Historical Data Research Articles

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Permanent areas and changes in forests, grasslands, and wetlands in the North European Plain since the eighteenth century—a case study of the Kościan Plain in Poland

This study investigates the intricate and enduring interplay of historical events, human activities, and natural processes shaping the landscape of North European Plain in western Poland over 230 years. Topographic maps serve as reliable historical data sources to quantify changes in forest, grassland, and wetland areas, scrutinizing their fragmentation and persistence. The primary objectives are to identify the permanent areas of the landscape and propose a universal cartographic visualization method for effectively mapping these changes. Using topographic maps and historical data, this research quantifies land cover changes, especially in forest, grassland, and wetland areas. With the help of retrogressive method we process raster historical data into vector-based information. Over time, wetlands experienced a substantial reduction, particularly in 1960–1982, attributed to both land reclamation and environmental factors. Grassland areas fluctuated, influenced by wetland and drier habitat dynamics. Fragmentation in grassland areas poses biodiversity and ecosystem health concerns, whereas forested areas showed limited fluctuations, with wetland forests nearly disappearing. These findings highlight wetland ecosystems’ sensitivity to human impacts and emphasize the need to balance conservation and sustainable development to preserve ecological integrity. This study advances landscape dynamics understanding, providing insights into historical, demographic, economic, and environmental transformations. It underscores the imperative for sustainable land management and conservation efforts to mitigate human impacts on ecosystems and biodiversity in the North European Plain.

Asbestos exposures associated with the use and handling of drilling mud additives.

This paper summarizes historical asbestos exposure data collected during the handling of short-fiber chrysotile asbestos that was used as an additive to drilling fluid in oil and gas exploration. A total of 1171 industrial hygiene (IH) personal and area air samples were collected and analyzed from more than 20 drilling rigs between 1972 and 1985. The dataset consists of 1097 short-term samples (<240min) with more than 80% having sample durations less than 30min. Average airborne fiber concentrations measured during asbestos handling activities ranged from 0.62 f/cc to 3.39 f/cc using phase-contrast microscopy (PCM). An additional 14 samples were considered long-term samples (>240min) and there were 60 samples with no reported sample duration. Eight-hour time-weighted average (8-h TWA) results, calculated using short-term samples, along with long-term samples greater than 240min, did not exceed contemporaneous Occupational Safety and Health Administration (OSHA) permissible exposure limits (PELs). This analysis fills a data gap in the evaluation of asbestos exposures from the use of drilling mud additives (DMAs) that contained chrysotile asbestos.

A study of the association between single nucleotide polymorphisms of the endoplasmic reticulum aminopeptidase 2 (ERAP2) gene and the risk of ankylosing spondylitis in Egyptians

BackgroundAnkylosing spondylitis (AS) is often regarded as the prototypical manifestation of spondylo-arthropathies that prevalently involves the axial skeleton with the potential attribution of ERAP2 polymorphisms to AS predisposition. The purpose of this study was to determine the genetic association between ERAP2 gene rs2910686, and rs2248374 single nucleotide polymorphisms (SNPs) and the risk of ankylosing spondylitis in the Egyptian population.Methods and resultsA cross-sectional work involved 200 individuals: 100 AS individuals diagnosed based on modified New York criteria in 1984 with 100 healthy controls matched in age and gender. The study included a comprehensive evaluation of historical data, clinical examinations, and evaluation of the activity of the disease using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). A comprehensive laboratory and radiological evaluation were conducted, accompanied by an assessment and genotyping of the ERAP2 gene variants rs2248374 and rs2910686. This genotyping was performed utilizing a real-time allelic discrimination methodology.Highly statistically substantial variations existed among the AS patients and the healthy control group regarding rs2910686 and rs2248374 alleles. There was a statistically significant difference between rs2910686 and rs2248374 regarding BASDAI, BASFI, mSASSS, ASQoL, V.A.S, E.S.R, and BASMI in the active AS group.ConclusionsERAP2 gene SNPs have been identified as valuable diagnostic biomarkers for AS patients in the Egyptian population being a sensitive and non-invasive approach for AS diagnosis especially rs2910686. Highly statistically significant variations existed among the AS patients and the healthy control group regarding rs2910686 alleles and genotypes.Further research is recommended to explore the potential therapeutic implications of these SNPs.

