Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship Between Depressive Symptoms and Psychological Variables in Patients With Schizophrenia

Introduction: Depressive symptoms are common in schizophrenia and can be seen at any stage of the disease. Although various models have been proposed to explain the development of depression in schizophrenia, studies investigating related psychological factors are scarce and the studies that have been done usually focus on only a small number of possible factors. Objective: The goal of this study was to investigate the predictability of some psychological factors on depression in patients with schizophrenia. For this purpose, patients with high and low depression scores were compared. Methods: Two groups of individuals with schizophrenia, with (n=29) and without (n=31) depression, as determined by scores on the Calgary Depression Scale in Schizophrenia, were compared using a sociodemographic data form, the Positive and Negative Syndrome Scale (PANSS), the Beck Anxiety Inventory, the Rotter Internal-External Locus 2024 of Control Scale, the Multidimensional Scale of Perceived Social Support, and the Stress Coping Styles Scale. Results: No differences were found between the 2 groups in terms of sociodemographic and clinical characteristics, social support scores, and coping styles. Statistically significant differences were found between the groups on the PANSS positive, negative, and general psychopathology subscales, in PANSS total scores, in anxiety scores, and in locus of control scores. Conclusions: This study showed that high levels of negative, positive, and general psychopathological symptoms, external locus of control, and high anxiety scores may be predictive of depression in individuals with schizophrenia. Studies that examine psychological factors in larger patient groups may provide the opportunity to detect and target these factors earlier in the course of schizophrenia, thereby reducing morbidity and mortality.
learning strategies associated with adopting different

Research suggests that implicit strategies adopted during learning help prevent break-down of automatic processes and subsequent performance decrements associated with the presence of pressure. According to the Constrained Action Hypothesis, automaticity of movement is promoted when adopting an external focus of attention. The purpose of the current experiment was to investigate if learning with an external focus of attention can enhance performance under subsequent pressure situations through promoting implicit learning and automaticity. Since previous research has generally used outcome measures of performance, the current study adopted measures of movement production. Specifically, we calculated within-subject variability in trajectory velocity and distance traveled every 10% of movement time. This detailed kinematic analysis allowed investigation into some of the previously unexplored mechanisms responsible for the beneﬁts of adopting an external focus of attention. Novice participants performed a 2.5 m golf putt. Following a pre-test, participants were randomly assigned to one of three focus groups (internal, external, control). Participants then completed 400 acquisition trials over two consecutive days before being subjected to both a low anxiety and high anxiety (HA) transfer test. Dependent variables included variability, number of successful putts and mean radial error. Results revealed that variability was greater in the internal compared to the external and control groups. Putting performance revealed that all groups increased performance following acquisition. However, only the control group demonstrated a decrement in performance in the HA transfer test.These ﬁndings suggest that adopting an appropriate focus of attention during learning can prevent choking; with an external focus inhibiting the breakdown of automatic processes and an internal focus acting as a self-focus learning strategy and thus desensitizing individuals to anxiety effects.
The e ﬀ ects of con ﬂ ict type and con ﬂ ict expression intensity on con ﬂ ict management

