Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening of Litter-Size-Associated SNPs in NOX4, PDE11A and GHR Genes of Sheep

Simple Summary NOX4, PDE11A and GHR have previously been screened as candidate genes for litter size in sheep. Therefore, it is necessary to validate these genes in the sheep population and identify loci that are associated with litter size. In this study, the results of association analysis showed that c.1057-4C > T in NOX4 and c.1983C > T in PDE11A were significantly associated with litter size in Small Tail Han sheep, and their effects were independent of each other. In summary, this study provided two new genetic markers for improving litter size in sheep. Abstract In previous studies, NOX4, PDE11A and GHR genes have been screened as important candidate genes for litter size in sheep by using the GWAS method; however, neither their effects on litter size nor the loci associated with litter size have been identified. In this study, three candidate loci (c.1057-4C > T in NOX4, c.1983C > T in PDE11A and c.1618C > T in GHR) were first screened based on our previous resequencing data of 10 sheep breeds. After the three loci were genotyped using Sequenom MassARRAY technology, we carried out population genetics analysis on the three loci and performed association analysis between the polymorphism of the three loci and the litter size of sheep. The results of population genetics analysis suggested that c.1057-4C > T in NOX4 and c.1983C > T in PDE11A may be subject to natural or artificial selection. The results of association analysis indicated that litter size was significantly associated with c.1057-4C > T in NOX4 and c.1983C > T in PDE11A (p < 0.05) in Small Tail Han sheep, and there was no significant interaction effect between the two loci on the litter size. In summary, c.1057-4C > T in NOX4 and c.1983 C > T in PDE11A can be considered candidate molecular markers for improving litter size in sheep.
Beyond Chiefdoms: Pathways to Complexity in Africa

1. Pathways to complexity: an African perspective Susan Keech McIntosh 2. The segmentary state and the ritual phase in political economy Aidan Southall 3. Perceiving variability in time and space: the evolutionary mapping of African societies Ann B. Stahl 4. Western representations of urbanism and invisible African towns Roderick J. McIntosh 5. Modelling political organization in large scale settlement clusters: a case study from the inland Niger Delta Susan Keech McIntosh 6. Sacred centres and urbanisation in West Central Africa Raymond N. Asombang 7. Permutations in patrimonialism and populism: the Aghem chiefdoms of Western Cameroon Igor Kopytoff 8. Wonderful society: the Burgess shale creatures, Mandara polities, and the nature of prehistory Nicholas David and Judy Sterner 9. Material culture and the dialectics of identity in the Kalahari: AD 700-1700 James Denbow 10. Seeking and keeping power in Bunyora-Kitara, Uganda Peter Robertshaw 11. The (in)visible roots of Bunyoro-Kitara and Buganda in the Lakes Region: 800-1300 David L. Schoenbrun 12. The power of symbols and the symbols of power through time: probing the Luba past Pierre de Maret 13. Pathways of political development in Equatorial Africa and neo-evolutionary theory Jan Vansina.
Integrasi Audit Trail dan Teknik Clustering untuk Segmentasi dan Kategorisasi Aktivitas Log

Dengan berkembangnya teknologi, organisasi dan perusahaan kini mengumpulkan data dalam jumlah yang sangat besar, yang tercatat dalam log sistem untuk tujuan audit keamanan, pemantauan, dan investigasi forensik. Namun, tantangan utama muncul saat analis keamanan harus menangani volume data log yang besar, yang seringkali membuat sulit untuk mengidentifikasi aktivitas mencurigakan atau abnormal. Dalam upaya mengatasi tantangan analisis log yang berskala besar dalam sistem informasi, penelitian ini bertujuan untuk mengintegrasikan teknik audit trail dengan metode clustering, khususnya menggunakan K-Means, untuk segmentasi dan kategorisasi aktivitas log. Penelitian ini mencari pendekatan yang dapat meningkatkan efisiensi dalam menelusuri dan menganalisis log aktivitas dengan mengelompokkan data log yang serupa. Metode yang digunakan mencakup desain eksperimental, pengumpulan data audit trail yang komprehensif, preprocessing data, implementasi algoritma K-Means, dan evaluasi hasil clustering. Hasil penelitian mengindikasikan bahwa penerapan K-Means pada audit trail memungkinkan identifikasi pola aktivitas yang signifikan, memudahkan deteksi anomali dengan cepat, dan menyederhanakan proses audit keamanan data, yang mengarah pada pemahaman yang lebih baik dalam pengelolaan risiko keamanan informasi.
Molecular Characterization and Seasonal Variation in Primary and Secondary Organic Aerosols in Beijing, China

