Collection Costs Research Articles

Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities

Monitoring domestic activities helps us to understand user behaviors in indoor environments, which has garnered interest as it aids in understanding human activities in context-aware computing. In the field of acoustics, this goal has been achieved through studies employing machine learning techniques, which are widely used for classification tasks involving sound recognition and other objectives. Machine learning typically achieves better performance with large amounts of high-quality training data. Given the high cost of data collection, development datasets often suffer from imbalanced data or lack high-quality samples, leading to performance degradations in machine learning models. The present study aims to address this data issue through data augmentation techniques. Specifically, since the proposed method targets indoor activities in domestic activity detection, room transfer functions were used for data augmentation. The results show that the proposed method achieves a 0.59% improvement in the F1-Score (micro) from that of the baseline system for the development dataset. Additionally, test data including microphones that were not used during training achieved an F1-Score improvement of 0.78% over that of the baseline system. This demonstrates the enhanced model generalization performance of the proposed method on samples having different room transfer functions to those of the trained dataset.

One size does NOT fit all – factoring weight into current stem cell transplant guidelines in multiple myeloma patients

Abstract Autologous hematopoietic stem cell (HPSC) transplantation serves as the standard of care treatment for multiple myeloma patients. The standard ASCT procedures for multiple myeloma (MM) patients often includes induction therapy, stem cell collection via apheresis and autologous hematopoietic cell transplantation (HCT) for eligible patients. The current guideline for HPSC collection for multiple myeloma established that 3x106 cells/kg of actual body weight (ABW) by the 1997 study of stem cell transplants in breast cancer. Recent research indicates that ideal body weight (IBW) utilization is a stronger predictor of post-autotransplantation neutrophil recovery than calculations based on ABW. Reducing the cost of HPSC collection and cryopreservation is critical to mitigate the cost incurred by the transplant center and the patient. This study evaluates whether using a patient’s IBW or Adjusted Ideal Body Weight (AIBW) would be satisfactory in determining the amount of stem cells required for transfusion when compared to ABW. After institutional review board approval, a retrospective chart review study analyzed a cohort of 224 deceased MM patients, who received HCT at the UAB Medicine Hospital within the fiscal years of 2017 to 2019. The ABW for each patient was used to calculate their IBW and AIBW using the formulas shown below. The target CD34+ cell amount for patients was calculated using each body weight parameter. The platelet count and absolute neutrophil count (ANC) defined a successful platelet and neutrophil engraftment. The engraftment date was determined as the day the platelet value exceeded 20 and ANC exceeded 0.5 for three consecutive days (without any transfusion within a seven day window) and a statistical analysis was utilized to determine whether the IBW and AIBW parameters were adequate to achieve successful engraftment and HCT. This study finds that the cohort of multiple myeloma patients are significantly more overweight than the prior study that established the current guidelines for autologous hematopoietic cell transplantation. The results for CD34+/kg cell targets for ideal and adjusted ideal body weight suggests that there are minimal differences in platelet and neutrophil engraftment and number of platelet transfusions when compared to CD34+/kg targets using actual body weight. Some methods to reduce excessive stem cell collection include decreasing target cell dosage from previous standards of 6.0 x 106 cells/kg or calculating total cell dosage with IBW or AIBW instead of ABW. Future studies should evaluate the efficacy of cell dosage decrease to lowered targets and total cell calculations using IBW or AIBW instead of ABW. Equation 1. ABW to IBW Conversion (H = height over 5 ft. in inches) IBW(males) = 50 kg + (2.3 kg x H) IBW(females) = 45.5 kg + (2.3 kg x H) Equation 2. ABW to AIBW Conversion AIBW = IBW + 0.5(ABW-IBW)

Modeling Urban Temperature Using Measurements from Mobile and Stationary Monitoring Stations

