Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense manual labor and lacks standardized assessment. In this work, we established two standard benchmark datasets of NHPs in the laboratory: MonkeyinLab (MiL), which includes 13 categories of actions and postures, and MiL2D, which includes sequences of two-dimensional (2D) skeleton features. Furthermore, based on recent methodological advances in deep learning and skeleton visualization, we introduced the MonkeyMonitorKit (MonKit) toolbox for automatic action recognition, posture estimation, and identification of fine motor activity in monkeys. Using the datasets and MonKit, we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome (RTT). MonKit was used to assess motor function, stereotyped behaviors, and depressive phenotypes, with the outcomes compared with human manual detection. MonKit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency, thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.
Understanding herding behavior among Indonesian stock market investors

This paper examined the factors that affect investor herd behavior. Herd behavior bias is a well- known phenomenon in financial markets in which individuals blindly follow the investing decisions of others without understanding the fundamental principles of investment. While extensive research has explored herd behavior in financial markets, there remains a significant research gap in understanding herding behavior at the level of individual investors. Previous studies have primarily focused on herding behavior within market contexts, neglecting the examination of specific factors that influence herding behavior among individual investors. Using a sample of 217 Indonesian investors this study investigates the factors that influence herding behavior. The findings showed that stock influencer credibility had a significant positive impact on herd behavior bias, suggesting that investors with high trust in stock influencers were likely to follow the herd. Financial literacy also had a significant negative impact on herd behavior bias, indicating that investors with higher financial knowledge are less likely to herd. However, financial literacy was found to have a significance towards herding behavior when mediated through risk perception. Lastly, risk perception had a significant positive impact on herd behavior bias, indicating that investors with higher risk perception are more likely to follow the herd. This study contributes to the existing literature by enhancing our understanding of herd behavior dynamics among investors, specifically at the individual level. By exploring the factors that influence herding behavior, it provides valuable insights into the mechanisms that drive this bias. The findings highlight the importance of targeted financial education programs and regulatory measures to address herding behavior bias among Indonesian investors, focusing on enhancing financial literacy and reducing risk perception.
Ground surface changes detectable by Earth observation and their impact on the stability of slopes

Abstrakt. Artyku3 przedstawia zastosowania teledetekcji satelitarnej (EO) w badaniu osuwisk egzaminuj1c g3ówne przyczyny ruchów osuwiskowych i typy deformacji stoków. Wprowadzono koncepcjê poœrednich indykatorów rozpoznawalnych na powierzchni Ziemi i wskazuj1cych na zmiany granicy równowagi stoków. Wp3yw tych zmian na wspó3czynnik bezpieczeñstwa i na deformacje stoków jest analizowany z uwzglêdnieniem ró¿nych klas materia3ów geotechnicznych. Wyniki s1 przedstawione syntetycznie w formie tabeli, wskazuj1cej ich zastosowanie do kartowania stopnia zagro¿enia osuwiskowego.
Governance and economic growth in developing countries: A panel threshold regression analysis

This study is aimed at analyzing the effect of the governance index and the governance components index on economic growth in 48 developing countries over the period 2002-2020. Corruption control, the effectiveness of the government, political stability, and regulatory quality are but a few of the many variables taken into account by the governance components index. The findings of the study show that governance has an asymmetric effect on economic growth. Moreover, the results indicate that enhancing governance in developing countries can obstruct economic growth in them. This outcome should not surprise and cast doubt on the positive effects of sound governance on economic growth, as improving governance requires numerous resources currently lacking in these countries. Therefore, policymakers must boost economic growth at the initial stage so that they can identify resources for improving governance and capitalize on them as well.
Carbon stable isotope paleoecology of carnivorans from the Miocene localities of Batallones-1 and Batallones-3 (Madrid Basin, Spain)