A PPP kinematic application on historical GPS data: the reprocessing of the ITASE98-99 Antarctica mission height profiles

The analysis of altimetric profiles in Antarctica and their evolution over the years is a sensitive topic for the scientific community since it helps understand the effects of climate change that the continent undergoes. Different geomatic techniques, including the GNSS technology, can be employed to obtain altimetric profiles. However, the GNSS differenced approaches, such as the Post Processing Kinematic, are hardly usable to define long profiles in Antarctica because of the low number of CORS stations. In these conditions, the Precise Point Positioning (PPP) approach is a valid alternative to avoid processing very long baselines. The aim of this article is to define a standard procedure for the processing of historical GPS data, thanks to the availability of a dataset from the International Trans-Antarctic Scientific Expedition, which took place between 1998 and 1999 (ITASE98-99). This expedition focused on mapping the Antarctic territory, subdividing it by nations of influence, using geophysical and geodetic technologies, including GPS. The altimetric profiles had already been calculated in 2002 by the Geomatics group of the University of Bologna using the Gipsy-OASIS II software. In this work, the new version of the JPL software, GipsyX, is used to apply the newly implemented models and reprocessed products. The calibration of the processing parameters leading to the final PPP solution is described in the paper, including details on the implementation of a post-processing filtering procedure. The average a posteriori elevation error is 4.6 cm, while 99% of them are within 27 cm. The comparison of the new results to both the previous processing and the REMA elevation model shown that about double the number of solutions are now available, meter-level elevation spikes have been avoided, and a half meter bias is now reduced to a few centimeters. Given the almost 15 years difference between the 1999.0 expedition epoch and the REMA reference epoch, the obtained results can be used to study accumulation/erosion effects on the Antarctica ice sheet.

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening

ABSTRACT Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the impact of the traffic flow parameters and their interaction terms on risk prediction performance, employing a variable pre-screening approach (i.e. Smoothly Clipped Absolute Deviation (SCAD)). A research framework is proposed for more efficient risk prediction, and a detailed case study is further conducted using the proposed approach. In the case study, real vehicle trajectory data from HighD are processed and used, which can be aggregated to extract both traffic flow parameters and corresponding risk data during a specific time interval. As for the risk detection, Time-to-Collision (TTC) index is utilized to identify risky conditions. For different lanes (i.e. inner, middle and outer lanes), the impact of variables, including interaction terms, on risk is explored using the SCAD-logistic models. Furthermore, machine learning methods are employed to compare the risk prediction performance before and after considering interaction terms, as well as before and after variable pre-screening. Finally, the superiority of the machine learning models after SCAD-based variable pre-screening is demonstrated. Results indicate that the interaction terms between traffic flow parameters have significant impacts on the traffic risk. Besides, considering interaction terms and variable pre-screening can improve risk prediction accuracy. Furthermore, the proposed models outperform Random Forest (RF) in terms of predicting traffic risk, achieving a maximum 21.24% accuracy improvement and reducing computational time by up to 31.51%. Findings of this study are expected to contribute to the high-precision prediction of real-time risk in the future.

