Miracle-dz/UniFAR-FTUs huggingface dataset:
Measured and unmeasured contributions of peer education: experiences from the Get Up, Speak Out for Youth Rights! (GUSO) programme in Kisumu and Siaya Counties, Kenya

ABSTRACT Peer education is a widely used strategy in sexual reproductive health and rights (SRHR) programmes for young people, yet measurement of its effectiveness often focuses on a narrow set of outcomes. This qualitative study explored how peer education was integrated into the Get Up, Speak Out for Youth Rights! (GUSO) programme in Kisumu and Siaya Counties, Kenya and the contribution it made to measured and unmeasured outcomes. Findings indicate that whilst peer educators were a central part of the GUSO theory of change, the intentionality of design varied between partners and sites, and their contributions were formally measured only in relation to two of five outcome areas. In addition to their contributions to these measured outcomes, the study found that peer educators also contributed to a range of other, unmeasured outcomes related to community support and mobilisation; gender norms; and economic empowerment. Findings show that peer educators may contribute to many unmeasured – and sometimes unexpected – outcomes that go beyond traditional measurement of their contributions. These merit further exploration in the literature and in programming. Programme developers are encouraged to be more intentional in the design and measurement of peer education, ensuring that the breadth of its contribution to programming are recognised.
Between Personal and Public Interest: How Algorithmic News Recommendation Reconciles with Journalism as an Ideology

Abstract Throughout modern history, the introduction of new technologies in journalism has challenged journalistic roles and the normative landscape of journalism, and the emergent use of personalised news recommender systems seems no exception given personalisation’s inherent democratic risk and commercial nature. However, current literature offers limited insight into how these technologies reconcile with the shared ideological norms and beliefs of journalists. This article identifies recurring tensions between journalism’s ideology and increasing commercial pressures in existing literature focusing on illustrative examples of digital technology implementations in journalism, and discusses how personalised news recommendation may recreate or mediate these tensions. The findings suggest that personalised news recommendation can facilitate journalism in service of the public by using value-sensitive algorithmic design that incorporates editorial input and nudges users to view diverse and serendipitous content. The findings additionally emphasise the importance of algorithmic transparency as a constituent element to distinguish nudging that prompts reflective behaviour from manipulation of choice, thus mediating tensions with journalism’s ideological aspirations to be fair and objective. Lastly, the paper calls for a higher level of understanding and interaction between journalists and algorithmic designers to mediate the impact on journalistic autonomy and ease the inherent transfer of editorial authority over distribution.
Infrared dim target detecting algorithm based on multi-feature and spatio-temporal fusion

Focusing on the detection of infrared dim targets in space-based imaging systems, a multi-feature and spatio-temporal fusion algorithm is proposed. By analyzing characteristics of target and background in the image, firstly, an algorithm combining TOP-HAT and improved partial differentiation method is proposed for image preprocessing; Secondly, the local entropy feature and local gradient feature of the small target image are extracted from images to fuse, an improved interframe method is used for spatio-temporal fusion to enhance the target signal, then threshold segmentation is used to obtain the detection result. Theoretical analysis and experiments show that the algorithm proposed in this paper can not only suppress the background and enhance the target well; in addition, it is possible to realize the on-chip transplantation of hardware.
Assessment, Interaction and the Transmission Process of Twin deficit Hypothesis: Fresh Evidence from India

In light of the persistent and coinciding internal and external imbalances, there remains an argument that worsening balance of payment (BoP) is the result of higher fiscal imbalance. However, no concrete consensus either theoretical or empirical exists, particularly in the context of India, and, therefore, this phenomenon of the twin deficit hypothesis becomes more of an empirical question. This study makes a timely and fresh revisit, especially in the backdrop of the fiscal expansions, to curb the recent recessionary situation that is engulfing the economy. To this end, an econometric exercise is undertaken on a quarterly data of financial year over 2000–2019. This work also extends the analysis with three reference variables, namely gross domestic product (GDP), private investment to GDP, real effective exchange rate together with the data for three dummy years to capture the impact of their specific occurrence in a particular year. The transmission mechanism describes how the budget deficit transcends and affects external sector variables. Empirical findings suggest a strong positive association between the budget deficit and current account deficit (CAD), which reinforces the validity of Mundell–Fleming and Keynesian theories. The effect of different exogenous variables explicitly indicates a simultaneous action on multiple fronts to improve the twin account balance.
Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming

Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there are target SV mismatch, covariance matrix estimation error and target contamination, the jamming suppression performance degrades severely. In this paper, a robust adaptive beamforming algorithm for anti-jammer application based on covariance matrix reconstruction is proposed in FDA-MIMO radar. In this method, the residual noise is further determined by using the spatial power spectrum estimation approach, which results in improved estimation accuracy of the signal covariance matrix and the desired target SV. The jamming SV is obtained from vectors in the intersection of two subspaces (namely, the signal-jamming subspace derived from the sample covariance matrix (SCM) and the jamming subspace generated from the jamming covariance matrix) by an alternating projection algorithm. Furthermore, the jamming power is obtained by exploiting the orthogonality between the different SVs. With the obtained parameters of target and jamming, the optimal adaptive beamformer weight vector is calculated. Simulation results demonstrate that the proposed algorithm can cope with the mainbeam deceptive jamming suppression under various model mismatches and has excellent performance over a wide range of signal-to-noise ratios (SNRs).
Resource costs for fault-tolerant linear optical quantum computing

Linear optical quantum computing (LOQC) seems attractively simple: information is borne entirely by light and processed by components such as beam splitters, phase shifters and detectors. However this very simplicity leads to limitations, such as the lack of deterministic entangling operations, which are compensated for by using substantial hardware overheads. Here we quantify the resource costs for full scale LOQC by proposing a specific protocol based on the surface code. With the caveat that our protocol can be further optimised, we report that the required number of physical components is at least five orders of magnitude greater than in comparable matter-based systems. Moreover the resource requirements grow higher if the per-component photon loss rate is worse than one in a thousand, or the per-component noise rate is worse than $10^{-5}$. We identify the performance of switches in the network as the single most influential factor influencing resource scaling.
Multi-objective methods for crop insurance premiums: framework proposal and a case study in sugarcane

The increase in extreme climate events due to climate change has resulted in crop losses, quality losses, environmental and social impacts in agricultural areas. Insurance against extreme events is a vital tool adaptation to deal with the impacts of those hazards. However, few works consider the optimization of different dimensions related to this tool. This work proposes a framework to use multi-objective optimization models to better design and evaluate crop insurance premiums and conducts a case study for sugarcane premiums at São Paulo state in 2010. The framework can be adopted for different crops, objectives, and models. The case study showed that around 20% of the policies evaluated were efficient solutions from the farmer's point of view.
Effects of Different Nanocarbon Materials on the Properties of Al/MoO3/NCM Thermite Prepared by Electrostatic Spinning

In order to improve thermal conductivity, energy performance, and combustion performance of the aluminum-containing thermite, nanocarbon materials were added to thermite. Aluminum/molybdenum and trioxide/nanocarbon materials (Al/MoO3/NCM) were fabricated by electrostatic spinning technology. The Al and MoO3 particles of the nAl/MoO3/NCM thermite are much smaller than nitrocellulose (NC); thus, the two components can be better attached to NC fibers. Results on thermal conductivity demonstrated that the addition of NCM can improve the thermal conductivity of Al/MoO3, and the addition of reduced graphene oxide (RGO) has a more significant impact on thermal conductivity. Energy performance analysis results indicated that the energy performance of Al/MoO3/NCM thermite spinning is the best when the value of combustion oxygen equivalent ratio (Φ) is 0.90–1.00. The combustion performance results show that the addition of NCM can significantly increase the combustion rate of thermites, and the addition of RGO improves its combustion rate the most, followed by carbon nanotubes (CNT) and nanoflake graphite (NFG) being the lowest. By changing the shape of the Al/MoO3/NCM charge and the internal composition of the charge, the sensitivity of the agent can be adjusted, and the matching performance and use performance of the electric igniter can be improved.
A new method for step height characterization based on K-means algorithm

This paper proposes a high-precision characterization method for step height of micro-structured surfaces based on the Kmeans algorithm. First, the original measured surface data obtained by the three-dimensional surface measuring instrument is dimensionally reconstructed. Secondly, use the K-means-based clustering algorithm and data mapping to identify and remove outliers, and the centroid distance of the reconstructed data in the three-dimensional space is mapped to the required step height value. Finally, through the iterative convergence design, the accuracy and robustness of the algorithm characterization results are further improved. Experiment on simulated data shows that this method is robust against outliers. It can effectively and accurately characterize step heights for measurement data of big size. Also, the method can simultaneously ignore outliers during parameterization.
Transcriptomic Analysis of Degradative Pathways for Azo Dye Acid Blue 113 in Sphingomonas melonis B-2 from the Dye Wastewater Treatment Process

