Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://github.com/nku-zhichengzhang/WECL.
Fermentation parameters in the rumen of goats supplemented with polyphenol oxidase derived from Gliricidia sepium leaves under in vitro conditions

Abstract. Huda AN, Sabarudin A, Jayanegara A, Soetanto H. 2023. Fermentation parameters in the rumen of goats supplemented with polyphenol oxidase derived from Gliricidia sepium leaves under in vitro conditions. Biodiversitas 24: 3282-3290. The growing demand for healthy foods derived from ruminant products is restricted by the high content of saturated fatty acids in meat or milk due to the process of rumen biohydrogenation. Recent reports from intensive studies using polyphenol oxidase (PPO), an enzyme in plants’ secondary metabolism, as the inhibitor of rumen biohydrogenation cost doubt of ruminant products. PPO is assumed to increase the supply of UFA by reducing the rate of biohydrogenation due to rumen microbes’ activity. Microbes of the rumen, including species of bacteria and protozoa are responsible for rumen biohydrogenation. The present study investigated the effect of adding PPO derived from Gliricidia sepium leaves on fermentation parameters in the rumen, including protozoa density and diversity, fatty acid profile, volatile fatty acid profile, and the efficiency of microbial protein synthesis under in vitro conditions. The experiment was carried out through an in vitro rumen fermentation method, consisting of five treatments, i.e., negative control, positive control, the addition of 0.1 ml, 0.3 ml, and 0.5 ml PPO emulsion. The most dominant type of protozoa among all treatments was Entodinium spp. PPO emulsion increases the efficiency of microbial protein synthesis and volatile fatty acid concentration. 0.5 ml PPO emulsion increases linoleic acid. In conclusion, this finding poses a question on the role of rumen protozoa in lipid biohydrogenation in the rumen. A further experiment on the benefits of adding PPO regarding rumen biohydrogenation under in vivo conditions is warranted.
Support for Scalable Emulation of Distributed Systems

Distributed Systems are becoming increasingly prevalent in our daily lives and their relevance has never been so prominent in everyday activities. This naturally leads systems to increase in complexity and dimension, and users to have much higher expectations about the performance and correctness of these systems. This creates a signiﬁcant pressure for conducting systematic and detailed performance assessments of novel proposals through experimentation. Conducting experiments on distributed systems can be challenging, since one has to launch multiple processes and retain control over them. While you can manually run and control experiments in small distributed systems, such possibility becomes daunting when large-scale distributed systems such as peer-to-peer systems or recent proposes in the context of Web 3.0. To be able to extract key performance metrics, which depend heavily on the underlying network, and since the networks where these systems operate are many times unstable, this can lead to inconclusive results. Moreover, when considering systems operating at a global scale, it is extremely hard to have access to competitive resources in many locations, to achieve realistic conditions. To emulate the underlying network realistically we can use linux tc to shape links between the end hosts to exhibit target properties. To do this for large systems with thousands of nodes is however, very complex. In this work, we propose a tool that assists in deﬁning and executing experiments for large scale distributed systems, using container technology and network emulation. We propose an architecture where a manager node exists per physical machine, and this manager is responsible for all the application nodes in that physical machine. This responsibility goes from receiving the experiment information (network topology, link properties, dynamic events, such as node failures), to ensuring that every application node receive their corresponding events. Lastly, we plan to evaluate our tool by conducting users tests, and comparing it to state-of-the-art tools. This work is being conducted in collaboration with Protocol Labs, as a means to test their DHT in a realistic environment.
Robust Bayesian Regression Analysis Using Ramsay-Novick Distributed Errors with Student-t Prior

