Efficiency Of Processes Research Articles

Modeling of a metal hydride energy storage tank dynamics using hybrid numerical, experimental, and machine learning methods

This study presents an integrated analysis combining numerical simulations, experimental investigations, and machine learning models to simulate the performance of metal hydride systems for hydrogen storage under various conditions by using a LaNi5 metal hydride cylindrical tank of 500 NL capacity, with a focus on PCM thermal enhancements and surface water heating and cooling. Numerical simulations offer insights into thermal and reaction dynamics, while experimental data are used for model validation and supplemented with additional numerical data for training a FFNN model to predict dynamic hydrogen flow, surface temperature, and surface heat flux. The obtained mean RMSEs for charging/discharging experimental data is 2.8 SOC% for the state of charge and 0.3 °C for surface temperatures. The study compares the effectiveness of water cooling and heating in metal hydride surface across different temperatures, highlighting their impact on the efficiency of hydrogen absorption and desorption processes. Energy consumption is shown to be non-linear; during charging, heat flux peaks before decreases rapidly within around 0.2 h. During discharging, heat flux rises steadily. The study also explores the impact of adding copper fins to the PCM covering metal hydride system, revealing significant improvements in charging and discharging efficiency, specifically, the integration of 16 copper fins led to a notable decrease in complete charging time from approximately 6.5 h to just over 5.2 h, and in complete discharging time from around 4.5 h to under 3.96 h. Using PCM, the highest heat flux is 5 times less during charge and 2 times less during discharge than that for water cooling mode.

Anion-Driven Bandgap Tuning of AgIn(SxSe1-x)2 Quantum Dots.

Accurate tuning of the electronic and photophysical properties of quantum dots is required to maximize the light conversion efficiencies in semiconductor-assisted processes. Herein, we report a facile synthetic procedure for AgIn(SxSe1-x)2 quantum dots with S content (x) ranging from 1 to 0. This simple approach allowed us to tune the bandgap (2.6-1.9 eV) and extend the absorption of AgIn(SxSe1-x)2 quantum dots to lower photon energies (near-IR) while maintaining a small QD size (∼5 nm). Ultraviolet spectroscopy studies revealed that the change in the bandgap is modulated by the electronic shifts in both the valence band and the conduction band positions. The negative overall charge of the as-synthesized quantum dots enabled us to make films of quantum dots on mesoscopic TiO2. Excited state studies of the AgIn(SxSe1-x)2 quantum dot films demonstrated a fast charge injection to TiO2, and the electron transfer rate constant was found to be 1.5-3.5 × 1011 s-1. The results of this work present AgIn(SxSe1-x)2 quantum dots synthesized by the one-step method as a potential candidate for designing light-harvesting assemblies.

Digitalization and Performance Management Systems: A Shipping Agency Case Study

This study examines how the digitalization of information processes embraced by shipping intermediaries (i.e. shipping agencies) within supply chain management (SCM) changes performance management systems (PMSs). This exploratory study uses the lens of stakeholder theory and adopts a case study methodology to investigate a shipping agency based in Italy. It uses primary data collected from semi-structured interviews and secondary data. The data were collected from No-vember 2022 to March 2023. The results indicate that the digitalization of infor-mation accounting processes has boosted the adoption of key performance indi-cators (KPIs) to monitor and evaluate the effectiveness and efficiency of opera-tional processes. The results also reveal that, in the agency relationship between agent and shipowner, the agent does not use the benefits of PMSs to identify stra-tegic KPIs for the business. This study highlights that when adopting PMSs in ship-ping agencies, it is necessary to include both an information accounting and an organizational dimension. Managers should be involved in training programs sup-porting a culture of sharing information through digitalized systems. The main lim-itation of this study, due to its exploratory nature, is the lack of analysis including the shipowner's perspective and the impossibility of generalizing based on the re-sults. To the best of our knowledge, this is the first study to investigate digitaliza-tion, PMSs, and SCM in terms of the organizational-accounting frameworks of shipping agencies.

