Miracle-dz/UniFAR-FTUs huggingface dataset:
Fraud Prevention Analysis: Perceptions of Bank Sumsel-Babel Employees

If a nation’s financial statements are stable and exist, and the financial system is stable, banking companies can contribute to economic growth. The growth or development of the banking world is not only dominated by certain banks such as BUMN Banks or Private Banks. However, this is also experienced by Regional Development Banks. In several cases in banking, Fraud occurs due to internal processes (human error), system failures, or external errors. This research uses a quantitative research method with a questionnaire observation approach, with the object respondents being Bank Sumsel-Babel employees, totaling 238 respondents and 34 question instruments—Smart-PLS analysis technique. Of the 32 indicators (2 indicators omitted), in the outer measurement model, data has validity and reliability with the approach that has been achieved. At the same time, in the inner path coefficient test model, all variables are negative with a value <- 420 (weak category). Meanwhile, at the R-square level of 0.688 (moderate), it can create a goodness of fit, robust value, and an accuracy of 95.0%. The estimation results show that all variables have a negative direction coefficient that significantly affects compensation, competence, and internal control of Fraud. At the same time, organizational culture, with a value of 0.138 > 0.05, indicates no effect on Fraud.
Towards data-driven microscopic traffic simulation models

Στόχος της έρευνας είναι η ανάπτυξη πιο αξιόπιστων μικροσκοπικών κυκλοφοριακών προτύπων. Αναπτύσσεται μια ολοκληρωμένη μεθοδολογία για την εκτίμηση προτύπων κυκλοφοριακής προσομοίωσης με τη χρήση καινοτόμων και ευέλικτων μεθόδων μηχανικής μάθησης, όπως η ταξινόμηση, η ομαδοποίηση, η τοπικά σταθμισμένη παλινδρόμηση (loess), οι καμπύλες splines, οι Gaussian διαδικασίες, οι διανυσματικές μηχανές υποστήριξης και τα νευρωνικά δίκτυα. Τα δεδομένα που χρησιμοποιήθηκαν στην έρευνα αυτή περιλαμβάνουν δεδομένα από τρεις διαφορετικές πηγές, δεδομένα από τη Νάπολη, τα NGSIM δεδομένα και δεδομένα από την Ινδία. Δίνεται έμφαση στα πρότυπα ακολουθίας οχημάτων και για τα ίδια δεδομένα εφαρμόζεται το μοντέλο του Gipps, ένα γνωστό μοντέλο ακολουθίας οχημάτων που χρησιμοποιείται ως μοντέλο αναφοράς στην παρούσα έρευνα. Επειδή πολλοί παράγοντες επηρεάζουν τη συμπεριφορά του οδηγού, εξετάζεται κατά πόσο βελτιώνεται το μοντέλο ενσωματώνοντας περισσότερες μεταβλητές. Επιπλέον, εξετάζεται η δυναμική βαθμονόμηση κυκλοφοριακών προτύπων λαμβάνοντας υπόψη τη δυναμική μεταβολή των παραμέτρων για κάθε οδηγό, στον χρόνο και το χώρο. Οι παράμετροι μεταβάλλονται σε έναν κυλιόμενο χρονικό ορίζοντα και επιτυγχάνεται πρόβλεψη της ταχύτητας έως 10% για δέκα βήματα στο μέλλον. Διερευνάται η χρήση μοντέλων καθοδηγούμενων από τα δεδομένα σε συνθήκες μεικτής κυκλοφορίας χωρίς τήρηση των λωρίδων κυκλοφορίας και με μεγάλη ποικιλία ως προς τον τύπο των οχημάτων, κοινά χαρακτηριστικά των αναπτυσσόμενων χωρών. Αν και τα κλασσικά πρότυπα ακολουθίας οχημάτων είναι θεωρητικά τεκμηριωμένα, τα πρότυπα βασισμένα σε δεδομένα προσφέρουν μεγαλύτερη ευελιξία και επιτρέπουν την εύκολη ενσωμάτωση νέων μεταβλητών. Τα αποτελέσματα υποδεικνύουν ότι τα πρότυπα που βασίζονται σε δεδομένα μπορούν να συμβάλλουν στην εκτίμηση πιο αξιόπιστων μικροσκοπικών προτύπων.
Sharp detection of oscillation packets in rich time-frequency representations of neural signals