This paper investigates bayesian treatment of regression modelling with Ramsay - Novick (RN) distribution specifically developed for robust inferential procedures. It falls into the category of the so-called heavy-tailed distributions generally accepted as outlier resistant densities. RN is obtained by coverting the usual form of a non-robust density to a robust likelihood through the modification of its unbounded influence function. The resulting distributional form is quite complicated which is the reason for its limited applications in bayesian analyses of real problems. With the help of innovative Markov Chain Monte Carlo (MCMC) methods and softwares currently available, here we first suggested a random number generator for RN distribution. Then, we developed a robust bayesian modelling with RN distributed errors and Student-t prior. The prior with heavy-tailed properties is here chosen to provide a built-in protection against the misspecification of conflicting expert knowledge (i.e. prior robustness). This is particularly useful to avoid accusations of too much subjective bias in the prior specification. A simulation study conducted for performance assessment and a real-data application on the famously known "stack loss" data demonstrated that robust bayesian estimates with RN likelihood and heavy-tailed prior are robust against outliers in all directions and inaccurately specified priors.
Wood Supply from Family Forests of the United States: Biophysical, Social, and Economic Factors

Wood products are an essential provisioning ecosystem service with US forests providing nearly one-fifth of global wood supply. As of 2018, an estimated 46% of the annual wood harvested came from corporate forests, 42% came from family forests, and the remainder came from other private, public, and Tribal forests. The supply of wood from corporate forests is well described by traditional economic models, but the supply from family forests is much less well understood. This article combines data from three components of the USDA Forest Service’s Forest Inventory and Analysis program—plots, landowner surveys, and mill surveys—with other data to model the wood supply from family forests in the United States. Results are summarized in terms of bivariate relationships and a logistic regression model. The model results show that basal area, stand origin, forest type, having timber as an ownership objective, the amount of annual income derived from their forestland, proximity to a mill, management advice, and region are significantly associated with family forest timber harvesting. The results should be useful for forest industry analysts and others interested in understanding the current and potential future supply of wood from family forests.
The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan’s innovative AI hospital system

Public and private investments into developing digital health technologies—including artificial intelligence (AI)—are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an “Innovative AI Hospital System. ” Yet, there has been little critical examination of the SIP Research Plan, particularly from an ethics approach. This paper reports on an analysis of the Plan to identify the extent to which it addressed ethical considerations set out in the World Health Organization’s 2021 Guidance on the Ethics and Governance of Artificial Intelligence for Health. A coding framework was created based on the six ethical principles proposed in the Guidance and was used as the basis for a content analysis. 101 references to aspects of the framework were identified in the Plan, but attention to the ethical principles was found to be uneven, ranging from the strongest focus on the potential benefits of AI to healthcare professionals and patients (n = 44; Principle 2), to no consideration of the need for responsive or sustainable AI (n = 0; Principle 6). Ultimately, the findings show that the Plan reflects insufficient consideration of the ethical issues that arise from developing and implementing AI for healthcare purposes. This case study is used to argue that, given the ethical complexity of the use of digital health technologies, consideration of the full range of ethical concerns put forward by the WHO must urgently be made visible in future plans for AI in healthcare.
Feasibility of challenging treadmill speed-dependent gait and perturbation-induced balance training in chronic stroke patients with low ambulation ability: a randomized controlled trial

Background Treadmill training shows advantages in the specificity, amount, and intensity of gait and balance practice for the rehabilitation of stroke patients. Objective To investigate the feasibility and effectiveness of challenging treadmill speed-dependent gait and perturbation-induced balance training in chronic stroke patients with low ambulation ability. Methods For this randomized controlled trial (Chinese Clinical Trials.gov registration number ChiCTR-IOR-16009536) with blinded testers, we recruited 33 ambulatory stroke participants with restricted community ambulation capacity and randomly assigned them into two groups: the experimental group with 2 week treadmill speed-dependent gait training combined with 2 week treadmill perturbation-induced balance training (EXP) or the control group with traditional gait and balance training (CON). Various variables were recorded during EXP training, including the rating of perceived exertion, heart rate, causes of pauses, treadmill speed, and perturbation intensity. Outcome measures were examined before training and at 2 and 4 weeks after training. They included gait velocity during five-meter walk test at comfortable and fast speed and reactive balance ability in the compensatory stepping test as primary outcome measures, as well as dynamic balance ability (timed up-and-go test and 5 times sit-to-stand test) and balance confidence as secondary outcome measures. Results All participants completed the study. The treadmill speed and perturbation intensity significantly increased across training sessions in the EXP group, and no adverse effects occurred. The normal and fast gait velocities showed significant time and group interaction effects. They significantly increased after 2 and 4 weeks of training in the EXP group (p < 0.05) but not in the CON group (p > 0.05). Likewise, dynamic balance ability measured using the timed up-and-go test at a fast speed significantly improved after 2 and 4 weeks of training in the EXP group (p < 0.05) but not in the CON group (p > 0.05), although without a significant time and group interaction effect. Surprisingly, the reactive balance ability did not show improvement after treatment in the EXP group (p > 0.05). Conclusion Challenging treadmill speed-dependent gait and treadmill perturbation-induced balance training is feasible and effective to improve ambulation function in chronic stroke patients with low ambulation ability.
Characterization of a mechanical bender for x-ray mirrors at European XFEL

