Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing the recovery of human-edible nutrients in milk and nitrogen efficiency throughout the lactation cycle by feeding fatty acid supplements

Dairy cows convert low-quality feedstuffs unsuitable for human consumption into milk, a high-quality protein source for humans. Even under grazing conditions of well-managed pastures, dietary energy limits the efficiency of animal production, contributing to the excretion of nutrients to the environment. Thus, our study aimed to assess the effect of supplying sources and levels of supplemental fat to grazing dairy cows during early lactation on nutrient excretion and recovery of human-edible (HE) nutrients in milk throughout the lactation cycle. Two experiments were conducted with grazing Jersey × Holstein dairy cows. In Experiment 1, five dairy cows were used in a 5 × 5 Latin Square design. Treatments were a control diet (concentrate without supplemental fat) or diets supplemented with calcium salts of either soybean fatty acids (CSSO) or palm fatty acids (CSPO) fed at two levels of supplementation (400 g/day or 700 g/day) for 24 days. In general, the level of supplemental fat had a minor effect on production parameters and methane emission and intensity. Feeding cows with CSPO increased milk yield by 8.1%, milk energy output by 20%, and milk fat yield by 35.2% compared with CSSO. A tendency was observed for CSPO to decrease methane per unit of milk energy compared with CSSO. In experiment 2, 24 dairy cows were used in a randomized block design. Treatments were a control diet or diets supplemented with calcium salts of either CSSO or CSPO fed at 400 g/day for 13 weeks. The carryover effect of the diets was evaluated until 42 weeks of lactation. During the supplementation period, feeding CSPO improved the recovery of HE energy, protein, and indispensable amino acids compared with control and CSSO. Interestingly, we demonstrated that feeding CSPO improved the recovery of HE energy, protein, and indispensable amino acids compared with control over the lactation cycle. Overall, our results suggested that the dose of supplemental fat used in our study had a minor influence on production and rumen fermentation parameters. We demonstrated that feeding dairy cows with a fatty acid source based on palm oil under tropical grazing conditions reduced nutrient excretion and improved the recovery of HE nutrients throughout the lactation cycle.
Identification and comparison of pandemic-to-symptom networks of South Korea and the United States

Background The Coronavirus (COVID-19) pandemic resulted in a dramatic increase in the prevalence of anxiety and depression globally. Although the impact on the mental health of young adults was especially strong, its underlying mechanisms remain elusive. Materials and methods Using a network approach, the present study investigated the putative pathways between pandemic-related factors and anxiety and depressive symptoms among young adults in South Korea and the U.S. Network analyses were conducted on cross-country data collected during the COVID-19 lockdown period (n = 1,036). Our model included depression symptoms (PHQ-9), generalized anxiety symptoms (GAD-7), and COVID-19-related factors (e.g., COVID-19-related traumatic stress, pandemic concerns, access to medical/mental health services). Results The overall structure of pandemic-to-symptom networks of South Korea and the U.S. were found to be similar. In both countries, COVID-related stress and negative future anticipation (an anxiety symptom) were identified as bridging nodes between pandemic-related factors and psychological distress. In addition, worry-related symptoms (e.g., excessive worry, uncontrollable worry) were identified as key contributors in maintaining the overall pandemic-to-symptom network in both countries. Conclusion The similar network structures and patterns observed in both countries imply that there may exist a stable relationship between the pandemic and internalizing symptoms above and beyond the sociocultural differences. The current findings provide new insights into the common potential pathway between the pandemic and internalizing symptoms in South Korea and in the U.S. and inform policymakers and mental health professionals of potential intervention targets to alleviate internalizing symptoms.
Providing freedom or financial remuneration? A cross-sectional study on the role of monetary and legal incentives on COVID-19 further booster vaccination intention in the Italian context

Vaccine hesitancy became a more and more important issue during the COVID-19 pandemic. Due to the emergence of new variants, many international health agencies have already begun administering booster doses of the vaccine in response to these threats. Studies have emphasized the effectiveness of different types of incentive-based strategies to increase vaccination behaviors. The purpose of the present study was to identify the correlation between different types of incentives (legal or financial) with people’s intentions to get a COVID-19 booster vaccine. We conducted a cross-sectional study between 29 January 2022 and 03 February 2022. An online quantitative survey was carried out in Italy. One thousand and twenty-two Italian adults were recruited by a professional panel provider. Descriptive statistics were computed for the five variables concerning the incentives (monetary, tax, fee, health certification, travel) toward vaccination. A general linear model (GLM) was then computed to compare the scores of the five different variables within the subjects. The general linear model showed a significant within-subjects main effect. Post-hoc comparisons showed that among the financial incentive, the monetary reward is rated lower than all the others. Tax and fees both resulted lower than both the legal incentives. Finally, COVID-19 health certification and travel did not result significantly different from each other. This study offers an important contribution to public policy literature and to policymakers in their efforts to explain and steer booster vaccination acceptance while facing an ongoing pandemic.
The implications of socioeconomic factors on salivary bioscience methodological variables in a large pediatric multi-site study

