Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Food Insecurity and Dietary Diversity on Recovery from Wasting among Hospitalized Children Aged 6–23 Months in Sub-Saharan Africa and South Asia

Background: Current guidelines for the management of childhood wasting primarily focus on the provision of therapeutic foods and the treatment of medical complications. However, many children with wasting live in food-secure households, and multiple studies have demonstrated that the etiology of wasting is complex, including social, nutritional, and biological causes. We evaluated the contribution of household food insecurity, dietary diversity, and the consumption of specific food groups to the time to recovery from wasting after hospital discharge. Methods: We conducted a secondary analysis of the Childhood Acute Illness Network (CHAIN) cohort, a multicenter prospective study conducted in six low- or lower-middle-income countries. We included children aged 6–23 months with wasting (mid-upper arm circumference [MUAC] ≤ 12.5 cm) or kwashiorkor (bipedal edema) at the time of hospital discharge. The primary outcome was time to nutritional recovery, defined as a MUAC > 12.5 cm without edema. Using Cox proportional hazards models adjusted for age, sex, study site, HIV status, duration of hospitalization, enrollment MUAC, referral to a nutritional program, caregiver education, caregiver depression, the season of enrollment, residence, and household wealth status, we evaluated the role of reported food insecurity, dietary diversity, and specific food groups prior to hospitalization on time to recovery from wasting during the 6 months of posthospital discharge. Findings: Of 1286 included children, most participants (806, 63%) came from food-insecure households, including 170 (13%) with severe food insecurity, and 664 (52%) participants had insufficient dietary diversity. The median time to recovery was 96 days (18/100 child-months (95% CI: 17.0, 19.0)). Moderate (aHR 1.17 [0.96, 1.43]) and severe food insecurity (aHR 1.14 [0.88, 1.48]), and insufficient dietary diversity (aHR 1.07 [0.91, 1.25]) were not significantly associated with time to recovery. Children who had consumed legumes and nuts prior to diagnosis had a quicker recovery than those who did not (adjusted hazard ratio (aHR): 1.21 [1.01,1.44]). Consumption of dairy products (aHR 1.13 [0.96, 1.34], p = 0.14) and meat (aHR 1.11 [0.93, 1.33]), p = 0.23) were not statistically significantly associated with time to recovery. Consumption of fruits and vegetables (aHR 0.78 [0.65,0.94]) and breastfeeding (aHR 0.84 [0.71, 0.99]) before diagnosis were associated with longer time to recovery. Conclusion: Among wasted children discharged from hospital and managed in compliance with wasting guidelines, food insecurity and dietary diversity were not major determinants of recovery.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks

The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks. However, existing GNNs are mainly devised to classify nodes in a (partially) labeled graph. To classify nodes in a newly-collected unlabeled graph, it is desirable to transfer label information from an existing labeled graph. To address this cross-graph node classification problem, we propose a graph infomax method that is domain adaptive. Node representations are computed through neighborhood aggregation. Mutual information is maximized between node representations and global summaries, encouraging node representations to encode the global structural information. Conditional adversarial networks are employed to reduce the domain discrepancy by aligning the multimodal distributions of node representations. Experimental results in real-world datasets validate the performance of our method in comparison with the state-of-the-art baselines.
Improved YOLOv5-S object detection method for optical remote sensing images based on contextual transformer

Abstract. To address the problems of error and omission detection in remote sensing image detection caused by the diverse scale changes of remote sensing object scales and the abundant proportion of small-scale objects, as well as the global and dense distribution of remote sensing objects, a remote sensing image detection improvement method based on YOLOv5-S is proposed. First, according to the characteristics of remote sensing objects, the data enhancement strategy is adopted to expand the dataset samples for the characteristics of remote sensing objects to improve the generalization ability of the model. Second, the contextual transformer module is introduced to the backbone feature extraction network and the feature fusion network to ensure the local feature extraction capability while improving the global information acquisition capability of the model, making full use of the input contextual information and guiding the dynamic attention matrix learning to improve the visual representation ability. Third, based on the original model, a shallow detection scale is added, and then a multiscale complex fusion structure is adopted. Meanwhile, the K-means++ algorithm replaces the original K-means algorithm and then clusters 12 anchor box sizes. Fourth, the efficient intersection over union loss is used to improve the accuracy of the remote sensing object recognition prediction. In the experiment on the on two optical remote sensing image datasets, a comparison with several object detection algorithms based on convolutional neural network is made, the results show that the mAP@0.5 tested on the remote sensing datasets is higher than the original YOLOv5-S. Compared with other models, the detection efficiency is higher, and the problems of small-scale object detection in remote sensing image have been significantly improved.
There's More Than One Perspective to Take Into Account for Successful Customer Integration Into Radical New Product Innovation: A Framework and Research Agenda

