Miracle-dz/UniFAR-FTUs huggingface dataset:
A Method for Behavior Change Support by Controlling Psychological Effects on Walking Motivation Caused by Step Count Log Competition System

Systems presenting information that encourages competition by using rankings and scores (hereafter referred to as competition information) have become widespread to support behavioral change. However, users without high levels of motivation, such as behavior change support targets, do not necessarily benefit from competition information. In this study, we propose a method to control the psychological effects caused by competition information to support behavior change. We implemented a competition information presentation system using step counts logs to support increasing one’s daily steps. We designed two patterns of competition information considering psychological effects. One is likely to have good effects, using three mechanisms to easily obtain results corresponding to the effort, make closely matched rivals with similar abilities, and pay attention to a small number of rivals. The other is unlikely to have positive effects and may potentially even have negative ones, using a mechanism that brings about the opposite results of the former pattern. We evaluated 42 participants with low levels of motivation over six weeks. The results showed that the former information pattern increased participants’ step counts by about 1000 steps per day, and the latter information pattern did not lead to an increase. We confirmed the feasibility of the proposed method and discussed the possibility of the appropriate use and potential abuse of such techniques for manipulating motivation. Our study can be helpful in designing a competition information presentation system considering psychological effects.
Krill-Oil-Dependent Increases in HS-Omega-3 Index, Plasma Choline and Antioxidant Capacity in Well-Conditioned Power Training Athletes

There is evidence that both omega-3 polyunsaturated fatty acids (n-3 PUFAs) and choline can influence sports performance, but information establishing their combined effects when given in the form of krill oil during power training protocols is missing. The purpose of this study was therefore to characterize n-3 PUFA and choline profiles after a one-hour period of high-intensity physical workout after 12 weeks of supplementation. Thirty-five healthy power training athletes received either 2.5 g/day of Neptune krill oilTM (550 mg EPA/DHA and 150 mg choline) or olive oil (placebo) in a randomized double-blind design. After 12 weeks, only the krill oil group showed a significant HS-Omega-3 Index increase from 4.82 to 6.77% and a reduction in the ARA/EPA ratio (from 50.72 to 13.61%) (p < 0.001). The krill oil group showed significantly higher recovery of choline concentrations relative to the placebo group from the end of the first to the beginning of the second exercise test (p = 0.04) and an 8% decrease in total antioxidant capacity post-exercise versus 21% in the placebo group (p = 0.35). In conclusion, krill oil can be used as a nutritional strategy for increasing the HS-Omega-3 Index, recover choline concentrations and address oxidative stress after intense power trainings.
Erratum to: Systematics of the rodent genus Neacomys Thomas (Cricetidae: Sigmodontinae): two new species and a discussion on carotid patterns

The taxonomy of the oryzomyine genus Neacomys currently is in a state of flux: systematic studies in the last 20 years increased its diversity from four to 16 species, with an additional several undescribed phylogenetic lineages. Despite this progress, morphological variation and species limits remain poorly known for several species groups and complexes within the genus, such as N. tenuipes, N. musseri, and N. dubosti. Here we analyze the variation of morphological characters and morphometric patterns of two new candidate species of Neacomys for northern Venezuela and Pará state, Brazil, that are characterized by the presence of derived carotid circulation, a rare character state for the genus. Analyzed material included holotypes, type series, and/or topotypes of almost all described species of Neacomys, including type series of N. musseri, the holotype of N. tenuipes, and sequenced specimens of N. xingu. Qualitative comparison showed that 25 characters are informative for the distinction of the two new forms, and morphometric analyses corroborated the morphological separation of the new species. Results also point that N. tenuipes might represent a species complex. The new forms are sympatric with other species, including N. tenuipes and N. xingu, and can be identified by a suite of morphological characters, including the presence of a derived carotid pattern and, for the Pará form, by a unique morphology of the first upper molar. We review the occurrence of carotid circulation patterns within Sigmodontinae and discuss its use for the systematics of the subfamily. Further studies involving the new Neacomys species will contribute to understanding the biogeographic patterns and evolutionary trends within this distinct and diverse genus.
Duration of Untreated Disorder and Cannabis Use: An Observational Study on a Cohort of Young Italian Patients Experiencing Psychotic Experiences and Dissociative Symptoms

