Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis during Jakarta Flood for Emergency Responses and Situational Awareness in Disaster Management using BERT

Social media provide valuable information for disaster risk reduction as well as a disaster emergency response. Previous studies claim that social media data can be used in the emergency response effort, which is worthwhile for the Government, BNPB, as well as the community, to get a direct and credible information about the ongoing disaster situations. However, the social media data has not been widely used for emergency response and situational awareness in Indonesia. Therefore, in this paper we implement BERT method applied to a set of tweets related to Jakarta Flood in early 2020. We have crawled the tweet dataset during the Jakarta flood disaster in early 2020 which had become a trending topic on Twitter. We aim to identify the relevance tweets that may provide useful information related to emergency responses in disaster management. The experimental results indicate promising outcomes. However, the quality of the dataset greatly influences the system performance.
Transportation Mode Detection by Embedded Sensors Based on Ensemble Learning

Context-aware computing has become a certainty due to the widespread use of smartphone devices equipped with sensors. A wide range of services, such as vehicular traffic monitoring and smart parking, can be accomplished with the help of awareness of user mobility. Transportation mode detection (TMD) using machine learning algorithms and the data captured from smartphone embedded sensors have attracted research community attention. In this research, ensemble learning is utilized to differentiate between transportation modes, including walking, standing, riding a train, driving a car, and riding a bus. The ensemble learning consists of three classifiers; each classifier votes independently on the instances, and the majority vote is applied for robust generalization. The proposed method was validated using three datasets; the samples included in these datasets were gathered by smartphone sensors (belonging to heterogeneous users), such as rotation vector sensors, accelerometers, uncalibrated gyroscopes, linear acceleration, orientation, speed, game rotation vector, sound, and gyroscopes. The proposed ensemble learning method achieves an accuracy of 89%, 93%, and 95% on the first, second, and third datasets, respectively, when 10% and 90% of the data are used for testing and training, respectively. On the other set of experiments, in which 30% and 70% of the data are used for testing and training, respectively, the proposed method yields accuracies of 86.8%, 92.1%, and 94.9% on the first, second, and third datasets, respectively. The proposed method shows promising results compared to existing human activity recognition (HAR) methods.
A Rapid Scalable Embedding and Mapping Model for Recommendation

The recommendation systems based on deep neural networks show high accuracy and precision in advertising prediction and rating prediction tasks, but the performance declines when they encounter sparsity problems. Furthermore, the low scalability framework makes them expand new users or items inefficiently. We propose a Rapid Scalable Embedding and Mapping model for Recommendation (RSEMR) to solve the problems. RSEMR uses the bi-directional mapping structure to build the transformation between the embedding vector and the unified vector space, which can rapidly expand new users or items into the existing domains and achieve a better initial performance of prediction. Two effective mapping structures, including vector elements-based single connection and autoencoder-based fully connection, can be used in RSEMR to capture linear or non-linear mapping functions, respectively. Besides, RSEMR does not require any additional information other than a very small amount of user behavior data, and it deals with the relationships between subjects based on the spatial distance in the unified vector space, which is more adaptable to the scarce attribute labels and sparse interaction behaviors. We select several open datasets for experiments and the results have demonstrated RSEMR’s superiority over the mainstream neural network-based recommendation models in sparsity situation, in terms of accuracy, efficiency, and scalability.
The Effects of Habitat Complexity on the Cognitive Performance of Two Fish Species and Their Hybrids

The Effects of Habitat Complexity on the Cognitive Performance of Two Fish Species and Their Hybrids (April 2008) Heather Anne Chance Department of Biology Texas A&M University Research Advisor: Dr. Gil G. Rosenthal Department of Biology The complexity of an animal’s habitat plays a significant role in behavior, morphology as well as other life history traits. Habitat complexity may also play a significant role in an animal’s cognitive capabilities such as learning and memory attributes. In the field, I quantified habitat complexity in populations of two species of swordtail fishes – Xiphophorus birchmanni and X. malinche – as well as two populations of naturally occurring X. birchmanni/X. malinche hybrids found in the Rio Calnali, Hidalgo, Mexico. Habitat complexity differed between populations with X. malinche residing in the most complex and X. birchmanni in the least complex. In the laboratory, I tested spatial learning and memory capabilities of X. birchmanni, X. malinche, and their hybrids; I then investigated the relationship between habitat complexity and cognitive performance as well as the performance of hybrids compared to the parental species. Cognitive performance does not seem effected by habitat complexity. The hybrid populations do not seem to deviate in cognitive performance from that of the parental species.
An Off-Grid STAP Algorithm Based On Local Mesh Splitting With Bistatic Radar System

