Human Data Research Articles

A chronic intermittent haemodialysis pig model for functional evaluation of dialysis membranes.

Performance evaluation of new dialysis membranes is primarily performed in vitro, which can lead to differences in clinical results. Currently, data on dialysis membrane performance and safety are available only for haemodialysis patients. Herein, we aimed to establish an in vivo animal model of dialysis that could be extrapolated to humans. We created a bilateral nephrectomy pig model of renal failure, which placed a double-lumen catheter with the hub exposed dorsally. Haemodialysis was performed in the same manner as in humans, during which clinically relevant physiologic data were evaluated. Next, to evaluate the utility of this model, the biocompatibility of two kinds of membranes coated with or without vitamin E used in haemodiafiltration therapy were compared. Haemodialysis treatment was successfully performed in nephrectomized pigs under the same dialysis conditions (4 h per session, every other day, for 2 weeks). In accordance with human clinical data, regular dialysis alleviated renal failure in pigs. The vitamin E-coated membrane showed a significant reduction rate of advanced oxidation protein products during dialysis than non-coated membrane. In conclusion, this model mimics the pathophysiology and dialysis condition of patients undergoing haemodialysis. This dialysis treatment model of renal failure will be useful for evaluating the performance and safety of dialysis membranes.

Analysis of the Role of Human Capital on Economic Growth in the Beijing-Tianjin-Hebei

Abstract This paper uses some human capital related data in the Beijing-Tianjin-Hebei region as empirical data to study the impact of human capital on the economic growth of the Beijing-Tianjin-Hebei region; based on the selected data, the principal component analysis method is used to analyze health human capital, education human capital, and comprehensive human capital. Measure and compare the human capital differences of the three places in Beijing, Tianjin and Hebei. Finally, based on the economic growth model, the three places are regression analysis, and the results of different types of human capital on the three places are obtained. The analysis results show that different types of human capital measurement values in the Beijing-Tianjin-Hebei region are indeed different. In general, the human capital in the Beijing-Tianjin-Hebei region contributes more to economic growth than physical capital, and the contribution of health human capital is better than that of education human capital. Keywords: Human capital, Economic growth, Regression analysis, Principal component analysis, Health; Education.

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties.

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug. A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression. Initial analysis showed that compounds with logD7.4>1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize. A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developedto assign candidate compounds to the appropriate proposed classes.

Inferring causal protein signalling networks from single‐cell data based on parallel discrete artificial bee colony algorithm

AbstractInferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

The Role of Olorofim in the Treatment of Filamentous Fungal Infections: A Review of In Vitro and In Vivo Studies

Invasive fungal infections have recently been recognized by the WHO as a major health, epidemiological, and economic issue. Their high mortality rates and the emergence of drug resistance have driven the development of new molecules, including olorofim, an antifungal belonging to a new family of compounds, the orotomides. A review was conducted on the PubMed database and the ClinicalTrials.gov website to summarize the microbiological profile of olorofim and its role in the treatment of filamentous fungal infections. Twenty-four articles were included from the search and divided into two groups: an “in vitro” group focusing on minimum inhibitory concentration (MIC) results for various fungi and an “in vivo” group evaluating the pharmacokinetics (PK), pharmacodynamics (PD), efficacy, and tolerability of olorofim in animal models of fungal infection and in humans. Olorofim demonstrated in vitro and in vivo activity against numerous filamentous fungi, including azole-resistant Aspergillus fumigatus, various dermatophytes, and endemic and dimorphic fungi. in vitro results showed higher MICs for certain Fusarium species and dematiaceous fungi Alternaria alternata and Exophiala dermatitidis; further in vivo studies are needed. Published PK-PD data in humans are limited. The results of the ongoing phase III clinical trial are eagerly awaited to evaluate olorofim’s clinical impact.