The European XFEL will generate extremely short and intense X-ray laser pulses of high coherence and nearly diffraction-limited divergence. Guiding these X-rays beams over a distance of more than 1 km to the experiments requires an extreme precision in pointing stability of beamline components like mirrors and gratings and also a control of the divergence of the beam. The specifications of the X-ray mirrors that will be able to transport, distribute and focus the beam are quite challenging. The European XFEL mirrors for the beam transport are 950 mm long and the optical surface specifications are 2 nm Peak-To-Valley. Some of the mirrors will have bending capabilities in order to focus the beam in the right position and with nanometer accuracy. This is implemented using a mechanical bender that will ensure stability of the optics in the nanometer range and will also offer the possibility to correct for mechanical or temperature drifts. We present here the characterization of a mechanical bender that was done using two instruments, a Large Aperture Fizeau interferometer and a system of three capacitive sensors. The bender is designed in a way that the mirror is hold with clamps on both ends and a symmetric torque is applied on the clamps, inducing a cylindrical shape on the mirror surface. Several long-term stability measurements were done, as well as the characterization of bending capabilities. The parameters retrieved from the measurements are the sagitta and therefore the radius of curvature for different bending positions. The behavior of the variation of the shape of the mirror was also studied. The information gathered from our measurements will be used to optimize the final design of the bender.
Phenolics Extracted from Jasminum sambac Mitigates Diabetic Cardiomyopathy by Modulating Oxidative Stress, Apoptotic Mediators and the Nfr-2/HO-1 Pathway in Alloxan-Induced Diabetic Rats

Diabetes mellitus is a chronic metabolic disorder defined as hyperglycemia and pancreatic β-cell deterioration, leading to other complications such as cardiomyopathy. The current study assessed the therapeutic effects of phenolic acids extracted from Jasminum sambac phenols of leaves (JSP) against diabetes-induced cardiomyopathy in rats. The rats were divided into four groups, with each group consisting of 20 rats. The rats were given intraperitoneal injections of alloxan monohydrate (150 mg/kg) to induce diabetes. The diabetes-induced groups (III and IV) received treatment for six weeks that included 250 and 500 mg/kg of JSP extract, respectively. In the treated rats, the results demonstrated that JSP extract restored fasting glucose, serum glucose, and hyperlipidemia. Alloxan induced cardiomyopathy, promoted oxidative stress, and altered cardiac function biomarkers, including cardiac troponin I, proBNP, CK-MB, LDH, and IMA. The JSP extract-treated rats showed improved cardiac function indicators, apoptosis, and oxidative stress. In diabetic rats, the mRNA expression of caspase-3, BAX, and Bcl-2 was significantly higher, while Bcl-2, Nrf-2, and HO-,1 was significantly lower. In the treated groups, the expression levels of the BAX, Nrf-2, HO-1, Caspase-3, and Bcl-2 genes were dramatically returned to normal level. According to our findings, the JSP extract prevented cardiomyopathy and heart failure in the hyperglycemic rats by improving cardiac biomarkers and lowering the levels of hyperlipidemia, oxidative stress, apoptosis, hyperglycemia, and hyperlipidemia.
Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today’s era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.
The Potential of Maritime Museums for Heritage Education