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.
Data Driven Distributed Bipartite Consensus Tracking for Nonlinear Multiagent Systems via Iterative Learning Control

This article explores a data-driven distributed bipartite consensus tracking (DBCT) problem for discrete-time multi-agent systems (MASs) with coopetition networks under repeatable operations. To solve this problem, a time-varying linearization model along the iteration axis is first established by using the measurement input and output (I/O) data of agents. Then a data-driven distributed bipartite consensus iterative learning control (DBCILC) algorithm is proposed considering both fixed and switching topologies. Compared with existing bipartite consensus, the main characteristic is to construct the proposed control protocol without requiring any explicit or implicit information of MASs’ mathematical model. The difference from existing iterative learning control (ILC) approaches is that both the cooperative interactions and antagonistic interactions, and time-varying switching topologies are considered. Furthermore, through rigorous theoretical analysis, the proposed DBCILC approach can guarantee the bipartite consensus reducing tracking errors in the limited iteration steps. Moreover, although not all agents can receive information from the virtual leader directly, the proposed distributed scheme can maintain the performance and reduce the costs of communication. The results of three examples further illustrate the correctness, effectiveness, and applicability of the proposed algorithm.
Time-Domain Software Correction of Nonlinear Faulty Lossy Transmission Line Networks

In this article, we use Newton's method and the nonlinear least squares method to solve a nonlinear problem in electromagnetic compatibility. Both techniques provide a way to identify the temporal profile of a voltage source that would produce a target voltage over a given time interval at a point of the transmission line network presenting nonlinear components. After comparing the results of a test problem, the most efficient method is considered to identify the temporal source in order to bring a software correction to a nonlinear faulty lossy transmission line network. The software correction process may cancel any unintentional perturbations produced by one or more faults regardless of their type and location.
Profiling Energy Usage of Software Configurations for Virtualized Environment

A Software Product Line development configures consolidation of components that include modules and artefacts of a software ecosystem to aim its goals where different combinations may consume different amount of energy. In a virtualized system like Cloud Computing energy efficiency means a cost reduction that gives more revenue to their stakeholders. Moreover, many software in Cloud computing system were developed in a typical configuration way where reusability gives benefit to the ecosystem. This work combines Software Production Line Engineering, Cloud Computing and Energy Measurement technique to profile the consumption of energy that taking into account the workload of the system. In order to reveals the relationship between energy usage and processes within the system under measurement, we make use of Gaussian Process Regression to charac-terize the impact of workloads to software configurations in a virtualized environment. To demonstrate our method, a case study that combines of 12 software configurations for Cloud Computing environment, that retrieved from the Ubuntu software repository, will be presented. In order to reveals the information of energy usage, we developed a method to utilize the infrastructure of the virtualized system. Our profiling energy usage has open a chance to extend further research on dynamic energy modeling for virtualized environment.
An improved method for estimating fatigue life under combined stress

The paper deals with a complicated problem of estimating fatigue lives of machine parts exposed to various kinds of dynamic loading. An origin of the presented method can be found 25 years ago when a simple algorithm of strain energy density cumulation was applied for evaluation of uniaxial harmonic and random fatigue tests. Recently, the method has been adopted for multiaxial loading. Unfortunately, results of its application have shown rather big variance. This paper presents the improved method, which yields much lower scattering of estimated fatigue lives compared with earlier approaches. c © 2007 University of West Bohemia. All rights reserved.
Temporal trends in decompensated heart failure and outcomes during COVID‐19: a multisite report from heart failure referral centres in London

Admission rates for acute decompensated heart failure (HF) declined during the COVID‐19 pandemic. However, the impact of this reduction on hospital mortality is unknown. We describe temporal trends in the presentation of patients with acute HF and their in‐hospital outcomes at two referral centres in London during the COVID‐19 pandemic.
Behavioral comorbidity, overweight, and obesity in children with incontinence: An analysis of 1638 cases

Children with nocturnal enuresis (NE), daytime urinary incontinence (DUI), and fecal incontinence (FI) are at risk for behavioral problems, overweight, and obesity. The aim of this study was to analyze the specific behavioral and weight comorbidity in subtypes of incontinence.
Supplementation with Fermented Rice Bran Attenuates Muscle Atrophy in a Diabetic Rat Model