Maximizing similarity: using correlation coefficients to calibrate kinetic parameters in population balance models

Crystallization plays a crucial role as a separation and purification technique, particularly in the chemical and pharmaceutical industries. By adjusting process parameters, the productivity, product quality, and efficiency of downstream processes can be improved. In complex processes, model-based design becomes invaluable. Population Balance Models (PBMs) have successfully aided the chemical industry in achieving more effective production processes for decades. These models can utilize various input data sources to identify the dominant mechanisms and calibrate model parameters. While inline particle monitoring tools serve as excellent qualitative descriptors, challenges arise from experimentation and data interpretation, hindering their direct application in the kinetic parameter estimation of PBMs. In this study, we present a novel approach that utilizes information from inline particle monitoring tools for the kinetic parameter estimation of PBMs, bypassing the associated obstacles. Our pioneering approach relies on offline product size data and the correlation-based utilization of inline particle monitoring information. The paper compares this novel strategy with two parameter estimation techniques: the classical method using solute concentration and product size data and a somewhat naïve approach, which assumes that the inline particle monitoring data can directly be compared with the simulations. The prediction capabilities are evaluated through two in-silico case studies. The results indicate that the precision and predictive capability of the correlation-based technique are comparable to the classical approach, both for noisy data and for a system undergoing significant agglomeration and deagglomeration. The use of Pearson’s correlation coefficient yields the best results in the novel cases. These findings in in-silico datasets provide a foundation and motivation for the practical application of this idea, unleashing the so-far hidden model development potential of such measurements.

Risk management in international supply chains: geopolitical and geoeconomic dimensions

In the current climate of global instability driven by dramatic geopolitical and geoeconomic transformations, international supply chains face numerous risks that significantly impact the continuity and efficiency of logistics processes. Threats such as military conflicts, port blockades, border instability, climate changes, and cyber threats introduce new challenges for companies, particularly Ukrainian ones, which require adaptive strategies to ensure the resilience of their supply chains. The purpose of this study is to systematize key geopolitical and geoeconomic risks affecting international supply chains and to identify optimal approaches to managing these risks under conditions of instability, especially for Ukrainian companies facing heightened threats. The study includes an analysis of global risks based on international scientific literature, specific risks in Ukraine and their impact on import, export, and transit chains, and the development of a generalized risk management model tailored to Ukrainian enterprises. The research identified effective risk management methods such as supply chain diversification, the creation of alternative routes, and the use of insurance mechanisms. A practical case study on risk management in the import of industrial fasteners illustrates the stages of collaboration with forwarders and carriers, partner selection, and response to disruptions, demonstrating approaches that can be used to minimize the impact of risks. The findings provide practical recommendations for Ukrainian companies, highlighting an adaptive approach to risk management that will help them maintain competitiveness and ensure supply chain resilience in a rapidly changing global environment. Future research should focus on integrating digital technologies, such as blockchain, artificial intelligence, and big data analytics, to strengthen the resilience and flexibility of international supply chains.

Модель оцінки конкурентоспроможності транспортно-логістичної системи в умовах цифрової трансформації економіки

Introduction. In modern conditions of development of the world economy, the digital economy is one of the most relevant and important factors in ensuring economic growth. The digital transformation of the economy is a primary direction of innovative development of socio-economic systems, and therefore a tool for creating long-term competitive advantages of the transport and logistics system. The article examines the issue of economic problems associated with obtaining an integrated assessment of the level of competitiveness of the transport and logistics system. The purpose of the article is to analyze the impact of the digitalization of the economy on the competitiveness of the transport and logistics system and to create a model for obtaining an integrated evaluation of its competitiveness based on factors that reflect the specifics of the services provided by the system in the conditions of the digital transformation of the economy. Research methods. The digital transformation of the transport and logistics system is carried out based on the implementation of digitalization at all levels of business processes of economic structures: from the optimization of the logistics of physical flows and the optimization of data exchange to customer service. To assess the level of competitiveness of the transport and logistics system, it is proposed to calculate the competitiveness index by group of services in the process of transporting material flow. The model for calculating the integrated indicator of the competitiveness of the transport and logistics system is described, which is a three-stage multifactor model. Results. The economic evaluation of the digital transformation of the development of logistics services in railway transport allows for specifying the specifics of service services and clarifying the possibilities of using logistics methods of service response in carrying out cargo transportation. This makes it necessary to clarify the place and role of service support of cargo flow in the transport and logistics system and points to bottlenecks that must be overcome in the future to increase the level of competitiveness not only of the system but also of the country as a whole. Conclusions. The development of the digital transformation of the transport and logistics system consists of solving various socio-economic and technological tasks, which include the emergence of new products and new markets; reduction of costs for conducting business activities; and increasing productivity and efficiency of logistics and transport processes. As a result is increased competition in the transport and logistics system.