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

Cardiac Left Ventricular miRNA-26a Is Downregulated in Ovariectomized Mice, Upregulated upon 17-Beta Estradiol Replacement, and Inversely Correlated with Collagen Type 1 Gene Expression

Heart failure with preserved ejection fraction (HFpEF) is more prevalent in post- compared to pre-menopausal women. The underlying mechanisms are not fully understood. Data in humans is confounded by age and co-morbidities. We investigated the effects of ovariectomy and estrogen replacement on the left ventricular (LV) gene expression of pro-inflammatory and pro-fibrotic factors involved in HFpEF and putative regulating miRNAs. Nine-week-old C57BL/6 female mice were subjected to ovariectomy (OVX) or SHAM operation. OVX and SHAM groups were sacrificed 1-, 6-, and 12-weeks post-surgery (T1/SHAM; T1/OVX; T6/SHAM; T6/OVX, T12/SHAM). 17β-estradiol (E2) or vehicle (VEH) was then administered to the OVX groups for 6 weeks (T12/OVX/E2; T12/OVX/VEH). Another SHAM group was sacrificed 12-weeks post-surgery. RNA and miRNAs were extracted from the LV apex. An early 3-fold increase in the gene expression of IL-1α, IL-6, Mmp9, Mmp12, Col1α1, and Col3α1 was observed one-week post-surgery in T1/OVX vs. T1/SHAM, but not at later time points. miRNA-26a was lower in T1/OVX vs. T1/SHAM and was inversely correlated with Col1α1 and Col3α1 expression 1-week post-surgery (r = −0.79 p < 0.001; r = −0.6 p = 0.007). miRNAs-26a, 29b, and 133a were significantly higher, while Col1α1, Col3α1, IL-1α, IL-6, Tnfα, Mmp12, and FasL gene expression was significantly lower in E2- compared to vehicle-treated OVX mice. miRNA-26a was inversely correlated with Col3α1 in T12/OVX/ E2 (r = −0.56 p = 0.02). OVX triggered an early increase in the gene expression of pro-inflammatory and pro-fibrotic factors, highlighting the importance of the early phase post-cessation of ovarian function. E2 replacement therapy, even if it was not immediately initiated after OVX, reversed these unfavorable changes and upregulated cardiac miRNA-26a, previously unknown to be affected by menopausal status.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

HME-KG: A method of constructing the human motion encoding knowledge graph based on a hierarchical motion model

The diversity, infinity, and nonuniform description of human motion make it challenging for computers to understand human activities. To explore and reuse captured human motion data, this work defines a more comprehensive hierarchical theoretical model of human motion and proposes a standard human posture encoding scheme. We construct a domain knowledge graph (DKG) named the human motion encoding knowledge graph (HME-KG) based on posture codes and action labels. Community detection, similarity analysis, and centrality analysis are used to explore the potential value of motion data. This paper conducts an evaluation and visualization of HME-KG.

Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

A Robust Examination of Cheating on Unproctored Online Exams

The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning, and significant levels of cheating have been reported in the academic literature for over sixty years. Much of this literature, however, has focused on student behavior in a classroom utilizing proctored, in-class exams. Grades on exams usually determine most of a student’s final grade in a course, and GPAs are used by employers and graduate schools to indicate a student’s subject matter mastery. As more conventional colleges and universities expand their online course offerings it is natural to wonder if academic dishonesty is more prevalent in online classes than in face-to-face classes. In particular, are students more likely to cheat when no one is watching (i.e., on unproctored assessment assignments) than they do when someone is watching (i.e., on proctored assessment assignments)? The purpose of this study is to investigate whether students cheat more on unproctored online exams than they do on proctored in-classroom exams, and if so, is there any pattern to their cheating behavior. Our findings are derived from careful empirical analysis of 741 undergraduate students who completed three unproctored online exams, several collaboration-encouraged assignments, and a proctored in-class comprehensive final exam in the same course with the same instructor. Additionally, we collected demographic and human capital data for every student. Using bivariate and regression analysis, we find significant evidence of more cheating on unproctored online exams than on proctored in-class exams even though students were given stern honor code violation warnings. Moreover, we discover that student cheating increased with each unproctored online exam, implying that students learn how to cheat as they become more familiar with taking online assessment assignments. Finally, we find that students with certain demographic and human capital characteristics tend to cheat more than others. This research strongly supports the use of proctoring for all evaluation assignments in online classes to ensure that grades in these classes properly reflect student aptitude as opposed to merely reflecting their ability to cheat.