Introduction Salivary bioscience has found increased utilization within pediatric research, given the non-invasive nature of self-collecting saliva for measuring biological markers. With this growth in pediatric utility, more understanding is needed of how social-contextual factors, such as socioeconomic factors or status (SES), influence salivary bioscience in large multi-site studies. Socioeconomic factors have been shown to influence non-salivary analyte levels across childhood and adolescent development. However, less is understood about relationships between these socioeconomic factors and salivary collection methodological variables (e.g., time of saliva collection from waking, time of day of saliva collection, physical activity prior to saliva collection, and caffeine intake prior to saliva collection). Variability in salivary methodological variables between participants may impact the levels of analytes measured in a salivary sample, thus serving as a potential mechanism for non-random systematic biases in analytes. Methods Our objective is to examine relationships between socioeconomic factors and salivary bioscience methodological variables within the Adolescent Brain Cognitive Development Study© cohort of children aged 9–10 years old (n = 10,567 participants with saliva samples). Results We observed significant associations between household socioeconomic factors (poverty status, education) and salivary collection methodological variables (time since waking, time of day of sampling, physical activity, and caffeine intake). Moreover, lower levels of household poverty and education were significantly associated with more sources of potential bias in salivary collection methodological variables (e.g., longer times since waking, collections later in the day, higher odds of caffeine consumption, and lower odds of physical activity). Consistent associations were not observed with neighborhood socioeconomic factors and salivary methodological variables. Discussion Previous literature demonstrates associations between collection methodological variables and measurements of salivary analyte levels, particularly with analytes that are more sensitive to circadian rhythms, pH levels, or rigorous physical activity. Our novel findings suggest that unintended distortions in measured salivary analyte values, potentially resulting from the non-random systematic biases in salivary methodology, need to be intentionally incorporated into analyses and interpretation of results. This is particularly salient for future studies interested in examining underlying mechanisms of childhood socioeconomic health inequities in future analyses.
How do you build a nectar spur? A transcriptomic comparison of nectar spur development in Linaria vulgaris and gibba development in Antirrhinum majus

Nectar spurs (tubular outgrowths of floral organs) have long fascinated biologists. However, given that no model species possess nectar spurs, there is still much to learn about their development. In this study we combined morphological analysis with comparative transcriptomics to gain a global insight into the morphological and molecular basis of spur outgrowth in Linaria. Whole transcriptome sequencing was performed on two related species at three key developmental stages (identified by our morphological analysis), one with a spur (Linaria vulgaris), and one without a spur (Antirrhinum majus). A list of spur-specific genes was selected, on which we performed a gene enrichment analysis. Results from our RNA-seq analysis agreed with our morphological observations. We describe gene activity during spur development and provide a catalogue of spur-specific genes. Our list of spur-specific genes was enriched for genes connected to the plant hormones cytokinin, auxin and gibberellin. We present a global view of the genes involved in spur development in L. vulgaris, and define a suite of genes which are specific to spur development. This work provides candidate genes for spur outgrowth and development in L. vulgaris which can be investigated in future studies.
The influence of self-esteem, depression, and life satisfaction on the future expectations of Peruvian university students

Introduction Strengthening the self-confidence of future professionals can help them achieve professional goals with a likelihood of being able to achieve them in the future. The objective was to determine whether self-esteem, depression, and life satisfaction influence the future expectations of Peruvian university students. Methods A total of 708 university students participated (64.8% were female, mean age = 21.49; SD = 4.83). The Rosenberg Self-Esteem Scale, the Patient Health Questionnaire-2, the Life Satisfaction Scale, and the Adolescent Future Expectations Scale were used. Structural equation modeling was employed to test the impact of selfesteem, depression, and life satisfaction on future expectations. Results The model relating the study variables demonstrated an adequate fit (p < 0.001, CFI = 0.940, RMSEA = 0.073), confirming the relationship between self-esteem (β = 0.33, p < 0.001), life satisfaction (β = 0.28, p < 0.001), and depression (β = −0.14, p < 0.010.) with future expectations. This model explained 36% of future expectations. Discussion In conclusion, future expectations may be affected by low levels of self-esteem and life satisfaction and high levels of depression.
Methods of generating two-dimensional body-fitted grids