Tracking Differentiator-Based Identification Method for Temperature Predictive Control of Uncooled Heating Processes

The temperature control of uncooled heating processes presents challenges due to a substantial lag and the absence of active cooling mechanisms, which can lead to overshoot and oscillations. To address these issues, we propose an anti-disturbance identification method based on a tracking differentiator (TD) and an input-constrained temperature predictive control (ICTPC) strategy. Our approach specifically considers the impact of unknown disturbances on model identification within a second-order heating process. By employing a TD to differentiate the input and output signals, we effectively minimize the identification error caused by low-frequency disturbances, yielding a robust anti-disturbance identification technique. Following this, we establish input constraints to limit the amplitude and variation of the control input, ensuring a more controlled and predictable system response. Using the identified model, an ICTPC algorithm is designed to achieve stable temperature control in uncooled heating processes. Experimental results from a typical uncooled heating system demonstrate that our method not only significantly reduces overshoot but also effectively mitigates temperature fluctuations, leading to enhanced control performance and system stability. This study provides a practical solution for temperature control in systems without cooling capabilities, offering substantial improvements in the efficiency and quality of industrial production processes.

SWQC: Efficient sequencing data quality control on the next-generation sunway platform

Sequencing data quality control can significantly prevent low-quality data from impacting downstream applications in bioinformatics. The enormous growth of biological sequencing data in recent years introduces new challenges to the efficiency of quality control processes and motivates the need for fast implementations on modern compute systems. The powerful next-generation heterogeneous Sunway platform holds significant potential for addressing this challenge. However, there are currently no dedicated quality control applications that can fully utilize its computational power. To bridge this gap, we introduce SWQC, a novel quality control application specifically designed for the Sunway platform. We present an efficient distributed FASTQ I/O framework for Sunway-based workstations and supercomputers to take advantage of fast SSDs and the parallel file system. In order to support both process-level and thread-level (CPE-level) parallelism to leverage the computational power, we refactor and optimize all standard quality control modules for the heterogeneous Sunway architecture. When using a single node, SWQC achieves speedups between 2 and 40 over highly optimized quality control applications executed on a high-end 48-core AMD server. Additionally, when using 16 nodes, SWQC achieves parallel efficiencies of 70% (for reading and writing a single file) and 95% (for reading one file and writing split files) compared to a single node. Overall, SWQC is able to perform quality control operations for a 140GB FASTQ file within only 70 s using a single Sunway node. It is publicly available at https://github.com/RabbitBio/SWQC.

CO2(OH)2CO3 and Fe doped Co2(OH)2CO3 prepared as efficient and stable catalysts for activation of PMS: Two systems comparison and key role of high-valent metal-oxo species in mechanism revealing

High-valent metal-oxo species [M(IV) = O] exhibited high selectivity and chemical efficiency in advanced oxidation processes for removing organic pollutants, making their manipulation essential in catalytic systems. However, the low and unsustainable efficiency of generating M(IV) = O posed significant challenges. In this research, Co2(OH)2CO3 were successfully prepared via a one-pot hydrothermal method to efficiently activate peroxymonosulfate (PMS) with high-valent cobalt-oxo species [Co (IV) = O] as the primary active oxidation species. Building upon this foundation, COC was modified through variations in metal types and elemental ratios, in which magnetically enriched FexCo2-x(OH)2CO3 was synthesized by gradient Fe doping with higher catalytic efficiency and stability. The insertion of Fe resulted in the lengthening of the Co-O bond and improved the valence distribution of cobalt on the COC surface. Besides, it enabled part of the electrons to be transferred from the Fe active center to the Co active center, breaking the limitation of the traditional two-electron transfer and realizing the stable generation of more high-valent metals. Both reaction systems achieved efficient degradation of sulfadimethoxine (SDM) within 30 min and were able to completely degrade SDM within 30 min even after three cycles. FexCo2-x(OH)2CO3 maintained excellent catalytic performance even after 30 days of exposure to air. In both systems, high-valence metals played a dominant role, while the contributions of hydroxyl radicals and sulfate radicals were negligible. Overall, the two groups of developed catalysts both showed remarkable stability and pollutant removal performance in anions, humic acid, Different antibiotics and real water systems, which provided a new perspective on antibiotic removal.

