Historical Database Research Articles

Change point detection in text data

The analysis of text data using artificial intelligence and statistical methods has become increasingly important in recent years. One application is the automatic assignment of documents. For this purpose, a classification model is trained on the basis of historical data. If the structure of the texts to be classified changes over time, the quality of the classification will decrease. Change point detection algorithms can counteract this. Such algorithms automatically detect changes in the structure of the texts and indicate that the trained classification model has to be adapted. However, the undesired influence of the length of the document needs to be handled when modeling the text data. We present a multinomial change-point model detecting changes in text structures. The results are supported by simulation studies.

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods.

In search of founding era registers: automatic modeling of registers from the corpus of Founding Era American English

Abstract Registers are situationally defined text varieties, such as letters, essays, or news articles, that are considered to be one of the most important predictors of linguistic variation. Often historical databases of language lack register information, which could greatly enhance their usability (e.g. Early English Books Online). This article examines register variation in Late Modern English and automatic register identification in historical corpora. We model register variation in the corpus of Founding Era American English (COFEA) and develop machine-learning methods for automatic register identification in COFEA. We also extract and analyze the most significant grammatical characteristics estimated by the classifier for the best-predicted registers and found that letters and journals in the 1700s were characterized by informational density. The chosen method enables us to learn more about registers in the Founding Era. We show that some registers can be reliably identified from COFEA, the best overall performance achieved by the deep learning model Bidirectional Encoder Representations from Transformers with an F1-score of 97 per cent. This suggests that deep learning models could be utilized in other studies concerned with historical language and its automatic classification.

310.7: Development of the Nova Scotia potential donor audit tool and historic performance database: lessons learned from the first 1000 chart reviews.

310.7: Development of the Nova Scotia potential donor audit tool and historic performance database: lessons learned from the first 1000 chart reviews.

Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms

The wholesale electricity market is composed of real-time market and procurement. Since the fully liberalization of the energy market in Singapore in 2018, competition among the market participants has become intensive. Therefore, the forecast of the electricity price, including both Uniform Singapore Energy Price (USEP) and regulation price (REG), is essential for market participants to trade at the best possible price. This study proposes an event-driven forecast model to provide a real-time automatical price forecast of the wholesale electricity price using the cutting-edge regression tree ensembled algorithm, XGBoost. The model is triggered every half of an hour by the dispatch of the latest electricity price, which will be also used as the input together with other necessary training data from the historical price database. The training is finished within 1–2 min and the model makes the predictions on prices of the next 6 h. A better prediction has been demonstrated benchmarked to the forecast provided commercially by Energy Market Company. The forecast model only takes commercially available data as the input and is embedded with messaging broker, enabling online collaboration of different modules. The model is readily implemented, and thus can serve as an effective supplementary tool in virtual power plant operation with a higher renewable energy penetration in the future.

Classifying the risk of forest loss in the Peruvian amazon rainforest: An alternative approach for sustainable forest management using artificial intelligence

Peruvian Amazonian rainforests are constantly threatened by forest loss. Understanding changes in forest cover and assessing the level of risk is a permanent concern for numerous scientists and forest authorities. There are many conservation programs for Peruvian forests that involve collaborative efforts and employ diverse methodologies for forest monitoring. In this study, we propose an alternative approach to decision-making for forest preservation, aiming to classify the risk of forest loss in districts within the Peruvian Amazon rainforest. This classification enables sustainable forest management. To accomplish this, we utilized unsupervised learning artificial intelligence through Kohonen's neural network. The network was trained using a historical database spanning from 2001 to 2021, which includes variables such as forest cover and loss, climate, topography, hydrographic networks, and timber forest concessions. Through this approach, the network successfully established five clusters. Following preliminary analysis, we designated these clusters as: low, medium, high, very high, and extremely high risk of forest loss. Kohonen networks demonstrated their effectiveness in clustering forest loss and forest cover. The results indicate a shifting trend among the classes over time, with an increase in the categories exhibiting high and very high risk of forest cover loss. This study provides valuable information for decision-making in the prevention and conservation of Peruvian forests. We strongly recommend maintaining vigilance, particularly in districts classified as a very high or extremely high risk of losing forest cover.