Over the past few decades, the impact of customer integration on radical new product (RNP) innovation has been extensively investigated. To date, this important topic presents inconsistent empirical findings that must be converged. In this paper, our systematic literature review addresses these inconsistencies by taking a consolidated view of customer integration's effects on the development of RNPs. This extensive review of 153 empirical papers has two main objectives. First, we provide the primary reasons for inconsistent findings by scrutinizing the operationalizations of customer types (i.e., current customers, potential customers, ordinary users, or users with domain-specific skills) and RNPs (i.e., technological innovativeness, or both technological and market innovativeness) used in the studies, as well as the different perspectives on customer integration [i.e., customer-based idea evaluation, participation in direct and indirect idea generation, research and development (R&D) partnerships with customers, having a customer orientation, and disseminating customer knowledge via R&D-marketing collaborations]. Second, we present a synthesized view on factors in the sphere of the innovating company and the customer that influence customer integration's success along three phases of the radical innovation development process (i.e., discovery, incubation, and acceleration). Finally, we present avenues for future research and discuss managerial implications of our synthesized view.
Sulla convergenza di soluzioni di equazioni paraboliche ed ellittiche

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Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.
Impact of Learning Capability on Organizational Performance in the Nepalese Banking Sector

The Nepalese banking sector must retain and attract qualified human resources to achieve its goals. Job satisfaction is crucial for the retention and attraction of competent people in the organization. The learning capability has been considered a vital index of an organization’s effectiveness to innovate and grow (Jerez-Gómez et al., 2005). The purpose of the study was to examine the association and impact of learning capability on organizational performance in Nepalese commercial banks. The present study is descriptive and analytical in nature. The questionnaire survey method was adopted with a five-point Likert scale. This survey has conducted in Kathmandu Valley, focusing on employees of public and private banks such as managers, accountants, cashiers, and assistant level workers in the respective department respondents. Fifty respondents were chosen from four public and private banks. To choose the respondents for the study, the judgmental sampling technique was applied. In this research, correlation and regression analysis has been carried out to assess an association and the impact of learning capability on organizational performance. The study found a positive association between learning capability and organizational performance. It also found an effect of learning capability on organizational performance in Nepalese commercial banks. It is also found that gender has no impact on learning capability and organizational performance in the Nepalese banking sector.
Effects of Acute Sepsis on Cellular Dynamics and Amyloid Formation in a Mouse Model of Alzheimer’s Disease

Our objective was to investigate how sepsis influences cellular dynamics and amyloid formation before and after plaque formation. As such, APP-mice were subjected to a polymicrobial abdominal infection resulting in sepsis at 2 (EarlySepsis) and 4 (LateSepsis) months of age. Behavior was tested before sepsis and at 5 months of age. We could not detect any short-term memory or exploration behavior alterations in APP-mice that were subjected to Early or LateSepsis. Immunohistochemical analysis revealed a lower area of NeuN+ and Iba1+ signal in the cortex of Late compared with EarlySepsis animals (p = 0.016 and p = 0.01), with an increased astrogliosis in LateSepsis animals compared with WT-Sepsis (p = 0.0028), EarlySepsis (p = 0.0032) and the APP-Sham animals (p = 0.048). LateSepsis animals had larger areas of amyloid compared with both EarlySepsis (p = 0.0018) and APP-Sham animals (p = 0.0024). Regardless of the analyzed markers, we were not able to detect any cellular difference at the hippocampal level between groups. We were able to detect an increased inflammatory response around hippocampal plaques in LateSepsis compared with APP-Sham animals (p = 0.0003) and a decrease of AQP4 signal far from Sma+ vessels. We were able to show experimentally that an acute sepsis event before the onset of plaque formation has a minimal effect; however, it could have a major impact after its onset.
Second-generation biofuel production from the organic fraction of municipal solid waste

