Miracle-dz/UniFAR-FTUs huggingface dataset:
The Prevalence of Underweight, Overweight/Obesity and Their Related Lifestyle Factors in Indonesia, 2014–2015

Objective To quantify the prevalence of underweight and overweight or obesity and its related factors (socio-demographic, health behavior, health status) in a national adult population in Indonesia. Material and Methods In a national cross-sectional population-based survey in 2014–15 in Indonesia, 29509 adults (median age 41.0 years, Inter Quartile Range = 22.0, age range of 18–103 years) completed questionnaires and anthropometric measurements. Multinomial logistic regression modelling was used to determine the association between socio-demographic, health behavior and health status factors and underweight and overweight or obesity. Results Of total sample (n = 29509), 11.2% measured underweight (13.5% among men and 9.1% among women) (<18.5 kg/m2), 39.8% normal weight (48.1% among men and 32.0% among women) and 49.0% had overweight or obesity (≥23 kg/m2) (38.3% among men and 58.9% among women); 24.6% of the overall sample had class I obesity (25–29.9 kg/m2), and 8.5% had class II obesity (30 or more kg/m2). Among different age groups, underweight was the highest among 18–29 year-olds (20.0%) and those 70 years and older (29.8%), while overweight or obesity was the highest in the age group 30 to 59 years (more than 53%). In adjusted multinomial logistic regression, having less education, living in rural areas and not having chronic conditions were associated with underweight status. While better education, higher economic status, urban residency, dietary behavior (infrequent meals, frequent meat, fried snacks and fast food consumption), physical inactivity, not using tobacco, having chronic conditions (diabetes, hypertension, hypercholesterol), and better perceived health and happiness status were associated with overweight or obesity. Conclusions A dual burden of both adult underweight and having overweight or obesity was found in Indonesia. Sociodemographic, health risk behavior and health status risk factors were identified, which can guide public health interventions to address both these conditions.
Sugiyamaella xiaguanensis f.a., sp. nov., a yeast species isolated from rotting wood.

Three strains representing a novel yeast species, Sugiyamaella xiaguanensis f.a. , sp. nov. (type strain NYNU 161041T=CICC 33167T=CBS 14696T), were isolated from rotting wood samples collected in Henan and Yunnan Provinces, PR China. The novel species is able to assimilate cellobiose, salicin and d-xylose, which was typical of the species of the genus Sugiyamaella. Analysis of the D1/D2 domains of the large subunit rRNA gene and internal transcribed spacer regions of these strains showed that this species was related to Sugiyamaella lignohabitans and Sugiyamaella marionensis, its closest relatives. Su. xiaguanensis sp. nov. differed by 1.4 % nucleotide substitutions from Su. lignohabitans, and by 1.9 % nucleotide substitutions from Su. marionensis in the D1/D2 sequences. The ITS sequences of Su. xiaguanensis sp. nov. displayed more than 6.5 % nucleotide substitutions from the latter two species, showing that it is a genetically separate species.
Optimum Designed Multilayer Switched Reluctance Motors for use in Electric Vehicles to Increase Efficiency

The purpose of this paper was to investigate the optimal design of multilayer switched reluctance motors for use in electric vehicles. The method presented in this paper is to raise the efficiency of switched reluctance motor. Toyota Prius motor design was based on the true size of geometry motor with a conventional motor with the same power rating Switched Reluctance Motor 6 to 4 compared with the same surface area and Size, Using optimal design constraints Switched Reluctance Motor 4 to 4 five-layer, Switched Reluctance Motor 4 to 4 seven-layer is studied. By examining the different modes including changing arc stator and rotor, change teeth stator and rotor and the change in the air gap, we select the best structure. The motor design and relations governing method was developed using simulation software Magnet diagrams torque around the axis Z, speed, flux density was obtained. Switched Reluctance Motor Multilayer also increase more space occupied, but due to the empty space between the layers is cooling easier. Efficiency of Switched Reluctance Motor Multilayer to more Switched Reluctance Motor conventional compared. Use Switched Reluctance Motor 4 to 4 Multilayer in an electric vehicle, due to the low cost of this motor is more advantageous than the Toyota Prius motor.
On the Trade-off Analysis of Centralized and Distributed Routing in Delay Tolerant Satellite Networks