2Departamento de Geología Sedimentaria y Cambio Medioambiental, Instituto de Geociencias IGEO-CSIC-UCM, C/ José Antonio Novais 12, 28040, Madrid, Spain. 3Earth and Planetary Sciences Department, University of California Santa Cruz, 1156 High Street, Santa Cruz, California 95064, U.S.A. 4Instituto de Investigación Científica y Desarrollo tecnológico (INCYT-UPSE), Universidad Estatal Península de Santa Elena, Avda. Principal La Libertad, 240210, Santa Elena, Ecuador. 5Institut Català de Paleontologia Miquel Crusafont, Campus Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Barcelona, Spain. 6Departamento de Paleobiología, Museo Nacional de Ciencias Naturales-CSIC, C/ José Gutiérrez Abascal 2, 28006, Madrid, Spain. M. Soledad Domingo1*, Laura Domingo1, 2, 3, Juan Abella4, 5, Alberto Valenciano1, 2 y Jorge Morales6 Paleoecología de mamíferos carnívoros de los yacimientos miocenos de Batallones-1 y Batallones-3 (Cuenca de Madrid, España) a través de isótopos estables del carbono Carbon stable isotope paleoecology of carnivorans from the Miocene localities of Batallones-1 and Batallones-3 (Madrid Basin, Spain)
Simultaneous size and shape structural optimization using enhanced comprehensive learning particle swarm optimization

This study proposes a simultaneous size and shape structural optimization on truss structures using a metaheuristic algorithm, namely Enhanced Comprehensive Learning Particle Swarm Optimization (ECLPSO) which is based on the�CLPSO algorithm. CLPSO itself was originated from a famous Particle Swarm Optimization (PSO) that was invented�based on behaviour or movement of a bird flock. CLPSO is an improved PSO algorithm which has good exploration ability but is poor in exploitation. Thus, ECLPSO introduces of two enhancements, including perturbation-based exploitation and adaptive learning probabilities, to improve the exploitation ability and to adjust the learning probabilities of each particle, respectively. The objective of the study is to obtain the minimum weight of the benchmark truss structures under the design constraints required specifically for each problem. By combining size variables and shape variables of the problems, the optimizations�shall produce better results than those of size optimizations alone. In this research, the optimizations are implemented in Python code and performed several times individually to conclude the robustness and accuracy of the algorithm. The results including the minimum weight and standard deviation of the solutions shall be compared and discussed with those of some other metaheuristic algorithms in the literatures.
Consensus With a Linear Constraint

In this article, we consider a network of agents running a linear or nonlinear consensus algorithm with the following goal: in addition to requiring that the outputs of all agents converge to the same value, we also require that a specified linear function of the agent states remains constant during the evolution of the consensus algorithm. To achieve this goal requires the construction of a matrix of weighting parameters with specific properties. In this article, we present a noniterative centralized algorithm and an iterative decentralized algorithm for determining the weighting parameters. In the decentralized algorithm, the weighting parameters are specified by the agents and each agent only specifies the weighting parameters associated with the agents to which it communicates. The results do not require that the communication graph of the network be bidirectional. The results of this article can also be applied to consensus problems where one wants to achieve consensus to a specified weighting of the initials states of the network.
A New MHD Model with a Rotated-hybrid Scheme and Solenoidality-preserving Approach

In this paper, the rotated-hybrid scheme is applied for the first time to 3D magnetohydrodynamics (MHD) equations in the finite-volume frame. This scheme is devised by decomposing a cell-face normal vector into two orthogonal directions and combining the Roe solver, a full-wave or complete Riemann solver, and the Rusanov solver, an incomplete Riemann solver, into one rotated-hybrid Riemann solver. To keep the magnetic field divergence-free, we propose two kinds of divergence-cleaning approaches by combining the least-squares reconstruction of magnetic field with the divergence-free constraints. One is the locally solenoidality-preserving method designed to locally maintain the magnetic solenoidality exactly, not just in a least-squares sense, and another is the globally solenoidality-preserving (SP) approach that is implemented by adding a global constraint but abandons the exactness of the locally divergence-free condition. Both SP methods are employed for 3D MHD with a rotated-hybrid scheme in the finite-volume frame. To validate and demonstrate the capabilities of the rotated-hybrid scheme for MHD, we perform an Orszag–Tang MHD vortex problem and a numerical study for the steady-state coronal structures of Carrington rotation 2068 during the solar activity minimum. The numerical tests show the robustness of the proposed scheme and demonstrate the capability of these two SP approaches to keep the magnetic divergence errors to the expected accuracy.
Mechanical optimisation of Ni coatings produced by low-pressure cold spray