Purpose – This paper aims to add to the current knowledge about con ﬂ ict management by examining the relationshipsbetweencon ﬂ icttype,con ﬂ ictexpressionintensityandtheuseofthecon ﬂ ictmanagementapproach. Design/methodology/approach – The authors test theory-based hypotheses using a ﬁ eld study of new product development teams in an interdisciplinary Masters program (Study 1) and an experimental vignette study(Study 2). Findings – Results show that people are more likely to respond to task con ﬂ ict and con ﬂ icts expressed with less intensity using collectivistic con ﬂ ict management approaches (i.e. problem-solving, compromising and yielding), and to relationship con ﬂ icts and con ﬂ icts expressed with higher intensity through forcing, an individualistic con ﬂ ict management approach. Information acquisition and negative emotions experienced by team members mediate theserelationships. Practical implications – Knowing how the characteristics of the con ﬂ ict (type and expression intensity) affect con ﬂ ict management, managers can counteract the tendency to use dysfunctional, forcing con ﬂ ict management approaches in response to high intensity con ﬂ icts, as well as to relationship con ﬂ icts and support the tendency to use collectivistic con ﬂ ict management approaches in response to low intensity con ﬂ ict, aswell astask con ﬂ icts. Originality/value – The authors examine an alternative to the prevailing view that con ﬂ ict management serves as a moderator of the relationship between con ﬂ ict and team outcomes. The research shows that con ﬂ icttype and intensity of con ﬂ ict expression in ﬂ uence the con ﬂ ict management approach as a result of the information and emotion they evoke. The authors open avenues for future research on the complex and intriguingrelationships between con ﬂ ictcharacteristicsandthecon ﬂ ictmanagement approach.
THREE-STAGE RANDOMIZED RESPONSE MODEL FOR ESTIMATING A RARE SENSITIVE ATTRIBUTE IN PROBABILITY PROPORTIONAL TO SIZE SAMPLING USING POISSON DISTRIBUTION

This paper has a great potential for estimating mean number of individuals in the population who possess a rare sensitive attributes using Poisson distribution for two situations: clustered population and stratified population with clusters are strata units. Properties of the proposed estimation procedures are deeply examined when the proportion of a rare unrelated non-sensitive attributes is assumed to be known as well as unknown. Empirical studies are accomplished to show the superiority of the resultant estimators over well-known contemporary estimators.
Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario

This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based on driver’s cognition (DCSeg-Net), our approach optimizes the graph construction process. The SE-GNN module enhances the extraction of critical features and relations within diverse driving scenarios, elevating both accuracy and the system’s adaptability to complex driving scenarios. The introduced graph re-match module refines classification discrepancies, significantly boosting segmentation accuracy and refining the understanding of autonomous driving scenes. Beyond technological enhancements, this work outlines the expansion and diversification of our original dataset, strengthening the learning capabilities of our model by including specific classification labels and incorporating a broader range of driving scenarios. The utility of the improved SBS-Net is demonstrated through superior performance in Graph Construction Accuracy and Intersection over Union (IoU) measures, as highlighted in our evaluation metrics. These advances underscore the practical applicability of scenario-based segmentation in real-world autonomous driving scenarios, enhancing overall scene comprehension capabilities. The developments presented signal substantial progress in the field of autonomous driving technology.
A Dual-Feature-Based Adaptive Shared Transformer Network for Image Captioning

Current models exhibit notable efficacy in image-captioning tasks. Mainstream research shows that combining dual visual features enhances visual representations and brings a performance boost. However, the incorporation of dual visual features complicates computation and expands parameters, hindering streamlined model deployment. The selection of region features requires a pretrained object detector, neglecting the model’s ease of use for new scenarios and data. In this article, we propose a dual-feature adaptive shared transformer network, capitalizing on the merits of grid and shallow patch features, while circumventing the extra complexity from dual channels. Specifically, we eschew complex features such as region features to facilitate straightforward dataset compilation and expedite inference. We propose an adaptive shared transformer block (AST) to conserve parameters and diminish the model’s FLOPs. A gating mechanism is employed to adaptively compute the importance of each feature, thereby obtaining stronger visual features. Since using flattening grid features before a transformer often leads to a loss of crucial spatial information, we incorporate the learning of relative geometric information based on grid features into our proposed method. Our analysis of various feature fusion techniques reveals that the AST approach outperforms its counterparts in terms of FLOPs and model size while still achieving high performance. Extensive experiments on different datasets indicate that our model demonstrates competitive performance on MSCOCO and outperforms state-of-the-art models on small-scale datasets.
Multisource Heterogeneous Specific Emitter Identification Using Attention Mechanism-Based RFF Fusion Method