Given that satellite networks cannot assume continuous connectivity among nodes, an architecture known as Delay Tolerant Networking (DTN) has been proposed as an appealing communication paradigm. However, as space networks continue to grow in both number and size, the process of routing and forwarding traffic is rapidly becoming computationally expensive and challenging. Since these networks usually exhibit predictable trajectories and communication opportunities, the research community has proposed to provide nodes with scheduled contact plans in advance in order to make distributed routing decisions as they generate or receive traffic. On the other hand, a more attractive solution for the conservative space industry seems to be a centralized scheme. Based on a Software-Defined Network (SDN) approach, a route table can be computed on ground and then provisioned to nodes in space, which would no longer run any routing intelligence on board, only forwarding. In this work we analyze the dichotomy between distributed and centralized routing schemes in the context of DTN and provide insights regarding important aspects to take into account when designing and managing satellite networks. Copyright c © by the paper’s authors. Copying permitted for private and academic purposes. In: Proceedings of the IV School of Systems and Networks (SSN 2018), Valdivia, Chile, October 29-31, 2018. Published at http://ceur-ws.org
BABA-Primed Histone Modifications in Potato for Intergenerational Resistance to Phytophthora infestans

In this paper we analyzed β-aminobutyric acid (BABA)-primed epigenetic adjustment of potato cv. “Sarpo Mira” to Phytophthora infestans. The first stress-free generation of the potato genotype obtained from BABA-primed parent plants via tubers and seeds showed pronounced resistance to the pathogen, which was tuned with the transcriptional memory of SA-responsive genes. During the early priming phase before the triggering stress, we found robust bistable deposition of histone marks (H3K4me2 and H3K27me3) on the NPR1 (Non-expressor of PR genes) and the SNI1 gene (Suppressor of NPR1, Inducible), in which transcription antagonized silencing. Switchable chromatin states of these adverse systemic acquired resistance (SAR) regulators probably reprogrammed responsiveness of the PR1 and PR2 genes and contributed to stress imprinting. The elevated levels of heritable H3K4me2 tag in the absence of transcription on SA-dependent genes in BABA-primed (F0) and its vegetative and generative progeny (F1) before pathogen challenge provided evidence for the epigenetic mark for intergenerational memory in potato. Moreover, our study revealed that histone acetylation was not critical for maintaining BABA-primed defense information until the plants were triggered with the virulent pathogen when rapid and boosted PRs gene expression probably required histone acetyltransferase (HAT) activity both in F0 and F1 progeny.
Collective Fast Delivery by Energy-Efficient Agents

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths) that have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0 with respect to the following: Each agent i has a weight w_i that defines the rate of energy consumption while travelling a distance in the graph, and a velocity v_i with which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time T (time when the package arrives at t), and small total energy consumption E. Concretely, we ask for a schedule that: either (i) Minimizes T, (ii) Minimizes lexicographically (T,E) (prioritizing fast delivery), or (iii) Minimizes epsilon*T + (1-epsilon)*E, for a given epsilon, 0<epsilon<1. We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n + k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 3-approximation algorithm for (iii) using a single agent.
Highly Reliable Two-Step Charge-Pump Read Scheme for 1.5 F2/Bit Nonlinear Sub-Teraohm 0TNR Vertical ReRAM

Among the emerging types of memory, resistive random-access memory (ReRAM) units offer faster write speeds and consume less power than those of flash memory units. With the advancement of 3-D stack technology, 3-D nonvolatile memories (NVMs) are under active development to satisfy the requirements of new applications. This brief proposes a 1.5 F2/bit nonlinear sub-teraohm vertical ReRAM (V-ReRAM) and a sensing ultrahigh-resistance read scheme that not only accurately senses sub-picoampere currents but also reduces sneak current effects. A 2-Kb V-ReRAM macro unit was fabricated using a 0.15- $\mu \text{m}$ CMOS process and the Industrial Technology Research Institute’s zero-transistor-four-ReRAM V-ReRAM back-end-of-line process. The proposed read scheme increased the sensing margin by eight times when compared with the current-mirror type, a commonly used read scheme for NVMs. Additionally, the memory bit size was smaller than one-transistor-N-ReRAM V-ReRAM.
The bidirectional interaction of the gut microbiome and the innate immune system: Implications for chemotherapy‐induced gastrointestinal toxicity