In the computation of fluid dynamic fields,finite difference methods and finite volume methods are usually used,but it is necessary to generate curvilinear grids in computing complicated areas.In this paper,the general principle of generating 2D body-fitted grids is introduced,and some typical grid-generating methods which are used currently are compared.The analysis shows that the method based on the potential theory is more effective in some selected physical areas.
A Clustering Bipartite Graph Anonymous Method for Social Networks

Privacy preserving in social networks has been raised serious concerns in recent years. One of the privacy disclosures is brought about through structural attacks by malicious users. In this paper, we consider the privacy preserving publication against sensitive edges identification attacks in social networks, which are expressed by bipartite graphs. Firstly, we present a positive one-way (c1, c2)-security algorithm, a negative one-way (c1, c2)-security algorithm, and a two-way (c1, c2)-security algorithm. The proposed algorithms are based on the k-security group theory and the sensitive edges identification attacks in social networks. Furthermore, a bipartite graph anonymous problem is formally defined to against sensitive edges identification attacks. Meanwhile, a clustering-based bipartite graph anonymous method, Clustering-based bipartite (c1, c2)-security algorithm (CBB(c1, c2)-security), is proposed. The experimental results show that under the equal conditions, our method not only produces less information loss than that of the existing method, but also effectively resists sensitive edges identification attacks and realize security release of bipartite graphs.
DrSMC : a sequential Monte Carlo sampler for deterministic relationships on continuous random variables

Computing posterior distributions over variables linked by deterministic constraints is a recurrent problem in Bayesian analysis. Such problems can arise due to censoring, identifiability issues, or other considerations. It is well-known that standard implementations of Monte Carlo inference strategies break down in the presence of these deterministic relationships. Although several alternative Monte Carlo approaches have been recently developed, few are applicable to deterministic relationships on continuous random variables. In this thesis, I propose Deterministic relationship Sequential Monte Carlo (DrSMC), a new Monte Carlo method for continuous variables possessing deterministic constraints. My exposition focuses on developing a DrSMC algorithm for computing the posterior distribution of a continuous random vector given its sum. I derive optimal settings for this algorithm and compare its performance to that of alternative approaches in the literature.
Numerical Investigation on the Spectral Broadening of Acoustic Waves by a Turbulent Layer

When acoustic waves are propagating through turbulence, a scattering phenomenon occurs leading to a broadening of the acoustic spectrum. This phenomenon has been observed experimentally for a harmonic source radiating through the shear layer of a cold jet at low speed. The observed spectra are displaying a characteristic shape consisting in a more or less reduced peak amplitude at the source frequency, surrounded by sidebands (also called haystacks). The levels and width of these sidebands appear to be evolving with the source and ﬂow parameters. This spectral broadening is studied in this paper for a simpliﬁed conﬁguration consisting in a monopole radiation propagating through a turbulent layer with a constant thickness and convected by a uniform mean ﬂow. The numerical method used relies on a ﬁnite diﬀerence code solving the linearized Euler equations in the time domain. The turbulence is synthesized using a stochastic method based on the ﬁltering of white noise to impose prescribed statistical properties to the turbulent velocity ﬁeld. These turbulent ﬂuctuations are then added to the steady mean ﬂow to form an unsteady base ﬂow around which the Euler equations are linearized. This new set of equations contains terms involving products between the turbulent and acoustic ﬂuctuations, which are responsible for the scattering. In this paper, the trends deduced from this numerical study can be compared to analytical models and experimental data obtained for a jet shear layer. They can also be related to the trends observed in a previous study on the scattering of sound by a single convected vortex.
Development of ESP32-Based Smart Greenhouse Controller