Migratory destinations and spatial structuring of humpback whales (Megaptera novaeangliae) wintering off Nicaragua

Understanding the migratory patterns of large whales is of conservation importance, especially in identifying threats to specific populations. Migration ecology, including migratory destinations, movements and site fidelity for humpback whales (Megaptera novaeangliae) remain poorly studied in parts of the range of the Central America population, considered endangered under the United States Endangered Species Act. This study aimed to investigate the migratory destinations of humpback whales sighted at two study sites in Nicaragua, which are part of the Central America population. A ten-year photographic database of humpback whales observed off Nicaragua was combined with citizen science contributions and sightings from dedicated research programs. The resulting image collection was compared with available historical photo identifications and databases using an automated image recognition algorithm. This approach yielded 36 years of photographic identification totaling 431 recaptures in Nicaragua (2006–2008 and 2016–2021) and 2539 recaptures (1986–2020) in both feeding and breeding grounds of 176 unique individuals sighted in Nicaragua. Our results showed that photo-identified whales were recaptured between October and April in breeding grounds and year-round in feeding grounds between British Columbia and California, with peak recaptures between June and October. Our study provided first-time evidence on fine-scale site affinity of individual humpback whales within Nicaraguan waters and to other breeding and feeding grounds.

Modeling soybean cultivation suitability in China and its future trends in climate change scenarios

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topography-socio-economy environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. Three shared socio-economic paths (SSPs) that set up in the future climate section were considered, including SSP126 (sustainable path), SSP245 (intermediate path), and SSP585 (fossil fuel dominated development path). The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, portion of primary industry, topsoil gravel content, mean diurnal temperature range and accumulated temperature ≥10 °C. High-suitability and moderate-suitability area are respectively 26.51 Mha and 41.93 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. From 2021 to 2060, the total area highly and moderately suitable for soybean cultivation is projected to increase first and then decrease under both SSP126 and SSP245 scenarios. However, it shows a continued upward trend under SSP585, the rising part accounting for more than 10% in the base of historical data. Specifically, under SSP585, the suitability grade in most parts of Northeast China (eastern Inner Mongolia, northern Heilongjiang and western Jilin and Liaoning) will have a general promotion, opposite to the result under SSP126. Moreover, parts of southwest China (Yunnan, Chongqing, northern Guizhou and eastern Sichuan) may be more suitable for soybean cultivation in both scenarios. This study provides a practical reference for current and future soybean planting layout and relative countermeasures.

Exploring Concomitant Acetylcholinesterase Inhibitor and Overactive Bladder Anticholinergic Use and Risk of Hospitalization in Medicare and Dual-Eligible Medicare-Medicaid Populations in a Historic Database.

Overactive bladder prevalence increases in older adults often complicating the management of other comorbidities. The theoretical antagonism between the parasympathetic-blocking anticholinergic agent and the parasympathetic stimulatory agents concomitantly used by patients is only recently being explored. The primary aim was to determine the frequency of the annual use of acetylcholinesterase inhibitors, overactive bladder anticholinergics, and the use of both agents in the same year. The secondary aim was measurement of the association between annual hospitalization and same-year use of both acetylcholinesterase inhibitors and anticholinergics. The US nationally representative MarketScan® Medicare databases were analyzed. In the Medicare enrollees, there were 122 020, 141 920, and 15 639 users of acetylcholinesterase inhibitors, anticholinergics, and both agents, respectively. The percentage of acetylcholinesterase inhibitor users who also used anticholinergics was 12.8%. Comparing users of both acetylcholinesterase inhibitors and anticholinergics to those using AChEI alone, 5 608 of the former experienced a hospitalization (35.9%) compared to 33 182 of the latter (31.2%). There was an increased risk of hospitalization for those using both acetylcholinesterase inhibitors and anticholinergics in the same year, with an odds ratio (OR) of 1.23 (95% CI, 1.19, 1.28). Clinicians should consider improved monitoring of the usage of both medications and clarify alternative regimens that avoid anticholinergics in at-risk older adults.