Biofuels are playing an increasingly important role since they are an attractive alternative to fossil fuels, access to which has been limited. Moreover, they can help the European Union countries meet their climate and energy objectives. There is a growing interest in the production of biofuels from the organic fraction of municipal solid wastes as second-generation biofuels. Such a solution supports a circular economy as an example of an effective approach in terms of effective waste management. The study presents a review of different biofuels with an emphasis on second-generation biofuels from lignocellulose biomass, highlighting the BioRen process, which is one of the waste-to-fuel conversion technologies. It allows for maintaining the value of materials within the economy. All stages of the process are described and material flow is presented. The framework of the BioRen project includes the bio-fermentation of the organic fraction by a two-stage enzymatic process simultaneously into bioethanol and isobutanol, which are then used to produce glycerol tertiary butyl ether. The post-fermentation sludge is treated by the hydrothermal carbonization method increasing the efficiency of the whole waste treatment process and closing the loop. The bio-coal resulting from the process of carbonization can be used as a fuel with a high-calorific value. The remaining fraction undergoes a physical–chemical and catalytic conversion of inorganic particles into their clean, dry, and inert components. The results show that in the BioRen project 78% of the heat that is produced during the process is recovered and used for electricity production. Moreover, 53% of waste is recovered and re-used, because the process is profitable, efficient, environmentally friendly, and maintains high-security standards. Such an example of advanced technology, with a high readiness level, obtained results, and experience may encourage European countries and facilities to implement such an effective solution, which supports the development of bioenergy.
Introduction to 3+1 Numerical Relativity

1. Brief Review of General Relativity 2. The 3+1 Formalism 3. Initial Data 4. Gauge Conditions 5. Hyperbolic Reductions of the Field Equations 6. Evolving Black Hole Spacetimes 7. Relativistic Hydrodynamics 8. Gravitational Wave Extraction 9. Numerical Methods 10. Examples of Numerical Spacetimes A. Total Mass and Momentum in General Relativity B. Spacetime Christoffel Symbols in 3+1 Language C. BSSNOK with Natural Conformal Rescaling D. Spin-weighted Spherical Harmonics References Index
Vortex Shedding Lock-In due to Pitching Oscillation of a Wind Turbine Blade Section at High Angles of Attack

The unsteady flow around a pitching two-dimensional airfoil section (NREL S809) has been simulated using unsteady RANS with the transition SST turbulence model. This geometry is chosen to represent a wind turbine blade in a standstill configuration. The Reynolds number is Re=106 based on a chord length of 1 m. A prescribed sinusoidal pitching motion has been applied at a fixed amplitude of 7° for a range of high angles of attack 30°<α<150°. At these incidences, the airfoil will behave more like a bluff body and may experience periodic vortex shedding. It is well known that, in bluff body flows, oscillations can lead to a lock-in (lock-in) of the vortex shedding frequency, fv, with the body’s motion frequency, fp. In order to investigate the susceptibility of airfoil to lock-in, the frequency ratio r (r=fp/fv0) has been varied around r=1. The lock-in region boundaries have been proposed, and an analysis of the effect of the oscillation amplitude has been conducted. The lock-in map obtained suggests that, for the vibration amplitude considered, the risk of vortex-induced vibration is more significant in the regions of α≈40° and α≈140°, i.e., for shallower characteristic lengths. Finally, a lumped parameter wake oscillator model has been proposed for pitching airfoils. This simple model is in qualitative agreement with the CFD results.
A New Model Organism to Investigate Extraocular Photoreception: Opsin and Retinal Gene Expression in the Sea Urchin Paracentrotus lividus

Molecular research on the evolution of extraocular photoreception has drawn attention to photosensitive animals lacking proper eye organs. Outside of vertebrates, little is known about this type of sensory system in any other deuterostome. In this study, we investigate such an extraocular photoreceptor cell (PRC) system in developmental stages of the sea urchin Paracentrotus lividus. We provide a general overview of the cell type families present at the mature rudiment stage using single-cell transcriptomics, while emphasizing the PRCs complexity. We show that three neuronal and one muscle-like PRC type families express retinal genes prior to metamorphosis. Two of the three neuronal PRC type families express a rhabdomeric opsin as well as an echinoderm-specific opsin (echinopsin), and their genetic wiring includes sea urchin orthologs of key retinal genes such as hlf, pp2ab56e, barh, otx, ac/sc, brn3, six1/2, pax6, six3, neuroD, irxA, isl and ato. Using qPCR, in situ hybridization, and immunohistochemical analysis, we found that the expressed retinal gene composition becomes more complex from mature rudiment to juvenile stage. The majority of retinal genes are expressed dominantly in the animals’ podia, and in addition to the genes already expressed in the mature rudiment, the juvenile podia express a ciliary opsin, another echinopsin, and two Go-opsins. The expression of a core of vertebrate retinal gene orthologs indicates that sea urchins have an evolutionarily conserved gene regulatory toolkit that controls photoreceptor specification and function, and that their podia are photosensory organs.
Elliptic Localization With Imperfect Clock Synchronization for Known and Unknown Propagation Speed Scenarios

