Miracle-dz/UniFAR-FTUs huggingface dataset:
K-Means Spreading Factor Allocation for Large-Scale LoRa Networks

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by 1.53 times.
An enhanced broadband class-J mode power amplifier for 5G smart meter applications

Background: With the tremendous increase in the usage of smart meters for industrial/ household purposes, their implementation is considered a crucial challenge in the Internet of Things (IoT) world, leading to a demand for emerging 5G technology. In addition, a large amount of data has to be communicated by smart meters efficiently, which needs a significant enhancement in bandwidth. The power amplifier (PA) plays a major role in deciding the efficiency and bandwidth of the entire communication system. Among the various modes of PAs, a newly developed Class-J mode PA has been proven to achieve high efficiency over a wide bandwidth by maintaining linearity. Methods: This paper proposes a Class-J mode PA design methodology using a CGH40010F-GaN device that operates at a 3.5 GHz frequency to meet the requirements of 5G wireless communication technology for the replacement of existing 4G/LTE technology used for advanced metering infrastructure (AMI) in smart grids. This research's main objective is to design the proper matching networks (M.Ns) to achieve Class-J mode operation that satisfies the bandwidth requirements of 5G smart grid applications. With the target impedances obtained using the load-pull simulation, lumped element matching networks are analyzed and designed in 3 ways using the ADS EDA tool. Results: The simulation results reveal that the proposed Class-J PA provides a maximum drain efficiency (D.E) of 82%, power added efficiency (PAE) of 67% with 13 dB small-signal gain at 3.5 GHz, and output power of 40 dBm (41.4 dBm peak) with a power gain of approximately 7 dB over a bandwidth of approximately 400 MHz with a 28 V power supply into a 50 Ω load. Conclusion: The efficiency and bandwidth of the proposed Class-J PA can be enhanced further by fine-tuning the matching network design to make it more suitable for 5G smart meter/grid applications.
Long-term effects of network-based fMRI neurofeedback training for sustained attention

Neurofeedback allows for learning voluntary control over one’s own brain activity, aiming to enhance cognition and clinical symptoms. A recent study improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network (SAN) minus the default mode network (DMN). However, long-term learning effects of functional magnetic resonance imaging (fMRI) neurofeedback training remain under-explored. Here, we evaluate the effects of network-based fMRI neurofeedback training for sustained attention by assessing behavioral and brain measures before, one day after, and two months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs. Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training-induced increase in FC between the DMN and occipital gyrus was maintained during follow-up transfer runs, but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior long-term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool. Key points Participants were still able to self-regulate the differential activity between large-scale networks two months after the end of neurofeedback training and this during transfer runs without feedback. Lasting brain changes were also observed in the functional connectivity of trained regions in runs during which participants engaged in active self-regulation as well as during resting-state runs without concomitant self-regulation. The increased sustained attention we observed right after the end of neurofeedback training did not persist two months later.
Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome

Protecting plants from the inside out Some soils show a remarkable ability to suppress disease caused by plant pathogens, an ability that is attributed to plant-associated microbiota. Carrión et al. investigated the role of endophytes, the intimate microbial community found within roots, in fungal disease suppression (see the Perspective by Tringe). The wilt fungus Rhizoctonia solani infects sugar beets, whereupon transcriptional analysis shows that several bacterial endophyte species activate biosynthetic gene clusters to cause disease suppression. These organisms produce antifungal effectors, including enzymes that can digest fungal cell walls, and secondary metabolites, including phenazines, polyketides, and siderophores, which may contribute to the antifungal phenotype. Science, this issue p. 606; see also p. 568 Bacteria living between and within the roots of plants exposed to a fungal pathogen can help generate a disease-suppressing phenotype. Microorganisms living inside plants can promote plant growth and health, but their genomic and functional diversity remain largely elusive. Here, metagenomics and network inference show that fungal infection of plant roots enriched for Chitinophagaceae and Flavobacteriaceae in the root endosphere and for chitinase genes and various unknown biosynthetic gene clusters encoding the production of nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). After strain-level genome reconstruction, a consortium of Chitinophaga and Flavobacterium was designed that consistently suppressed fungal root disease. Site-directed mutagenesis then revealed that a previously unidentified NRPS-PKS gene cluster from Flavobacterium was essential for disease suppression by the endophytic consortium. Our results highlight that endophytic root microbiomes harbor a wealth of as yet unknown functional traits that, in concert, can protect the plant inside out.
Eliciting Design Requirements for a Knowledge Management System in Cultural and Historical Heritage