ABSTRACT Nickel-based coatings are widely used as thermal barriers in several sectors thanks to their remarkable corrosion and wear resistance and outstanding stability at high temperatures. Recently, Ni coatings, produced with thermal spraying and vacuum techniques, have been investigated for solar power energy applications. In the present manuscript, low-pressure cold spray (LPCS) was used to deposit pure Nickel onto a steel substrate. The influence of gas temperature, nozzle stand-off distance, and advancing speed on morphological and mechanical properties were studied. The optimal deposition conditions were derived by ANOVA analysis. The hardness and the adhesion strength were approximately 160 HV and 26 MPa, respectively. The highest thickness obtained under the optimised deposition with a single pass was around 900 µm.
Managing Carbon Foot Prints in Supply Chain for Imperfect Quality Items

Producing 100% high-quality items is a challenging task in today’s era, which often results in the delivery of defective products. Therefore, inspecting the lot becomes a necessity. This paper investigates the impact of environmental concerns on the inventory model by incorporating fuel costs and emission taxes. The objective of the study is to minimize the expected total cost and determine the optimal order quantity. The study includes a numerical example with sensitivity analysis to determine the model’s robustness. The findings of the study indicate that minimizing emissions also reduces the expected total cost. The paper highlights the significance of considering environmental concerns in inventory management. The study highlights the importance of addressing environmental issues in inventory management and provides a framework for minimizing costs while considering the impact on the environment.
Information Technology as a catalyst for the professionalisation of academic administrators: A case study of a health sciences faculty

As academic administrators become a key staffing component of efficiently run universities, their role as knowledge workers is being reframed. This is especially evident as universities increasingly rely on information technologies and student information systems to process vast amounts of student data in the pursuit of improving student success and assisting senior management with future planning. In this study, a qualitative research approach was employed to explore the enabling factors that contributed to academic administrators becoming central to various data quality management processes. Twenty-six (26) participants were purposively selected to participate in this study. Data collection strategies included the use of semi-structured interviews, focus groups, observations and documentary sources. The findings demonstrate the interrelationship between the professionalisation of academic administrators and enhanced data quality management processes. The results reassert the argument for reconceptualising the role of academic administrators as knowledge workers. This position has implications for how such staff are perceived within the organisational structure of the university and the types of staff development and training required.
A personalized diet intervention improves depression symptoms and changes microbiota and metabolite profiles among community-dwelling older adults

Introduction The impact of diet on mental well-being and gut microorganisms in humans is well recognized. However, research on the connections between food nutrients, gut microbiota, and mental health remains limited. To address this, the present study aimed to assess the effects of a personalized diet, based on individual needs and aligned with the Mediterranean diet principles, on depression symptoms, quality of life, nutritional intake, and gut microbiota changes among older adults living in the community. Methods The intervention involved regular visits from a registered dietitian, who provided tailored dietary recommendations. During the 6-month study, participants completed questionnaires to evaluate their depression levels, quality of life, and dietary habits. Additionally, they provided stool samples for analysis of gut microbiota and metabolites. Results The results demonstrated that the personalized dietary intervention reduced depression symptoms and improved the quality of life among older adults. Furthermore, significant changes in the intake of certain nutrients, such as folate, lutein, zeaxanthin, EPA, and DHA, were observed following the intervention. Moreover, the intervention was associated with increased diversity in the gut microbiome and reduced total short-chain fatty acids, the main metabolites produced by gut microorganisms. The study also revealed correlations between food nutrients, gut microbiota, and mental health parameters. Discussion In conclusion, this research highlights the potential advantages of personalized dietary interventions in managing depression and enhancing overall well-being among older populations. It also sheds light on the role of gut microbiota and its metabolites in these effects. The findings offer valuable insights into the significance of nutrition and gut health for mental well-being in older adults.
Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Today's concern about climate change and the country's economic growth have made adopting hybrid electric vehicles (HEV) a worthy solution. Since the gas emissions of HEVs are closely related to their motor performance, developing an effective HEV motor control technique is essential. The performance and acceleration of an HEV motor are determined by the internal combustion engine (ICE) and the electronic throttle plate (ETP), which employ an air‐fuel mixture for vehicle propulsion. In order to deal with this problem, this article presented an optimal fuzzy controller that controls the throttle plate's angular position and motor speed in HEVs. However, the efficiency and reliability of fuzzy controllers depend upon their gains factor. So, a different heuristic algorithm is employed to self‐tuning the fuzzy logic controller (FLC) gain factor. The performance of the suggested ChOA‐fuzzy‐based controller was evaluated utilizing various control error indices and time‐domain stability. A comprehensive controller performance analysis using different metaheuristic techniques has been carried out to validate the proposed scheme. The findings show that the suggested ChOA‐fuzzy‐based controller performs better than other techniques.
Microshear bond strength and microleakage of glass ionomer cement on aged dentin