Smart farming is currently getting much attention from around the globe. There are a lot of works discussing on how to enable smart farm. One of the ways to make this happen is to have a smart controller that is durable, reliable, easy to use, IoT -enabled and cost-saving. This paper will describe the in-lab development smart greenhouse controller that responding to the aforementioned properties. The hardware of the propose prototype consist of power block, LCD, air temperature/humidity, soil moisture and 4 relays. All the components are embedded into a single Printed Circuit Board (PCB) that make a prototype consume low current around 223 mA or power consumption of 2.7 W. This device support three irrigation modes: scheduling, automatic and manual mode. Each mode can be configured by using UI in LCD that designed entirely to support local Khmer language. An IoT dashboard powered by Thingsboard is built to keep track the operation of the proposed prototype and is configured to communicate with the ESP32-based smart greenhouse controller via MQTT protocol. To demonstrate the performance, the proposed device is put to test in an actual greenhouse for a week and the result shows that the device can read the air temperature/humidity while control the valve and soil moisture correctly without showing any sign of error or bugging.
The Need for an Enhanced Process Methodology for Ethical Data Science Projects

This paper proposes a practice-based approach to ethics by expanding the scope of process framework CRISPDM for managing ethical risks arising in data science projects. The paper discusses the benefits and shortcomings of CRISPDM with respect to minimizing ethical risks during data science project execution. Next, to help ensure ethical data science projects, CRISP-DM is enhanced to minimize ethical risks. This refinement offers viable and specific work actions for data science practitioners. Furthermore, future research to integrate this enhanced CRISP-DM with a collaboration framework such as Scrum or Data Driven Scrum (DDS) is proposed to enable better coordination and efficient testing before final deployment of data science system.
Efficient Receiver for DVB-RCS2 Synchronization Problems

The second generation digital television broadcasting interactive satellite system (DVB-RCS2) plays a very important role in the interactive satellite communication system. It is a burst communication system and works under multifrequency time division multiple access (MF-TDMA). In practical applications, serious synchronization problems will be encountered, especially the system adopts the Turbo coding method, which is very sensitive to the carrier frequency offset. Therefore, we propose a solution for DVB-RCS2 synchronization problem. Combined with previous research on synchronization problems, timing synchronization is combined with oversampling and correlation with known sequences to locate the burst position. In terms of carrier frequency offset, for the special burst format such as preamble and postamble in DVB-RCS2, the phase difference is used to estimate the frequency offset, and the cyclic redundancy check (CRC) information in the burst is used as the check of the frequency offset estimate. The synchronization module we designed can correct the time offset and carrier frequency offset generated during the transmission of all waveforms in DVB-RCS2, and also has good performance under low signal-to-noise ratio.
A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual’s privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.
FMDiv: Functional Module Division on Binary Malware for Accurate Malicious Code Localization

In recent years, binary malware detection has attracted extensive attention from industry and academia. However, most of the existing work focuses on determining whether a sample is malicious or not, rather than identifying the malicious essence in malware. Few studies aim at locating malicious code at function granularity and suffer from inaccuracy. In this paper, we solve the problem by dividing malware into Functional Module (FM), which is a better granularity for locating malicious code, as it combines certain functions to express malicious behaviors in malware. We design a tool called FMDiv to automatically unpack and disassemble binary malware and then divide them into FMs based on the function call graph (CG). Meanwhile, one novel feature extraction and embedding method has been adopted to validate the effect of the FM division algorithm and provide one alternative method of characterization for subsequent malicious FM location. We evaluate FMDiv’s performance on 10,440 real-world samples from VIRUSSHARE. The results show that FMDiv can correctly characterize and make FM division of malware, outperforming current state-of-the-art work.
A Safe-Domain Generative Adversarial Network with Transformer for Noisy Imbalanced Fault Diagnosis

At present, data-driven fault diagnosis methods have made excellent achievements. In industrial scenarios, it is difficult to obtain sufficient amount of fault data, which means intelligent fault diagnosis is often faced with imbalanced data problem. Moreover, the label noise is usually brought due to manual recording errors so as to seriously affect the diagnosis performance. To address these problems, this paper proposed a safe-domain generative adversarial network with Transformer (SDGAN). A safe domain selecting method is used to remove the noisy samples and construct a pure dataset which poses no risk to the training process of GAN. Therefore, GAN is able to generate high-quality minority samples to balance the original dataset. In addition, the Vision Transformer (ViT) is also applied as a classifier to recognize the global information for each fault sample and achieve high diagnostic accuracy. The experimental results show that SDGAN achieves great diagnosis performance on various imbalanced ratios and noise ratios cases. Furthermore, SDGAN outperforms other baseline methods on imbalanced fault diagnosis with label noise, which indicates that the SDGAN can effectively solve real-world industrial problems.