Biomass burning (BB) aerosols, primary biological aerosol particles, and secondary organic aerosols, are critical components of particulate matter in the atmosphere. In this study, saccharides, lignin/resin acids, biogenic secondary organic aerosols tracers, aromatic acids, and hydroxyacids/polyacids were measured in total suspended particles collected from April 2012 to March 2013 at an urban site in Beijing using gas chromatography/mass spectrometry. The concentration of levoglucosan was elevated during 18–20 June. The high ratio of levoglucosan to mannosan and low ratio of galactosan to mannosan in June demonstrated the influence of wheat straw combustion in the North China Plain via long‐rang transport. Sucrose, glucose, and fructose were more abundant in April, the spring bloom season. The maximum trehalose content (298 ng/m3) occurred during a dust storm (27–29 April) with the highest stable carbon isotope ratio (δ13C, −21.2‰) of total carbon. Fungal spore tracers (arabitol and mannitol) peaked in summer. The largest concentrations of the oxidization products of isoprene and α/β‐pinene also occurred in summer. However, the β‐caryophyllene tracer was much more abundant on long‐lasting haze days in winter and was positively related to levoglucosan. The contributions of biogenic primary sources (plant debris, fungal spores, and BB) to organic carbon were estimated to be in range of 2.64–35.2% (11.8%), with BB being the predominant source. Biogenic secondary sources (isoprene, α‐pinene, and β‐caryophyllene) and anthropogenic secondary source (naphthalene) accounted for 0.23–10.9% (3.36%) of organic carbon with large contributions in summer and winter, respectively.
Design and Characterization of a Powered Wheelchair Autonomous Guidance System

The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver’s feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.
Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor

Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.
Sistem Monitoring dan Kontrol Rumah Pintar Berbasis Internet Of Things Untuk Peningkatan Efisiensi Energi

The urgency of research with the theme of designing and implementing a smart home monitoring and control system based on the Internet of Things (IoT) to improve energy efficiency and quality of life is very important to carry out, in general it can be described as being able to increase energy efficiency; improve the quality of life where the system can regulate temperature, humidity and lighting to create a comfortable and healthy environment; encourage innovation; providing solutions to environmental challenges; optimizing energy availability where the smart home IoT system can regulate the use of available energy. Thus, it is very important to carry out this research to face environmental challenges and improve people's quality of life. The main objective of this research is to develop a system that can monitor and control energy use in the home efficiently, so as to improve energy efficiency and the quality of life of home residents, to overcome weaknesses and develop a holistic and effective smart home monitoring and control system. IoT based. The method used in this research is the Research and Development method, where this method is a process or steps to develop a new product or improve an existing product, which can be accounted for. The research results show that the IoT system can be designed and function well based on the results of the tests that have been carried out.
Detection of Depression from Arabic Tweets Using Machine Learning

Depression has become the disease of the times and has caused suffering and disruption in the lives of millions of people around the world of all ages. Method: We obtained 16,581 Arabic tweets, whether they express depression or not, and the symptoms they contain for 1439 Arab Twitter users. We classified whether the user is depressed or not. We used many machine learning algorithms: DT, RF, Mutational Naïve Bayes, and AdaBoost , we also used feature extraction like BOW and TF-IDF. The result: Our experiments showed that Mutational Naïve Bayes with TF-IDF had the highest accuracy of 86% when rating tweets. Conclusion: Caring for the mental health of people is very important, as some measures must be taken to maintain the mental health of people in the early stages of infection.
Assessment of the Ecological Quality of Sousa River