Chemotherapy‐induced gastrointestinal toxicity (CIGT) occurs in up to 80% of all patients undergoing cancer treatment, and leads to symptoms such as diarrhoea, abdominal bleeding and pain. There is currently limited understanding of how to predict an individual patient's risk of CIGT. It is believed the gut microbiome and its interactions with the host's innate immune system plays a key role in the development of this toxicity and potentially other toxicities, however comprehensive bioinformatics modelling has not been rigorously performed. The innate immune system is strongly influenced by the microbial environment and vice‐versa. Ways this may occur include the immune system controlling composition and compartmentalisation of the microbiome, the microbiome affecting development of antigen‐presenting cells, and finally, the NLRP6 inflammasome orchestrating the colonic host‐microbiome interface. This evidence calls into question the role of pre‐treatment risk factors in the development of gastrointestinal toxicity after chemotherapy. This review aims to examine evidence of a bidirectional interaction between the gut microbiome and innate immunity, and how these interactions occur in CIGT. In the future, knowledge of these interactions may lead to improved personalised cancer medicine, predictive risk stratification methods and the development of targeted interventions to reduce, or even prevent, CIGT severity.
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation

Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance on later parses is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset.
Parameter sharing between dependency parsers for related languages

Previous work has suggested that parameter sharing between transition-based neural dependency parsers for related languages can lead to better performance, but there is no consensus on what parameters to share. We present an evaluation of 27 different parameter sharing strategies across 10 languages, representing five pairs of related languages, each pair from a different language family. We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies. Based on this result, we propose an architecture where the transition classifier is shared, and the sharing of word and character parameters is controlled by a parameter that can be tuned on validation data. This model is linguistically motivated and obtains significant improvements over a monolingually trained baseline. We also find that sharing transition classifier parameters helps when training a parser on unrelated language pairs, but we find that, in the case of unrelated languages, sharing too many parameters does not help.
Supervoxel-based 3D Segmentation in Complex Scenes

We present an unsupervised 3D point cloud segmentation method for household table-top scenes, which are usually unstructured and highly cluttered. The main idea of the algorithm is based on graph-cut which conducts upward merging steps from roar point cloud to adjacent supervoxles, homogeneous surface patches and to final object hypotheses. Our approach based only on 3D point cloud which contains more accurate spatial information than 2.5D images. The proposed approach together with other two state-of-the-art methods are compared and evaluated on a benchmark database. Advantages and weaknesses of our method are also discussed in the end.
Mobile App User Choice Engineering Using Behavioral Science Models

When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning. Such instances arise in (i) mobile crowdsensing campaigns, where tasks are assigned to users through the app, and the goal is to optimize the quality of fulfilled tasks; (ii) smart-energy apps, where energy-saving recommendations are issued through the app, and the goal is to optimize energy savings; (iii) mobile advertising, where ads or offers are projected to the user, and the aim is to optimize revenue through user response to ads. Each user is modeled as a vector of feature values for a set of features. In an important class of decision-making models in behavioral science, the lexicographic fast-and-frugal-tree (FFT) heuristics, user decision emerges through a ranking of features that in turn gives rise to a decision tree. Having the incentive as a controllable feature that guides the user decision process, we study and characterize the complexity of the problem of allocating choices and incentives to users out of a limited budget. Numerical results indicate important performance gains when the incentive allocation policy adapts to user lexicographic choices.
Detecting Asynchronous Periodic Patterns of Intervals in Temporal Sequence Data

Temporal sequence data, such as event logs collected from industrial operations, often contain both sequential and time interval information. These two types of information can be used to define periodic patterns. Due to random disturbance, the occurrence of such periodic patterns may be drifted from their expected positions or time, resulting in asynchronous periodic patterns (APPs). The major limitation of existing studies on APPs is their reliance on subjective and case-specific determination of tolerance to the asynchrony. In real-world practice, however, knowledge on such tolerance values may be extremely limited, if not unavailable. To address this limitation, this paper formulates the asynchrony tolerating as a hierarchical clustering of time intervals embedded in the temporal sequence data. The clustering method is improved to balance the trade-off between data similarity and pattern interpretability. Based on the symbol sequence generated by the improved clustering method, APPs are detected with the proposed convolution-based periodicity detection algorithm. The effectiveness of the proposed approach is demonstrated with both numerical simulation experiment and real-world case study.
Sparse-to-Dense Depth Reconstruction Using Non-Convex Optimization