Cyber security has always been an important issue in the Internet of Everything topic. In the physical layer of the Internet, specific emitter identification (SEI) technology is widely researched as a simple and effective intrusion prevention technology. Existing SEI research only focused on radio frequency (RF) signals from a single receiver. However, in real scenes such as the Industrial Internet of Things (IIoT), vehicle-to-everything applications, and intelligent sensing systems, etc., RF signals are received from different types of sensors deployed at different locations. Therefore, this paper proposes a multisource heterogeneous SEI (MH-SEI) method and proposes a multi-source heterogeneous attention-based feature fusion network (MHAFFN) to achieve excellent identification performance. The proposed MHAFFN utilizes a multi-channel convolutional network as the RF fingerprinting (RFF) extraction module for multisource heterogeneous RF signals and equips an attention-based RFF fusion module to obtain mixed RFF for the automatic classifier. The experimental results show that the identification accuracy of MHAFFN is 99.196% in a perfect environment. Furthermore, robustness verification has proved that MHAFFN keeps advantages in noisy environments. Through fault tolerance mechanism verification experiment, it is proved that MHAFFN is able to work stably in real-world complex scenarios.
Characterization of the complete chloroplast genome of Gastrochilus sinensis (Orchidaceae, Epidendroideae), a beautiful epiphytic orchid from China

Abstract Gastrochilus sinensis is a beautiful epiphytic orchid with high ornamental value. In this study, the first complete chloroplast genome sequence of G. sinensis was determined using next-generation sequencing (NGS). The de novo assembled chloroplast genome was 148,020 bp in length, including a pair of inverted repeat regions (IRs; 25,987 bp), a small single-copy region (SSC; 11,045 bp), and a large single-copy region (LSC; 85,001 bp). The chloroplast genome encodes 109 unique genes, including 75 protein-coding genes (PCGs), 30 tRNA genes, and four rRNA genes. The total GC content of the chloroplast genome was 36.8%. The phylogenetic analysis showed a close relationship between G. sinensis and G. formosanus species. The complete chloroplast genome provides fundamental information for genetic diversity and phylogenetic relationships in Gastrochilus.
A role for adverse childhood experiences and depression in preeclampsia

Introduction: Adverse childhood experiences (ACEs) are a measure of childhood adversity and are associated with life-long morbidity. The impacts of ACEs on peripartum health including preeclampsia, a common and dangerous hypertensive disorder of pregnancy, remain unclear, however. Therefore, we aimed to determine ACE association with peripartum psychiatric health and prevalence of preeclampsia using a case–control design. Methods: Clinical data were aggregated and validated using a large, intergenerational knowledgebase developed at our institution. Depression symptoms were measured by standard clinical screeners: the Patient Health Questionnaire-9 (PHQ-9) and the Edinburgh Postnatal Depression Scale (EPDS). ACEs were assessed via survey. Scores were compared between participants with (N = 32) and without (N = 46) prior preeclampsia. Results: Participants with ACE scores ≥4 had significantly greater odds of preeclampsia than those with scores ≤ 3 (adjusted odds ratio = 6.71, 95% confidence interval:1.13–40.00; p = 0.037). Subsequent speculative analyses revealed that increased odds of preeclampsia may be driven by increased childhood abuse and neglect dimensions of the ACE score. PHQ-9 scores (3.73 vs. 1.86, p = 0.03), EPDS scores (6.38 vs. 3.71, p = 0.01), and the incidence of depression (37.5% vs. 23.9%, p = 0.05) were significantly higher in participants with a history of preeclampsia versus controls. Conclusions: Childhood sets the stage for life-long health. Our findings suggest that ACEs may be a risk factor for preeclampsia and depression, uniting the developmental origins of psychiatric and obstetric risk.
Sentiment analysis of online licensing service quality in the energy and mineral resources sector of the Republic of Indonesia