Unveiling the potential role of virus-encoded polyphosphate kinases in enhancing phosphorus removal in activated sludge systems

While microbial phosphate removal in activated sludge (AS) systems has been extensively studied, the role of viruses in this process remains largely unexplored. In this study, we identified 149 viral auxiliary metabolic genes associated with phosphorus cycling from 2,510 viral contigs (VCs) derived from AS systems. Notably, polyphosphate kinase 1 (ppk1) and polyphosphate kinase 2 (ppk2) genes, which are primarily responsible for phosphate removal, were found in five unclassified VCs. These genes exhibited conserved protein structures and active catalytic sites, indicating a pivotal role of viruses in enhancing phosphorus removal. Phylogenetic analysis demonstrated a close relationship between viral ppk genes and their bacterial counterparts, suggesting the occurrence of horizontal gene transfer. Furthermore, experimental assays validated that viral ppk genes enhanced host phosphate removal capabilities. VCs carrying ppk genes were observed across diverse ecological and geographical contexts, suggesting their potential to bolster host functions in varied environmental and nutrient settings, spanning natural and engineered systems. These findings uncover a previously underappreciated mechanism by which viruses enhance phosphate removal in wastewater treatment plants. Overall, our study highlights the potential for leveraging virus-encoded genes to improve the efficiency of biological phosphorus removal processes, offering new insights into the microbial ecology of AS systems and the role of viruses in biogeochemical cycling.

Unraveling the Genetic Mechanisms of Maize Ear Diameter Heterosis

Hybridization has long been a crucial strategy for breeders aiming to develop high-yield crops vital for global food security. However, the exact molecular mechanisms driving heterosis (hybrid vigor) remain a topic of debate. Maize (Zea mays), which demonstrates pronounced heterosis, serves as an ideal model for studying this phenomenon. In our study, we carefully measured phenotypic changes in ear diameter, tracing its development from the inflorescence meristem (IM) to the floral meristem (FM) stages. Our findings revealed a complex progression: the hybrid's ear diameter followed an additive pattern during the IM and spikelet pair meristem (SPM) stages, shifted to incomplete dominance at the spikelet meristem (SM) stage, and ultimately displayed over-dominance at the FM stage. Notably, significant phenotypic changes occurred during the SM stage with gene expression primarily showing non-additive patterns. Gene Ontology (GO) enrichment analysis highlighted the role of cell redox homeostasis genes, which exhibited over-dominant expression in hybrids, as key contributors to heterosis. Furthermore, we identified a distinct gene expression category - dominant maternal or paternal gene expression in F1 hybrids (DMP) - characterized by exclusive expression in the hybrid and one parent, while remaining inactive in the other. This category of DMP genes plays a pivotal role in shaping the diverse gene expression patterns observed in hybrids, distinguishing them from their parental lines. In conclusion, the widespread occurrence of non-additive expression seems to enhance the efficiency of biological processes and energy distribution in hybrids, ultimately driving the manifestation of heterosis.

Performance analysis of Plate type Steam Condenser Using Nano-fluid

Abstract This paper investigates the application of nanofluid coolant, specifically a mixture of water and aluminium dioxide (Al2O3) nanoparticles, to augment heat transfer efficiency within flat plate heat exchanger. Beginning with an introduction highlighting the pivotal role of heat exchangers in industrial processes, the study emphasizes the imperative for enhanced efficiency and reduced energy consumption. The system description delineates the components and functionality of flat plate heat exchanger, elucidating operational and experimental conditions including initial temperatures and subsequent temperature transitions during cooling. Key parameters such as effectiveness and overall heat transfer coefficient are identified, with an exploration of their variation when comparing water alone to nanofluid coolant. Comparative data showcases experimentally obtained effectiveness and heat transfer coefficient values for both coolant types, revealing a substantial enhancement in efficiency with nanofluid coolant. Like effectiveness for water was 18% while that for nanofluid was 35%. The future potential and broader applications of nanofluid coolant in heat transfer technologies, advocating for continued research to unlock further advancements and efficiencies in industrial processes. By this technology we can reduce water losses as well as reduce power consumption in plants.