Background: The Duration of Untreated Psychosis (DUP) is the time between the first-episode psychosis (FEP) and the initiation of antipsychotic treatment. It is an important predictor of several disease-related outcomes in psychotic disorders. The aim of this manuscript is investigating the influence of cannabis on the DUP and its clinical correlates. Methods: During years 2014–2019, sixty-two FEP patients with and without cannabis use disorder (CUD) were recruited from several Italian psychiatric hospitals. The subjects were then divided into two groups based on the duration of the DUP and assessed at the beginning of the antipsychotic treatment and after 3 and 6 months, using the Positive and Negative Syndrome Scale (PANSS), the Global Assessment of Functioning (GAF) scale, and the Dissociative Experiences Scale (DES-II). Results: As expected, a longer DUP was associated with worse symptoms and cannabis use did not seem to affect the DUP, but both were related with more dissociative symptoms at onset and over time. Discussion: According to our study, cannabis use can be a predictor of FEP and DUP, and of disease outcome. However, several factors might influence the relationship between cannabis use and DUP. Preventing cannabis use and early diagnosis of psychotic disorders might impact the disease by reducing the persistence of symptoms and limiting dissociative experiences.
Research of one new adaptive mho relay

In this paper, a new mho relay based on the adaptive principle is proposed. The operation boundary of the new relay can be adjusted to adapt different earth-fault resistance automatically, and provide consistent relay zone coverage. Therefore the adaptive mho relay has a perfect performance for the protection of single phase-to-earth faults, especially the high impedance faults on transmission line. The performance of the relay has been verified on a simulated network using ATP program. The result shows that the new adaptive mho relay has excellent performance within the protective zone, and it can reflect inner single-phase faults with a high impedance of more than 800 /spl Omega/ on a simulated 300 km long transmission line of 500 kV.
Towards Adaptive Inspection for Fraud in I4.0 Supply Chains

The effective functioning of society is increasingly reliant on supply chains which are susceptible to fraud, such as the distribution of adulterated products. Inspection is a key tool for mitigating fraud, however it has traditionally been constrained by physical characteristics of supply chains such as their size and geographical distribution. The increasingly cyber-physical nature of supply chains, their autonomy, and their data richness, extends their attack surfaces and thus increases opportunities for fraud. However, it also presents new opportunities for increased and dynamic inspection, which in turn requires more targeted and flexible inspection regimes. In this paper we explore opportunities to engineer adaptive inspection of cyber-physical supply chains to support efforts to reduce fraud. Through using structural representations of supply chains (topological models) we propose defining optimal inspection zones. Such zones circumscribe assets of interest to optimise observation while reducing the intrusiveness of inspection. Using a motivating example of adulterated pharmaceuticals and a proof-of-concept tool we illustrate adaptive inspection, and surface challenges to its realisation, such as value metrics, forensic readiness integration and managing contrasting local and global perspectives.
Techniques for Broad-Banding Above Resonance Circulator Junctions without the Use of External Matching Networks

By using a modification of Bosma's approach, a theoretical explanation is given for the broad-banding effect wich can be achieved by the use of three open-circuited stubs on the center conductor circumference of above resonance circulators. It is also shown that similar broad-banding can be achieved by the use of materials with very large values of 4piM/sub s/ in the above resonance state. It appears that the frequency dependence of the conductance G(omega) is a limiting factor in the bandwidth improvements that can be obtained in these ways.
Graphene Family Nanomaterials (GFN)-TiO2 for the Photocatalytic Removal of Water and Air Pollutants: Synthesis, Characterization, and Applications

Given the industrial revolutions and resource scarcity, the development of green technologies which aims to conserve resources and reduce the negative impacts of technology on the environment has become a critical issue of concern. One example is heterogeneous photocatalytic degradation. Titanium dioxide (TiO2) has been intensively researched given its low toxicity and photocatalytic effects under ultraviolet (UV) light irradiation. The advantages conferred by the physical and electrochemical properties of graphene family nanomaterials (GFN) have contributed to the combination of GFN and TiO2 as well as the current variety of GFN-TiO2 catalysts that have exhibited improved characteristics such as greater electron transfer and narrower bandgaps for more potential applications, including those under visible light irradiation. In this review, points of view on the intrinsic properties of TiO2, GFNs (pristine graphene, graphene oxide (GO), reduced GO, and graphene quantum dots (GQDs)), and GFN-TiO2 are presented. This review also explains practical synthesis techniques along with perspective characteristics of these TiO2- and/or graphene-based materials. The enhancement of the photocatalytic activity by using GFN-TiO2 and its improved photocatalytic reactions for the treatment of organic, inorganic, and biological pollutants in water and air phases are reported. It is expected that this review can provide insights into the key to optimizing the photocatalytic activity of GFN-TiO2 and possible directions for future development in these fields.
Bibliometric Analysis of the Potential of Technologies in the Humanitarian Supply Chain