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics.
Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

Most existing air target intention recognition methods use only single-moment information, risking failure when acquiring data containing noise and many outliers. The robustness of methods that utilize continuous moment information has yet to be explored. This paper designs a robust recognition method for air target intention to address the above problems. The method takes data with noise and outliers as the object, based on a parallel time-channel Transformer Encoder and a weight self-learning unit. First, a detailed introduction to air target intention recognition and robust recognition is given, and the intention space and feature space are defined. Subsequently, the data samples are reconstructed using a fixed-step sliding window to increase the information utilized with multi-moment information as input. Finally, step-wise and channel-wise correlations are extracted using a time-axis Transformer Encoder and a channel-axis Transformer Encoder, respectively, and the weights of the two branches’ outputs are automatically learnt using a weight self-learning unit. This enhanced self-attention network allocates attention weights between elements in the time and channel domain sequences to capture their long-range and short-range relationships and extract recognizable representations, making it robust to outliers and noise. The experimental results show that the model’s recognition accuracy and composite F1 score reach 96.9% and 0.9676, and its performance remains well when the noise level and outliers proportion increase. The ablation and comparison experiments show its advantage in accuracy over other models.
Taking a Break: The Effects of Partaking in a Two-Week Social Media Digital Detox on Problematic Smartphone and Social Media Use, and Other Health-Related Outcomes among Young Adults

Despite their increasing popularity, especially among young adults, there is a dearth of research examining the effectiveness of digital detoxes focused on restricting or limiting social media use. As such, the purpose of this exploratory study was to create and carry out a social media digital detox among young adults and evaluate its effectiveness with regards to smartphone and social media addiction, as well as several health-related outcomes. Additionally, the study also sought to obtain an understanding of participants’ experiences and perceptions regarding the digital detox via semi-structured exit interviews in order to improve and maximize the effectiveness of future social media digital detox interventions. Thirty-one young adults completed a two-week social media digital detox (preceded by a two-week baseline period and followed up by a two-week follow-up period), whereby their social media use was limited to 30 min per day. A series of one-way repeated measures analyses of variance revealed that a two-week social media detox improved smartphone and social media addiction, as well as sleep, satisfaction with life, stress, perceived wellness, and supportive relationships. Thematic analysis of exit interviews also revealed eight themes: feelings, effort to detox, adjustment period, the Goldilocks effect, screen to screen, post-detox binge, progress not perfection, and words of wisdom, all of which provide contextualization of the quantitative findings and valuable insights for future detoxes. In conclusion, the findings of this exploratory study provide initial support for the use of social media digital detoxes, suggesting that limiting usage can have beneficial effects with regards to smartphone and social media addiction, as well as many other health-related outcomes.
Burnt Big Trees by the Aso-4 Pyroclastic Flow

佐賀県三養基郡上峰町堤の八藤遺跡において, Aso-4火砕流堆積物中から3本の巨木を含む多数の樹木群が出土した. すべての樹木は, 表面あるいは全体が炭化しており, 火砕流堆積物中には炭化木から派生する「煙の化石」が随所にみられたことから, これらはAso-4火砕流によって倒され, この場で焼かれたものであることが判明した. また, 火砕流堆積物の直下には森林古土壌とともに, 多くの樹根が発見された. このような事実は, 巨大火砕流災害の具体的証拠を示すものとして重要であるばかりか, Aso-4噴出当時の森林植生を直接復元する上でも重要である. また, 巨木は表面のみが焼かれて炭化しているが, 内部の保存は良好である. 樹木の枯死年代はAso-4火砕流の噴出年代と一致するとみられ, Aso-4によりこれらの樹木の年代決定が可能である. さらに, これらの樹木は当時の古環境やAso-4火砕流の性質を知るための分析用試料として価値が高い.
Off the beaten track. How ecosystems fail to respond to environmental change