Freshwater is undeniably an essential life-sustaining element. It is a key ingredient in the health and well-being of humans and ecosystems, and for socio-economic development. Therefore, it becomes essential to manage, in a balanced way, this natural resource. Most problems related to water quality are caused by intensive agriculture, industrial production, mining, untreated urban runoff and wastewater. The Sousa River belongs to the hydrographic basin of the Douro River, located in the north of Portugal, extending for approximately 95 km, and its spring is in Felgueiras. It crosses Felgueiras, Lousada, Penafiel, Paredes and flows into the Douro River in Gondomar. Thus, the study’s main goals were to evaluate the ecological status and quality of the Sousa River through the analysis of biological components (benthic diatoms and macroinvertebrates), as well as physicochemical and hydromorphological parameters. Research was performed during 9 months (November 2016 to July 2017), in four distinct sampling stations. The sampling included physicochemical and hydromorphological parameters as well as biological parameters. Samples were collected every three months, except in winter. Hence, through the assessment of the physicochemical water quality, it was recognised that high levels of organic pollution existed, especially during spring and summer. The evaluation of the physical habitat revealed slight transformations in the channel of the river and the poor ability for the riparian zone to function as a barrier to the anthropogenic impacts. Moreover, the study of the benthic macroinvertebrate community indicated that the river is somewhere between slightly contaminated to moderately contaminated presenting, in some sampling sites, “Good” to “Poor” Quality. The benthic macroinvertebrate diversity was high with populations dominated mostly by tolerant taxa to organic pollution. Regarding, the diatom community, these indicated that the Sousa River presented a "Reasonable" quality throughout the seasons and sampling sites, with populations dominated mainly by tolerant and ubiquitous taxa. The results obtained were important since they may serve as an incentive to the elaboration of future research works that can provide integrated management solutions of the natural resources.
How are indigenous forest-dependent communities in Bangladesh drawing on local knowledge to adapt to climate change?

The effects of climate change are increasingly being felt by indigenous communities living in the forests of Bangladesh. Md. Habibur Rahman and Khurshed Alam recently conducted a study to explore perceptions of climate change and how communities are adapting to changing weather patterns. They write that although impressive range of strategies were observed, efforts were inadequate to ensure sustainable livelihoods and greater technical support and awareness raising programs are therefore urgently needed.
A Comparison of Cloud Microphysical Properties Derived From MODIS and CALIPSO With In Situ Measurements Over the Wintertime Southern Ocean

In situ observations of cloud effective radius (reff), droplet number concentration (Nd), and thermodynamic phase from 11 wintertime flights over the Southern Ocean (43–45°S, 145–148°E) are compared to products from MODerate‐resolution Imaging Spectroradiometer (MODIS) and Cloud‐Aerosol Lidar with Orthogonal Polarization. The in situ observations were in close alignment with A‐train overpasses for a 30‐min window. For open mesoscale cellular convection, which was predominantly observed, clouds were commonly found to be intermittently drizzling, patchy, and mixed phase. Compared to the in situ observations of the cloud thermodynamic phase, the Cloud‐Aerosol Lidar with Orthogonal Polarization and MODIS cloud phase optical property products consistently underestimated the occurrence of mixed‐phase clouds, whereas the MODIS infrared‐based phase product showed a better qualitative agreement despite a frequent classification of uncertainty. The MODIS reff_2. 1 overestimated the in situ reff for nondrizzling clouds (by ~13 μm on average) and, to a lesser extent, for lightly drizzling cases. Conversely, MODIS reff_2. 1 underestimated the in situ reff for heavily drizzling cases by ~10 μm on average. The overestimation of reff is much greater than that for the stratocumulus over the Southeast Pacific shown in other studies. An examination on subpixel heterogeneity, droplet size variability, a bimodal distribution, and solar zenith angle suggests that all of these factors have measurable impacts on the MODIS reff bias. The MODIS Nd is largely consistent with the in situ observations. However, the Nd of the two high Nd cases (closed mesoscale cellular convection) are highly underestimated. An error analysis suggests that the Nd biases are likely a result of a compensating error effect.
Tourist satisfaction model: Structural relationship of destination image, electronic word of mouth, and service quality in Bali destination