This study presents a bibliometric analysis of research on technology in the humanitarian supply chain. The methodology includes performance analysis and science mapping to explore the application of technologies in humanitarian supply chains. This paper contributes to the literature by examining the most influential authors, trends, journals, countries, institutions, and the recent humanitarian supply chain collaboration. The information presented in this research was obtained with the Scopus database. The study identified 342 documents after applying filters to screen for duplicates and manuscripts unrelated to the topic. The articles were analyzed using MS Excel and VOSviewer. The research provides an overview of state of the art showing a high collaboration between the authors Ramesh A. and Kabra C, and the most relevant institutions were the Griffith Business School and the Delft University of Technology. Journal of Humanitarian Logistics and Supply Chain Management and Journal of Disaster Research were the most productive journals. The terms analysis shows that “disasters”, “disaster prevention”, “humanitarian logistics”, and “human” are the most used keywords. The study identifies future research lines related to the interaction between critical technologies to deliver real benefits to the humanitarian supply chain. As a result, it proposes integrating the significant contributions of new technologies, such as blockchain, big data, artificial intelligence, 3D printing, virtual and augmented reality, and the social media relief phase following the disaster. It also indicates gaps in knowledge in terms of research related to human-made disasters and health emergencies.
Classification of stroke with gradient boosting tree using smote-based oversampling method

The aim of this study is to classify the disease with the gradient increasing tree classification method in an open access dataset containing data from patients with and without stroke disease. In addition, it is aimed to compare the results by balancing the data with the oversampling method Synthetic Minority Over-sampling Technique (SMOTE) which is one of the data balancing methods in the study. In this study, a dataset containing information about patients with and without stroke disease obtained from the address "https://www.kaggle. com/asaumya/healthcare-problem-prediction-stroke-patients" was used. In the study, SMOTE was used as the data balancing method, and the gradient boosting tree method was used in the modeling. The performance of the model was evaluated by Specificity, sensitivity, accuracy, positive predictive value and negative predictive values. Specificity, sensitivity, accuracy, positive predictive value and negative predictive values were obtained as 0.0887, 0.9772, 0.9339, 0.9544 and 0.1679, respectively, according to the modeling result using the gardient boosting tree method using the original version of the dataset. Specificity, sensitivity, accuracy, positive predictive value and negative predictive values were obtained as 0.0887, 0.9772, 0.9339, 0.9544 and 0.1679, respectively, according to the modeling result using the gardient boosting tree method using the SMOTE applied version of the dataset. When the results obtained from the study were examined, the modeling results made with the SMOTE applied dataset were obtained more consistently and realistically. As a result, it is suggested that researchers use dataset balancing methods to acquire more accurate results whenever they come across an unbalanced dataset problem.
Plasma Levels of Decorin Increased in Patients during the Progression of Breast Cancer

Decorin (DCN), an extracellular matrix proteoglycan found in tumor surrounding tissues, is a natural inhibitor of tumor cell proliferation and invasion. We conducted a cross-sectional observation study to evaluate the association of the pathological stage with the levels of DCN in plasma or tumor surrounding tissue. Among 118 patients who underwent breast surgery, 35 were designated as carcinoma in situ (Stage 0), 39 were Stage I, and 44 were Stage II or III. The stromal expression of DCN was quantified using a semiquantitative digital image analysis after immunohistochemical staining. The concentration of DCN was evaluated with a specific ELISA. As we have previously shown, stromal DCN expression was attenuated in the patients with Stage I, whereas stromal and plasma DCN was elevated paradoxically in those with Stage II/III. The elevated plasma DCN is an independent predictive factor of Stage II/III by the multivariate logistic regression analysis. The plasma level of DCN was negatively correlated with stromal DCN expression only in patients with advanced disease (Stage II/III). The plasma level of DCN could become a useful biomarker for patients in the advanced stages. Extensive studies and further assessments are warranted for evaluating the prognostic significance and tumor characteristics to understand the clinical significances of stromal and systemic DCN.
Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis

Purpose Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment. Patients and Methods We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models. Results Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability. Conclusion Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.
Inhibition of Prostaglandin F2α Receptors Exaggerates HCl-Induced Lung Inflammation in Mice