Heat waves are occurring more frequently worldwide as global warming continues, and urban heat islands can threaten conventional life in cities. Measuring, analyzing, and simulating weather data at fine spatial and temporal scales are essential to prevent and reduce the damage caused by extreme heat waves. In urban environments, handling complex micrometeorological situations using current meteorological stations and global simulation models (e.g., weather research forecasting models) is challenging. In this study, the thermal environments of urban areas were measured using a mobile meteorological measurement platform. Both mobile and stationary datasets were incorporated into the meteorological modeling process to simulate the spatial and temporal distribution of temperature. Additionally, various mobile observation implementation scenarios for temperature modeling were examined. We compared simulation combinations with the temperature field generated from the total dataset to obtain a better sampling campaign and properly incorporate mobile data scenarios. When collecting mobile data, it is important to consider spatial features to improve the efficiency of sampling programs. This can substantially reduce the cost of mobile data collection, together with the sensor error bound.

Bias Mitigation and Representation Optimization for Noise-Robust Cross-modal Retrieval

The remarkable progress in cross-modal retrieval relies on accurately-annotated multimedia datasets. In practice, most existing datasets used for training cross-modal retrieval models are automatically collected from the Internet to reduce data collection costs. However, it inevitably contains mismatched pairs, i.e. , noisy correspondences, thus degrading the model performance. Recent advances utilize the predicted similarity distribution of individual samples for noise validation and correction, which easily faces two challenging dilemmas: 1) confirmation bias and 2) unstable performance with increasing noise. In light of the above, we propose a generalized B ias M itigation and R epresentation O ptimization framework (BMRO). Specifically, we propose a Bias Estimator (BE) to estimate the unbiased confidence factor of a sample by contrasting it against its nearest neighbors. Unbiased confidence factor can precisely adjust sample contribution and enhance accurate sample division. This facilitates the Adaptive Representation Optimizer (ARO) in providing tailored optimization strategies for clean and noisy samples. ARO performs contrastive learning between clean samples and generated hard samples, thus promoting the generalizability and robustness of the representation. Besides, it utilizes complementary learning to reduce incorrect guidance from noisy samples. Extensive experiments on five visual-text benchmarks verify that our BMRO can significantly improve the matching accuracy and performance stability against noisy correspondences.

Participatory Renewal of Historic Districts Based on Bayesian Network

Public participation is a requirement for contemporary urban governance in China, especially in the protection planning of historic districts. In the rapid development stage, many historic districts have lost their functional positioning in cities and are gradually disappearing due to outdated facilities and services. Previous studies have pointed out that protecting historic and cultural districts requires the joint efforts of multiple stakeholders. Integrating multiple stakeholders’ interests and needs is a complex problem in related research fields and planning practices. The traditional forms of public participation, such as questionnaire surveys, interviews, and symposiums, have the problem of high time costs for collection and analysis. At the same time, the information has been translated multiple times, making it challenging to avoid misunderstandings and errors. Therefore, there is an urgent need for an alternative platform for public participation in the renewal of China’s historic districts. Taking Houzaimen Street in Jinan City as an example, this paper uses the Bayesian network to establish a spatial evaluation model of historic districts. Our aims are to translate natural semantics into the design semantics of the architectural field, accurately locate the spatial problems of the historic district, and provide targeted improvement measures. In this paper, a public feedback channel for Houzaimen Street is established to provide a reference for the renewal of the historic district and the advancement of public participation.

Creating a comprehensive framework for design, construction and management of healthy buildings

While healthy buildings are investigated in academic research, practice, and policy, they often fail to address the needs and gaps due to insufficient knowledge transfer between them. Moreover, there is no general definition of healthy buildings that could act as a reference for different stakeholders of the building sector. Therefore, this paper presents a novel framework for healthy buildings and a definition for compiling existing knowledge and addressing the needs and gaps. The holistic approach of the framework was developed through an extensive literature review and tested with 12 building case studies in the major European climate zones. The framework outlines five different dimensions (improving mental and physical health, designed for human needs, sustainably built and managed, resilient and adaptive, empowering people), including 24 indicators and sub-indicators. The validation of the framework revealed that it is possible to assess different building projects against different indicators under each dimension. However, challenges remain due to issues such as lack of time, cost of data collection, or lack of documentation. The developed framework provides a balanced approach that promotes both individual well-being and broader sustainability goals. It provides necessary guidance to inform practitioners and policy makers as well as other building sector stakeholders to integrate healthy buildings thinking into their project development, assessment, and decision making.