Purpose: This research aims to investigate the interplay between destination image, Electronic Word of Mouth (e-WoM), service quality, and tourist satisfaction during their stay in Bali. Design/Methodology/Approach: The research design uses quantitative causality. The data collection technique employed in this study involves a questionnaire distributed to 600 respondents, determined through incidental sampling. Findings: Service quality, while the services provided by the Monkey Forest staff are well-received by tourists; destination image, while the attractiveness of Monkey Forest tourist attractions is unique and appealing; and the beauty of the natural landscape. Tourist satisfaction exerts a positive and significant influence on e-WoM, while a satisfied traveler with the image and service quality of the monkey forest destination is willing to engage in marketing through electronic word of mouth. Additionally, two moderation relationships are discovered: the relationship between service quality and e-WoM, which is positively and significantly influenced by the variable of tourist satisfaction, and the clause relationship, wherein destination image positively and significantly influences e-WoM, with tourist satisfaction as a moderating variable. Conclusions: So, it can be concluded that there are five clause relationships between variables as hypotheses, including three direct effects and two moderating relationships (indirect effects). Research Limitations: The limitations of this research lie in the limited coverage of the research object, focusing on either a specific area or solely on one destination. Practical Implications: The implication of this study on the e-WoM variable, which serves as an endogenous variable, significantly impacts the Normated Fit Index, albeit within reasonable limits.
Understanding the Warm Water Volume Precursor of ENSO Events and its Interdecadal Variation

A wind forced ocean model is used to decompose the equatorial Pacific warm water volume (WWV) between 1980 and 2016 into two components: the (i) adjusted wind response, which is found by letting the model evolve unforced for three months, and (ii) instantaneous wind response, which are the instantaneous WWV changes due to Ekman transports. Our results suggest that roughly half of WWV variability is only as predictable as the winds that drive the instantaneous change. Separate examinations of pre‐2000 and post‐2000 periods reveal (i) nearly equal importance of instantaneous and adjusted responses for the pre‐2000 period and (ii) dominance of the instantaneous response during the post‐2000 period, which is most apparent during the recharged phase. This increasing instantaneous contribution prominence explains the post‐2000 reduction in WWV/El Niño‐Southern Oscillation sea surface temperature lead times (from six to nine months pre‐2000 down to three months post‐2000) and is consistent with the reduction in post‐2000 El Niño‐Southern Oscillation prediction skill.
Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power efficiency gain of 2.7% and an average reduction of 21% in the required cycles. This study elucidates the potential of different architectural choices in enhancing energy efficiency and performance in IoT systems.
Sediment motion beneath surges and bores

Positive surges and bores can induce significant bed-load transport in estuaries and river channels. Based upon physical modelling, the present study investigated the sediment motion beneath bores on a relatively long gravel bed. The free-surface measurements at a series of locations showed that the bore shape varied during its upstream propagation. An ultrahigh-speed camera captured the details of gravel motion at 1200 fps. Frame-by-frame analysis of slow-motion video movies demonstrated three basic modes of pebble motion: rotation, rolling, and saltation. More complicated pebble motion was a combination of 2 or 3 basic modes. The synchronous measurements of near-bottom velocity and bed-load material trajectories highlighted the importance of the adverse longitudinal pressure gradient and transient flow recirculation on the inception of particle motion. Long durations of gravel motion also indicated that the weak negative flow under secondary waves played some role in maintaining the upstream transient sediment transport.
Population parameters and exploitation rate of two dominant fish species in Tovè River (Southern Benin)

Most fisheries in Africa are overexploited or are at the peak of exploitation. A key contributor to this over fishing is poor data on fisheries, inefficient management strategies and unenforced policies. This study deals with growth, mortality parameters and the exploitation rate of Synodontis schall and Shilbe intermedius collected with a range of several fishing gears between October 2015 and September 2016 from Tovè River in Benin to contribute to sustainable management of these fisheries. The von Bertalanffy growth constants for S. schall were TL∞ = 21.84 cm, K = 0.93 yr, and t0 = 0.49 yr with a derived growth performance index of φ'= 2.64. The corresponding estimates for S. intermedius were TL∞ = 25.20 cm, K = 0.52 yr, t0 = -0.52yr and φ' = 2.51. The total mortality rate, Z, for S. schall was estimated as 4.13 yr, with the fishing mortality, F, being calculated as 2.31 yr. The mortality estimates for S. intermedius were Z = 1.28 yr and F = 0.08 yr. The size at first capture was estimated at 5.35 cm and 7.20 cm for S. schall and S. intermedius, respectively. The current exploitation rates for S. schall (0.55) and S. intermedius (0.06) suggest that only the stocks of S. schall was slightly above the assumed optimum value (Eopt = 0.5). Rational measures must be taken to effectively manage for exploitation of these 2 stocks particularly the one of S. schall. © 2017 The Authors. Published by JFLS. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0). A R T I C L E I N F O Received: 4 August 2017 Accepted: 21 November 2017 Available online: 1 December 2017