Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of small plasmid sequences via Oxford Nanopore sequencing

Oxford Nanopore Technologies (ONT) sequencing platforms currently offer two approaches to whole-genome native-DNA library preparation: ligation and rapid. In this study, we compared these two approaches for bacterial whole-genome sequencing, with a specific aim of assessing their ability to recover small plasmid sequences. To do so, we sequenced DNA from seven plasmid-rich bacterial isolates in three different ways: ONT ligation, ONT rapid and Illumina. Using the Illumina read depths to approximate true plasmid abundance, we found that small plasmids (<20 kbp) were underrepresented in ONT ligation read sets (by a mean factor of ∼4) but were not underrepresented in ONT rapid read sets. This effect correlated with plasmid size, with the smallest plasmids being the most underrepresented in ONT ligation read sets. We also found lower rates of chimeric reads in the rapid read sets relative to ligation read sets. These results show that when small plasmid recovery is important, ONT rapid library preparations are preferable to ligation-based protocols. Impact statement Researchers who use Oxford Nanopore Technologies (ONT) platforms to sequence bacterial genomes can currently choose from two library preparation methods. The first is a ligation-based approach, which uses ligase to attach sequencing adapters to the ends of DNA molecules. The second is a rapid approach, which uses a transposase enzyme to cleave DNA and attach adapters in a single step. There are advantages to each preparation, for example ligation can produce better yields but rapid is a simpler procedure. Our study reveals another advantage of rapid preparations: they are more effective at sequencing small plasmids. We show that sequencing of ligation-based libraries yields fewer reads derived from small plasmids, making such plasmids harder to detect in bacterial genomes. Since small plasmids can contain clinically relevant genes, including antimicrobial resistance (AMR) or virulence determinants, their exclusion could lead to unreliable conclusions that have serious consequences for AMR surveillance and prediction. We therefore recommend that researchers performing ONT-only sequencing of bacterial genomes should consider using rapid preparations whenever small plasmid recovery is important. Data summary Supplementary figures, tables, data and code can be found at: github.com/rrwick/Small-plasmid-Nanopore
Carrier aggregation‐enabled non‐orthogonal multiple access approach towards enhanced network performance in 5G Ultra‐Dense Networks

The need for fifth generation (5G) wireless technology to meet stringent demands on high throughput, ultra low latency, high reliability, massive connectivity, and fairness among users has seriously constrained the limited radio spectrum. Non‐orthogonal multiple access (NOMA) has been identified as one of the promising enabling radio access techniques to solve this problem. The basic principle of NOMA is the ability to serve multiple users in the same time and frequency resources but differentiated by their power domains. However, the inherent power‐domain multiplexing requires channel gain disparity between the strong and weak users, thereby reducing the sum‐rate gain of the network. In this paper, we propose NOMA scheme, integrated with carrier aggregation (CA) to further increase the bandwidth and data rate of users. Simulation results show considerable improvement in terms of throughput, energy efficiency, and fairness among users.
Editors' Review and Forthcoming Topic Event-Predictive Cognition: From Sensorimotor via Conceptual to Language-Based Structures and Processes.

Our minds navigate a continuous stream of sensorimotor experiences, selectively compressing them into events. Event-predictive encodings and processing abilities have evolved because they mirror interactions between agents and objects-and the pursuance or avoidance of critical interactions lies at the heart of survival and reproduction. However, it appears that these abilities have evolved not only to pursue live-enhancing events and to avoid threatening events, but also to distinguish food sources, to produce and to use tools, to cooperate, and to communicate. They may have even set the stage for the formation of larger societies and the development of cultural identities. Research on event-predictive cognition investigates how events and conceptualizations thereof are learned, structured, and processed dynamically. It suggests that event-predictive encodings and processes optimally mediate between sensorimotor processes and language. On the one hand, they enable us to perceive and control physical interactions with our world in a highly adaptive, versatile, goal-directed manner. On the other hand, they allow us to coordinate complex social interactions and, in particular, to comprehend and produce language. Event-predictive learning segments sensorimotor experiences into event-predictive encodings. Once first encodings are formed, the mind learns progressively higher order compositional structures, which allow reflecting on the past, reasoning, and planning on multiple levels of abstraction. We conclude that human conceptual thought may be grounded in the principles of event-predictive cognition constituting its root.
Featured Hybrid Recommendation System Using Stochastic Gradient Descent

Beside cold-start and sparsity, developing incremental algorithms emerge as interesting research to recommendation system in real-data environment. While hybrid system research is insufficient due to the complexity in combining various source of each single such as content-based or collaboration filtering, stochastic gradient descent exposes the limitations regarding optimal process in incremental learning. Stem from these disadvantages, this study adjusts a novel incremental algorithm using in featured hybrid system combing the feature of content-based method and the robustness of matrix factorization in collaboration filtering. To evaluate experiments, the authors simultaneously design an incremental evaluation approach for real data. With the hypothesis results, the study proves that the featured hybrid system is feasible to develop as the future direction research, and the proposed model achieve better results in both learning time and accuracy
Investigation of the Acid-Catalysed Alkylation of Lignins by Means of NMR Spectroscopic Methods