Depth reconstruction aims at recovering accurate depth maps from sparse measurements. Recent works reconstruct depth maps based on the assumption that neighboring pixels have similar depth when they have similar color values. However, such methods tend to blur depth boundaries due to color bleeding. We address this problem by a non-convex penalty. First, we formulate depth recovery problem as a non-convex optimization problem in which depth boundaries are preserved by an L1-L2 penalty. Second, we solve the problem based on iteratively reweighted least squares. Numerical experiments demonstrate that our method outperforms state-of-the-art algorithms in terms of PSNR and visual quality.
Energy-Efficient Resource Allocation Based on Hybrid Particle Swarm Optimization in Power Telecommunication Networks

The Orthogonal Frequency Division Multiplexing (OFDM) will be widely used in 5G network. However, the energy consumption has become one of main challenges in the wireless network. In this paper, we investigate the energy efficiency (EE) optimization problem in the OFDM-based cellular network. The energy efficiency performance in cellular networks is strongly affected by channel and power allocations. Then we construct an objective function to attain maximum energy efficiency with the QoS requirements of users. Then the Hybrid particle swarm optimization algorithm has been utilized to address it by obtaining the optimal channel and power allocations. We also make a numerical simulation to verify the algorithm proposed in this paper. A comparison with the Max-Min energy efficiency algorithm is also carried. The simulation results show that EE performance of our algorithm can reach convergence and is better than the Max-Min EE algorithm.
Variations in perinatal mortality associated with different polluting fuel types and kitchen location in Bangladesh

ABSTRACT This study examines the association between household air pollution from use of polluting cooking fuels and perinatal mortality in Bangladesh. We analysed the data from the Bangladesh Demographic and Health Surveys (BDHS) 2004, 2007, 2011, and 2014. The two outcome variables were stillbirth and early neonatal mortality. The exposure variable was type of primary cooking fuel used in the household (clean vs. polluting). Bivariate and multivariable analyses were conducted to obtain the crude and adjusted odds ratio (aOR), respectively. In the adjusted model, the exposure to polluting fuels was associated with early neonatal mortality (aOR: 1.46, 95% confidence interval [CI]: 1.01–2.10), but not with stillbirth (aOR: 1.25, 95% CI: 0.85–1.84). The effect of cooking with agricultural crop waste was greater for stillbirth (aOR: 1.76, 95% CI: 1.10–2.80) and for early neonatal mortality (aOR: 1.78, 95% CI: 1.13–2.80) which was also associated with wood as the main fuel (aOR: 1.52, 95% CI: 1.04–2.21). Using polluting fuels in an indoor kitchen was associated with an increased odds of stillbirth (aOR: 4.12, 95% CI: 1.49–11.41). Cooking with polluting fuels is associated with perinatal mortality. The combined association of polluting cooking fuels and indoor kitchen location was greater for stillbirth. This effect and the effect of different fuel types need further investigation. Although this is a large sample, there are some limitations with the BDHS data in both recording the exposure and the outcomes. A large prospective trial is needed to determine the precise effect size.
Discontinuous modulation method for isolating DC-DC converter to mitigate grid-side DC link voltage fluctuation

This paper proposes a discontinuous modulation strategy of an isolating DC-DC converter on the photovoltaic plant side to reduce the grid side inverter DC link voltage fluctuation. Cascaded H-bridges (CHB) can be used to connect a photovoltaic plant directly to a medium voltage grid without a grid frequency transformer, as the topology has an inherent requirement of having an isolation between the series connected cells. Cascaded H-bridges has a feature that the DC link voltages of the cells have a voltage fluctuation at twice the output frequency. This fluctuation can be reduced by limiting the power towards the capacitor during intervals when the output power is low. The effect of discontinuous mode interval length on the photovoltaic side is studied. The benefits of the proposed method are reduced switching losses at the isolating DC-DC converter and reduced switching losses at the grid inverter due to smaller peak voltages of the DC links.
MMSE Precoding for Receive Spatial Modulation in Large MIMO Systems

Receive spatial modulation (RSM) schemes enable simple and energy efficient multiple-input-multiple-output (MIMO) transceivers and yet attain high spectral efficiency, which renders them promising schemes for millimeter wave (mmWave) communication in massive MIMO systems. When these schemes are designed to include zero forcing (ZF) precoders, performance can be impaired in the presence of highly spatially correlated channels. Extending these schemes for minimum mean square error (MMSE) precoding is not trivial due to the hardware constraints of the energy efficient user terminal architecture. In this paper, we adapt the MMSE precoder to the low complexity RSM architecture and develop detection methods for the spatial and modulation symbols. The proposed MMSE RSM scheme with total and per-antenna power constraints have been compared with ZF RSM in terms of average and outage mutual information by simulations showing superior gain for mmWave channels.