: Designing a Knowledge Management System (KMS) from a socio-technical approach, usually includes elicitation of distinct, yet related, requirements from a user, system and domain level. However, this perspective presents shortcomings in terms of emergent and co-evolving interactions between agencies, particularly in highly-dynamic use contexts. To address these issues, this paper reports on a design science study that develops three design requirements grounded on sociomaterial (SM) tenets, and based on the study of imbrications between social and material agencies. We do so in the context of an interorganizational knowledge sharing network in the cultural and historical heritage domain, showing how the sociomaterial coordination practices could be better understood and directed to design practices. The findings reveal that a KMS derived from sociomaterial lens could potentially address different coordination issues that arise when sharing knowledge between heritage projects. Our artefact will be helpful in applying what have been mostly theoretical discussions on sociomateriality in highly-dynamic design settings.
AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users’ perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.
Link Prediction for Wikipedia Articles based on Temporal Article Embedding

: Wikipedia articles contain a vast number of hyperlinks (internal links) connecting subjects to other Wikipedia articles. It is useful to predict future links for newly created articles. Suggesting new links from/to existing articles can reduce editors’ burdens, by prompting editors about necessary or missing links in their updates. In this paper, we discuss link prediction on linked and versioned articles. We propose new graph embeddings utilizing temporal random walk, which is biased by timestamp difference and semantic difference between linked and versioned articles. We generate article sequences by concatenating the article titles and category names on each random walk path. A pretrained language model is further trained to learn contextualized embeddings of article sequences. We design our link prediction experiments by predicting future links between new nodes and existing nodes. For evaluation, we compare our model’s prediction results with three random walk-based graph embedding models DeepWalk, Node2vec, and CTDNE, through ROC AUC score, PRC AUC score, Precision@k, Recall@k, and F1@k as evaluation metrics. Our experimental results show that our proposed TLPRB outperforms these models in all the evaluation metrics.
Vehículos aéreos no tripulados como alternativa de solución a los retos de innovación en diferentes campos de aplicación: una revisión de la literatura

Objetivo: Identificar los principales campos de aplicación en los que los vehículos aéreos no tripulados son una alternativa de solución innovadora, así como las tecnologías implementadas en ellos, articulando información fragmentada. Metodología: Mediante la herramienta Tree of Science se desarrolló una búsqueda inicial en Web of Science y con base en la información obtenida se complementó con el modelo de revisión sistemática tomado como referencia las bases de datos Scopus, ScienceDirec, IEEE y Web of Science aplicando una selección y descarte de información de las diferentes investigaciones relacionadas. Resultados y Conclusiones: Se seleccionaron un total de 81 artículos que fueron analizados y estudiados acorde a las aplicaciones de enfoque donde los vehículos aéreos no tripulados han presentado aportes, soluciones y alternativas a las labores diarias implementadas en áreas como agricultura, ambiente, militar, logística, mapeo y geociencias. Los vehículos aéreos más utilizados como alternativa de solución son los multirrotor que obtuvieron un 58.82 % de participación, seguido de los ala fija o flexible con un 31.76 %. Además, se encontró que el 91.36 % adaptan cámaras en estas aeronaves para el desarrollo de sus estudios. Finalmente, se observó que el área de la logística, en términos generales, es un tema potencial para estudios futuros.
Investigation of a Novel Ecofriendly Electrolyte-Solvent for Lithium-Ion Batteries with Increased Thermal Stability

