Miracle-dz/UniFAR-FTUs huggingface dataset:
Future Expectations and Internet Addiction Among Adolescents: The Roles of Intolerance of Uncertainty and Perceived Social Support

Internet addiction is a common and challenging problem among adolescents. Previous studies have shown that future time orientation is an important protective factor against internet addiction. In this study, the mediating effect of intolerance of uncertainty and the moderating role of perceived social support were examined on the association between future expectations, regarded as the “prospective life course” perspective of future time orientation, and internet addiction among adolescents. A total of 1,006 Chinese adolescents (54% male and 46% female; Mage = 15.42 years, SD = 1.32) recruited from middle schools completed questionnaires. Results indicated that future expectations were significantly negatively associated with internet addiction, and the link was mediated by intolerance to uncertainty in adolescents. Further, the latent moderated structural equation showed that perceived social support moderated the association between future expectations and intolerance of uncertainty. The association was significant only for adolescents with a higher level of perceived social support. The findings of this study provide specific guidelines for how to prevent adolescent internet addiction.
Predictive Value of Metabolic Parameters Derived From 18F-FDG PET/CT for Microsatellite Instability in Patients With Colorectal Carcinoma

Background Microsatellite instability (MSI) is one of the important factors that determine the effectiveness of immunotherapy in colorectal cancer (CRC) and serves as a prognostic biomarker for its clinical outcomes. Purpose To investigate whether the metabolic parameters derived from18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) can predict MSI status in patients with CRC. Materials and Methods A retrospective analysis was performed on CRC patients who underwent 18F-FDG PET/CT examination before surgery between January 2015 and April 2021. The metabolic 18F-FDG PET/CT parameters of the primary CRC lesion were calculated and recorded with different thresholds, including the maximum, peak, and mean standardized uptake value (SUVmax, SUVpeak, and SUVmean), as well as the metabolic tumor volume (MTV) and the total lesion glycolysis (TLG). The status of MSI was determined by immunohistochemical assessment. The difference of quantitative parameters between MSI and microsatellite stability (MSS) groups was assessed, and the receiver operating characteristic (ROC) analyses with area under ROC curves (AUC) was used to evaluate the predictive performance of metabolic parameters. Results A total of 44 patients (24 men and 20 women; mean ± standard deviation age: 71.1 ± 14.2 years) were included. There were 14 patients in the MSI group while there were 30 in the MSS group. MTV30%, MTV40%, MTV50%, and MTV60%, as well as TLG50% and TLG60% showed significant difference between two groups (all p-values <0.05), among which MTV50% demonstrated the highest performance in the prediction of MSI, with an AUC of 0.805 [95% confidence interval (CI): 0.657–0.909], a sensitivity of 92.9% (95% CI: 0.661–0.998), and a specificity of 66.7% (95% CI: 0.472–0.827). Patients’ age and MTV50% were significant predictive indicators of MSI in multivariate logistic regression. Conclusion The metabolic parameters derived from18F-FDG PET/CT were able to preoperatively predict the MSI status in CRC, with MTV50% demonstrating the highest predictive performance. PET/CT imaging could serve as a noninvasive tool in the guidance of immunotherapy and individualized treatment in CRC patients.
Category Congruence of Display-Only Products Influences Attention and Purchase Decisions

In e-commerce settings, shoppers can navigate to product-specific pages on which they are asked to make yes-or-no decisions about buying a particular item. Beyond that target, there are often other products displayed on the page, such as those suggested by the retailers’ recommendation systems, that can influence consumers’ buying behavior. We propose that display items that come from the same category as the target product (matched) may enhance target purchase by increasing the attractiveness of the presented opportunity. Contrasting this, mismatched display items may reduce purchase by raising awareness of opportunity costs. Eye-tracking was used to explore this framework by examining how different types of displays influenced visual attention. Although target purchase rates were higher for products with matched vs. mismatched displays, there was no difference in fixation time for the target images. However, participants attended to mismatched display items for more time than they did for matched ones consistent with the hypothesized processes. In addition, increases in display attractiveness increased target purchase, but only for matched items, in line with supporting the target category. Given the importance of relative attention and information in determining the impact of display items, we replicated the overall purchase effect across varying amounts of available display information in a second behavioral study. This demonstration of robustness supports the translational relevance of these findings for application in industry.
Multi-Head Attention-Based Long Short-Term Memory for Depression Detection From Speech

Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the inherent information from speech. To focus on more emotionally salient regions of depression speech, in this research, we propose a multi-head time-dimension attention-based long short-term memory (LSTM) model. We first extract frame-level features to store the original temporal relationship of a speech sequence and then analyze their difference between speeches of depression and those of health status. Then, we study the performance of various features and use a modified feature set as the input of the LSTM layer. Instead of using the output of the traditional LSTM, multi-head time-dimension attention is employed to obtain more key time information related to depression detection by projecting the output into different subspaces. The experimental results show the proposed model leads to improvements of 2.3 and 10.3% over the LSTM model on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) corpus, respectively.
Fluid Secretion by Malpighian Tubules of Rhodnius prolixus: Neuroendocrine Control With New Insights From a Transcriptome Analysis

Rhodnius prolixus (the kissing bug and a major vector of Chagas disease) is an obligate blood feeder that in the case of the fifth instar consumes up to 10 times its unfed body weight in a single 20-minute feed. A post-prandial diuresis is initiated, within minutes of the start of gorging, in order to lower the mass and concentrate the nutrients of the meal. Thus, R. prolixus rapidly excretes a fluid that is high in NaCl content and hypo-osmotic to the hemolymph, thereby eliminating 50% of the volume of the blood meal within 3 hours of gorging. In R. prolixus, as with other insects, the Malpighian tubules play a critical role in diuresis. Malpighian tubules are not innervated, and their fine control comes under the influence of the neuroendocrine system that releases amines and neuropeptides as diuretic or antidiuretic hormones. These hormones act upon the Malpighian tubules via a variety of G protein-coupled receptors linked to second messenger systems that influence ion transporters and aquaporins; thereby regulating fluid secretion. Much has been discovered about the control of diuresis in R. prolixus, and other model insects, using classical endocrinological studies. The post-genomic era, however, has brought new insights, identifying novel diuretic and antidiuretic hormone-signaling pathways whilst also validating many of the classical discoveries. This paper will focus on recent discoveries into the neuroendocrine control of the rapid post-prandial diuresis in R. prolixus, in order to emphasize new insights from a transcriptome analysis of Malpighian tubules taken from unfed and fed bugs.
Abnormal Functional Connectivity of Posterior Cingulate Cortex Correlates With Phonemic Verbal Fluency Deficits in Major Depressive Disorder

Background: Major depressive disorder (MDD) patients face an increased risk of developing cognitive impairments. One of the prominent cognitive impairments in MDD patients is verbal fluency deficit. Nonetheless, it is not clear which vulnerable brain region in MDD is interactively linked to verbal fluency deficit. It is important to gain an improved understanding for verbal fluency deficit in MDD. Methods: Thirty-four MDD patients and 34 normal controls (NCs) completed resting-state fMRI (rs-fMRI) scan and a set of verbal fluency tests (semantic VFT and phonemic VFT). Fourteen brain regions from five brain networks/systems (central executive network, default mode network, salience network, limbic system, cerebellum) based on their vital role in MDD neuropathology were selected as seeds for functional connectivity (FC) analyses with the voxels in the whole brain. Finally, correlations between the z-score of the FCs from clusters showing significant between-group difference and z-score of the VFTs were calculated using Pearson correlation analyses. Results: Increased FCs in MDD patients vs. NCs were identified between the bilateral posterior cingulate cortex (PCC) and the right inferior frontal gyrus (triangular part), in which the increased FC between the right PCC and the right inferior frontal gyrus (triangular part) was positively correlated with the z score of phonemic VFT in the MDD patients. Moreover, decreased FCs were identified between the right hippocampal gyrus and PCC, as well as left cerebellum Crus II and right parahippocampal gyrus in MDD patients vs. NCs. Conclusions: The MDD patients have altered FCs among key brain regions in the default mode network, the central executive network, the limbic system, and the cerebellum. The increased FC between the right PCC and the right inferior frontal gyrus (triangular part) may be useful to better characterize pathophysiology of MDD and functional correlates of the phonemic verbal fluency deficit in MDD.
Positive and Negative Emotion Classification Based on Multi-channel

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.
NLP-UNED at eRisk 2021: self-harm early risk detection with TF-IDF and linguistic features

Mental health problems such as depression and anxiety are conditions that can have very serious consequences. Self-harm is a lesser-known symptom mostly associated with young people that has been linked to depression. Research suggests that the way people write can reflect mental well-being and mental health risks, and social media provides a source of user-generated text to study. Early detection is crucial for mental health problems. In this context, the shared task eRisk was proposed. This paper describes the participation of the group NLP-UNED on the 2021 T2 subtask. Participants were asked to create systems to detect early signs of self-harm on users from Reddit. We propose a feature-driven classifier with features based on text data, TF-IDF terms, first-person pronoun use, sentiment analysis and self-harm terminology. The official task results show that a relatively simple model can achieve fast and competent results.
UAIC-AI at SnakeCLEF 2021: Impact of convolutions in snake species recognition