The acid-catalysed (catalyst, 0.15 M p-toluenesulfonic acid in the reaction medium) alkylation of lignins with rnethano) or ethanol in dioxane (alcohol-dioxane (2:1)) at 30°C has been studied by NMR spectroscopic methods. Complete alkylation required 4—6 days and resulted in the introduction of about one alkoxyl group in every second phenylpropane unit. The alkylation can be explained by etherification of benzy) alcohols (and, to a minute extent, cinnamyl alcohols), reetherification of benzyl ethers, esterification of carboxylic acids and formation of acetals from benzaldehydes, cinnamaldehydes and glyceraldehyde-2-aryl ethers. The methodology used permitted the conclusion that reactions other than alkylation occurred to only a minor extent. The wine-red colour acquired by the reaction mixture from lignin alkylation could be related to the occurrence of cinnamaldehyde groups. According to model compound studies, alkylation of arylglycerol-ß-aryl ethers resulted in a mixture of the erythro (60%) and threo (40%) forms of the benzyl ethers.
Ultrasmall Dispersible Crystalline Nickel Oxide Nanoparticles as High‐Performance Catalysts for Electrochemical Water Splitting

Ultrasmall, crystalline, and dispersible NiO nanoparticles are prepared for the first time, and it is shown that they are promising candidates as catalysts for electrochemical water oxidation. Using a solvothermal reaction in tert‐butanol, very small nickel oxide nanocrystals can be made with sizes tunable from 2.5 to 5 nm and a narrow particle size distribution. The crystals are perfectly dispersible in ethanol even after drying, giving stable transparent colloidal dispersions. The structure of the nanocrystals corresponds to phase‐pure stoichiometric nickel(ii) oxide with a partially oxidized surface exhibiting Ni(iii) states. The 3.3 nm nanoparticles demonstrate a remarkably high turn‐over frequency of 0.29 s–1 at an overpotential of g = 300 mV for electrochemical water oxidation, outperforming even expensive rare earth iridium oxide catalysts. The unique features of these NiO nanocrystals provide great potential for the preparation of novel composite materials with applications in the field of (photo)electrochemical water splitting. The dispersed colloidal solutions may also find other applications, such as the preparation of uniform hole‐conducting layers for organic solar cells.
A sulfur-assisted strategy to decorate MWCNTs with highly dispersed Pt nanoparticles for counter electrode in dye-sensitized solar cells

A novel strategy was introduced to prepare hybrid nanomaterials of platinum nanoparticles and multi-walled carbon nanotubes (Pt–MWCNTs). A thin layer of sulfur on MWCNTs could absorb and bind the Pt precursors from solution on the MWCNTs’ surface and also restrict their particle growth during reduction which resulted in ultrafine dispersions of metal nanocrystallites due to the strong affinity of sulfur for noble metals. The composite material was applied as the counter electrode (Pt–MWCNTs CE) for dye-sensitized solar cells (DSCs) and the fill factor (FF) and power-conversion efficiency (η) of the DSC with Pt–MWCNTs CE were 0.63 and 7.69%, respectively. The corresponding values of the DSC with bare Pt CE were 0.55 and 6.31%. The morphology and elemental composition of Pt–MWCNTs were characterized by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The dispersion and attachment of Pt nanoparticles on the surface of MWCNTs were observed by transmission electron microscopy (TEM). Electrochemical impedance spectra (EIS), Tafel polarization measurement and cyclic voltammetry (CV) were performed to characterize the catalytic activities of this hybrid CE.
A Multimode Anomaly Detection Method Based on OC-ELM for Aircraft Engine System

The practical industrial processes possess the characteristics of multimode, unbalanced data distribution, and complex types of abnormalities, which are challenging to the anomaly detection task of complex industrial systems. In this paper, a novel anomaly detection framework based on one-class extreme learning machine (OC-ELM) for the multimode system is presented. To tackle the multiple operation modes, a clustering algorithm is first applied to distinguish the operation modes of the system. The corresponding detection models are built under different operation modes resulting in the multiple models operated in parallel. In addition, the proposed method constructs the reasonable boundary of the complex data distribution, reflecting the equipment running in the healthy or the normal state. The anomaly detection index is obtained according to the deviation degree between the testing sample and the normal model. As a result, a global monitoring index reflecting the degradation state is obtained by combining the anomaly monitoring indices of the equipment under multiple operation modes. The proposed method is verified on a public dataset of aircraft engines, and the advantages are demonstrated by comparing with the implemented detection model without handling the information of operation modes, and the multiple principal component analysis method.
IMCA: An Efficient In-Memory Convolution Accelerator