The Ministry of Energy and Mineral Resources of the Republic of Indonesia regularly assessed public satisfaction with its online licensing services. User rated their satisfaction at 3.42 on a scale of 4, below the organization's average of 3.53. Evaluating public service performance is crucial for quality improvement. Previous research relied solely on survey data to assess public satisfaction. This study goes further by analyzing user feedback in text form from an online licensing application to identify negative aspects of the service that need enhancement. The dataset spanned September 2019 to February 2023, with 24,112 entries. The choice of classification methods on the highest accuracy values among decision tree, random forest, naive bayes, stochastic gradient descent, logistic regression (LR), and k-nearest neighbor. The text data was converted into numerical form using CountVectorizer and term frequency-inverse document frequency (TF-IDF) techniques, along with unigrams and bigrams for dividing sentences into word segments. LR bigram CountVectorizer ranked highest with 89% for average precision, F1-score, and recall, compared to the other five classification methods. The sentiment analysis polarity level was 36.2% negative. Negative sentiment revealed expectations from the public to the ministry to improve the top three aspects: system, mechanism, and procedure; infrastructure and facilities; and service specification product types.
The 2006 Red List and an updated checklist of bryophytes of the Iberian Peninsula (Portugal, Spain and Andorra)

Sergio, C. , Brugues, M. , Cros, R. M. , Casas, C. and Garcia, C. 2006. The2006 Red List and an updated checklist of bryophytes of the Iberian Peninsula(Portugal, Spain and Andorra). – Lindbergia 31: 00–00.The objective of this work is to provide an updated threatened and endangeredstatus of all bryophytes in the Iberian territory and also to provide a checklistfor Portugal, Spain and Andorra. With respect to these species, the recentIUCN criteria are used for evaluation in the respective categories.There are a total of 1101 known Iberian bryophytes (811 mosses and 290hepatics including 6 hornworts), with 370 of these taxa on the Red List (33.5%). There are 10 Regionally Extinct taxa comprised of 7 mosses and 3 hepatics;16 Critically Endangered taxa comprised of 11 mosses and 5 hepatics; 40 En-dangered taxa comprised of 26 mosses and 14 hepatics; 114 Vulnerable taxacomprised of 83 mosses and 31 hepatics; 26 Near Threatened taxa comprisedof 11 mosses and 15 hepatics. Of the Non-Threatened categories, there are732 Least Concern taxa, which include 21 on the Attention list (18 mosses and3 hepatics). In addition, 163 taxa are considered to be Data Deficient (DD andDD-va) with DD-n including the new taxa or new references for the IberianPeninsula.
Does Firm Size Matter for ESG Risk? Cross-Sectional Evidence from the Banking Industry

The ambiguous evidence regarding the linkages between firm size and ESG risk in the relevant literature justifies the need for their further scientific investigation. A particularly interesting context for this task is offered by the banking industry, where financial institutions face both strong incentives to expand the scale of their activities and high reputational risk sensitivity. Given the above, this paper aims to systematize and enhance the theoretical underpinnings of the relationship between firm size and ESG risk in banks, highlighting its likely non-linear character, and to investigate it empirically in the cross-section of the international banking industry. This research employs uni- and multivariate, and linear and non-linear regression analyses applied to a sample of 668 banks that were assigned the Morningstar Sustainalytics ESG Risk Rating for the year 2021. The results demonstrate that, although, on average, size seems to be associated negatively with ESG risk in the cross-section, the relationship is in fact non-linear and follows a U-shaped pattern. The findings are robust regarding the impact of both country-specific contextual factors and outliers. This study emphasizes the importance of diseconomies of scale in ESG risk management, thus offering some important lessons and recommendations for bank executives and equity investors.
Research on Adaptive Prediction Algorithm

The paper aims to solve the problem that the nonlinear model of control system being hard to fetch parsing and impossible to be completely accurately described by mathematical model, and thus adopting adaptive predictive control algorithm for its capability of on-line identifying the variation characteristics of uncertain parameters. At the beginning, we analyze the advantages and disadvantages of many kinds of adaptive control method. Then the characteristics of cascade control system are put forward. According to the principle, we study adaptation prediction open-loop control by simulation. Later, taken interference’s effect on main variable into account, we design adaptive prediction closed loop scheme to improve the system’s adaptive capacity at the time the load changing, and then identify a cascade control program whose inner loop is PID controller and outer loop is self-adaptation predictive controller. At last, we analyze the simulation results and make a conclusion that closed loop self-adaptation predictive control improves stability and dynamic property when the system is under interference.
AmpNorm: an effective style normalization method for single domain generalization