In this article, we address the elliptic localization problem in the presence of clock synchronization errors at the receivers and transmitters, when the propagation speed is known as well as not known. For the known propagation speed scenario, we first show that the clock synchronization errors, when modeled as Gaussian random variables, can be treated as part of the composite noise in the measurement model without incurring performance loss in localization. Motivated by this result, we propose a generalized trust region subproblem solution for estimating the object position. For the unknown propagation speed scenario, we first formulate a nonconvex weighted least-squares problem. Such a nonconvex problem is solved by either one of the two proposed methods, semidefinite relaxation or grid search, to jointly estimate the object position and the propagation speed. Furthermore, we conduct the mean square error analysis to show that the proposed methods are able to reach the Cramer–Rao lower bound accuracy under Gaussian noise, which is validated by using simulated and semireal data.
MALO-LSTM: Multimodal Sentiment Analysis Using Modified Ant Lion Optimization with Long Short Term Memory Network

: In recent times, multimodal sentiment analysis is the most researched topic, due to the availability of huge amount of multimodal content. Generally, multimodal sentiment analysis uses text, audio and visual representations for effective sentiment recognition. The detection of sentiment in the natural language is a tricky process even for humans, so making it automation is more complicated. In this article, the input multimodal data is collected from Surrey Audio-Visual Expressed Emotion (SAVEE) and YouTube datasets, and then hybrid feature extraction is performed to extract feature vectors from the different modalities like textual, audio and visual. The Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) techniques are applied to extract features from textual modality. Further, AlexNet, spectral centroid features, spectral flux features, and short term energy are employed to extract features from the visual and audio modalities. The extracted active feature values are given as the input to the Modified Ant Lion Optimizer (MALO) based Long Short Term Memory (LSTM) network for sentiment classification. In the MALO algorithm, two new processes are performed to select optimal hyper-parameters of LSTM network that improves the training and testing mechanism and reduces the computational complexity. The results with the MALO-LSTM model obtained were 98.62% and 98.81% of accuracy on YouTube and SAVEE datasets, relatively higher than some of the existing methods, the proposed approach provided comparatively better performance.
NONLINEAR STRONG ERGODIC THEOREMS FOR ASYMPTOTICALLY NONEXPANSIVE SEMIGROUPS WITH COMPACT DOMAINS

In this paper, we prove a nonlinear ergodic theorem for a commuta- tive semigroup of asymptotically nonexpansive mappings from a compact convex subset of a strictly convex Banach space into itself. Using this result, we obtain two nonlinear ergodic theorems proved by Yoshimoto [15].
Commonality Analysis for Detecting Failures Caused by Inspection Tools in Semiconductor Manufacturing Processes

Semiconductor fabrication involves hundreds of process steps through various manufacturing tools. These processing steps are composed of many manufacturing and inspection steps. Inspection is an important step in the fabrication process to determine whether a process is in or out of control. Abrupt manufacturing or inspection tool excursion can lead to a serious low yield problem. Although commonality analysis is a proven tool for detecting abrupt tool excursion, it has gained only limited success in detecting manufacturing tool excursion outside of inspection tools. Compared with manufacturing tools, only a small number of lots or wafers pass through inspection tools. Therefore, it is difficult to construct a sufficient lot history log for inspection commonality analysis in contrast to that of manufacturing tools. Furthermore, inspection may stress a wafer during its own processing, therefore, the target wafer is changed sequentially or randomly. Accordingly, a lot history is apt to include missing traces, which hinders finding inspection tool excursion effectively. In this paper, we propose a comparative analysis framework for commonality analysis algorithms. Performance measures are suggested. To compare the performance of the algorithms effectively, we use a synthetically generated dataset in a simulation experiment. In addition, we apply the algorithms to a real problem that occurred in the fabrication process. Our proposed algorithm demonstrates superiority over the other commonality analysis algorithms in the experiments.