Background: Acid Blue 113 (AB113) is a typical azo dye, and the resulting wastewater is toxic and difficult to remove. Methods: The experimental culture was set up for the biodegradation of the azo dye AB113, and the cell growth and dye decolorization were monitored. Transcriptome sequencing was performed in the presence and absence of AB113 treatment. The key pathways and enzymes involved in AB113 degradation were found through pathway analysis and enrichment software (GO, EggNog and KEGG). Results: S. melonis B-2 achieved more than 80% decolorization within 24 h (50 and 100 mg/L dye). There was a positive relationship between cell growth and the azo dye degradation rate. The expression level of enzymes involved in benzoate and naphthalene degradation pathways (NADH quinone oxidoreductase, N-acetyltransferase and aromatic ring-hydroxylating dioxygenase) increased significantly after the treatment of AB113. Conclusions: Benzoate and naphthalene degradation pathways were the key pathways for AB113 degradation. NADH quinone oxidoreductase, N-acetyltransferase, aromatic ring-hydroxylating dioxygenase and CYP450 were the key enzymes for AB113 degradation. This study provides evidence for the process of AB113 biodegradation at the molecular and biochemical level that will be useful in monitoring the dye wastewater treatment process at the full-scale treatment.
Groundwater Modeling System (GMS) software

GMS software was used more and more widely because of its friendly interface, powerful pre and postprocessing capability and excellent 3D visual effect when usual standard software were used to simulate groundwater in foreign countries. On base of simple introduction to modules of GMS, We analysed virtues and flaws of software systematically. Combining using experience, it also presents some common problems appearing in application, so that it could be used for reference by those use GMS software. 
On the Influence of Image Settings in Deep Learning-based Malware Detection

Considering the inadequacy of signature-based approaches for detecting malware, especially in the mobile environment, the research community is developing methodologies for detecting malware, especially using deep learning techniques, modeling applications like images. In state-of-the-art, several methods are proposed, each of one using a different kind of images and a different dimension of images: currently these are not standard settings for image preprocessing in Android malware detection. The aim of this paper is to compare different deep learning models performances to understand the best settings in terms of kind of image and image dimension. The idea is to trace a path in order to indicate the optimal settings for processing a dataset for malware detection using deep learning.
Manuscript title: Genomic profiling of HIV-1 integration in microglia links viral insertions to TAD organization

HIV-1 persists in anatomically distinct cellular and tissue reservoirs as a stably integrated provirus that is a major barrier to HIV-1 cure. Proviral insertions are largely characterized in blood cells, while HIV-1 integration patterns remain unexplored in microglia, the major brain reservoir. Here, we employ genomics approaches to obtain the first HIV-1 integration site (IS) profiling in microglia and perform in-depth analysis of transcriptome, specific histone signatures and chromatin accessibility on different genomic scales. We show that HIV-1 follows genic insertion patterns into introns of actively transcribed genes, characteristic of blood reservoirs. HIV-1 insertional hotspot analysis by nonnegative matrix factorization (NMF)-based approach clusters IS signatures with genicand superenhancers. Chromatin accessibility transcription factor (TF) footprints reveal that increased CTCF binding marks latently infected microglia compared to productively infected one. We identify CTCFenriched topologically associated domain (TAD) borders with signatures of active chromatin as a neighborhood for HIV-1 integration in microglia and CD4+ T cells. Our findings further strengthen the notion that HIV-1 follows the patterns of host cell genome organization to integrate and to establish the silent proviral state and reveal that these principles are largely conserved in different anatomical latent reservoirs.
“Forget about the learning”? Technology expertise and creativity as experiential habit in hacker-/makerspaces

Abstract This paper discusses to what extent hacker- and makerspaces (HMS) facilitate technology expertise. It draws on a combined qualitative interview and survey study of current/former community members. Study participants relate that HMS encourage learning-by-doing and self-directed creativity involving digital technology and crafts. Despite some being hesitant to label what they do as learning, a notion strongly associated with primary/secondary school, creativity itself is considered a learning ability and an experiential habit: a skill to be nurtured in practice. Members tend to expect that a self-directed approach to technological creativity is cultivated by new members too. As a “rite of passage”, this has implications for members’ in- and exclusion: notably creating challenges for individuals from already underrepresented groups and those perceiving themselves as comparatively low-skilled in technology. While learning and technology expertise are thus potentially facilitated in HMS, this is not equally the case for all members.
THE ADVANCES OF NUMERICAL SIMULATION FOR GRAVEL PACKING IN HORIZONTAL WELLS

This paper reviews the latest advances of numerical simulation for gravel packing in horizontal wells. The discussions focus on the following topics:gravel packing process mechanism and technology;calculation model for equilibrium bank height and critical velocity in packing.A description is made on slurry flow in screen/wellbore annuli,carried fluid flow in wash-pipe/screen annuli and fluid transfer between two flow system and fluid leak-off into formation.The mathematical model for bank height inαwave packing,factors affecting horizontal well gravel-pack efficiency and existing problems are also discussed.
A systematic literature review of city competitiveness: A 30-year assessment and future agenda for public administration