This study presents tributyl acetylcitrate (TBAC) as a novel ecofriendly high flash point and high boiling point solvent for electrolytes in lithium-ion batteries. The flash point (TFP=217∘C) and the boiling point (TBP=331∘C) of TBAC are approximately 200 K greater than that of conventional linear carbonate components, such as ethyl methyl carbonate (EMC) or diethyl carbonate (DEC). The melting point (TMP=−80∘C) is more than 100 K lower than that of ethylene carbonate (EC). Furthermore, TBAC is known as an ecofriendly solvent from other industrial sectors. A life cycle test of a graphite/NCM cell with 1 M lithium hexafluorophosphate (LiPF6) in TBAC:EC:EMC:DEC (60:15:5:20 wt) achieved a coulombic efficiency of above 99% and the remaining capacity resulted in 90 percent after 100 cycles (C/4) of testing. As a result, TBAC is considered a viable option for improving the thermal stability of lithium-ion batteries.
Field inversion and machine learning strategies for improving RANS modelling in turbomachinery

Turbulence and transition modelling are critical aspects in the prediction of the ﬂow ﬁeld in turbomachinery. Recently, several research efforts have been devoted to the use of machine learning techniques for improving Reynolds-averaged Navier-Stokes (RANS) models. In this framework, a promising technique is represented by ﬁeld inversion which requires to ﬁnd an optimal correction ﬁeld that minimises the error between numerical predictions and experimental data. In this work, Artiﬁcial Neural Networks and Random Forests are investigated as tools to generalise the correction provided by ﬁeld inversion. An approach to automatically identify the regions where the correction model should be computed is proposed: this improves the ﬁtting and reduces the calls to the model during the predictions. Furthermore, a correction-based weighting of the database is introduced in order to improve the training performances. The potential and the issues of the methods are investigated on a high-lift gas turbine cascade at low Reynolds number.
Late Eocene–early Miocene evolution of the southern Australian subtropical front: a marine palynological approach

Abstract. Improvements in our capability to reconstruct ancient surface-ocean conditions based on organic-walled dinoflagellate cyst (dinocyst) assemblages from the Southern Ocean provide an opportunity to better establish past position, strength and oceanography of the subtropical front (STF). Here, we aim to reconstruct the late Eocene to early Miocene (37–20 Ma) depositional and palaeoceanographic history of the STF in the context of the evolving Tasmanian Gateway as well as the potential influence of Antarctic circumpolar flow and intense waxing and waning of ice. We approach this by combining information from seismic lines (revisiting existing data and generating new marine palynological data from Ocean Drilling Program (ODP) Hole 1168A) in the western Tasmanian continental slope. We apply improved taxonomic insights and palaeoecological models to reconstruct the sea surface palaeoenvironmental evolution. Late Eocene–early Oligocene (37–30.5 Ma) assemblages show a progressive transition from dominant terrestrial palynomorphs and inner-neritic dinocyst taxa as well as cysts produced by heterotrophic dinoflagellates to predominantly outer-neritic/oceanic autotrophic taxa. This transition reflects the progressive deepening of the western Tasmanian continental margin, an interpretation supported by our new seismic investigations. The dominance of autotrophic species like Spiniferites spp. and Operculodinium spp. reflects relatively oligotrophic conditions, like those of regions north of the modern-day STF. The increased abundance in the earliest Miocene of Nematosphaeropsis labyrinthus, typical for modern subantarctic zone (frontal) conditions, indicates a cooling and/or closer proximity of the STF to the site . The absence of major shifts in dinocyst assemblages contrasts with other records in the region and suggests that small changes in surface oceanographic conditions occurred during the Oligocene. Despite the relatively southerly (63–55∘ S) location of Site 1168, the rather stable oceanographic conditions reflect the continued influence of the proto-Leeuwin Current along the southern Australian coast as Australia continued to drift northward. The relatively “warm” dinocyst assemblages at ODP Site 1168, compared with the cold assemblages at Antarctic Integrated Ocean Drilling Program (IODP) Site U1356, testify to the establishment of a pronounced latitudinal temperature gradient in the Oligocene Southern Ocean.
A Study of American Slangs in Billy Bathgate Translated by Yang Renjing from the Perspective of Translation Memetics