Fermented rice bran (FRB), a prospective supplement, has been proven to ameliorate certain medical conditions. However, its nutraceutical effect on muscle atrophy has never been investigated. The present study aimed to evaluate the effect of FRB on muscle atrophy in a streptozotocin (STZ)-induced diabetic rat model. Three groups of Sprague-Dawley rats, namely the control, STZ, and FRB groups, were treated as follows. The diabetic groups (STZ and FRB) were injected intraperitoneally with STZ (40 mg/kg BW), whereas the control group was injected with the vehicle. The STZ and control groups were fed the AIN93M diet, and the FRB group was fed 10% of FRB based on the AIN93M diet. The diabetic groups had reduced muscle size compared to the control group; however, these changes were alleviated in the FRB group. Moreover, the FRB group had a significantly lower expression of FBXO32/Atrogin-1 and TRIM63/MuRF1 (p < 0.05) due to blocked NF-κB activation. In conclusion, the anti-inflammatory effect of FRB may be beneficial for ameliorating muscle atrophy in diabetic conditions.
Tick-Borne Encephalitis Virus: A Quest for Better Vaccines against a Virus on the Rise

Tick-borne encephalitis virus (TBEV), a member of the family Flaviviridae, is one of the most important tick-transmitted viruses in Europe and Asia. Being a neurotropic virus, TBEV causes infection of the central nervous system, leading to various (permanent) neurological disorders summarized as tick-borne encephalitis (TBE). The incidence of TBE cases has increased due to the expansion of TBEV and its vectors. Since antiviral treatment is lacking, vaccination against TBEV is the most important protective measure. However, vaccination coverage is relatively low and immunogenicity of the currently available vaccines is limited, which may account for the vaccine failures that are observed. Understanding the TBEV-specific correlates of protection is of pivotal importance for developing novel and improved TBEV vaccines. For affording robust protection against infection and development of TBE, vaccines should induce both humoral and cellular immunity. In this review, the adaptive immunity induced upon TBEV infection and vaccination as well as novel approaches to produce improved TBEV vaccines are discussed.
Investigating predictors of trauma induced data-driven processing and its impact on attention bias and free recall

Abstract Background: Whilst data-driven processing (DDP) during trauma has been shown to play a role in poor memory integration and is associated with post-traumatic stress disorder (PTSD) re-experiencing symptoms, the pre-trauma risk factors and related cognitive mechanisms are uncertain. Aims: This experimental study aimed to investigate predictors of peri-traumatic DDP, as well as its role in attention bias to threat and free recall. Method: A virtual reality video was used to simulate an analogue trauma. Questionnaires, a free recall task, and an eye-tracking measure assessed cognitive changes after exposure. Results: Regression analysis demonstrated that trait dissociation at pre-exposure to trauma significantly predicted DDP. Attention bias towards threat-related images was found. Results showed that DDP and poorer free recall predicted attention bias to threat images and higher levels of DDP actually predicted higher overall scores in the free recall task. Conclusions: This study showed that DDP is strongly linked to dissociative traits, and along with memory disintegration it may predict attention changes after exposure to a trauma.
Diet of the Lesser Horseshoe Bat (Rhinolophus hipposideros) in Central Germany and Its Seasonal and Site-Specific Variation

As a K-strategist and comparatively sedentary species, the lesser horseshoe bat Rhinolophus hipposideros is considered sensitive to changes in habitat quality. Knowledge of the species' dietary requirements and use of foraging habitats is thus considered an essential prerequisite to manage its habitats adequately. Based on four large annual samples of faecal pellets from three different nursery colonies, including two consecutive years of sampling from one colony, we studied the diet of Central German populations of R. hipposideros. Consistent with findings of similar studies carried out in other parts of the distribution range of R. hipposideros, in our study, Diptera, Lepidoptera, and Neuroptera represented the most important groups of prey for the studied colonies. However, Hemiptera made a comparatively larger contribution in our study than in others, and so did Hymenoptera in one of the colonies. We found seasonal compositional variation in all four annual samples, as well as compositional variation between samples from different colonies, but not between the two annual samples obtained in consecutive years from the same colony. Differences between colonies appeared at least to some extent to reflect differences in availability of foraging habitats. Our results are thus in agreement with the assumption of R. hipposideros being a largely opportunistic, generalist forager. Our findings are also consistent with a known preference by R. hipposideros of woodland as main foraging habitat, as previously established by other studies carried out in the northern part of the distribution range. However, the relative importance of Hemiptera, and in particular of Psyllidae, at certain times during the foraging season, suggests that the Central German colonies of R. hipposideros might have utilized commercial orchards and private fruit gardens for foraging during seasonal peaks in abundance of pest species of fruit trees. The implied ability of R. hipposideros to respond to seasonal abundance peaks of particular groups of prey in a range of habitats suggests that structural diversity might be key in maintaining viable populations of this species. The potential importance of orchards and fruit gardens in regions where such habitats are prevalent is likely to have relevant management implications.
Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach

Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time consuming. Therefore, prediction models based on diverse approaches have been established in recent decades. Computational prediction of molecular properties has become an important step in drug discovery, aiming to identify potential drug-like candidates and reduce costs. However, limitations related to dataset capacity and algorithm adaptation still place restrictions on the applicability of the related models. In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. Benchmark datasets of aqueous solubility (LogS), lipophilicity (LogD) and membrane permeability measured using the Caco-2 cell line (LogPapp) were constructed by merging and calibrating experimental data from diverse articles and databases. Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. MESN achieves performance improvements over other state-of-the-art methods for the prediction of aqueous solubility, lipophilicity and membrane permeability. Results were also obtained using several other algorithms and independent validation datasets as a control study. Moreover, a dimension reduction analysis (based on t-distributed stochastic neighbor embedding, t-SNE) and an atomic feature similarity analysis showed that the molecular embeddings extracted from the MESN model exhibit good clustering and diversity. Overall, considering the fundamental role of the data and the superior prediction performance of the model, we highlight the applicability of MESN on benchmark datasets for further utility in drug-discovery-related molecular property prediction.
Ochratoxin A presence in Cabernet Sauvignon wine changes antioxidant activity in vitro and oxidative stress markers in vivo

ABSTRACT Ochratoxin A (OTA) is a mycotoxin found in grape products and oxidative stress has been reported as an important mechanism involved in its toxicity, classified as possible carcinogenic to humans. Conversely, phenolics are known bioactive compounds in grapes and display great antioxidant properties. However, the biological effects of the concomitant presence of phenolic compounds and OTA remains unclear. The aim of this study was to evaluate, for the first time, the effect of OTA presence in Cabernet Sauvignon wine on antioxidant activity in vitro and on oxidative stress markers in vivo. In addition, the phenolic composition of wine was evaluated by LC-DAD-MS/MS. In vitro assays were based on spectrophotometric methods, while in vivo assays were performed evaluating oxidative stress markers in the nematode Caenorhabditis elegans, an alternative model to animal testing. A total of 23 phenolic compounds were identified in the Cabernet sauvignon red wine, including the anthocyanins delphinidin-3-O-glicoside and malvidin-3-O-glicoside, the flavonol quercetin-3-O-glucuronide and the phenolic acids caffeic, verbascoside and caftaric. Trans-resveratrol and trans-piceid were the only stilbenes found in the samples. OTA presence in the red wine was accompanied by reduction in GSH content and increase in hydroxyl radical generation in vitro. The presence of OTA in wine also increased lipoperoxidation and induced overexpression of the antioxidant enzymes superoxide dismutase and catalase in vivo. This study demonstrates that OTA presence in red wine can reduce its antioxidant potential in vitro and induces oxidative stress in vivo, without affecting the phenolic compounds levels in the samples. Thus, this work provides insights into the negative effects of the presence of OTA in wine, not only by its known toxicity, but also by prejudicing the antioxidant potential of wine. It is important to be aware of these effects when developing a complete description of OTA toxicity in humans.
Cochlear Implantation in Cases of Asymmetric Hearing Loss: Subjective Benefit, Word Recognition, and Spatial Hearing

A prospective clinical trial evaluated the effectiveness of cochlear implantation in adults with asymmetric hearing loss (AHL). Twenty subjects with mild-to-moderate hearing loss in the better ear and moderate-to-profound hearing loss in the poorer ear underwent cochlear implantation of the poorer hearing ear. Subjects were evaluated preoperatively and at 1, 3, 6, 9, and 12 months post-activation. Preoperative performance was evaluated unaided, with traditional hearing aids (HAs) or with a bone-conduction HA. Post-activation performance was evaluated with the cochlear implant (CI) alone or in combination with a contralateral HA (bimodal). Test measures included subjective benefit, word recognition, and spatial hearing (i.e., localization and masked sentence recognition). Significant subjective benefit was reported as early as the 1-month interval, indicating better performance with the CI compared with the preferred preoperative condition. Aided word recognition with the CI alone was significantly improved at the 1-month interval compared with preoperative performance with an HA and continued to improve through the 12-month interval. Subjects demonstrated early, significant improvements in the bimodal condition on the spatial hearing tasks compared with baseline preoperative performance tested unaided. The magnitude of the benefit was reduced for subjects with AHL when compared with published data on CI users with normal hearing in the contralateral ear; this finding may reflect significant differences in age at implantation and hearing sensitivity across cohorts.