Conventional Management Systems Research Articles

Improving Decision Support Tools for Quantifying GHG Emissions from Organic Production Systems

AbstractAs food companies have adopted sustainability metrics to quantify the environmental impacts of supply chains, we need data-driven decision support tools that represent organic management practices. Decision support tools such as COMET-Farm and the Cool Farm Tool have been developed to estimate management practice impacts on soil carbon and greenhouse gas emissions from agricultural systems, but these tools have primarily been developed and used to evaluate conventional management systems. We provide an overview of the research, outreach, and educational activities used to improve these tools to better integrate organic management practices with a focus on cover crops. We summarize our previously published findings from a meta-analysis of the average potential soil carbon benefits of cover crops in temperate climates that identified planting window, biomass production, and soil texture as important predictors of cover crop soil carbon outcomes. We demonstrate how these findings were applied to improvements in process-based models and the parameterization of empirical models. In addition, we solicited feedback from organic community members on the utility of these tools and identified barriers to adoption. Finally, we evaluated both tools as resources for teaching undergraduate students about organic management systems and their impacts on greenhouse gas emissions. While both tools contain a range of customizable, organic amendment options, grazing management options still need further improvement. These improved decision support systems can help identify opportunities for enhancing the sustainability of organic systems.

Integrated quality 4.0 framework for quality improvement based on Six Sigma and machine learning techniques towards zero-defect manufacturing

PurposeThis research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”Design/methodology/approachThe research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.FindingsThis research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.Originality/valueThis research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

An Intelligent Web-based Energy Management System for Distributed Energy Resources Integration and Optimization

The integration of renewable energy sources into power distribution systems frequently presents challenges for conventional energy management systems (EMS) due to the unpredictable and unstable characteristics of such energy sources. As a result, novel and cutting-edge solutions are required. This paper presents an intelligent web-based energy management system (iW-EMS) specifically designed to address the integration and optimization of distributed energy resources, as outlined in the proposed approach. The system incorporates a hybrid novel optimization approach that integrates simulated annealing and cone programming to effectively manage the distribution of energy resources and attain optimal outcomes from the proposed EMS. Additionally, it leverages generative AI services to create optimal scenarios based on historical data and real-time information, ensuring adaptability to the dynamic nature of renewable energy generation, providing a user-friendly and flexible web environment for scenario planning. The proposed framework facilitates seamless communication and collaboration among stakeholders involved in renewable energy integration, while also enabling the incorporation of real-world data sources such as weather forecasts and energy consumption patterns into the planning process.

Effects of conventional and natural management systems on backgrounding and finishing performance of beef steers

Effects of conventional and natural management systems on backgrounding and finishing performance of beef steers

Evaluation of soil fertility index in organic, semi-organic, and conventional rice field management systems

Rice farming in Madiun Regency implements three different management systems, that is organic, semi-organic, and conventional. The different implementation of these rice field management systems impacts soil fertility index and rice productivity. This purpose of this research was to know the effect of rice field management systems on soil fertility index and rice productivity in the Madiun Regency. The research uses an explorative descriptive qualitative method with a survey approach. Soil samples were taken using a random sampling method and 3 types of soil management systems (conventional, semi-organic and organic), and rice production samples were taken using an estimation method. The research results show that the soil fertility index ranges from 0.78 to 0.82, which is high. The highest soil fertility index is in the semi-organic management system and the lowest is in the organic management. The management system of semi-organic gave a response to the highest rice production of 6.89 tons/ha. Management system of semi-organic is a better management for increasing and maintaining soil fertility and crop production than conventional or organic. Farming activities results show that organic management systems increase the highest income, increasing 115.58% and 53.30% in semi-organic compared to conventional. The rice field management system has the effect of increasing the worm population density with the highest in the organic system, namely 4.19 individuals m-2. The Pontoscolex worm type dominates the three management systems. There is a correlation between organic matter content and earthworms.

Sustainable aquatic resource management and inland fisheries in tropical Asia: Interdisciplinary and transdisciplinary approaches

The intensive utilization of tropical inland water bodies for multiple and sometimes competing activities underlines the necessity for their integrated and holistic co-management. This paper presents our synthesis on lake and reservoir fisheries in South and Southeast Asia as social–ecological systems, based on a synopsis of our research findings from a previous EU-funded research programme in Sri Lanka, Thailand and the Philippines (FISHSTRAT project). The paper attempts to merge our results with recent developments in research, policy and practice. We explore the effects of the main external and internal control mechanisms of the trophic state and pinpoint to the high production potential of traditionally unexploited small indigenous fish species. The limitations of conventional centralized management systems highlight the importance of introducing transdisciplinary approaches which integrate limnology, fish ecology and fisheries with the interests of other resource using stakeholders and decision makers in order to develop locally appropriate co-management strategies for sustainable aquatic resource use.