The analysis and comparison to the translation of American slangs in Yang Renjing’s translation of Billy Bathgate through the notions of translation memetics can deeply indicate the cultural nature behind American slangs, and further promote the exchange and communication of Chinese and foreign cultures with the conversion results of slang translation. By integrating the strategies adopted by the translator in the translation of slang memes to analyze the motivations and rules behind the transmission of American slangs, the discussions to the modes of spreading and developing slang memes in translation can be advanced. Also, the fact of cultural mutation and transformation in translation can be proved from the perspective of memetics to demonstrate the inevitability of the variation of American slang in communication due to the limitation of cultural differences.
Analytical Design and Optimization of Axial Flux Permanent Magnet Machines With Slotless Structure

The three-dimensional (3-D) finite element (FE) analysis is an extremely time-consuming task in the design and optimization of axial flux permanent magnet (AFPM) machines. The aim of this article is to present a fast and accurate design model of slotless AFPM machines. Thanks to the separation of active- and end-winding parts, the overhang structure, which has widely been used in the air gap winding, can be handled by the proposed method. The winding eddy current loss, a primary source of losses in the air-core structure, is incorporated into the analytical model. In addition to the high accuracy, this approach makes it possible to evaluate machine performances only once without needing to rotate the rotor position. As a result, the proposed analytical model can significantly reduce the computational time, preferred in applying an optimization algorithm. Finally, the accuracy of the proposed method is verified by both FE and experimental results.
Use of Functional Near-Infrared Spectroscopy to Predict and Measure Cochlear Implant Outcomes: A Scoping Review

Outcomes following cochlear implantation vary widely for both adults and children, and behavioral tests are currently relied upon to assess this. However, these behavioral tests rely on subjective judgements that can be unreliable, particularly for infants and young children. The addition of an objective test of outcome following cochlear implantation is therefore desirable. The aim of this scoping review was to comprehensively catalogue the evidence for the potential of functional near infrared spectroscopy (fNIRS) to be used as a tool to objectively predict and measure cochlear implant outcomes. A scoping review of the literature was conducted following the PRISMA extension for scoping review framework. Searches were conducted in the MEDLINE, EMBASE, PubMed, CINAHL, SCOPUS, and Web of Science electronic databases, with a hand search conducted in Google Scholar. Key terms relating to near infrared spectroscopy and cochlear implants were used to identify relevant publications. Eight records met the criteria for inclusion. Seven records reported on adult populations, with five records only including post-lingually deaf individuals and two including both pre- and post-lingually deaf individuals. Studies were either longitudinal or cross-sectional, and all studies compared fNIRS measurements with receptive speech outcomes. This review identified and collated key work in this field. The homogeneity of the populations studied so far identifies key gaps for future research, including the use of fNIRS in infants. By mapping the literature on this important topic, this review contributes knowledge towards the improvement of outcomes following cochlear implantation.
Gutenberg és a MOOC

Összefoglaló. Tanulmányunkban a Tömeges Nyitott Online Kurzusok (angolul: Massive Open Online Education, rövidítése MOOC) kialakulásával és óriási méretű növekedésével foglalkozunk. A rövid előzményekkel rendelkező, mai formájában 2011-ben startolt új típusú oktatási forma jelenleg már alapképzéses és mesterképzéses diplomát is ad. Ily módon beszélhetünk MOOC egyetemekről, melyek sok szerző szerint a Christensen-féle romboló újítás megtestesítői a felsőoktatásban. A MOOC népszerűsége egyúttal jelzi, hogy a napjainkban lezajló generációváltás egyben oktástechnikai változással is párosul. Cikkünkben összehasonlítjuk a MOOC egyetemek jóléti hatásait a Gutenberg-féle könyvnyomtatás jóléti hatásaival. A szolgáltatói oldalhoz kapcsolódóan pedig bemutatjuk a MOOC-platformok jelenleg működő finanszírozási modelljeit. Summary. The study analyses the emergence and huge growth of the Massive Open Online Education. This new type of education which started in 2011 in its current form and so has only a very short history, now offers both bachelor’s and master’s degree. MOOC universities are seen by many authors as the embodiment of Christensen’s destructive innovation. At the same time, the popularity of MOOC indicates that the generational change taking place today is accompanied by a change in educational technology. The present paper compares the welfare effects of MOOC universities with the welfare effects of Gutenberg’s book printing. In connection with the service provider side, the paper presents the financing models of the MOOC platforms as well.