Three-dimensional spring-loaded inverted pendulum walking model considering human moving speed and frequency.

The spring-loaded inverted pendulum (SLIP) model is an effective model to capture the essential dynamics during human walking and/or running. However, most of the existing three-dimensional (3D) SLIP model does not explicitly account for human moving speed and frequency. To address this knowledge gap, this paper develops a new SLIP model which includes roller foot, massless spring and concentrated mass. The governing equations-of-motion for SLIP model during its double support phase are derived. It is noted that in the current formulation, the motion of roller foot is prescribed so only the equations for the concentrated mass needs to be solved. In order to yield model parameters leading to a periodic walking gait, a constrained optimization problem is formulated and solved using gradient-based approach with a global search strategy. The optimization results show that when the attack angle ranges from 68° to 74° the 3D SLIP model can yield a periodic walking gait with walking speeds varying from 0.5 to 2.0 m/s, the predicted human walking data is also compared with published experimental data, finding reasonable accuracy.

A Novel Approach to Integrate Human Biomonitoring Data with Model Predicted Dietary Exposures: A Crop Protection Chemical Case Study Using Lambda-Cyhalothrin.

The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

FAIR compliant database development for human microbiome data samples

IntroductionSharing microbiome data among researchers fosters new innovations and reduces cost for research. Practically, this means that the (meta)data will have to be standardized, transparent and readily available for researchers. The microbiome data and associated metadata will then be described with regards to composition and origin, in order to maximize the possibilities for application in various contexts of research. Here, we propose a set of tools and protocols to develop a real-time FAIR (Findable. Accessible, Interoperable and Reusable) compliant database for the handling and storage of human microbiome and host-associated data.MethodsThe conflicts arising from privacy laws with respect to metadata, possible human genome sequences in the metagenome shotgun data and FAIR implementations are discussed. Alternate pathways for achieving compliance in such conflicts are analyzed. Sample traceable and sensitive microbiome data, such as DNA sequences or geolocalized metadata are identified, and the role of the GDPR (General Data Protection Regulation) data regulations are considered. For the construction of the database, procedures have been realized to make data FAIR compliant, while preserving privacy of the participants providing the data.Results and discussionAn open-source development platform, Supabase, was used to implement the microbiome database. Researchers can deploy this real-time database to access, upload, download and interact with human microbiome data in a FAIR complaint manner. In addition, a large language model (LLM) powered by ChatGPT is developed and deployed to enable knowledge dissemination and non-expert usage of the database.

Edge-Community Entropy is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition.

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogues of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age-dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.Significant Statement Many potential clinical applications of fMRI necessitate examining localized function to assess and guide behavioral, pharmacological, surgical, and neuromodulatory interventions. Edge-community entropy offers the mathematical and conceptual advantage of calculating the functional specialization of individual nodes within an overlapping and interdependent system of communities using whole-brain, temporally-informed data. We suggest entropy during cognitive tasks may be a more conceptually appropriate functional connectivity analogue of age-related neural dedifferentiation than network segregation metrics, and demonstrate entropy strongly correlates with age and moderates cognition. Notably, entropy was strongly related to age at the whole-brain level and more strongly related than participation coefficient at the nodal level, offering greater potential to interrogate how local age-related dysfunction influences distributed systems supporting cognition and behavior.

