Operational Data Research Articles

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PurposeThis study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications. Materials and MethodsWe designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses. ResultsFrom August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001). ConclusionIntegrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

A Current Development of Energy Harvesting Systems for Energy-Independent Bioimplantable Biosensors.

Biosensors have emerged as vital tools for the detection and monitoring of essential biological information. However, their efficiency is often constrained by limitations in the power supply. To address this challenge, energy harvesting systems have gained prominence. These off-grid, independent systems harness energy from the surrounding environment, providing a sustainable solution for powering biosensors autonomously. This continuous power source overcomes critical constraints, ensuring uninterrupted operation and seamless data collection. In this article, a comprehensive review of recent literature on energy harvesting-based biosensors is presented. Various techniques and technologies are critically examined, including optical, mechanical, thermal, and wireless power transfer, focusing on their applications and optimization. Furthermore, the immense potential of these energy harvesting-driven biosensors is highlighted across diverse fields, such as medicine, environmental surveillance, and biosignal analysis. By exploring the integration of energy harvesting systems, this review underscores their pivotal role in advancing biosensor technology. These innovations promise improved efficiency, reduced environmental impact, and broader applicability, marking significant progress in the field of biosensors.

A Current Development of Energy Harvesting Systems for Energy-Independent Bioimplantable Biosensors.

Biosensors have emerged as vital tools for the detection and monitoring of essential biological information. However, their efficiency is often constrained by limitations in the power supply. To address this challenge, energy harvesting systems have gained prominence. These off-grid, independent systems harness energy from the surrounding environment, providing a sustainable solution for powering biosensors autonomously. This continuous power source overcomes critical constraints, ensuring uninterrupted operation and seamless data collection. In this article, a comprehensive review of recent literature on energy harvesting-based biosensors is presented. Various techniques and technologies are critically examined, including optical, mechanical, thermal, and wireless power transfer, focusing on their applications and optimization. Furthermore, the immense potential of these energy harvesting-driven biosensors is highlighted across diverse fields, such as medicine, environmental surveillance, and biosignal analysis. By exploring the integration of energy harvesting systems, this review underscores their pivotal role in advancing biosensor technology. These innovations promise improved efficiency, reduced environmental impact, and broader applicability, marking significant progress in the field of biosensors.

A hierarchical framework for minimising emissions in hybrid gas-renewable energy systems under forecast uncertainty

Developing and deploying renewables in existing energy systems are pivotal in Europe’s transition to net-zero emissions. In this transition, gas turbines (GTs) will be central for balancing purposes. However, a significant hurdle in minimising emissions of GTs operating in combination with intermittent renewables arises from the reliance on unreliable meteorological forecasts. Here, we propose a hierarchical framework for decoupling this operational problem into a balancing and emissions minimisation problem. Balancing is ensured with a high-level stochastic balancing filter (SBF) based on data-driven stochastic grey-box models for the uncertain intermittent renewable. The filter utilises probabilistic forecasting and less conservative chance constraints to compute safe bounds, within which a proposed low-level economic predictive controller further minimises emissions of the GTs during operations. As GTs exhibit semi-continuous operating regions, complementarity constraints are utilised to fully exploit each GT’s allowed operational range. The proposed method is validated in simulation for a gas-balanced hybrid renewable system with batteries, three GTs with varying capacities, and a wind farm. Using real historical operational wind data, our simulation shows that the proposed framework balances the energy demand and minimises emissions with up to 4.35% compared with other conventional control strategies in simulation by minimising the GT emissions directly with complementarity constraints in the low-level controller and indirectly with less conservative chance constraints in the high-level filter. The simulations show that the computational cost of the proposed framework is well within requirements for real-time applications. Thus, the proposed operational framework enables increased renewable share in hybrid energy systems with GTs and renewable energy and subsequently contributes to de-carbonising these types of isolated or grid-connected systems onshore and offshore.