Traditional convolutional neural network (CNN) architectures suffer from two bottlenecks: computational complexity and memory access cost. In this study, an efficient in-memory convolution accelerator (IMCA) is proposed based on associative in-memory processing to alleviate these two problems directly. In the IMCA, the convolution operations are directly performed inside the memory as in-place operations. The proposed memory computational structure allows for a significant improvement in computational metrics, namely, TOPS/W. Furthermore, due to its unconventional computation style, the IMCA can take advantage of many potential opportunities, such as constant multiplication, bit-level sparsity, and dynamic approximate computing, which, while supported by traditional architectures, require extra overhead to exploit, thus reducing any potential gains. The proposed accelerator architecture exhibits a significant efficiency in terms of area and performance, achieving around 0.65 GOPS and 1.64 TOPS/W at 16-bit fixed-point precision with an area less than 0.25 mm2.
Rumor Detection of COVID-19 Pandemic on Online Social Networks

The new coronavirus epidemic (COVID-19) has received widespread attention, causing the health crisis across the world. Massive information about the COVID-19 has emerged on social networks. However, not all information disseminated on social networks is true and reliable. In response to the COVID-19 pandemic, only real information is valuable to the authorities and the public. Therefore, it is an essential task to detect rumors of the COVID-19 on social networks. In this paper, we attempt to solve this problem by using an approach of machine learning on the platform of Weibo. First, we extract text characteristics, user-related features, interaction-based features, and emotion-based features from the spread messages of the COVID-19. Second, by combining these four types of features, we design an intelligent rumor detection model with the technique of ensemble learning. Finally, we conduct extensive experiments on the collected data from Weibo. Experimental results indicate that our model can significantly improve the accuracy of rumor detection, with an accuracy rate of 91% and an AUC value of 0.96.
Detecting Individuals High in Neuroticism based on the Color Features of the Facebook Profile Picture

Previous research has mostly focused on the link between the linguistic and behavioral footprints found on social media on the one hand and personality on the other. Despite the high amount of image-based contents posted and shared online and the valuable implicit information they might conceal about users' preferences and tendencies, the study of visual traces is in its infancy. The goal of the current paper is to test whether the color characteristics of the Facebook profile picture could mirror the level of neuroticism on a sample of 508 Romanian users. For this purpose, we assessed the classification performance of four machine learning algorithms having as input three sets of visual features: (1) the colorfulness, which indicates how colorful is an image; (2) the proportion of cold colors, along with the mean and standard deviation for saturation and value; (3) the emotional load, defined as pleasure, arousal, and dominance. None of the models showed good accuracy. However, this paper contributes to the literature by being part of a line of research that requires development not only in Romania but also worldwide.
Aligning PMBOK and COBIT for Project Management in Banking Industry: Case Study of BankXYZ

Bank XYZ is a well-known financial organization that faces problems related to the application of IT governance and project management practices. The problem that faced Bank XYZ is some policy related with IT governance, it is formed in an ad hoc manner as a result of years of implementing IT and learning from experiences. To improvement the IT project management with standard this research used COBIT6. Thus, this exploratory research was conducted to identify the alignment of IT project management with business objectives in the organization by combining COBIT5 and PMBOK6 best practices. This result used interview and secondary data such as document to collect the data at Bank XYZ. There is 7 from total of the quantity from 4 respondents at Bank XYZ. Result of this research is the project management practice Bank XYZ Should fulfilled 5 activity output process group PMBOK. The output that Bank XYZ should be consider are plan WBS, sequences activity, plan communication engagement, develop project management plan, and plan quality management.
Design of Multidimension-media Streaming Protocol Based on RTSP

In recent years, with the rise of the concept of virtual reality, multidimension-media that contains multiple sensory stimulations such as vision, hearing, and somatosensory have also sprung up. However, existing network streaming platforms are still limited to traditional video and audio streaming, lack the transmission of somatosensory data, and cannot support multidimension-media that requires natural interaction. This paper proposes a multidimension-media streaming system composed of three parties: control center, client platform, and multidimension-media producer. It specifies the login procedures, link procedures, interaction mechanisms, and logout procedures in detail. This architecture adopts the method of separate transmission of audiovisual information and body joint information to solve the problem of the unreturnable somatosensory information in the current streaming system. In this paper, a complete system has been set up to actually verify the flow of producers and client platforms from login, data transmission, stream creation to logout. It proposes an objective function for the system delay and tests the influence of the network cache size on the client platform. The delay can be reduced to less than one second under the best settings. This paper also measured the relationship between the body joints and the network traffic of the control center when a single pair of client-producer were connected and proposed related formulas for reference when building systems in the future.
TagMic: Listening Through RFID Signals