Unlocking Inventory Efficiency: Harnessing Machine Learning for Sales Surge Prediction

Purpose: Sales forecasting plays a crucial role in inventory optimization for retail stores, especially during special events such as promotions, advertisements, holiday season, weather, social and economic situations etc. These events drive significant changes in customer buying patterns. Some of these events are captured in the current forecasting models as part of trend, seasonality, and cyclicality. But many times, unexpected local events such as extreme weather conditions, riots, and regional events such as marathons, concerts have a significant impact on sales surges which are usually not captured in the sales forecast. This leads to inventory being out of stock and store managers placing last-minute manual orders. By accurately predicting these sales pattern changes, retailers can make informed decisions, ensuring optimal inventory levels and maximizing profits. Methodology: In this paper, a data-driven solution was discussed that leverages machine learning models to predict sales pattern changes and surges during local events in a specific geographical location. Web scraping can be used to gather data on local events from a range of online sources, including Google News, local news channels, and social media platforms. By extracting pertinent details about upcoming events, it is possible to compile a thorough database of local happenings. Findings: Data Analysis of historical sales data mapped to its local events can provide insights on key department-categories where there is a surge in sales. Machine learning models can be used to analyze, experiment and train historical sales data in conjunction with the events data. Unique contribution to theory, practice and policy: Machine learning models would be trained to capture the complex relationships between different local events and their impact on sales. Therefore the study recommends using the machine learning approaches specified to consider various factors, such as event type, location, duration, and historical sales patterns, our models can effectively predict sales fluctuations during specific events.

MEDIS—A comprehensive spatial database on Mediterranean islands for biogeographical and evolutionary research

AbstractMotivationThe intrinsic characteristics of islands make them a unique study system for the investigation of ecological and evolutionary dynamics. The Mediterranean Basin, an island‐rich biodiversity hotspot, still lacks a comprehensive spatial database for these geographic features. This study presents the first comprehensive spatial database of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research.Main types of variable containedThe MEDIS spatial database offers detailed information on 39 geographic, climatic, ecological and land‐use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, palaeogeography, road networks and geological information, providing a multifaceted view of each island's characteristics.Spatial location and grainThe study encompasses 2217 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain of the datasets on which the selected variables are based varies from 10 m (ESA WorldCover) to 1 km (CHELSA‐BIOCLIM+).Time period and grainThe spatial database incorporates data from various sources, each with its own timeframe, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum.Major taxa and level of measurementWhile not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids' coordinates.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

Updated geographic distributions for Texas reptiles

As is the case for many taxa in the Anthropocene, reptile conservation is challenged by an assortment of human-mediated factors. Distributional data for reptile species can be highly useful for informing conservation action. For example, species occurrence data can be used to model suitable habitat as well as quantify contraction, expansion, or shift in the distribution of a species. Texas, USA has a rich reptile fauna including one crocodilian, 32 testudine, 55 lacertilian, and 82 serpent species. However, literature pertaining to the distribution of these species has not been synthesized for over a decade. The goals of our study were to visually summarize all published distributional data for reptile species in the state with updated distribution maps; to tabulate all novel and historic distribution data that is absent from the last statewide synthesis; and to characterize any taxonomic, geographic, or temporal trends of distribution record reporting that have occurred in the state over the last decade. We discovered a total of 659 records that supplement the maps provided in the last statewide synthesis, 40% of which were found in published materials that predate that synthesis. Regarding distributional records published over the last decade, there was no apparent temporal trend – record reporting across years appeared to be stochastic. The number of records published for reptile families generally followed patterns of species richness, although several families had fewer or more records than would be expected. These results might be due to several factors including species biology, variable rates of potential range spread or contraction, and variable research attention (historically and contemporarily). Spatially, we found hot spots of record reporting in the southern, western, and northern portions of the state. We suspect these represent either a contemporary geographic bias of research attention, historically poor range characterization for reptile species occupying these regions, or a combination of both. We also found cold spots of record reporting in the northwestern portion of the Panhandle. These are likely due to natural patterns of reptile diversity as well as geographically biased research attention. Future work to be conducted on reptile distributions in Texas should include a thorough synthesis and revisiting of the voucher specimen data associated with these records.