B-184 Purifying High-Quality Genomic DNA From Frozen Blood Samples Stored up to 1.5 Years at -20°C

Abstract Background Maintaining DNA integrity in blood samples, from transport to storage to processing, is crucial to the success of downstream applications, especially regarding diagnostic applications. Additionally, due to the invasive nature of collecting blood samples, minimizing resampling to avoid excess patient pain and cost of collection and transportation are important to consider. Having the option to store and maintain portions of blood samples in the freezer enables future retesting without recollection. Since -20°C freezers generally are more affordable, require less energy, and have smaller footprints than -80°C freezers, demonstrating frozen blood stability at -20°C temperatures could decrease sampling burdens for labs with facility constraints. Methods Whole blood was collected into 9mL vacuum tubes (HemaSure-OMXP) from healthy donors. Aliquots of whole blood and buffy coat samples were stored at -20°C and thawed for genomic DNA extractions (QIAamp DNA Blood Mini Kit, Qiagen) at a range of different timepoints; frozen whole blood samples were processed at 0, 49, 141, and 148 days and frozen buffy coat samples were processed at 7, 27, 36, 185, 246, 373, 394, 584, and 604 days. Purified gDNA was quantified with Qubit (Broad Range dsDNA Assay, ThermoFisher) and qualified with Nanodrop One Microvolume UV-Vis Spectrophotometer (ThermoFisher) and TapeStation (Genomic DNA ScreenTape, Agilent Technologies). Results No significant decrease was observed for DNA yield and quality between fresh and frozen whole blood samples. Average DNA yields from 200 μL whole blood were 4.5 ± 2.2 μg and 5.3 ± 1.2 μg for fresh and frozen blood respectively. Average DNA integrity (DIN) was 6.9 ± 0.4 and 7.1 ± 0.3 for fresh and frozen blood respectively. Donor differences in blood samples can help explain the variance in DNA yields reported. No significant decrease was observed for DNA yield and quality in buffy coat samples frozen for longer at -20°C. In total, 84 whole blood and 25 buffy coat samples were processed in this study. Conclusions Although buffy coat samples tended to have higher yields than whole blood, due to the higher amount of white blood cells present, both sample types provided sufficient amounts of gDNA for downstream applications. Freezing blood samples for future testing can be effective at reducing the need for resampling without sacrificing the nucleic acid yield or quality of fresh samples. Additionally, as technological advancements are made for equipment and assays, frozen blood samples can be retested and compared to previous results for improved diagnostic decisions, done without requiring recollection from labs and patients.

A partitioning Monte Carlo approach for consensus tasks in crowdsourcing

The planner’s role in crowdsourcing involves determining the time to stop collecting information (i.e., timed decision, TD). Previous studies have modeled the uncertain crowdsourcing environment as Partially Observable Markov Decision Processes (POMDPs) and utilized the value of information (VOI) to balance the utilities and costs of information collection. However, there is still room for optimization in solving the TD problem within single-agent POMDPs. In this paper, we propose a partitioning Monte Carlo approach for consensus tasks based on the option-candidate (OC) model partitioning. We simplify the state representation for the OC model and introduce a multi-agent POMDP representation. We establish a correspondence between the single-agent and multi-agent models using two consistency theorems. We propose three progressively improved partitioning Monte Carlo (PMC) algorithms to solve the TD problem within the multi-agent POMDP. We conducted experiments in synthetic domains and a citizen science project, demonstrating that the proposed algorithms exhibit continuous improvements in runtime advantages and consistently outperform the SOTA single-agent Monte Carlo sampling-based algorithm while providing nearly consistent output results.