High-resolution harvester data for estimating rolling resistance and forest trafficability

AbstractInformation on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

High-resolution harvester data for estimating rolling resistance and forest trafficability

AbstractInformation on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

Assessment of the financial performance and sustainability of water management companies in Spain from a quantitative and technological perspective (2018-2022)

Introduction: Water collection, treatment and distribution companies in Spain face challenges due to climate variability, obsolete infrastructures and the need for efficient water resource management. This study provides a comprehensive and up-to-date analysis of the financial and operational performance of the sector, addressing climate change adaptations and innovative technologies, areas little explored in previous studies. Methodology: A quantitative analysis of financial and operational data from official sources was carried out, complemented by a literature review on water resources management and technological trends. Includes financial data collection and analysis, profitability assessment, and analysis of climate change adaptation technologies and strategies. Results and discussions: An improvement in the profitability of the sector was observed, due to efficient management and modernisation of infrastructures. Public-private collaboration and the adoption of innovative technologies, such as desalination and reuse of recycled water, are crucial to address climate challenges and ensure sustainability. It also highlights the need for a global approach to water management due to the externalisation of water stress. Conclusions: In conclusion, it emphasises innovation and collaboration to improve water management in Spain, ensuring its sustainability and future resilience.

Differences in driver takeover performance and physiological responses in conditionally automated driving: Links to emotional instability

When the automated driving system reaches its technical limit, drivers will have to take over control from the vehicle within a limited time. The drivers’ emotional instability may affect whether they can safely control the vehicle during takeover transitions. Accordingly, this paper explores the association between emotional instability and physiological responses and driver takeover performance. 42 drivers engaged in negative emotion induced by movie clips were involved in a sequence of takeover events in a driving simulator. Personality questionnaires were used to assess the driver’s level of emotional instability. Their physiological data before the takeover request (TOR) and operational behavior data fragments for the takeover process were extracted. The results demonstrated that integration of subjective evaluations with electroencephalogram (EEG) and electrocardiogram (ECG) signals allows for more objective measurement of drivers’ level of emotional instability. The statistical analyses indicated that emotional instability has a significant effect on drivers’ heartbeat interval, heart rate, the frequency bands of θ(4–8 Hz), α(8–12 Hz), β(13–30 Hz), γ (30–50 Hz) and lateral takeover performance. Those findings of differences in driver physiological responses and takeover performance under emotional instability may provide additional support for the design of driver state monitoring and adaptive warning systems.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&amp;D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge’s operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil.

Operation Model Based on Artificial Neural Network and Economic Feasibility Assessment of an EV Fast Charging Hub

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&amp;D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge’s operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil.

Development of a DNN model using calibrated simulation for the optimal control of HVAC systems

Acquiring actual data for developing data-based prediction models for the optimization of control setpoints (hereinafter referred to as optimal control (OC)) in heating, ventilation, and air conditioning (HVAC) systems is challenging, especially when changing control setpoints while considering all possible boundary conditions. Thus, considering the scalability of OC, it is a promising solution to generate sufficient learning data according to the control settings using a highly accurate simulation model, build a source model, and expand the learning model based on the actual data of the target system. This is the first study to improve the scalability of OC aiming to develop a deep neural network (DNN) model to predict the power consumption of a heat source system. First, this study will construct a calibrated heat source system simulation model and then use the generated learning data to train a DNN model. The actual operation conditions of the target building and heating system located in Vietnam were analyzed using operation data. A physics-based simulation model of the target heat source system was developed and calibrated using actual operation data to improve accuracy. By using the calibrated simulation, sufficient learning data was generated for all control settings allowing for the development of a learning model based on DNN that predicted the power consumption of the heat source system. In addition, a highly accurate prediction model through hyperparameter optimization was secured.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

A study on multiple types of yard cranes rescheduling in ports considering carbon emission

ABSTRACT This paper investigates the rescheduling problem of multiple oil-driven and electric-driven yard cranes across multiple container yards in semi-automated container terminals under random faults. Efficient fault diagnosis and handling mechanisms are provided to mitigate the effects of faults on the performance of yard operations. At the same time, a rescheduling model is proposed, aiming to minimize weighted carbon emissions and completion time. By analyzing the features of the problem, a tailored adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The effectiveness of the ALNS is verified through problem instances generated from the operational data of the port of Dalian in China. The factors considered in this paper are relevant to actual port operations, thus offering reliable solutions for scheduling and greening of ports.