A Quantitative Precipitation Estimation Method Based on 3D Radar Reflectivity Inputs

Quantitative precipitation estimation (QPE) by radar observation data is a crucial aspect of meteorological forecasting operations. Accurate QPE plays a significant role in mitigating the impact of severe convective weather. Traditional QPE methods mainly employ an exponential Z–R relationship to map the radar reflectivity to precipitation intensity on a point-to-point basis. However, this isolated point-to-point transformation lacks an effective representation of convective systems. Deep learning-based methods can learn the evolution patterns of convective systems from rich historical data. However, current models often rely on 2 km-height CAPPI images, which struggle to capture the complex vertical motions within convective systems. To address this, we propose a novel QPE model: combining the classic extrapolation model ConvLSTM with Unet for an encoder-decoder module assembly. Meanwhile, we utilize three-dimensional radar echo images as inputs and introduce the convolutional block attention module (CBAM) to guide the model to focus on individual cells most likely to trigger intense precipitation, which is symmetrically built on both channel and spatial attention modules. We also employ asymmetry in training using weighted mean squared error to make the model concentrate more on heavy precipitation events which are prone to severe disasters. We conduct experiments using radar data from North China and Eastern China. For precipitation above 1 mm, the proposed model achieves 0.6769 and 0.7910 for CSI and HSS, respectively. The results indicate that compared to other methods, our model significantly enhances precipitation prediction accuracy, with a more pronounced improvement in forecasting accuracy for heavy precipitation events.

MTTLA‐DLW: Multi‐task TCN‐Bi‐LSTM transfer learning approach with dynamic loss weights based on feature correlations of the training samples for short‐term wind power prediction

AbstractWind power prediction for newly built wind farms is usually faced with the problem of no sufficient historical data. To efficiently extract the useful features from related wind farms, a novel transfer learning method based on temporal convolutional network (TCN)‐Bi‐long short‐term memory (LSTM) with dynamic loss weights is proposed. Firstly, a novel multi‐task TCN‐Bi‐LSTM model is designed to extract common features. The separate TCNs, and common Bi‐LSTM layers of the proposed model are designed to extract the temporal features from related wind farms. Secondly, in the pre‐training stage, to optimize the training process of the neural networks, a dynamic loss‐weighting strategy is proposed for multi‐task learning (MTL) to select the most related features, which increase the prediction accuracy by providing a suitable optimization object. Thirdly, the multi‐task TCN‐Bi‐LSTM model is re‐trained based on the samples from the target wind farm. Finally, a dataset of seven wind farms was employed to evaluate the efficiency of the proposed MTL structure and the dynamic loss‐weighting strategy. The result shows that the root mean squared error of the 12‐h short‐term prediction can be decreased by 4.19% compared with the traditional single‐task learning model, which verifies the validity of the proposed multi‐task transfer learning method.

Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation.

Identification of Beach Erosion in Colombo- North Coast Line: Effectiveness of the Engineered Solutions

Coastal erosion is a silent disaster in Sri Lanka due to the loss of valuable land. Already, millions of rupees have been spent on coastal protection. This study assessed the long-term effectiveness of those engineered solutions, especially focusing on the Colombo- North coastline. The effectiveness was assessed by comparing erosion rates in the last 20 years with available historical data. Shorelines of the last 20 years were observed and digitized by Google Earth image analysis and erosion rates were calculated by GIS and Digital Shoreline Analysis (DSAS) tools. The evolution of the structures was observed and analysed by satellite image analysis and other available data from reliable sources such as reports produced by the Government. Cost estimation for the observed structures was carried out using BOQ analysis and discounted methods. Results showed that more than 60 numbers of structures have been constructed during the last 20-30 years along the study area. Uswetakeyyawa to Estuary of Kelani River stretch was identified as the region with the highest structure density. When analyzing the results, it was revealed that the structures built in that region have reduced the erosion rate (0.43 m/year) compared to the 1996 rates (2.5 m/year). Digital shoreline analysis further indicated effectiveness as most of the protected areas from Negombo to Maha Oya estuary have been accreted during the last 20 years while unprotected areas from Uswetakeyyawa to Negombo lagoon, have been subjected to erosion. According to cost estimations, approximately Rs 1.23 billion have been spent in the last 20-30 years on the entire study area. Further, the cost analysis of the Uswetakeyyawa to Kelaniya stretch revealed that the investment for 1m accretion was Rs 28.5 million. However, the cost-effectiveness of the structures should be further assessed against the land loss value with an integrated Coastal Vulnerability Index.