Globally, ecosystems are exposed to human-induced changes in the environment. The effect of environmental changes on ecosystems can be understood with dynamical models. Such models have suggested that environmental change can trigger so-called critical transitions in ecosystems. A critical transition is a shift of a system to a qualitatively different dynamical regime. Critical transitions occur when conditions change beyond some critical threshold and include discontinuous, hard to reverse shifts to alternative stable regimes (catastrophic shifts), transitions from static to cyclic dynamics and the formation of spatially periodic patterns out of uniform states. Through equilibrium analysis the mechanisms behind these critical transitions can be studied in models by assuming that ecosystems are spatially uniform and in equilibrium. However, although frequently assumed, ecosystems often do not reside in a uniform equilibrium state. This dissertation investigates the response of ecological models that are "off the beaten track", i.e. that are not in a uniform equilibrium state, because of spatial patterning (Chapter 2), because of pulsed resource input (Chapter 3), because of cyclic dynamics (Chapter 4) or because of rapid environmental change (Chapter 5).
Dual Consistency-Constrained Learning for Unsupervised Visible-Infrared Person Re-Identification

Unsupervised visible-infrared person re-identification (US-VI-ReID) aims at learning a cross-modality matching model under unsupervised conditions, which is an extremely important task for practical nighttime surveillance to retrieve a specific identity. Previous advanced US-VI-ReID works mainly focus on associating the positive cross-modality identities to optimize the feature extractor by off-line manners, inevitably resulting in error accumulation of incorrect off-line cross-modality associations in each training epoch due to the intra-modality and inter-modality discrepancies. They ignore the direct cross-modality feature interaction in the training process, i.e., the on-line representation learning and updating. Worse still, existing interaction methods are also susceptible to inter-modality differences, leading to unreliable heterogeneous neighborhood learning. To address the above issues, we propose a dual consistency-constrained learning framework (DCCL) simultaneously incorporating off-line cross-modality label refinement and on-line feature interaction learning. The basic idea is that the relations between cross-modality instance-instance and instance-identity should be consistent. More specifically, DCCL constructs an instance memory, an identity memory, and a domain memory for each modality. At the beginning of each training epoch, DCCL explores the off-line consistency of cross-modality instance-instance and instance-identity similarities to refine the reliable cross-modality identities. During the training, DCCL finds credible homogeneous and heterogeneous neighborhoods with on-line consistency between query-instance similarity and query-instance domain probability similarities for feature interaction in one batch, enhancing the robustness against intra-modality and inter-modality variations. Extensive experiments validate that our method significantly outperforms existing works, and even surpasses some supervised counterparts. The source code is available at https://github.com/yangbincv/DCCL.
Kv1.3 Blockade Alleviates White Matter Injury through Reshaping M1/M2 Phenotypes via the NF-κB Signaling Pathway after Intracerebral Hemorrhage.

BACKGROUND White matter injury (WMI) in basal ganglia usually induces long-term disability post intracerebral hemorrhage (ICH). Kv1.3 is an ion channel expressed in microglia and induces neuroinflammation after ICH. Here, we investigated the functions and roles of Kv1.3 activation-induced inflammatory response in WMI and the Kv1.3 blockade effect on microglia polarization after ICH. METHODS Mice ICH model was constructed by autologous blood injection. The expression of Kv1.3 was measured using immunoblot, real-time quantitative polymerase chain reaction (RT-qPCR), and immunostaining assays. Then, the effect of administration of 5-(4-Phenoxybutoxy) psoralen (PAP-1), a selectively pharmacological Kv1.3 blocker, was investigated using open field test (OFT) and basso mouse score (BMS). RT-qPCR, immunoblot, and enzyme-linked immunosorbent assay (ELISA) were taken to elucidate the expression of pro-inflammatory or anti-inflammatory factors around hematoma. PAP-1's function in regulating microglia polarization was investigated using immunoblot, RT-qPCR, and immunostaining assays. The downstream PAP-1 signaling pathway was determined by RT-qPCR and immunoblot. RESULTS Kv1.3 expression was increased in microglia around the hematoma significantly after ICH. PAP-1 markedly improved neurological outcomes and the WMI by reducing pro-inflammatory cytokine accumulation and upregulating anti-inflammatory factors. Mechanistically, PAP-1 reduces NF-κB p65 and p50 activation, thus facilitating microglia polarization into M2-like microglia, which exerts this beneficial effect. CONCLUSIONS PAP-1 reduced pro-inflammatory cytokines accumulation and increased anti-inflammatory factors by facilitating M2-like microglia polarization via the NF-κB signaling pathway. Thus, the current study shows that the Kv1.3 blockade is capable of ameliorating WMI by facilitating M2-like phenotype microglia polarization after ICH.
Effectiveness of Sternal Intraosseous Device in Patients Presenting with Circulatory Shock: A Retrospective Observational Study.