Physicochemical Properties and Microbiome of Vineyard Soils from DOP Ribeiro (NW Spain) Are Influenced by Agricultural Management.

Agricultural management influences the soil ecosystem by affecting its physicochemical properties, residues of pesticides and microbiome. As vineyards grow crops with the highest incidence of pesticides, the aim of this study was to evaluate the impact of conventional and sustainable management systems of vineyards from DOP Ribeiro on the soil's condition. Samples from soils under three different management systems were collected, and the main soil physicochemical properties were evaluated. A selection of 50 pesticides were investigated by liquid chromatography with tandem mass spectrometry. The bacterial and fungal microbiomes were characterized through amplicon sequencing. The results show that organic agriculture positively influences soil pH and the concentration of some nutrients compared to conventional management. Our microbiome analysis demonstrated that transitioning from conventional to organic management significantly improves several BeCrop® indexes related to key microbial metabolism and soil bio-sustainability. Such a transition does not affect soil alpha diversity, but leads to a higher interconnected microbial network structure. Moreover, differential core genera and species for each management system are observed. In addition, the correlation of the microbiome with geographical distance is evidence of the existence of different microbial terroirs within DOP Ribeiro. Indeed, sustainable management leads to higher nutrient availability and enhances soil health in the short term, while lowering pesticide usage.

Multimedia-based Information System for Technology and Vocational Education Laboratory

The significance of Technological and Vocational Education (TVE) lies in producing professionals with competencies that are required to support the academic community. The aim of this study was to build an Information System of Laboratory (ISLab) that would improve the performance of the technical and vocational educational laboratory. The proposed information system was built based on multimedia featuring text, graphics, 3D images, grassroots access, and top-down reviews. The study used the System Development Life Cycle (SDLC) technique, and the data was analyzed using descriptive, gain-score, and inferential factorial design. The study findings proved that ISLab operated in accordance with laboratory standards and achieved a performance of 90.3% according to user needs. Furthermore, laboratory management using ISLab was found more effective than conventional management system, and the proposed method of management enabled interaction with user groups that led to an increase in performance of laboratory staff and lecturers. The results of this research indicate that ISLab was suitable to be used in managing technical and vocational educational laboratories.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates—total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation

Corporate procurement management assumes a pivotal role within the contemporary business landscape, yet confronts an array of challenges as markets continue to evolve and globalize. Conventional procurement management systems frequently grapple with issues of inefficiency, resource depletion, and noncompliance, necessitating the exploration of innovative avenues for optimization. This paper delves into the realm of risk mitigation associated with collusion behavior in the administration of intelligent procurement systems, presenting a novel procurement collusion identification model founded on a convolutional neural network (CNN) with reinforcement learning techniques. This framework commences with the application of a CNN and Long Short-Term Memory (LSTM) network for in-depth feature analysis and initial identification of historical procurement data, subsequently leveraging reinforcement learning methodologies to enhance the model's autonomy and intelligence for the purpose of optimization. Throughout the experimental phase, diverse domains of procurement data were meticulously selected for analysis. The empirical findings unequivocally demonstrate the model's proficiency, with an average recognition accuracy of 95.1% across five publicly available datasets. This performance surpasses existing machine learning methodologies employed in contemporary research and common recognition networks, thereby offering a pioneering reference point for the intelligent administration and optimization of future procurement systems.

A survey of smart dustbin systems using the IoT and deep learning

With massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works.

Production, Composition and Nutritional Properties of Organic Milk: A Critical Review.

Milk is one of the most valuable products in the food industry with most milk production throughout the world being carried out using conventional management, which includes intensive and traditional systems. The intensive use of fertilizers, antibiotics, pesticides and concerns regarding animal health and the environment have given increasing importance to organic dairy and dairy products in the last two decades. This review aims to compare the production, nutritional, and compositional properties of milk produced by conventional and organic dairy management systems. We also shed light on the health benefits of milk and the worldwide scenario of the organic dairy production system. Most reports suggest milk has beneficial health effects with very few, if any, adverse effects reported. Organic milk is reported to confer additional benefits due to its lower omega-6-omega-3 ratio, which is due to the difference in feeding practices, with organic cows predominantly pasture fed. Despite the testified animal, host, and environmental benefits, organic milk production is difficult in several regions due to the cost-intensive process and geographical conditions. Finally, we offer perspectives for a better future and highlight knowledge gaps in the organic dairy management system.