Objective: To compare the microshear bond strength and microleakage of glass ionomer cements when bonded to aged and young dentin. Background: Root caries commonly occurs in elderly patients. Glass ionomer cement (GIC) is frequently used to restore root caries. Many studies of GIC have been conducted using young dentin, however few studies have assessed adhesion and microleakage of GICs to aged dentin. � Materials and methods: Seventy-eight non-carious human molars (patient age 16-30 = 39; patient age ? 65 = 39) were tested with three commercial GICs (Fuji II LC, Equia Forte Fil, and Fuji BULK). For microshear bond strength (? SBS), teeth were horizontally sectioned and embedded in resin and three tubes attached to the sectioned surface. Materials were mixed and injected into the tubes, allowed to set and the tubes removed leaving the GIC cylinders. Specimens were stored in deionized water for 24 h at 25? C and subjected to a shear force in a universal testing machine. For microleakage, a buccocervical cavity was prepared, restored with GIC, and stored in deionized water for 24 h at 25? C. The specimens were polished, coated with nail varnish, placed in 0.5% basic fuchsin for 24 h, sectioned at the midpoint and evaluated for microleakage under a stereomicroscope and scanning electron microscope (SEM). Results: For ? SBS, Fuji II LC showed the highest bond strength among all three products (young dentin, 7.29 MPa; aged dentin, 8.59 MPa; P < 0.001). There was no significant difference between age groups. For microleakage, Fuji II LC had more dye penetration (P < 0.01) and there was significant difference between age groups. (Occlusal: P = 0.007, Gingival: P = 0.02) Conclusion:� After bonding of GICs, aged dentin showed no difference in ? SBS but less microleakage when compared to young dentin.
Application of Normalized Compression Distance and Lempel-Ziv Jaccard Distance in Micro-electrode Signal Stream Classification for the Surgical Treatment of Parkinson’s Disease

Abstract Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No new features needed to be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted.
Reliability of the multipeg™ transducer in meas-uring dental implant stability by using a reso-nance frequency analysis device (Osstell®): An observational clinical study

Background: Measuring implant stability is an important issue in predicting treatment success. Dental implant stability is usually measured through resonance frequency analysis (RFA). Osstell® RFA devices can be used with transducers (Smartpeg™) that correspond to the implants used as well as with transducers designed for application with Penguin® RFA devices (Multipeg™). Aims: This study aims to assess the reliability of a MultiPeg™ transducer with an Osstell® device in measuring dental implant stability. Materials and Methods: Sixteen healthy participants who required dental implant treatment were enrolled in this study. Implant stability was measured by using an Osstell® device with two transducers, namely, Smartpeg™ and Multipeg™. Insertion torque was also measured and recorded as >50 and ≤50 N·cm. Unpaired t-test and Mann–Whitney U test were conducted to assess the relationships of the implant stability values obtained by the two transducers with insertion torque, whereas Pearson and Spearman's correlations were utilized to investigate correlations between the two transducers. Interclass correlation coefficients were applied to assess the reliability between the two transducers. Results: Implant stability measurements (primary and secondary) showed strong positive correlations between Smartpeg™ and Multipeg™. The reliability values between both transducers in primary and secondary implant stability measurements were 0.922 and 0.981, respectively. The use of both transducers revealed higher implant stability measurements for implants inserted with insertion torque > 50 N·cm than those inserted with insertion torque ≤ 50 N·cm. Conclusions: This study demonstrated that the Multipeg™ transducer is reliable in measuring the stability of dental implants using an Osstell® device.