A lightweight progressive joint transfer ensemble network inspired by the Markov process for imbalanced mechanical fault diagnosis

Owing to safety limitations and data collection costs, scenarios with imbalanced data usually arise, posing a great challenge for precise fault diagnosis. Targeting imbalanced fault diagnosis and the high computational cost of mainstream ensemble learning methods currently used, this article proposes a lightweight and accurate scheme based on a progressive joint-transfer ensemble network (PJTEN) and a Markov-lightweight strategy (MLS). Specifically, a PJTEN is developed, incorporating a multiple excitation-channel attention basic estimator and progressive joint-transfer strategy (PJTS) to maintain diversity of basic estimators better and focus more on key information from minority classes. Besides, the MLS guided by Markov transition probabilities is for the first time constructed for ensemble learning to reduce the network redundancy by alternating optimization. Using a standard dataset and a brand-new dataset of a real ship propulsion system, the proposed method achieves leading results in Accuracy, F1 score and MCC, compared with eight cutting-edge methods, thereby validating its substantial value. In terms of lightweight operation, such as temporal complexity (TC), spatial complexity (SC), and time efficiency, it is also ahead of the latest ensemble-based methods.

PPAT: An effective scheme ensuring privacy-preserving, accuracy, and trust for worker selection in mobile crowdsensing networks

The data content privacy protection and data accuracy are two important research issues in Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy privacy protection as well as data accuracy at the same time, thus hindering the development of MCS. To solve the above issues, for the first time, we have proposed a Privacy Preserving, Accuracy, and Trust data collection scheme (PPAT) for MCS, which can protect the privacy of data content and maintain high accuracy at low-cost style. In PPAT scheme, First, we proposed a scrambled data privacy protection framework which can protect the data of each worker from being known to any third party, which can protect the data privacy of workers. The second, more importantly, we propose a truth value estimation method based on trust computing, which can obtain the truth value more accurately compared to the classic methods under privacy-preserving. In the proposed trust-based truth value calculation, the worker's trust is determined by comparing it with the weight of the trusted worker. Then, the truth value is calculated by the trust of the workers, so that the truth value obtained is more accurate. Through theoretical analysis, it is proved that the proposed PPAT scheme has good worker data content, worker trust, and truth value content privacy protection. Through a large number of simulation experiments, the strategy proposed in this paper has a good ability to protect data content privacy compared to the previous strategy, while improving data quality by 0.5%∼5.7%, and reducing data collection costs by 35.6%∼54.9%.

Preemptive Approach to Plerixafor Use Is Optimal in Patients With Relapsed/Refractory Germ Cell Tumors Undergoing Peripheral Blood Hematopoietic Stem Cell Collection: Effect on Collection Days, Yields, and Cost.

Evidence describing the use of plerixafor in the off-label population of relapsed/refractory germ cell tumors (GCT) is limited. Weaim to describe the effect of rescue versus preemptive plerixafor use on apheresis collection days, collection yields, and cost. We retrospectively collected data on 77 consecutive patients (at least 15 years of age) with GCT who underwent peripheral blood stem cell (PBSC) collection for autologous stem cell transplant between January 1, 2020 and May 1, 2022. Depending on insurance approval, plerixafor was given either as "rescue" (after a first apheresis collection of < 5 × 106 CD34+ cells/kg) or as "preemptive" on Day 4 of granulocyte-colony stimulating factor (G-CSF) prior to the first apheresis collection, if the Day 4 peripheral blood CD34+ count was < 40 cells/μL. A total of 66% of patients who received preemptive plerixafor completed collection in 1 day, similar to good mobilizers who only needed G-CSF (71%, p = 0.366). In contrast, all poor mobilizers in the rescue group required at least 2 days of collection and had lower CD34+ cell yields than the preemptive group (7.15 vs. 9.81 × 106/kg, p = 0.0055). A cost analysis revealed that preemptive plerixafor may save approximately $7000 per patient compared with a rescue approach. Preemptive plerixafor in GCT patients undergoing PBSC collection allows relatively poor mobilizers to collect in fewer days and with lower overall cost. Fewer apheresis procedures result in less risk to the patient, increased patient satisfaction, and the ability to schedule more patients within the constraints of staffing.