Olericulture No-Till System at Mountain Region: Physical and Biological Attributes of the Soil

The production of vegetables and grains by the family farming in the mountains of the Atlantic Forest is characterized by intensive soil management with ploughing and harrowing practices. These practices are promoting hydric erosion and losses of soil quality in the region. In this context, the objective of this work was to evaluate soil physical and biological characteristics at two seasons of the year in agroecosystems producing vegetables and grains in the no-tillage system (NTS) for 3, 5, and 9 years compared to the conventional management system (CT) in the Atlantic Forest Biome, Brazil. Physical and organic matter attributes and carbon (C) and nitrogen (N) stock were evaluated. NTS showed, in general, greater total porosity than CT systems. The main differences between the systems were found in the organic attributes and C and N stocks. The content of microbial biomass C in NTS with 3, 5, and 9 years was 767.5, 326.5, and 210.0 mg·kg−1, while the areas with CT had 93.75, 78.25, and 45.75 mg·kg−1, respectively. The stock of C in winter at the 9NTS area was 33.0 and 41.5 Mg·ha−1, and the respective area in CT presented only 21.75 and 25.00 Mg·ha−1 in the depths of 0–10 and 10–20 cm, respectively. The metabolic quotient of the NTS areas did not differ from the reference ecosystems and is promoting lower C-CO2 emissions than the CT system. The adoption of NTS in vegetable production improves soil quality in family farm areas of mountains relief at the Atlantic Forest Biome.

When tomorrow comes: A prospective risk assessment of a future artificial general intelligence-based uncrewed combat aerial vehicle system

There are concerns that Artificial General Intelligence (AGI) could pose an existential threat to humanity; however, as AGI does not yet exist it is difficult to prospectively identify risks and develop requisite controls. We applied the Work Domain Analysis Broken Nodes (WDA-BN) and Event Analysis of Systemic Teamwork-Broken Links (EAST-BL) methods to identify potential risks in a future ‘envisioned world’ AGI-based uncrewed combat aerial vehicle system. The findings suggest five main categories of risk in this context: sub-optimal performance risks, goal alignment risks, super-intelligence risks, over-control risks, and enfeeblement risks. Two of these categories, goal alignment risks and super-intelligence risks, have not previously been encountered or dealt with in conventional safety management systems. Whereas most of the identified sub-optimal performance risks can be managed through existing defence design lifecycle processes, we propose that work is required to develop controls to manage the other risks identified. These include controls on AGI developers, controls within the AGI itself, and broader sociotechnical system controls.

Can natural undisturbed revegetation restores soil organic carbon to levels under native climax vegetation under tropical semiarid climate?

AbstractLand‐use change has driven soil carbon stock losses in ecosystems worldwide. Implementing agricultural crops and exploiting forest resources trigger the breakdown of soil aggregates, thus exposing organic matter to microbial decomposition and enhancing carbon dioxide emissions, especially in biomes more susceptible to climate extremes as in the tropical semiarid regions. This study was based on the hypothesis that the undisturbed soil from the dry forest (Caatinga biome under natural revegetation in Brazilian semiarid) would have an improvement in the mass of macroaggregates and recover more than 50% of the soil C stock within 10 years. Thus, a field experiment was conducted to investigate soils from the Caatinga biome under native vegetation, “cowpea cropping” for over 30 years, and soil under natural revegetation for over 10 years, after conventional soil cultivation of maize and cowpea, to determine soil and soil‐aggregates carbon stocks and to estimate the recovery rate of these stocks. The proportional mass of aggregates of different sizes and the total stock of particulate organic carbon (POC) were also quantified. The results showed that soil under preserved native vegetation of dry forest Caatinga biome had higher total soil C stock (50.9 Mg ha−1) than that under cowpea cropping (23.2 Mg ha−1) and natural revegetation (45.1 Mg ha−1). The proportional mass of large macroaggregates was higher in soil under native vegetation for all depths. However, soil under cowpea cropping had lower C stocks in macroaggregates, and recovered roughly 63% of the original C stocks, while revegetation recovered 78% of the stock in 10 years. Although the conventional management system for cowpea monoculture aggravated losses in soil carbon stock by more than 50% of the original C stocks, dry forest under natural revegetation recovered 79% of this stock and almost 100% of POC stock in 10 years (~12 Mg ha−1). Furthermore, soil under undisturbed Caatinga dry forest achieved C stock levels equivalent to that of the global average range for semiarid tropical environments. The high recovery rate of C stock in forest soil under natural revegetation indicates the resilience potential of organisms responsible for structural protection of aggregates and the encapsulated soil organic matter content.