: Deep neural networks have demonstrated remarkable efficacy in numerous computer vision tasks. However, due to the training and testing sets of data coming from different domains, the domain gap limits the performances of deep neural networks. To enhance the generalization performance of deep neural networks, we provide a unique amplitude normalization (AmpNorm) approach to decrease the domain gap from the frequency domain perspective. Specifically, ours AmpNorm collects the amplitude spectrum from the source domain during training and then converts the images from the unseen target domain into those with a style similar to the source domain during testing. Our AmpNorm is simple yet effective, as well as plug-and-play, which is readily implemented into the majority of single-domain generalization (SDG) methods. Extensive results on three public benchmarks demonstrate that our AmpNorm can greatly improve these models ’ performance on the invisible target domain.
Palaeogeographic Condition for Origin of Underground Brine in Southern Coast of Laizhou Bay,Bohai Sea

Underground brine formed in south coast of the Laizhou bay distributes widely with large volume since Late Pleistocene.Its origin is complicated.According to the analysis,it is the fluvial delta sedimentary system that creates a good replenishment,migration,filtration,capping condition for the generation of Underground brine.In the early stage of brine evolution,the lagoon water consisted of continuously injected normal seawater,which ran across the bar under the process of storm surge,and fresh groundwater lying below the river sediment.High salinity brine generated in lagoon,running into the tidal flat and the delta front belt under the effect of backflow—infiltration.Lagoon water would remain stable with relatively high concentrations for a long time.In the regression period,a large amount of long-term evaporation and seasonal multi-source river sediment transport modes formed brine-producing-reservoir.The terrestrial deposit covered early brine,which became Underground brine formations,and in this way,several large scale sea—land changes had formed alternative distribution characteristics.Taking the sand bar—lagoon as the brine generated reactor,dissolved components were fractionated under the effect of pump evaporation,and took ion exchange in sediments,which led to mineral alteration.As a result,Underground brine was generated in the tidal flat and delta frontier under the effect of backflow—infiltration.
Green Synthesis of Er-Doped ZnO Nanoparticles: An Investigation on the Methylene Blue, Eosin, and Ibuprofen Removal by Photodegradation

We present a study on the green synthesis of undoped and Er-doped ZnO compounds using Mangifera indica gum (MI). A set of tests were conducted to assess the structure of the material. The tests included X-ray diffraction, Raman, and Fourier-transform infrared spectroscopy. Optical properties were studied using diffuse reflectance and photoluminescence. Morphological and textural investigations were done using SEM images and N2 adsorption/desorption. Furthermore, photocatalytic tests were performed with methylene blue (MB), yellow eosin (EY), and the pharmaceutical drug ibuprofen (IBU) under UV irradiation. The study demonstrated that replacing the stabilizing agent with Mangifera indica gum is an effective method for obtaining ZnO nanoparticles. Additionally, the energy gap of the nanoparticles exhibits a slight reduction in value. Photoluminescence studies showed the presence of zinc vacancies and other defects in both samples. In the photocatalytic test, the sample containing Er3+ exhibited a degradation of 99.7% for methylene blue, 81.2% for yellow eosin, and 52.3% for ibuprofen over 120 min. In the presence of methyl alcohol, the degradation of MB and EY dyes is 16.7% and 55.7%, respectively. This suggests that hydroxyl radicals are responsible for the direct degradation of both dyes. In addition, after the second reuse, the degradation rate for MB was 94.08%, and for EY, it was 82.35%. For the third reuse, the degradation rate for MB was 97.15%, and for EY, it was 17%. These results indicate the significant potential of the new semiconductor in environmental remediation applications from an ecological synthesis.