Impact of dioxins on reproductive health in female mammals

Extensive research has been conducted to investigate the toxicological impact of dioxins on mammals, revealing profound effects on the female reproductive system in both humans and animals. Dioxin exposure significantly disrupts the intricate functions of the ovary, a pivotal organ responsible for reproductive and endocrine processes. This disruption manifests as infertility, premature ovarian failure, and disturbances in sex steroid hormone levels. Comprehensive studies, encompassing accidental human exposure and experimental animal data, have raised a wealth of information with consistent yet varied conclusion influenced by experimental factors. This review begins by providing an overarching background on the ovary, emphasizing its fundamental role in reproductive health, particularly in ovarian steroidogenesis and hormone receptor regulation. Subsequently, a detailed examination of the Aryl hydrocarbon Receptor (AhR) and its role in governing ovarian function is presented. The review then outlines the sources and toxicity of dioxins, with a specific focus on AhR involvement in mediating reproductive toxicity in mammals. Within this context, the impact of dioxins, notably 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), on Folliculogenesis and Preimplantation embryos is discussed. Furthermore, the review delves into the disruptions of the female hormonal system caused by TCDD and their ramifications in endometriosis. Notably, variations in the effects of TCDD on the female reproductive and hormonal system are highlighted in relation to TCDD dose, animal species, and age. As a forward-looking perspective, questions arise regarding the potential involvement of molecular mechanisms beyond AhR in mediating the female reproductive toxicity of dioxins.

Resolving the Loss of Intermediate-Size Speech Aerosols in Funnel-Guided Particle Counting Measurements

Modeling of airborne virus transmission and protection against it requires knowledge of the amount of biofluid emitted into the atmosphere and its viral load. Whereas viral concentrations in biofluids are readily measured by quantitative PCR, the total volume of fluids aerosolized during speaking, as measured by different researchers using various technologies, differs by several orders of magnitude. We compared collection methods in which the aerosols first enter into a low-humidity chamber either by direct injection or via commonly used funnel and tubing arrangements, followed by standard optical particle sizer measurement. This “collect first, measure later” approach sacrifices the recording of the temporal correlation between aerosol generation and sound types such as plosives and vowels. However, the direct-injection mode prevents inertia deposition associated with the funnel arrangements and reveals far more intermediate-size (5–20 μm in diameter) particles that can dominate the total mass of ejected respiratory aerosol. The larger aerosol mass observed with our method partially reconciles the large discrepancy between the SARS-CoV-2 infectious dose estimated from superspreader event analyses and that from human challenge data. Our results also impact measures to combat airborne virus transmission because they indicate that aerosols that settle faster than good room ventilation rates can dominate this process.

Integrating human-centric simulations in educational production lines: advancing ergonomics for industry 5.0 applications

This research in the Industry 5.0 field focuses on a human-centered simulation of the FAS200 SMC educational production line, utilizing Tecnomatix Process Simulate Human software for developing a virtual human environment. A key aspect of this study is the integration of inertial sensors, enhancing the accuracy and depth of ergonomic analysis. These sensors play a pivotal role in capturing precise human movement data, crucial for ergonomic assessments. Adopting a defined working methodology, the study extensively employs the RULA method to evaluate operator postures in the production line. This approach has led to significant ergonomic improvements, evidenced by a 40 percent reduction in the RULA index at each workstation. The integration of inertial sensors has been instrumental in achieving these results, providing detailed insights into human movements and interactions with the production environment. The research transcends traditional ergonomic assessments by incorporating a new human-centered approach, emphasizing the well-being of individuals working alongside machines. This approach, bolstered by the use of inertial sensors, marks a significant advancement in ergonomic studies, aligning with the principles of Industry 5.0. The findings hold substantial potential for application in industrial settings, signaling a shift towards more human-friendly and efficient industrial practices.

Developing Time Series Forecasting Models with Generative Large Language Models

Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software projects. Nevertheless, their potential as assistants to write the code needed to generate and model Machine Learning (ML) or Deep Learning (DL) architectures has not been fully explored to date. For this reason, this work focuses on evaluating the extent to which different tools based on GLLMs, such as ChatGPT or Copilot, are able to correctly define the source code necessary to generate viable predictive models. The use case defined is the forecasting of a time series that reports the indoor temperature of a greenhouse. The results indicate that, while it is possible to achieve good accuracy metrics with simple predictive models generated by GLLMs, the composition of predictive models with complex architectures using GLLMs is still far from improving the accuracy of predictive models generated by human data scientists.