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe respiratory disorders that are caused by aspiration, sepsis, trauma, and pneumonia. A clinical feature of ALI/ARDS is the acute onset of severe hypoxemia, and the mortality rate, which is estimated at 38–50%, remains high. Although prostaglandins (PGs) are detected in the bronchoalveolar lavage fluid of patients with ALI/ARDS, the role of PGF2α in ALI remains unclear. We aimed to clarify the role of PGF2α/PGF2α receptor (FP) signaling in acid-induced ALI using an FP receptor antagonist, AL8810. Intratracheal injection of hydrochloric acid (HCl) increased neutrophil migration into the lungs, leading to respiratory dysfunction. Pre-administration of AL8810 further increased these features. Moreover, pre-treatment with AL8810 enhanced the HCl-induced expression of pro-inflammatory cytokines and neutrophil migratory factors in the lungs. Administration of HCl decreased the gene expression of lung surfactant proteins, which was further reduced by co-administration of AL8810. Administration of AL8810 also increased lung edema and reduced mRNA expression of epithelial sodium channel in the lungs, indicating that AL8810 reduced fluid clearance. Furthermore, AL8810 also increased lipopolysaccharide-induced expression of adhesion molecules such as intracellular adhesion molecule-1 and E-selectin in human umbilical vein endothelial cells. These results indicate that inhibition of FP receptors by AL8810 exacerbated HCl-induced ALI.
MUC13 promotes lung cancer development and progression by activating ERK signaling

Mucin 13 (MUC13) is a glycoprotein that is expressed on the cell surface and participates in the tumorigenesis of multiple malignancies, including pancreatic cancer, colorectal cancer and renal cancer. However, to the best of our knowledge, the expression levels and function of MUC13 in lung cancer progression have not yet been demonstrated. Therefore, the present study examined the expression pattern and regulatory role of MUC13 in lung cancer tumorigenesis. The results demonstrated that MUC13 was highly expressed in lung cancer tissues and cell lines compared with that in normal tissues and cell lines. Functionally, knockdown of MUC13 inhibited cell proliferation and enhanced the apoptosis of A549 and NCI-H1650 lung cancer cells. Furthermore, silencing of MUC13 suppressed the migration and invasion of lung cancer cells. Additionally, a xenograft tumor model demonstrated that knockdown of MUC13 delayed the development of the lung cancer xenograft and suppressed the expression of proliferation marker Ki-67 in tumor tissues. Mechanistically, MUC13 activated the ERK signaling pathway by enhancing the phosphorylation of ERK, JNK and p38 in lung cancer tissues compared with that in normal tissues. Knockdown of MUC13 inhibited the phosphorylation of ERK/JNK/p38 in A549 and NCI-H1650 cells. Overall, these findings suggested that MUC13 could act as an oncogenic glycoprotein to accelerate the progression of lung cancer via abnormal activation of the ERK/JNK/p38 signaling pathway and might serve as a therapeutic target for lung cancer treatment.
Multi-Domain-Simulation in der Leistungselektronik

Power electronics is developing more and more into a multi-disciplinary engineering area. Besides the circuit technology itself, other aspects such as control issues, the thermal behavior of the system, electromagnetism and electromagnetic compatibility (EMC) are becoming important for a successful design of power electronics hardware. The requirements for high power density, high efficiency, and system integration are the main reasons why different physical domains cannot be considered as separate anymore. Thermal behavior, for instance, has to be considered during system design to ensure long-term functionality and reliability. Conduction and switching losses of semiconductor components must be modeled thoroughly for parametrizing thermal models. Furthermore, temperature variations have a feedback effect on semiconductor losses. Parasitic circuit components such as stray inductances and capacitive couplings lead to switching overvoltages, additional losses and degraded EMC performance of the converter system. In order to be able to handle the complexity of system design, modern simulation tools are required, which are probably the only alternative to time-consuming and expensive trial-and-error optimization of hardware prototypes. Many different existing software packages for 3D field computations are available, and due to their advanced algorithms, these tools have the ability to solve complex modeling problems. However, the coupling between different simulation domains is often not forseen. Some multiphysics finite element solvers are available which tightly couple different physical domains, e.g. thermal and electrical co-simulation. The tight coupling leads to very large models and simulation times that are not acceptable for daily development tasks. This work therefore focuses on the intelligent combination of 3D field simulations and a fast power electronics circuit simulator. Within this PhD project, an electromagnetic simulation tool was developed which is based on the Partial Element Equivalent Circuit method (PEEC). The PEEC solver is able to efficiently simulate the three-dimensional elec-