BACKGROUND Hemorrhagic shock requires timely administration of blood products and resuscitative adjuncts through multiple access sites. Intraosseous (IO) devices offer an alternative to intravenous (IV) access as recommended by the massive hemorrhage, A-airway, R-respiratory, C-circulation, and H-hypothermia (MARCH) algorithm of Tactical Combat Casualty Care (TCCC). However, venous injuries proximal to the site of IO access may complicate resuscitative attempts. Sternal IO access represents an alternative pioneered by military personnel. However, its effectiveness in patients with shock is supported by limited evidence. We conducted a pilot study of two sternal-IO devices to investigate the efficacy of sternal-IO access in civilian trauma care. METHODS A retrospective review (October 2020 to June 2021) involving injured patients receiving either a TALON® or a FAST1® sternal-IO device was performed at a large urban quaternary academic medical center. Baseline demographics, injury characteristics, vascular access sites, blood products and medications administered, and outcomes were analyzed. The primary outcome was a successful sternal-IO attempt. RESULTS Nine males with gunshot wounds transported to the hospital by police were included in this study. Eight patients were pulseless on arrival, and one became pulseless shortly thereafter. Seven (78%) sternal-IO placements were successful, including six TALON devices and one of the three FAST1 devices, as FAST1 placement required attention to Operator positioning following resuscitative thoracotomy. Three patients achieved return of spontaneous circulation, two proceeded to the operating room, but none survived to discharge. CONCLUSIONS Sternal-IO access was successful in nearly 80% of attempts. The indications for sternal-IO placement among civilians require further evaluation compared with IV and extremity IO access.
A Self-Aligning Upper-Limb Exoskeleton Preserving Natural Shoulder Movements: Kinematic Compatibility Analysis

NESM- $\gamma $ is an upper-limb exoskeleton to train motor functions of post-stroke patients. Based on the kinesiology of the upper limb, the NESM- $\gamma $ includes a four degrees-of-freedom (DOF) active kinematic chain for the shoulder and elbow, along with a passive chain for self-aligning robotic joint axes with the glenohumeral (GH) joint’s center of rotation. The passive chain accounts for scapulohumeral rhythm and trunk rotations. To assess self-aligning performance, we analyzed the kinematic and electromyographic data of the shoulder in eight healthy subjects performing reaching tasks under three experimental conditions: moving without the exoskeleton (baseline), moving while wearing the exoskeleton with the passive DOFs properly functioning, i.e., unlocked (human-in-the-loop(HIL)-unlocked), and with the passive DOFs locked (HIL-locked). Comparison of baseline and HIL-unlocked conditions showed nearly unchanged anatomical movement patterns, with a root-mean-square error of shoulder angle lower than 5 deg and median deviations of the GH center of rotation below 20 mm. Peak muscle activations showed no significant differences. In contrast, the HIL-locked condition deviated significantly from the baseline, as observed by the trunk and GH trajectory deviations up to 50 mm, accompanied by increased peak muscle activations in the Deltoid and Upper Trapezius muscles. These findings highlight the need for kinematic solutions in shoulder exoskeletons that can accommodate the movements of the entire shoulder complex and trunk to achieve kinematic compatibility.
Effect of a geothermal heat pump system on cooling residential buildings in a hot, dry climate