Experimental Research of the Drying Behavior of White Cabbage Leaves With Three Different Geometries in a Wind Energy‐Assisted Hybrid Hot Air Dryer

ABSTRACTIn this paper, the performance of a wind energy‐supported hybrid hot air dryer was examined experimentally. Wind energy provided overall electrical energy needed for the activation of the dryer. In the experiments, the drying behavior of white cabbage leaves with the same surface area and three different geometries: circle, rectangle, and square cross‐section was examined using three different constant temperatures (50°C, 60°C, and 70°C), and two different constant air speeds (0.5 and 1 m/s). The main purpose of using different geometries during the drying process was to determine the optimum cross‐section and working parameters for white cabbage leaves. The based aim of the study was to contribute to both increasing the energy efficiency of processes that cause high energy consumption, such as drying, and ensuring their continuity throughout the four seasons, by using a clean renewable energy source such as wind. Consequent the experiments, it was seen that the drying time decreased by approximately 52.63% and the energy consumption reduced by approximately 47.9% with the increase in temperature and air speed in rectangular‐sectioned cabbages compared to other geometries. In addition, in the longest‐term experiment, a maximum of 40% of the stored energy was used. This revealed that the developed hybrid system has approximately 52.5% higher energy efficiency than other drying units supporting with PV panels and also provides an average at the rate of 45% advantage in terms of product drying time.

Fast biodiesel production via potassium ferrate catalysis at room temperature using refined coconut oil and vegetable oil

Abstract Traditionally, the transesterification reaction for biodiesel production takes place at elevated temperatures, around 60-65°C, for up to two hours of reaction time, entailing a high energy consumption. As the University of the Philippines Los Baños – Interdisciplinary Biofuels Research Studies Center (UPLB-IBRSC) continuously aims to conduct studies on improving process efficiencies in biofuels production, this research investigated the potential of potassium ferrate as a catalyst for fast biodiesel production at room temperature. Particularly, the catalytic activity of potassium ferrate in the transesterification of refined coconut oil and vegetable oil was examined. The effects of the parameters such as methanol-to-oil molar ratio, catalyst loading, and reaction time on biodiesel yield and purity were studied. Thin layer chromatography, with the aid of ImageJ software, was applied to analyze the purity of the biodiesel through the developed spots’ area estimation. Characterization experiments in terms of free fatty acid (FFA) content and acid value were performed to confirm the low FFA content of the feedstocks prior to transesterification. Results of the parametric and optimization study revealed that a biodiesel yield of 87.67% using vegetable oil was experimentally achieved at the optimum conditions of 6.75 wt% catalyst loading, 6:1 methanol-to-oil molar ratio, and 30 minutes reaction time. Using coconut oil, on the other hand, resulted in a higher biodiesel yield of 94.90% at a catalyst loading of 6 wt%, a methanol-to-oil molar ratio of 6:1, and a reaction time of 60 minutes. Generally, findings show that catalyst loading has a positive effect on biodiesel yield and purity only up to a certain optimum amount. Among all the parameters analyzed, the methanol-to-oil molar ratio was found to have the most significant effect as an individual factor. Given the results of this study, it can be concluded that the potassium ferrate-catalyzed transesterification reaction at room temperature can potentially reduce the energy consumption in biodiesel production and the production cost in general.

The utilization of Industry 4.0 technologies to enhance the circular economy through the engagement of stakeholders in Brazilian food technology companies

AbstractSustainable business models tend to have greater potential for the transition to the circular economy. The present study aims to investigate how Brazilian startups in the food segment foodtech are integrating Industry 4.0 technologies to boost the circular economy, emphasizing the importance of stakeholder engagement in the process. The choice of this unit of analysis is associated with companies that were born recently, with a high chance of having sustainable and circular business models. The research involved eight food technology startups, using questionnaires, interviews, and content analysis to collect data on circular supply chain practices, Industry 4.0 technologies adopted, and the level of stakeholder engagement. The results indicate that foodtech startups are progressively incorporating technologies such as automation, big data, biotechnology, and the Internet of Things, to promote circular economy initiatives. The active participation of stakeholders has been fundamental, contributing to innovation and efficiency in processes. However, the complete transition to the circular economy and the full adoption of Industry 4.0 technologies are challenged by obstacles, including high costs and regulatory barriers. The study found that startups play a crucial role in this transition, due to their agility and capacity for innovation, which can serve as an example for larger companies. Collaboration between businesses, regulators, academic institutions, and society is essential to overcome these challenges and facilitate effective transformation. This work offers valuable practical insights for foodtech startups looking to adopt circular practices, highlighting technologies, and practices that can result in economic and environmental benefits. Startups emerge not only as adopters of new technologies but also as agents of change in industrial practices, shaping a more sustainable future for the food sector. It is recommended that future research explore the impact of regulatory policies and incentives on accelerating the adoption of these technologies by startups, in addition to evaluating the long‐term economic viability of these technological innovations in promoting a more circular economy.