Modeling of Applying Road Pricing to Airport Highway Using VISUM Software in Jordan

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method.

Prioritizing industrial wastes and technologies for bioenergy production: Case study

Energy supply stability in the industrial sector is crucial to maintain operational efficiency and avoiding costly disruptions. In the face of pressing environmental challenges, transitioning to sustainable, efficient, and eco-friendly energy sources is imperative. This study aims to assess the potential of industrial waste for bioenergy production in Khorasan Province, Iran, addressing the research gap of developing a comprehensive framework of criteria that was lacking in previous studies. Employing a combined technology and material assessment methodology, the research began with the collection and analysis of questionnaires to identify and weight critical criteria using Shannon Entropy and expert insights. The Additive Ratio Assessment method was then used to rank various types of industrial waste and technologies according to established value criteria. The Findings reveal that anaerobic digestion of organic waste emerges as the most viable bioenergy solution with an 85 % desirability score, followed by anaerobic digestion of sewage sludge and gasification of plastic waste, scoring 77.31 % and 69.41 %, respectively. The innovative aspect of this study is the development and implementation of five novel evaluation criteria, including process temperature, technology lifetime, production cost, waste collection cost, and waste separation cost, which have not been previously applied to the assessment of industrial waste for renewable energy production, especially in developing countries.

Crowdsourced Data Collection Opens New Avenues for the Behavioral Sciences to Impact Real-World Applications

The behavioral sciences have had great success in their study of the mechanisms that drive behavior. However, they have had less impact on applied settings or policy. This gap results from the very adaptability that makes human behavior useful. Adaptability implies that behavior will be highly specific to the context in which it occurs. Thus, building a bridge between the lab and application requires testing in the specific applied setting, which runs afoul of the high cost of data collection. This cost has also led to a focus on simple paradigms that poorly match applied settings. However, crowdsourcing enables data collection at vastly reduced budgets and schedules. This new cost regime also enables paradigms better suited to applied settings. Behavioral science should now be used throughout applied- and policy-focused projects.

A waste reduction strategy through autonomation under a closed-loop supply chain management

Currently, apart from manufacturing processes, the remanufacturing of products is considerably important. Appropriate remanufacturing requires the operation of long-run manufacturing systems. However, in long-run processes, the production system may convert to an out-of-control state due to machine breakdowns. Then, defective products are frequently produced; this increases wastage and disrupts environmental sustainability. In this model, a smart autonomation policy is deliberated for an error-free inspection in separating defective products during production. The autonomation policy facilitates waste reduction through remanufacturing. This paper concentrates on customer awareness and service-dependent demand, which directly improves the overall profitability of the system. A discrete investment to reduce setup cost, continuous investment to collect used goods, and cap-and-trade strategy to limit carbon emission are considered to obtain a more realistic model. Classical optimization method is applied for global maximum profit test of the profit function with respect to cycle length, customer awareness, service investment, discrete investment to reduce setup cost, number of shipments, and container capacities. Numerical testing, sensitivity to total profit in different cost parameters, and comparisons with previous research are explained. Some special scenarios including graphical representations are discussed to prove that a large investment is more beneficial than the cost of specific setup and collection.

Spillover effects of an information intervention on food handling behavior and the stimulus of survey in rural China

A substantial proportion of foodborne illnesses arise at home as a result of poor food handling practices. Consumer information intervention has usually been conducted to prevent such illness; however, few studies have examined the spillover effect of these interventions. This study aimed to identify whether an information intervention focused on one target food handling behavior has spillover effects on other aspects of food handling behaviors (nontargeting behaviors) through a randomized controlled trial (RCT) conducted in rural China. The results estimated by the difference-in-difference model reveal that information intervention has a significant impact on both target and nontarget food handling behaviors. The existence of positive spillover effects is mainly due to the release of limited attention resources from information interventions and the motivation of consumers to maintain other aspects of behaviors as regulated as the target behaviors. In addition, the results also show that being surveyed spurs a stimulus to subsequent behavior. The survey effect is taken into account when calculating the spillover effects in this paper, so a more accurate estimate is obtained. These findings can motivate governments to conduct more consumer education campaigns, especially in emerging countries, and can also contribute to experimental design, data collection methods and cost‒benefit analyses of interventions, thereby helping researchers avoid biased parameter estimation.