RFID is an increasingly ubiquitous technology widely adopted in both the industry and our daily life nowadays. But when it comes to eavesdropping, people usually pay attention to devices like cameras and mobile phones, instead of small-volume and battery-free RFID tags. This work shows the possibility of using prevalence RFIDs to capture and recognize the acoustic signals. To be specific, we attach an RFID tag on an object, which is located in the vicinity of the sound source. Our key innovation lies in the translation between the vibrations induced when the sound wave hits the object surface and the fluctuations in the tag’s RF signals. Although the inherent sampling rate of commercial RFID devices is deficient, and the vibrations are very subtle, we still extract characteristic features from imperfect measurements by taking advantage of state-of-the-art machine learning and signal processing algorithms. We have implemented our system with commercial RFID and loudspeaker equipment and evaluated it intensively in our lab environment. Experimental results show that the average success rate in detecting single tone sounds can reach as high as 93.10%. We believe our work would raise the attention of RFID in the concern of surveillance and security.
Size effects in polarization switching in ferroelectric thin films

Abstract The scenarios of the size effects which may account for the difference in the switching properties of ferroelectric thin films and bulk ferroelectrics are critically reviewed. It is shown that the two often used models (the Janovec-Kay-Dunn surface nucleation size effect and the dielectric layer model) are irrelevant to the experimentally observed thickness dependence of the coercive field in the films. The surface pinning model and the depletion-assisted nearby electrode nucleation model are shown to be consistent with the experimental observations. It is shown that the Miller-Weinreih law for wall velocity cannot be expected in thin films.
A Collaborative Filtering Approach to Successfully Completing The Marathon

For elite and recreational runners alike, preparing for the marathon is a complex problem. In this work, we propose a recommender system that is capable of helping runners devise a strategy for the marathon. Using high-resolution performance data from runners’ training and race performance we recommend both a realistic target time for the marathon and use collaborative filtering techniques to generate a pacing plan that allows runners to achieve that time. We demonstrate that our recommendations are capable of helping runners attain their goal time, while also helping them avoid premature fatigue and hitting the wall during the course of the race.
Post Implementation Review of Electronic Customer Relationship Management (E-CRM) Implementation in Port Services Company, Indonesia

PT XYZ, a company engaged in port services, has implemented E-CRM since 2016. The goal of E-CRM is to upgrade, update, and standardize customer relationship systems and some legacy systems. To achieve this goal, PT XYZ developed and used many E-CRM features. There are no tools for an organization to help gain use of the details and impact of an E-CRM implementation after several years of implementation. Companies find it difficult to detail the use and impact of E-CRM documentation. The company needs to complete this task because companies need to make post-implementation documentation of E-CRM to comply with Good Corporate Governance (GCG), namely transparency and accountability to stakeholders and the public. If transparency and accountability are not implemented, this will impact management rights in the port area, especially in the sub-directorate that implements E-CRM. Companies need to know the detailed impact and list of E-CRM usage. This impact and usage list will help the company create post-implementation documentation to help the company fulfill this project with GCG. The research question is what E-CRM is used for and the impact of E-CRM for the company. Researchers used mixed methods to analyze using quantitative and qualitative methods using questionnaires, document analysis, observation, and interviews. For the implementation of E-CRM, the features that have been developed, used, and support business processes are customer data management features, customer profiles, customer service, customer visit management, customer surveys, and marketing features. E-CRM impacts companies in several areas that are strategic, managerial, and operational. The most significant impact on the organization is product quality (operational), decision making (managerial), productivity (operational), partner synergy (strategic), business innovation (strategic), performance control (managerial), competitive advantage (strategic), resource management (managerial). With a post-implementation review, companies can get details of E-CRM's use and impact and help make detailed documentation of E-CRM's post-implementation use and impact to comply with GCG. If an audit is carried out for this project, the company is ready.
Adaptive Policy Tree Algorithm to Approach Collision-Free Transmissions in Slotted ALOHA

A new adaptive transmission protocol is introduced to improve the performance of slotted ALOHA. Nodes use known periodic schedules as base policies with which they collaboratively learn how to transmit periodically in different time slots so that packet collisions are minimized. The Adaptive Policy Tree (APT) algorithm is introduced for this purpose, which results in APT-ALOHA. APT-ALOHA does not require the presence of a central repeater and uses explicit acknowledgements to confirm the reception of packets. It is shown that nodes using APT-ALOHA quickly converge to transmission schedules that are virtually collision-free, and that the throughput of APT-ALOHA resembles that of TDMA, where slots are pre-allocated to nodes. In particular, APT-ALOHA attains a successful utilization of time slots- over 70% on saturation mode.