US respectively. Summer soil moisture is strongly driven by spring soil moisture as well as remote extra-tropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal 10 models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reductions in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niña’s can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such 15 information may increase preparedness against climate related global soybean supply shocks.
The role of hydrogenation and gettering in enhancing the efficiency of next‐generation Si solar cells: An industrial perspective

We discuss the importance of gettering and hydrogenation for next‐generation silicon solar cells in the context of industrial cell fabrication. Gettering and hydrogenation play a vital role for p‐type cell technologies in improving the silicon material's minority charge carrier lifetime. These mechanisms are naturally incorporated during screen‐printed cell fabrication through the phosphorus emitter diffusion, silicon nitride deposition and subsequent metallisation firing processes. While the transition towards emitters with lower dopant concentrations and/or thermal oxide passivation can reduce surface recombination, it can negatively impact the ability to getter common impurities such as iron. For cell technologies with alternative low‐temperature metallisation approaches, the ability to hydrogenate bulk defects is greatly reduced. Ultra‐high efficiency n‐type technologies tend to use heterojunction structures rather than diffused layers, but in doing so, do not benefit from phosphorus gettering. Also, particularly for amorphous silicon‐based heterojunction structures, the imposed temperature constraints strongly limit the ability to passivate bulk defects. As a result, high‐efficiency n‐type technologies rely on the use of ‘high‐quality’ wafers or would require the deliberate addition of gettering and hydrogenation processes before cell fabrication. A potential high‐efficiency hybrid homojunction/heterojunction structure is then discussed that could naturally enable gettering and bulk hydrogenation throughout cell fabrication.
Developing the Security for Cloud Information Via Alexnet Learning Model versus the accuracy of Artificial Neural Network

The main objective of the study is to protect cloud from different types of attacks by using AlexNet classifier compared accuracy with Artificial Neural Network and user’s data to be stored in the cloud safely by Advanced Encryption Standard. Materials and Methods: This research examines two groups AlexNet withArtificial Neural Network. Statistical study used 1300 training and 403 testing datasets from UNSW-NB15 dataset. ClinCalc programme utilised N=10, 0.05 is alpha value, 0.8% is G-Power, and 95% confidence interval.Result and Discussion: Novel Alexnet (91.081%) has an increased precision over ANN (90.075%) with the P value is 0.012(p<0.05) from the results of Independent samples t-test. There is a statistical significant difference between these two algorithms.Conclusion: This study concludes that the Novel AlexNet classifier algorithm seems fundamentally better than the ANN in terms of increasing the accuracy of secure cloud data and holding sensitive data with the dataset of UNSW-NB15.
Leveraging Android OS to Secure Diverse Devices in Residential Networks

As the complexity of home networks has grown in recent years, the security of multi-type devices connected in the residential environment, such as Internet-of-Things (IoT) devices, is increasingly important due to their widespread presence in homes and their physical capabilities. Many people are concerned about potential security weaknesses in these devices, since home networks lack the powerful security tools and trained personnel available in enterprise networks. However, securing various devices connected in home networks is challenging due to their different communication protocol, varied designs, multiple manufacturers, and potentially limited availability of long-term firmware updates. Home networks could introduce new physical hardware to enable security screening locally for securing in-network devices, but the capital and purchase costs may impede deployment. In this work, we propose several methods to utilize the Android smartphone operating system to secure varied devices connected in home networks. We explore access control mechanisms that tightly constrain access to IoT devices at the residential router, with the goal of precluding access that is inconsistent with legitimate users’ goals. This approach works across device types and manufacturers with a straightforward API and state machine construction. The results of our experiments show it identifies 100% of malicious traffic while correctly allowing more than 98% of legitimate network traffic. Moreover, we introduce a system to leverage existing available devices that are already inside a home network, such as Android smartphones, to create a platform for traffic inspection. This software-based solution avoids new hardware deployment and allows decryption of traffic without introducing new risk associated with new third parties. In terms of performance, we note it outperforms the state-of-the-art method in on-router traffic inspection. We also improve and evaluate an Android-based software-defined networking (SDN) system, which shows promising results on securing network traffic in Android applications.
The Dungeon Master’s Dashboard

—This paper introduces The Dungeon Master’s Dashboard, a suite of tools for the interactive visualization of Dun- geons&Dragons data. The suite is implemented with Vega-Lite, D3.js, OpenseaDragon, and customized AI tools. It consists of four modules proposed to facilitate a set of diverse tasks the narrator of the game (dungeon master) has to execute. We conducted pre-design interviews with domain experts to understand their needs. The Dice Tracker module allows the user to compare dice sets performance and the Party Tracker gives a clear and objective overview of the group’s attributes for easy comparison between player characters. Additionally, for a swift role-play improvisation, the Relationship Tracker displays whether the relationship of a character with others is positive or negative. Furthermore, this work describes an innovative tool for browsing for creatures, the Monster Shopper. This final module balances data filtering and visualization enjoyment.
Water cycle algorithm–based airflow control for oscillating water column–based wave energy converters

The stalling behavior is a feature of the Wells turbine that limits the generated output power of power plants using this turbine. The NEREIDA wave power plant installed in the harbor of Mutriku in the northern Spanish shoreline constitutes an excellent example of this phenomenon. This article deals with the modeling, simulation and control of an oscillating water column unit within the NEREIDA wave power plant. The stalling behavior is investigated and two control strategies are proposed to avoid it. The first control approach is the airflow control which aims to adjust the airflow in the turbine duct using a proportional–integral–derivative controller tuned with the water cycle algorithm. The second control approach is the rotational speed control adjusting the rotor speed using the rotor-side converter of the back-to-back converter which is wired to the doubly fed induction generator. Results of comparative studies show a power generation improvement even relative to the real measured data.