Forecasting the export competitiveness of Peruvian coffee in major importing countries usingautoregressive integrated moving average modeling

The study analyzes the export competitiveness of Peruvian coffee in international markets using the ARIMA model to forecast export trends. The objective is to identify factors influencing the sector's profitability and sustainability. Time series of export volumes and prices were evaluated, revealing that the ARIMA model allows for accurate predictions of future trends. The results suggest that the Peruvian coffee sector faces challenges related to international price fluctuations and global competition. Emphasis is placed on the importance of efficiency in production and export processes to maintain and enhance the competitiveness of Peruvian coffee in the global market.

Nanostructured materials as hazardous wastewater micropullutants: Sources, behavior, and impact on functional bacterial community of activated sludge

Unique properties of nanoscale materials make them attractive for industrial, medical, agricultural, and environmental applications. Nevertheless, the release of nanoparticles into the environment is a major concern due to the lack of knowledge about their behavior in the environment and potential widespread environmental impacts. On the one hand, nanomaterials are perceived as pollutants that may affect activated sludge microorganisms and, consequently, the efficiency of wastewater treatment processes. On the other hand, some nanomaterials can be intentionally added to activated sludge systems to improve their performance in terms of, e.g., sludge settling and removing heavy metals or organic pollutants. As a result, nanoparticles are frequently accumulated in wastewater, which is considered to be a major source of nanoparticle release to the surrounding environment. Processes that involve the action of activated sludge are used worldwide in wastewater treatment plants due to their excellent capacity of removing nutrients, degrading toxins, and retaining biomass. High concentrations of nanoparticles entering activated sludge systems can affect their growth and metabolism. The research studies, which are reviewed in the present article, show that nanoparticles significantly reduce the relative abundance of the activated sludge microbial community associated with nitrification, denitrification, and phosphorus removal. The knowledge about the structure of the activated sludge microbial community with an assessment of nanomaterial toxicity can contribute to optimizing the sludge population and improving the performance of wastewater treatment plants.

Development and Implementation of a Web-Based Management Information System for Enhancing Computer Programming Training in Education

In the current era of information technology, web-based training systems are critical for enhancing the efficiency of training processes. This study focuses on the development of a Management Information System (MIS) tailored for computer programming training, utilizing a web-based training (WBT) platform. The primary objective is to introduce a framework for a web-based MIS that optimizes the management and delivery of computer programming courses. Computer programming education presents unique challenges, such as real-time participant tracking and dynamic content management, which are addressed by this MIS. The system was developed using the waterfall model, with data collected through interviews, observations, and requirements analysis. Key features include training schedule management, participant tracking, material organization, session management, evaluation tools, and reporting capabilities. Real-time feedback enhances participant engagement, while session management improves the overall learning experience. The web-based platform offers flexible access and simplifies the management of training processes, enabling users to track schedules, access materials, and monitor progress. Validation results show a high validity score (V = 0.883), exceeding the required threshold, and the materials received a validity score of 0.864, confirming their suitability for basic programming competency training at Medan State University. These results demonstrate the system’s effectiveness and potential for wider application, making it a valuable tool for improving the management and delivery of programming courses across various educational contexts.

Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation Program

Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to detect potential errors in search relevance annotation tasks for three industry-scale ML applications (music streaming, video streaming, and mobile apps). Drawing on data from an extensive search relevance annotation program, we demonstrate that errors can be predicted with moderate model performance (AUC=0.65-0.75) and that model performance generalizes well across applications (i.e., a global, task-agnostic model performs on par with task-specific models). In contrast to past research, which has often focused on predicting annotation labels from task-specific features, our model is trained to predict errors directly from a combination of task features and behavioral features derived from the annotation process, in order to achieve a high degree of generalizability. We demonstrate the usefulness of the model in the context of auditing, where prioritizing tasks with high predicted error probabilities considerably increases the amount of corrected annotation errors (e.g., 40% efficiency gains for the music streaming application). These results highlight that behavioral error detection models can yield considerable improvements in the efficiency and quality of data annotation processes. Our findings reveal critical insights into effective error management in the data annotation process, thereby contributing to the broader field of human-in-the-loop ML.

SISTEMA DE MONITORAMENTO: BIODIGESTOR

This paper presents the development of an automated monitoring system for biodigesters, aiming to maximize operational efficiency, safety, and sustainability in biogas production processes. The system integrates sensors to measure critical parameters such as temperature, pressure, and volume, enabling real-time monitoring and remote control. The proposal seeks not only to ensure the proper functioning of the biodigester but also to optimize waste utilization and the generation of clean, renewable energy.