Mixed Mode Substantially Increases Hospital Consumer Assessment of Healthcare Providers and Systems Response Rates Relative to Single-Mode Protocols.

Low response rates (RRs) can affect hospitals' data collection costs for patient experience surveys and value-based purchasing eligibility. Most hospitals use single-mode approaches, even though sequential mixed mode (MM) yields higher RRs and perhaps better patient representativeness. Some hospitals may be reluctant to incur MM's potential additional cost and complexity without knowing how much RRs would increase. The aim of this study was to estimate the differences in RR and patient representation between MM and single-mode approaches and to identify hospital characteristics associated with the largest RR differences from MM of single-mode protocols (mail-only, phone-only). Patients were randomized within hospitals to one of 3 modes (mail-only, phone-only, MM). A total of 17,415 patients from the 51 nationally representative US hospitals participating in a randomized HCAHPS mode experiment. Mail-only RRs were lowest for ages 18-24 (7%) and highest for ages 65+ (31%-35%). Phone-only RRs were 24% for ages 18-24, increasing to 37%-40% by ages 55+. MM RRs were 28% for ages 18-24, increasing to 50%-60% by ages 65-84. Lower hospital-level mail-only RRs strongly predicted greater gains from MM. For example, a hospital with a 15% mail-only RR has a predicted MM RR >40% (with >25% occurring in telephone follow-up). MM increased representation of hard-to-reach (especially young adult) patients and hospital RRs in all mode experiment hospitals, especially in hospitals with low mail-only RRs.

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

An Improved Negotiation-Based Approach for Collecting and Sorting Operations in Waste Management and Recycling

This paper addresses the problem of optimal planning for collection, sorting, and recycling operations. The problem arises in industrial waste management, where distinct actors manage the collection and the sorting operations. In a weekly or monthly plan horizon, they usually interact to find a suitable schedule for servicing customers but with a not well-defined scheme. We proposal an improved negotiation-based approach using an auction mechanism for optimizing these operations. Two interdependent models are presented: one for waste collection by a logistics operator and the other for sorting operations at a recycling plant. These models are formulated as mixed-integer linear programs where costs associated with sorting and collection are to be minimized, respectively. We describe the negotiation-based approach involving an auction where the logistics operator bids for collection time slots, and the recycling plant selects the optimal bid based on the integration of sorting and collection costs. This approach aims to achieve an optimization of the entire waste management process. Computational experiments are presented.

Wages: The Worst Transistor Aging Analysis for Large-scale Analog Integrated Circuits via Domain Generalization

Transistor aging leads to the deterioration of analog circuit performance over time. The worst aging degradation is used to evaluate the circuit reliability. It is extremely expensive to obtain it since several circuit stimuli need to be simulated. The worst degradation collection cost reduction brings an inaccurate training dataset when a machine learning (ML) model is used to fast perform the estimation. Motivated by the fact that there are many similar subcircuits in large-scale analog circuits, in this article we propose Wages to train an ML model on an inaccurate dataset for the worst aging degradation estimation via a domain generalization technique. A sampling-based method on the feature space of the transistor and its neighborhood subcircuit is developed to replace inaccurate labels. A consistent estimation for the worst degradation is enforced to update model parameters. Label updating and model updating are performed alternately to train an ML model on the inaccurate dataset. Experimental results on the very advanced 5 nm technology node show that our Wages can significantly reduce the label collection cost with a negligible estimation error for the worst aging degradations compared to the traditional methods.