Flexible Ureteroscopy versus Retroperitoneal Laparoscopic Ureterolithotomy in the Management of upper Ureteric Stones Measuring from One to Two Centimeters: Randomized Prospective Study

Abstract Objective This prospective study aims to compare the clinical efficacy and safety of flexible ureteroscopy versus laparoscopic ureterolithotomy in the management of upper ureteric stones measuring from one to two centimeters. Methods his randomized-prospective study was conducted on 64 patients admitted from Urology unit, Ain Shams University in the period from October 2020 till September 2022; only 56 patients were analyzed statistically and 8 patients were excluded due to shortage in post- operative data and follow up. Patients were divided into two groups Group A consists of 31 patients and Group B consists of 25 patients with matched age, gender and body mass index (BMI). Results In our study, The VAS score at 6 hours post-operative and day 1 post-operative was found to be 5.32 and 2.71 in FURS group and was found to be 7.28 and 3.28 in RPLU group respectively showing significant difference with (p- value &amp;lt;0.001 and 0.016 respectively) between the two groups with more satisfaction in FURS group mostly due to the absence of trocars incisions. The complications were classified according to modified Clavien classification system. No major complications such as septic shock or death were reported in either treatment groups. As regards the recovery to work in postoperative and follow up periods in the present study, there was a significant difference between both groups with (P-value &amp;lt;0.001), as we found that it takes about 4.56 ± 0.82 days in RPLU group and 3.06 ± 1.91 days in FURS group to return daily routine activities. As regards the stone free rate in follow up periods in the present study, there was a significant difference between both groups with (P-value 0,02), as we found that it was 100% and 80,6% in RPLU and FURS group respectively. Our success rate in RPLU group was found to be 100% in comparison to Irfan Nazir Mir et al which was 93.4% (28/30) with two patients (2/30) converted to open ureterolithotomy, either due to technical snag or due to difficulty in localizing the ureter laparoscopically. Conclusion Both F-URS and RTLU were safe and effective surgical procedures for treatment of large proximal ureteral stones. However, RTLU had a higher stone-free rate with comparable operating time and complication rate as compared to F-URS. Also as regards to postoperative pain, shorter hospital stay and faster return to daily activities, both have almost similar results.

Voltage control method for multi-energy system based on the coupling of renewable energy hydrogen production and storage

The power system is gradually becoming an AC/DC hybrid MES (multi-energy system) with hydrogen energy. Due to the uncertainty of reactive power demand of load in MES and the multi-time scale characteristics of different energy flow transmission. It will lead to difficulties in reactive power coordination within the entire MES. Secondly, in response to this problem, this paper first establishes a MES cluster hybrid power flow equation formed by AC/DC and hydrogen transmission channels. The reactive power voltage fluctuation characteristics of AC/DC systems under MES cluster interconnection channel energy exchange, system energy conversion and renewable energy output fluctuation are analysed. Then according to the MES cluster hybrid power flow calculation. The main factors affecting the voltage reactive power of the AC system are studied, and the reactive power model based on electrothermal conversion, hydrogen storage and multi-energy transmission channels is established. Finally, proposed an algorithm for generating the safety and stability boundary of pipe network pressure. It’s used to construct the safe and stable boundary of the system pipe network. based on the actual operational data of multi-energy power system. The simulation model of reactive power dynamic optimization control for multi-energy interconnected systems is established

Exergy analysis of a gas turbine cycle power plant: a case study of power plant in Egypt

AbstractThis research presents an exergy analysis of a gas turbine power plant situated in Assiut, Egypt, operating under high-temperature conditions. The aim of the study is to assess the performance of the simple gas turbine cycle and identify the sources of thermodynamic inefficiencies using the second law of thermodynamics as a basis for analysis. To accomplish this, a model was developed in EES software utilizing real operational data obtained from the plant's control system. The investigation focused on the impact of varying ambient temperature on the exergy efficiency, exergy destruction, and net power output of the cycle. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 85.22%. This was followed by the compressor at 8.42% and the turbine at 6.36%. The overall energy and exergy efficiencies of the system were determined to be 28.8% and 27.17%, respectively. Furthermore, the study examined the effects of increasing ambient temperature from 0 to 45°C on the system's performance. It was observed that as the temperature rose, the overall exergy efficiency decreased from 27.91 to 26.63%. Simultaneously, the total exergy destruction increased from 126,407 to 138,135 kW. Additionally, the net power output exhibited a decline from 88,084 to 84,051 kW across the same ambient temperature range. These findings highlight the significant influence of ambient temperature on the thermodynamic performance of gas turbine power plants. As temperature rises, a greater amount of exergy is lost, resulting in reduced efficiency and diminished net power output. Therefore, optimizing the design of the combustion chamber is crucial for mitigating the adverse effects of hot weather conditions. The insights obtained from this study can be utilized to enhance the design and operation of gas turbine plants operating in hot climates.