Abstract PO3-17-04: HER2-directed biosimilar Ogivri in the treatment of breast cancer: real world reporting of symptoms and wellbeing using electronic patient reported outcome (ePRO)

Abstract Introduction: Trastuzumab has a major impact on the treatment of HER2 positive breast cancer (BC). Anti-HER2 biosimilars, such as OgivriTM, demonstrated safety and clinical equivalency to the reference product HerceptinTM in clinical trials. However, real-world reporting of side effects, quality of life (QoL) and outcome in patients treated with biosimilars has not yet been performed using electronic PROs. Here standardized and structured ePROs are dynamically recorded with the mediduxTM app during OgivriTM treatment and are applied to perform a comparative analysis with ePROs derived from 38 historical patients treated with HerceptinTM in two previous studies (NCT02004496, NCT03578731). Methodology: In this prospective observational study, patients with HER2-positive BC were treated with OgivriTM alone +/- Pertuzumab, +/- Chemotherapy and hormone therapy in neo-adjuvant, adjuvant and non-curative settings. Over an observational period of 6 weeks, patients used the mediduxTM app to dynamically record symptoms (according to CTCAE), well-being, EQ-5D-5L, cognitive capabilities, and vital parameters. Comparative analysis was performed with historical data from 38 patients that had reported 5217 ePROs and more than 1400 reports of well-being in similar therapeutic settings. Results: 52 females (age 37-86 yrs) were treated with OgivriTM. From 92 different symptoms available, 84 were entered (average &amp;gt; 2 symptoms/day) resulting in 10`532 individual patient entries. Among the most common symptoms reported in both groups were fatigue, taste disorder, nausea, diarrhea, dry mucosa, joint discomfort, tingling, sleep disorder, headache, appetite loss. As compared to HerceptinTM, overall, symptoms associated with OgivriTM were reported with a similar incidence and distribution among groups (p=0.68), although slightly but not significantly lower scores (p=0.24; CI: 0.08-0.31). Distribution of symptom grades in the OgivriTM cohort revealed that the vast majority of patients experienced mild and grade 1 toxicities, 13% grade 2, 2% grade 3 and 0.4% grade 4 toxicities, respectively. The latter finding could possibly also be attributed to a trend towards de-escalation in chemotherapeutic regimes during the past 5 years since data acquisition, affecting anthracyclines and taxanes, and that. Of note, when treatments were compared restricted to the trastuzumab antihormones and trastuzumab- paclitaxel therapies, this reported trend seamed weaker. Importantly, well-being of patients undergoing HER2 directed treatment did not differ (p=0.24; CI: 0.39-0.88) in both cohorts. Conclusion: No difference was reported for symptoms, adverse events, and well-being with respect to the Trastuzumab biosimilar OgivriTM in comparison with HerceptinTM. The integration of ePRO into research and clinical practice provides reliable information when investigating real world tolerability and outcomes of similar therapeutic compounds. Citation Format: Andreas Trojan, Sven Roth, Reinhard Zenhäusern, Yannick Kadvany, Christian Jackisch, Matti Aapro, Alexandru Eniu. HER2-directed biosimilar Ogivri in the treatment of breast cancer: real world reporting of symptoms and wellbeing using electronic patient reported outcome (ePRO) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-17-04.