Snake identification is crucial in quickly and effectively treating snake bites. With over 2.7 million snake envenomings happening yearly, medical personal are in desperate need of tools that will ease their work as well as save patient lives faster. SnakeCLEF 2021 challenge, which is part of the LifeCLEF laboratory, has exactly the aforementioned goal. This paper presents our team participation at SnakeCLEF 2021. We developed 3 models based on CNN: GoogLeNet , VGG16 and ResNet-18 and ranked 5 th with an F1-Country score of 0.785 using ResNet-18.
Development of a sensitive closed batch vessel hydride generation atomic absorption spectrometry method for the determination of cadmium in aqueous samples

ABSTRACT Hydride generation atomic absorption spectrometry was used as a simple, sensitive, and economical method for the determination of cadmium at trace levels. This study includes a stepwise optimization of all parameters that have an impact on the production, transport, and atomization of cadmium hydride for rapid determination due to the unstable nature of the hydride produced. The cell temperature for atomization, pump period, and flow rate of carrier gas were varied, and the best conditions were defined to obtain lower detection limits. In addition, the acid and sodium borohydride concentrations were optimized since they directly affect the production of the cadmium hydride. Under optimum experimental conditions, the hydride generation efficiency was greatly enhanced to record optimized analytical signals. The accuracy of the method was determined by the analysis of a certified reference material, and the results were in good agreement with the certified value. A linear dynamic range for cadmium was observed from 0.10 to 2.0 µg/L. High sensitivity was obtained with a limit of detection of 0.029 µg/L.
EfficientNets and Vision Transformers for Snake Species Identification Using Image and Location Information

The automatic classification of snake species based on non-standardized photographs is important to improve the care of patients suffering from snake bites. The SnakeCLEF 2021 challenge, provides a large database containing images and their recording location of 772 snake species to overcome this problem. This paper describes the participation of the FHDO Biomedical Computer Science Group (BCSG) in this challenge. In the experiments, deep learning-based EfficientNets and Vision Transformer (ViT) models were trained. In a subsequent step, the prior probabilities of the location information were multiplied with the model predictions. An ensemble of both deep learning models achieved the best results, which was a macro averaging 𝐹 1 -score across countries of 82.88 % for the independent test set.
Effect of inlet cone pipe angle in catalytic converter

The catalytic converter shows significant consequence to improve the performance of the vehicle start from it launched into production. Nowadays, the geometric design of the catalytic converter has become critical to avoid the behavior of backpressure in the exhaust system. The backpressure essentially reduced the performance of vehicles and increased the fuel consumption gradually. Consequently, this study aims to design various models of catalytic converter and optimize the volume of fluid flow inside the catalytic converter by changing the inlet cone pipe angles. Three different geometry angles of the inlet cone pipe of the catalytic converter were assessed. The model is simulated in Solidworks software to determine the optimum geometric design of the catalytic converter. The result showed that by decreasing the divergence angle of inlet cone pipe will upsurge the performance of the catalytic converter.
Universal Passage Weighting Mecanism (UPWM) in BioASQ 9b

This paper presents the participation of the University of Aveiro Biomedical Informatics and Techolo-gies group (BIT) in the ninth edition of the BioASQ challenge for document and snippet retrieval. Our proposed systems follow a two-stage retrieval pipeline, similar to our BioASQ 8B submissions. However, we completely rebuilt our neural ranking model, maintaining the key ideas of its inception while improving its computational efficiency as well as adding interoperability with the transformer architecture. This resulted in a novel universal passage weighting mechanism (UPWM), which offers a more powerful way to derive a document relevance score from the combination of its sentences. More concretely, we have built two variants that use our passage mechanism, the lightning UPWM and the transformer UPWM. The first uses a shallow interaction model and the second uses a BERT model. Additionally, we also propose an effective pairwise joint training mechanism that combines document retrieval with snippet retrieval. Our systems achieved competitive results scoring at the top and close to the top for all the batches, with MAP values ranging from 0.3573 to 0.4236 in the document retrieval task. Although we only submitted for the snippet retrieval task in the last two batches, our system scored at top position in the last batch by using rank reciprocal fusion of pointwise and pairwise joint training approaches. Code to reproduce our submissions are available on https://github.com/bioinformatics-ua/BioASQ9b.
Scaling Up Search with Partial Initial States in Optimization Crosswords