Soil microbes from conservation agriculture systems reduce growth of Bt-resistant western corn rootworm larvae

Sustainable agricultural management practices aimed at improving soil health can alter the soil microbiome, which can influence plant health and defenses against insects. The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, is a major belowground pest of corn. Pest management relies heavily on the planting of transgenic crops expressing Bacillus thuringiensis (Bt) toxins. In this study, we ask how corn-WCR interactions via the soil microbiome are affected by a conservation management system (extended corn-soybean-wheat rotation with cover crops under no-till) compared with a conventional management system (corn-soybean rotation under mulch tillage and no cover crops) when combined with transgenic Bt corn. To do this, we applied soil microbes from the conservation and conventional management systems to two corn lines, one producing Bt and one non-Bt. We then reared Bt-resistant and Bt-susceptible WCR on inoculated seedlings to examine plant and insect changes in fitness. We found that Bt was effective against susceptible larvae in both soil treatments. Bt-resistant larvae were ~ 20% smaller when reared in the presence of soil microbes from the conservation management system. Thus, control of Bt-resistant WCR may be improved in a conservation system without sacrificing Bt effectiveness in susceptible insects. Comparing the microbial communities using 16S rRNA sequencing revealed that management practices influenced the microbiomes associated with the soil and the plant rhizosphere, but not WCR. Our findings suggest value for growers in utilizing conservation management practices, such as no-till and cover crops, in agricultural systems through bottom-up changes to plant–insect interactions via the soil microbiome.

Research on intelligent file management system: a design strategy based on RFID technology and improved AICT algorithm.

In this modern era of technology and digitalization, keeping track of a manual file system in the office environment is a challenging task. This research proposes a radio frequency identification (RFID) technology to improve conventional file management systems. The proposed system includes advances in information and communication technology (AICT) algorithm that addresses tag collision problems, resulting in increased data collection and reduced communication traffic. The system consists of modules such as information collection, file management, and user management. Each is analyzed from various aspects. Simulation results show that the AICT algorithm outperforms the improved collision tree algorithm by increasing recognition efficiency by 10% and reducing communication traffic by 30%. Moreover, the proposed approach provides a simple and convenient way to manage files in real-time meeting the needs of modern times. The AICT algorithm excelled the other algorithms regarding recognition efficiency.

IoT Based Automated Waste Management System for Sustainable Cities

Innovative approaches are required to effectively manage waste disposal and recycling in light of the growing global waste crisis. Conventional waste management systems frequently require real-time data and inefficiencies to be addressed, which results in inefficient use of resources and damage to the environment. In order to assess waste management, this paper suggests utilizing data analytics and Internet of Things (IoT) technology advancements along with the integration of smart bins. Installing compactors to optimize space usage, sensors to detect fill levels, and connectivity to send real-time data to a central management system are all features of smart bins. This makes it possible for waste management firms and municipalities to keep an eye on waste generation trends, improve collection routes, and more efficiently allocate resources. Furthermore, by adding features like gamification and reward programs, smart bins can encourage recycling and promote public engagement in sustainable waste management practices. Comparing smart bins to conventional waste management techniques reveals a number of benefits. Municipalities that offer actionable insights into waste generation and disposal behaviors can lower operating costs, minimize their impact on the environment, and improve overall service quality. Additionally, incorporating smart bins improves the quality of life for locals and promotes sustainability in line with larger smart city initiatives. This study looks at the technical aspects of smart bin systems, looks at case studies of effective installations, and talks about possible obstacles and factors to take into account before broad adoption. Communities can set the path for a future that is more effective, sustainable, and environmentally conscious by reevaluating waste management with smart bins.

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

Data Streaming Pipelines in Life Sciences: Improving Data Integrity and Compliance in Clinical TrialS

Data streaming pipelines have become a revolutionary tool in the field of life sciences, providing novel functionalities for managing and examining the large volumes of data produced in clinical trials. By using real-time data processing, these pipelines improve data integrity and guarantee adherence to regulatory requirements, therefore effectively tackling some of the most crucial obstacles encountered in clinical research. Preserving the integrity and precision of data is of utmost importance in clinical studies. Conventional data management systems sometimes have difficulties in keeping up with the fast flow of data from many sources, resulting in delays, discrepancies, and even problems with compliance. In order to provide continuous, real-time processing of data as it is created, data streaming pipelines offer a solution. This methodology guarantees the prompt validation, cleansing, and processing of data, therefore preserving a consistent degree of precision and minimising the likelihood of mistakes. An inherent advantage of data streaming pipelines is their capacity to manage fast-moving data streams originating from diverse sources, including electronic health records (EHRs), wearable devices, and laboratory equipment. Through the integration of multiple data sources into a cohesive pipeline, researchers may get a holistic perspective of trial results, therefore enabling more informed decision-making and prompt interventions. The real-time characteristic of streaming pipelines also facilitates proactive monitoring, enabling the timely identification of abnormalitie r data quality problems that may affect the results of trials.