Abstract PS16-08: Histologic Pattern and Outcomes in High-Grade Metaplastic Breast Cancer Compared to Triple-Negative Ductal Breast Cancer Counterparts: A Single Institution Retrospective Study

Abstract Background Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer that presents with high tumor stage and a poor prognosis. MpBC tumors tend to have worse outcomes compared to other non-metaplastic triple-negative breast cancers (TNBC). Although MpBC has been described as chemotherapy resistant, chemotherapy remains a mainstay of treatment. MpBC tumors can be further characterized by their specific histologic pattern. The existing literature on outcomes of MpBC by histologic pattern is limited, with varying results regarding response rate and overall survival (OS). Identifying a pattern of MpBC that has better response to treatment could help tailor treatment recommendations. Methods A retrospective chart review was performed and identified 106 patients with early-stage, high-grade MpBC who were diagnosed at Levine Cancer Institute between January 1, 2010 and September 1, 2021. A matched control cohort (n=106) of patients diagnosed with non-metaplastic TNBC was selected based on propensity score matching on diagnosis date, age at diagnosis, race, pathologic staging, and tumor grade. Patient demographics, tumor characteristics, therapeutic interventions, residual cancer burden (RCB), and outcomes were collected. The associations between the histologic patterns of MpBC and disease characteristics were evaluated using chi-squared tests for categorical variables and Wilcoxon tests for continuous variables. We also examined the differences in RCB scores between the MpBC cohort and the non-metaplastic TNBC cohort who received neoadjuvant chemotherapy. Additionally, Kaplan-Meier and Cox proportional hazard analysis was performed to assess differences in recurrence free survival (RFS) and OS between the spindle cell/sarcomatous pattern versus all other patterns. Results For the entire group (n=212), median age at diagnosis was 58 years (range, 24-92); all patients were female; 66% White, 29% Black, and 5% unknown race. Most tumors were triple-negative (91%); 19%, 56% and 14% with Clinical Stage I, II, and III disease, respectively. A total of 63% of patients underwent radiation treatment and 76% received chemotherapy (43% neoadjuvant and 57% adjuvant). Recurrence after treatment occurred in 16% of all patients. In the MpBC cohort (n=106), the histologic patterns included spindle cell/sarcomatous (34%), squamous (17%), heterologous mesenchymal (31%), mixed (17%), and other (1%). Of those with MpBC who received neoadjuvant chemotherapy (n=32), RCB among the different histologic patterns was assessed. When comparing spindle cell/sarcomatous versus the other histologic patterns combined, there was no significant difference in RCB (p = 0.81), although more RCB III scores were recorded in the spindle cell/sarcomatous pattern compared to the other MpBC patterns (40% versus 30%). When assessing RFS and OS by histologic pattern in the entire MpBC cohort, spindle cell/sarcomatous tumors had inferior RFS (p = 0.0194) and OS (p = 0.0039) compared to the other MpBC patterns combined. When comparing RCB values between MpBC and non-metaplastic TNBC, there were numerically more RCB-III classes in the MpBC cohort as compared to the non-metaplastic TNBC. Conclusion MpBC is an aggressive and difficult breast malignancy to treat. Based on our single institution review, no correlation between RCB and the histologic pattern of MpBC was found, however, RFS and OS were worse among the spindle cell/sarcomatous pattern. Furthermore, the MpBC cohort had numerically higher RCB-III scores than the TNBC control cohort, which is in line with historical data. Citation Format: Ashley Matusz-Fisher, Annabel Chen, Erin Donahue, Courtney Schepel, Michelle Wallander, Chad Livasy, Richard White, Antoinette Tan, Lejla Hadzikadic-Gusic. Histologic Pattern and Outcomes in High-Grade Metaplastic Breast Cancer Compared to Triple-Negative Ductal Breast Cancer Counterparts: A Single Institution Retrospective Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS16-08.

A robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets

In order to better describe the uncertainty of renewable energy output, this paper proposed a novel robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets. First, an ellipsoidal uncertainty set was established using historical data on renewable energy output, and a data-driven convex hull polyhedral set was established by connecting high-dimensional ellipsoidal vertices; on this basis, an adaptive data-driven polyhedral set model was established to address the problem of high conservatism in the scaling process of convex hull polyhedral sets. Furthermore, a novel adaptive data-driven robust scheduling model for new distribution systems was established, and a column-and-constraint generation (C&amp;amp;CG) algorithm was used to solve the robust scheduling model. Finally, the improved IEEE-33 bus system simulation verification shows that the robust scheduling model for new distribution systems based on adaptive data-driven polyhedral sets can reduce conservatism and improve the robustness of optimization results.

Test case prioritization based on fault sensitivity analysis using ranked NSGA-2

AbstractThis paper discusses regression testing in software maintenance, focusing on test case prioritization to verify modifications to software functionality efficiently. The primary goal is to rank test cases, prioritizing those covering more code or faults with minimal execution time. The challenge lies in prioritizing numerous test cases generated during development and maintenance. Various algorithms, including greedy approaches and meta-heuristic techniques, address this challenge. The paper introduces a ranking-based non-dominated sorting genetic algorithm (NSGA-2) for test case prioritization, emphasizing cases sensitive to faults caused by modifications or new functionality. Historical data is prioritized, with key objectives including the sensitive index, execution cost, and average percentage of fault detection (APFD). The proposed model is tested on handcrafted and benchmark Java-based applications, comparing its performance to state-of-the-art algorithms in test case prioritization.

BITCOIN PRICE PREDICTION BY USING ARIMA

Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as pandas and NumPy, we cleanse and format the historical Bitcoin price data, laying the foundation for subsequent analysis. Key preprocessing tasks include handling missing values, normalization, and addressing any anomalies or outliers that may distort the underlying patterns. With the data prepared, our attention turns to assessing the stationarity of the Bitcoin price time series—a fundamental prerequisite for applying classical time series models. Through visual inspection and statistical tests such as the Augmented Dickey-Fuller (ADF) test, we ascertain the presence of trends or seasonality that could influence the modelling process. To mitigate such effects, we employ techniques such as differencing and transformations, including the Box-Cox transformation, to stabilize the variance of the data. Armed with a stationary time series, we embark on the core of our analysis: modelling Bitcoin prices using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. These models, renowned for their versatility and effectiveness in capturing temporal dependencies, offer a sophisticated framework for forecasting time series data. Guided by the principles of parsimony and model selection criteria such as the Akaike Information Criterion (AIC), we systematically explore the parameter space to identify the optimal specifications for our models. The efficacy of the chosen models is rigorously evaluated through diagnostic checks, encompassing residual analysis, model fit statistics, and out-of-sample validation. Insights gleaned from these assessments inform our confidence in the models' predictive capabilities and guide our interpretation of the forecasted outcomes. Finally, armed with a validated model, we turn our gaze to the future, employing it to generate forecasts of Bitcoin prices for forthcoming periods. Visualizations juxtaposing predicted prices against observed values provide a compelling narrative of the model's performance and offer stakeholders valuable insights into potential market trends and dynamics. In summary, this research contributes to the burgeoning field of cryptocurrency analytics by showcasing the application of time-tested statistical methodologies to forecast Bitcoin prices. By leveraging the power of ARIMA and SARIMAX models, we illuminate the intricate patterns underlying Bitcoin's price dynamics, empowering market participants with actionable intelligence for informed decision-making in an ever-evolving landscape. Keywords Cryptocurrency Markets, Bitcoin Price Prediction, Time Series Analysis, Python Programming, Data Preprocessing, Pandas, NumPy, Stationarity Testing, Augmented Dickey-Fuller (ADF) Test, Box-Cox Transformation, ARIMA Model, SARIMAX Model, Model Selection, Akaike Information Criterion (AIC), Diagnostic Checks, Residual Analysis, Out-of-Sample Validation, Forecasting, Visualization, Market Trends, Decision-Making, Cryptocurrency Analytics