Heuristic search remains a leading approach to difficult combinatorial optimization problems. Search algorithms can utilize pruning based on comparing a target score with an admissible (optimistic) estimate of the best score that can be achieved from a given state. If the former is larger they prune the state. However, when the target score is too high the search can fail by exhausting the space without finding a solution. In this paper we show that such failed searches can still be valuable. Specifically, best partial solutions encountered in such failed searches can often bear a high similarity to the corresponding part of a full high-quality or even optimal solution. Thus, a new search for a full solution, with a lower target score, can start with a best known partial solution, rather than starting from scratch. We demonstrate our ideas in a constraint optimization problem modelled on the Romanian Crosswords Competition, a challenging problem where humans perform much better than computers. Utilizing partial solutions produced by a failed search cuts down the running time of an existing state-of-the-art solver by orders of magnitude on competition-level crossword puzzle instances and allows to solve more instances.
Effect of tillage, cropping, and mulching pattern on crop yield, soil C and N accumulation, and carbon footprint in a desert oasis farmland

ABSTRACT The desert oasis is one of the major grain production areas in arid land, and many intensive farming practices have been adopted to improve the land utilization in the agriculture system. However, there remains little consensus on how to improve such farming practices for increasing both productivity and environment benefits in this system. A 4-year experiment was conducted in a typical desert oasis farmland to determine the effects of the farming practices on crop yield, soil carbon (C) and nitrogen (N) accumulation, and carbon footprint (CF). The farming practices included two tillage patterns: conventional (CT) and reduced tillage (RT), two cropping patterns: continuous (Con) and rotation cropping (Rot), and two mulching pattern: film (F) and straw mulching (S) with eight combined treatments. The RT did not significant decrease crop yield but increase soil C and N accumulation rate by 59% and 130%, and thus decrease CF for crop production compared with the CT. S can also improve soil C and N accumulation, and cause low CF for crop production, but leading to 14–41% decrease in maize yield compared with F. Rot result in a 14% increase on maize yield also has extra benefit to decrease CF for crop production, but no significant effect on soil C and N accumulation compared with Con. Our study adds a reasonable perspective on how to improve the conventional farming systems in desert oasis, the information about RT, straw mulching, and maize–soybean rotation have positive effect on improving soil quality and decreasing CF for crop production in this desert soil area is critical to develop the sustainable agriculture system in this desert oasis farmland, which both maintaining crop productivity and minimizing negative environmental impacts.
UAICS at CheckThat! 2021: Fake news detection

Social media growth in recent years has facilitated an enhancement in human communication. Platforms such as Facebook and Twitter are now ever-present in our lives, influencing how we speak, think and act. The growth of fake news greatly impacts this phenomenon as it lowers one’s trust in the content presented. One such example is related to the 2016 U.S. presidential election campaign where fake news was a deciding factor in tipping the balance of power. It is hence of critical importance to develop tools that detect and combat such destructive content. CLEF 2021 CheckThat! Task 3 tries to address the problem of fake news, posing a challenge to develop systems that could detect if the main claim made in an article is true, partially true, false, or other. Our team participated in this task with 5 models, ranking 6th place with an F1-macro of 0.44 and a model based on Gradient Boosting; in this paper we will present our methods, runs and results but also discuss future work.
Overview of the CLEF-2021 CheckThat! Lab: Task 3 on Fake News Detection

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting three tasks related to factuality, and it covers Arabic, Bulgarian, English, Spanish, and Turkish. Here, we present task 3 , which focuses on multi-class fake news detection and topical domain detection of news articles. Overall, there were 88 submissions by 27 teams for Task 3A, and 49 submissions by 20 teams for task 3B (two team from Task 3A and seven teams from Task 3B are excluding from the ranking due to wrong submission file). The best performing system for task 3A achieved a macro F 1 -score of 0.84 and was ahead of the rest by a rather large margin. The performance of the systems for task 3B was overall higher than for task 3A with the top performing system achieving a macro F 1 -score of 0.88. In this paper, we describe the process of data collection and the task setup, including the evaluation measures used, and we give a brief overview of the participating systems. Last but not least, we release to the research community all data sets from the lab as well as the evaluation scripts, which should enable further research in automatic classification of news articles with respect to their correctness and topical domain.
Conceptual Comparison of Compilation-based Solvers for Multi-Agent Path Finding: MIP vs. SAT

The task in multi-agent path finding (MAPF) is to find paths through which agents can navigate from their starting positions to given individual goal positions. The combination of two additional requirements makes the problem challenging: (i) agents must not collide with each other and (ii) the paths must be optimal with respect to some objective. We summarize and compare main ideas of contemporary compilation-based solvers for MAPF using MIP and SAT formalisms.