Data-Driven Management: The Impact of Big Data Analytics on Organizational Performance

Inevitably, in such a fast-moving landscape of big data analytics lies the transformative opportunity for any organization to unlock new avenues of growth, operational efficiency, and strategic decision-making. This paper contributes a comprehensive methodology that will help businesses take advantage of big data analytics to secure a continued competitive advantage. At the core of this methodology is to have a robust data governance framework that will establish the security, integrity, and accessibility of the enterprise data assets by specifying relevant policies, processes, and technologies. This, in turn, within such a framework, allows state-of-the-art AI-driven anomaly detection mechanisms for encryption and access control to implement protection measures around sensitive information while enabling secure yet efficient data utilization. The methodology also continues its approach of putting in place an integrated data ecosystem that brings together different pockets or sources of data, such as real-time operation data, customer interaction, and unstructured information. A critical component of this methodology is putting in place the advanced predictive analytics capabilities that could be run based on tapping into the power of machine learning algorithms; by doing so, the organization will be predicting market trends, customer behavior, and risks highly accurately. This will be a very proactive way of making decisions that would let the business efficiently use the available resources, innovate products and services ahead of time, and create a distinctly competitive advantage. The transformative ability of this methodology for big data analytics opened new channels toward growth and innovation and firmly established the organization as an industry leader with long-lasting competitive advantage. The place of data-driven insight, data culture, and responsible data practice has been the key to success in this organization.

Efficiency of eSource Direct Data Capture in Investigator-Initiated Clinical Trials in Oncology.

Clinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite implementation of eSource direct data capture (DDC), which we define as eCRFs for direct data entry in this study, on efficiency by analyzing data from a single investigator-initiated clinical trial in oncology. Operational data associated with the targeted study conducted in Japan was used to analyze time from data occurrence to data entry and data finalization, and number of visits to the site and time spent at the site by clinical research associates (CRAs). Additionally, simulations were performed on the change in hours at the clinical sites during the implementation of eSource DDC. No difference in time from data occurrence to data entry was observed between the DDC and the transcribed data fields. However, the DDC fields could be finalized 4days earlier than the non-DDC fields. Additionally, although no difference was observed in the number of visits for source data verification (SDV) by CRAs, a comparison among sites that introduced eSource DDC and those that did not showed that the time spent at the site for SDV was reduced. Furthermore, the simulation results indicated that even a small amount of data to be collected or a small percentage of DDC-capable items may lead to greater efficiency when the number of subjects per site is significant. The implementation of eSource DDC may enhance efficiency depending on the study framework and type and number of items to be collected.

Fault root cause analysis using degree of change and mean variable threshold limit in non-linear dynamic distillation column

The chemical industrial processes are both dynamic and complex. The presence of instability and danger in the production process poses significant challenges to safety management. Early and accurate fault diagnosis is essential to ensure a sustainable and stable chemical process performance. Most studies in literature do not provide detailed information about diagnosing the root cause of faults in distillation columns using contribution plots. Using principal component analysis (PCA) in conventional contribution plots leads to a large number of contributing variables, making it difficult to identify the root cause of a specific fault. In this study, the authors suggest employing the Degree of Change of Contribution Values (DCC) and Mean Variable Threshold Limit (LMVT) techniques alongside the Wavelet Transform-Principal Component Analysis (WT-PCA) method to decrease the quantity of fault identification variables in a non-linear system (distillation column). The Aspen Plus software is utilized to perform a dynamic simulation of a distillation column, generating data for both normal and faulty operations. Two distinct types of faults are considered, which include loss of feed flow and feed temperature changes. This study aims to analyze the results obtained from the Hotelling T2 and SPE contribution plots by calculating the average difference between the normal and faulty conditions using the Degree of Change in Contribution Values. This procedure is performed to determine the extent of deviation from the standard state. Subsequently, the variables that have surpassed the threshold limit for means variables (LMVT) are designated as the variables that serve as the primary underlying cause of the fault. The technique was employed in a case study involving a nonlinear distillation column, and the results indicate that only one "dominant" variable intersects the LMVT by Hotelling T2, which represents the true underlying cause of the corresponding fault.