ABSTRACT This article synthesizes the cross-disciplinary literature on city competitiveness published in the past three decades to bring attention to competitiveness as a key topic for public administration and local government performance. We systematically reviewed research regarding city competitiveness, its measurement, and its antecendents. Subsequently, to explore the link between local governments and competitiveness, we articulated multiple perspectives to investigate this connection, concluding that while the relationship between government and city competitiveness is still not entirely settled and results are far from consistent, the progress made to date charts a course for future research that centres on public administration contributions.
MAESTRO: Orchestrating Computational Offloading to Multiple FemtoClouds in Various Communication Environments

Many novel IoT-based applications demand low latency, large compute resources, and high privacy. These requirements have motivated the emergence of fog and edge computing to complement the low-privacy and high-latency cloud. The intention behind Fog computing is to place computational servers closer to the user, typically within the city’s vicinity, to reduce latency. However, because of the high deployment cost of these servers at scale, and unreliable network infrastructures in many countries or areas, edge computing was proposed. Edge computing advocates leveraging compute resources, typically 0-hops away, on distributed ensembles of colocated devices called FemtoClouds. In this paper, we propose MAESTRO, a system that enables users to offload computational jobs to multiple FemtoClouds in their immediate vicinity. For MAESTRO, we build an integrated architecture that includes two new scheduling algorithms for assigning computing workloads to FemtoClouds. Each of our scheduling algorithms is designed to allow the system to operate more efficiently given poor or strong network infrastructures. We implement a full prototype of our system to assess its performance on our experimental testbed. The results indicate that in communication-challenged environments, our specialized scheduler outperforms state-of-the-art schedulers by up to 55%, while in communication-friendly environments our other specialized scheduler outperforms state-of-the-art schedulers by up to 67%.
Multi-Carrier Waveforms and Multiple Access Strategies in Wireless Networks: Performance, Applications, and Challenges

Current generation mobile communications require high-quality services. Adopting multiple access (MA) and multi-carrier waveforms potentially enhances the quality of services offered to end-users. However, in the majority of literature, the integration of multi-carrier and multiple-access approaches have not been extensively examined. One possible solution is to review multiple access and multi-carrier waveforms simultaneously to create a favorable foundation for the integration of these schemes. Thus, we consider a comprehensive review of multiple-access systems and multi-carrier waveforms jointly from $1^{st}$ to $5^{th}$ -generation (1G-5G) cellular networks. Initially, we present orthogonal MA (OMA) schemes called: frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA) that have been utilized in 1G, 2G, 3G, and 4G, respectively. In addition, 5G wireless non-orthogonal multiple access (NOMA) techniques such as power domain NOMA (PD-NOMA), code domain NOMA (CD-NOMA), and other NOMA multiplexing methods are addressed in detail. On the other hand, we glanced at 5G cellular multi-carrier waveforms such as filter bank multi-carrier (FBMC), universal filtered multi-carrier (UFMC), generalized frequency division multiplexing (GFDM), and filtered orthogonal frequency division multiplexing (f-OFDM) waveforms. The assessment and comparison between different OMA, NOMA, and multi-carrier waveforms are carried out with the parameters: modulation schemes, bit error rate (BER), signal to noise ration (SNR), sum rate, peak-to-average power ratio (PAPR), latency, out of band emission (OOBE), and complexity. The analytical formulas of OMA, NOMA, and multi-carrier waveform schemes are also derived and verified using simulation data. Each multiple access strategy’s merits, shortcomings, applications, and factors influencing its performance are also addressed. Eventually, possible recommendations for the integration of multiple-access and modulation technologies for next-generation mobile networks are also included.
Internet News User Analysis Using Deep Learning and Similarity Comparison

Nowadays, many Korean users read news from portal sites like Naver and Daum. Users can comment on news articles on such sites, and some try to influence public opinion through their comments. Therefore, news users need to be analyzed. This study proposes a deep learning method to classify each user’s political stance. Further, a method is developed to evaluate how many similar comments each user writes, and another method is developed to evaluate the similarity of a user’s comments with other users’ comments. We collect approximately 2.68 million comments from hundreds of thousands of political news articles in April 2017. First, for the top 100 news users, we classify each user’s political stance with 92.3% accuracy by using only 20% of data for deep learning training. Second, an evaluation of how many similar comments each user writes reveals that six users score more than 80 points. Third, an evaluation of the similarity of each user’s comments to other users’ comments reveals that 10 users score more than 80 points. Thus, based on this study, it is possible to detect malicious commenters, thereby enhancing comment systems used in news portal websites.