Los museos marítimos permiten integrar diversas perspectivas y enfoques en el estudio de la cultura e historia marítimas. Este trabajo presenta un análisis cualitativo de los programas educativos de tres museos de la región mediterránea: el Museo Marítimo Galata de Génova (Italia), el Museo Marítimo de Barcelona (España) y el Museo Marítimo de Haifa (Israel). Esta investigación evalúa su potencial como agentes para la educación patrimonial y para el aprendizaje sobre historia, cultura y ciencia marítimas. La región mediterránea, con su historia marinera y diversidad cultural y étnica, constituye un lugar ideal para que estos museos actúen como plataformas de aprendizaje sobre la importancia del mar en la civilización y para abordar los retos a los que se enfrenta la región. Con este estudio subrayamos la importancia de promover la educación a través del patrimonio cultural en los museos y su potencial para facilitar la inclusión y el diálogo sobre la diversidad cultural.
Analysis of functional efficiency and risk of falls in patients with different types of dementia - preliminary observations.

INTRODUCTION AND OBJECTIVE Dementia is a multifactorial neurological disease that affects 50 million people worldwide. It is a disorder that impairs cognitive functions, functional efficiency, balance and gait. It contributes to an increased risk of falls, reduces independence in everyday activities and deepens disability. The aim of the study was to investigate the correlation between dementia and independence related to functional efficiency and risk of falls in the elderly. MATERIAL AND METHODS The eligibility criterion for participation in the study was age over 60, the presence of cognitive disorders, including dementia, and the ability to move with the use of orthopaedic equipment or independently. A total of 51 people participated in the study, including 13 people who underwent rehabilitation procedures. Each subject was evaluated once for cognitive abilities using two types of tests: the ADL scale, MMSE (Mini-Mental state Examination) and three physical fitness tests: SPPB (Short Physical Performance Battery), TUG (Timed Up & Go) and FRT (Functional Reach Test). RESULTS The average score of the MMSE test was 13.29±6.23 points, the average of the ADL scale was 4.20±1.23 points. A positive correlation was found between the level of dementia and the independence of the examined person, as well as a positive relationship between the MMSE test and the result of the Functional Reach Test, and the relationship between the ADL scale and the SPPB and 'Get-Up and Go' tests. CONCLUSIONS It has been demonstrated inter alia that static balance and functional efficiency depends on the patient's independence in everyday activities, and the level of dementia may suggest the patient's dynamic balance. In addition, the need for a broader analysis of targeted studies was recognized to confirm the conclusions obtained.
A Review of the Complex Flow and Heat Transfer Characteristics in Microchannels

Continuously improving heat transfer efficiency is one of the important goals in the field of energy. Compact heat exchangers characterized by microscale flow and heat transfer have successfully provided solutions for this purpose. However, as the characteristic scale of the channels decreases, the flow and heat transfer characteristics may differ from those at the conventional scale. When considering the influence of scale effects and changes in special fluid properties, the flow and heat transfer process becomes more complex. The conclusions of the relevant studies have not been unified, and there are even disagreements on some aspects. Therefore, further research is needed to obtain a sufficient understanding of flow structure and heat transfer mechanisms in microchannels. This article systematically reviews the research about microscale flow and heat transfer, focusing on the flow and heat transfer mechanisms in microchannels, which is elaborated in the following two perspectives: one is the microscale single-phase flow and heat transfer that only considers the influence of scale effects, the other is the special heat transfer phenomena brought about by the coupling of microscale flow with special fluids (fluid with phase change (pseudophase change)). The microscale flow and heat transfer mechanisms under the influence of multiple factors, including scale effects (such as rarefaction, surface roughness, axial heat conduction, and compressibility) and special fluids, are investigated, which can meet the specific needs for the design of various microscale heat exchangers.