ABSTRACT In the last century, electricity demand has doubled due to urban expansion, which has contributed to the formation of more urban heat islands (UHI) and the appearance of environmental hazards such as the global climate change phenomenon. Since residential buildings are considered the main electricity consumer sector in Egypt, they consume up to 42% of total energy consumption, which contributes to increasing temperatures and constitutes UHI in cities. In this context, the research aims to examine the effectiveness of using the closed vertical loop geothermal system (GSHPCV) for cooling residential buildings in a hot, dry climate, such as Cairo, Egypt. In addition to determining its effect on carbon emissions and the amount of energy consumed for cooling, this was done using TRNSYS-17 software as a simulation tool to compare the traditional building in a hot, dry climate before and after adding GSHPCV in Cairo, Egypt. The simulation revealed that using GSHPCV in hot, dry climate zones has a significant impact on residential buildings. It reduced the electricity consumption up to 19.7% of the electricity consumed for cooling, and it reduced temperatures indoors for buildings up to 15.8% on the ground floor, up to 11.3% on the first floor, and up to 3.5% on the roof floor. In addition, it reduced up to 19.7% of the carbon emissions and reached human thermal comfort on the ground floor for the study case at peak times during the summer period. Which provide a solution to reduce UHI and environmental hazards such as climate change.
Using a large ensemble of simulations to assess the Northern Hemisphere stratospheric dynamical response to tropical volcanic eruptions and its uncertainty

The observed strengthening of the Northern Hemisphere (NH) polar vortex after tropical volcanic eruptions appears to be underestimated by coupled climate models. However, there are only a limited number of observed eruptions, which makes the attribution of volcanic signals difficult, because the polar vortex is also influenced by other external forcing factors as well as internal variability. We show with a 100‐member ensemble of historical (1850–2005) simulations with the Max Planck Institute Earth System Model that an ensemble larger than what is provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models is needed to detect a statistically significant NH polar vortex strengthening. The most robust signal can be found when only the two strongest eruptions (Krakatau and Pinatubo) are considered in contrast to including smaller eruptions to increase the sample size. For these two strongest eruptions, the mean of 15 CMIP5 models shows a statistically significant strengthening of the NH polar vortex as well.
Syddansk Universitet Patient Reported Outcomes and Clinical Rating Scales for Fatigue and Cognition in Multiple Sclerosis

Background: Our objective was to validate the Danish translation of the Fatigue Scale for Motor and Cognitive Functions (FSMC) in multiple sclerosis (MS) patients. Materials and methods: A Danish MS cohort (n = 84) was matched and compared to the original German validation cohort (n = 309) and a healthy control cohort (n = 147). The Modified Fatigue Impact Scale (MFIS) was used as reference scale and Becks Depression Inventory-Fast Screen (BDI-FS) and Expanded Disability Status Scale (EDSS) for confounding factors. We assessed internal consistencies; convergent, divergent, and predictive validity; partial correlations correcting for depression; significant differences between the mean scores of the cohorts; and sensitivity and specificity with receiver operating characteristic (ROC) curves. Results: Excellent internal consistencies for the total scale and subscales were found (α = 0.91–0.95). Strong positive correlations between the two fatigue scales implied high convergent validity (total scores: r = 0.851, p< 0.01). The two cohorts corresponded well when divided into subgroups (EDSS score; age; gender). Correcting for depression did not result in any significant adjustments of the correlations. The area under the curve (AUC) for the ROC curves represented excellent accuracy (Danish MS cohort, AUC = 0.9190; German MS cohort, AUC = 0.9034). Conclusion: The Danish translation of the FSMC has a high convergent validity with another measure of fatigue as well as excellent internal consistency and accuracy. It is found to be an applicable and recommendable measure of fatigue in Danish MS patients.