One Health Approach to Leptospirosis: Dogs as Environmental Sentinels for Identification and Monitoring of Human Risk Areas in Southern Brazil.

Leptospirosis has been a neglected, widespread and reemerging zoonotic disease of global importance. The One Health holistic approach combining human, animal and environmental health has been important for control and prevention of zoonotic disease. An urban municipality in Southern Brazil with a high prevalence of leptospirosis was selected by convenience, with asymptomatic human and canine individuals serologically tested using MAT against 30 Leptospira spp. serovars. Epidemiological questionnaires were assessed along with the historical national database, with associated risk factors to dog and human seropositivity analyzed using multiple logistic regression. The spatial distribution of retrospective human leptospirosis cases was analyzed using a Kernel map and overlapped dog seropositivity and historical flooding areas, demonstrating that domestic and asymptomatic dogs may be important environmental sentinels for leptospirosis in such urban areas, even in the absence of human seropositivity and low canine seropositivity. Associated risk factors for leptospirosis in dogs and humans according to multiple regression included healthy dogs (p = 0.02, odds ratio-OR = 0.17, confidence interval of 95%CI 0.03-0.71) with indoor access (p = 0.059, OR = 5.12, 95%CI 1.10-37.7) and human males (p = 0.042, OR = 2.44, 95%CI 1.08-6.11) with presenting calf pain (p = 0.005, OR = 3.14, 95%CI 1.46-7. 14), jaundice (p < 0.001, OR = 4.61, 95%CI 2.11-10.10) and renal failure (p = 0.008, OR = 4.49, 95%CI 1.49-13.76). The greater the rain precipitation (above 3 mm of average annual precipitation), the higher the number of leptospirosis cases in humans. In conclusion, dog active serosurvey and rain precipitation should be systematically reported and plotted altogether as a basis for the early detection and monitoring of human risk areas. Such findings may serve as a basis for public health policies in Brazil and other endemic countries worldwide.

Variability of historical tropical cyclone best track databases and their impact on the developed stochastic track models and estimated wind hazard for mainland China

Variability of historical tropical cyclone best track databases and their impact on the developed stochastic track models and estimated wind hazard for mainland China

Navigating the Challenges of Deduplicating Actors in Historical Letter Exchanges

This paper outlines ongoing research to standardize the metadata of letters from Finnish cultural heritage organizations to build a comprehensive archive for studying 19th-century epistolary culture in the Grand Duchy of Finland (1808/09-1917), as a part of the Constellations of Correspondence (CoCo) project. The challenge of deduplicating and reconciling actors and their associated information is an important problem faced in historical knowledge databases. The actors, e.g., senders and recipients of letters, can be individual people, couples, or families as well as organizations like institutions or companies. The deduplication described in this article has its focus on the individual people. The solutions we propose in this paper are significant for the development of the efficient and accurate approaches in this field.

S09-04: Integrating exposure and a historical database into decision-making for DART testing in a second species under REACH

S09-04: Integrating exposure and a historical database into decision-making for DART testing in a second species under REACH

Introducing HiSCoD: A New Gateway for the Study of Historical Social Conflict

The Historical Social Conflict Database (HiSCoD) is an ongoing project designed to provide to scholars and society at large with a set of resources for analyzing social conflict from the Middle Ages to the second half of the nineteenth century (c. 1000–c. 1870). Based on original archival research and existing repositories, the aim is to provide a global database of social conflict in past societies by collecting, aggregating, documenting, and harmonizing instances of conflict. As of today, the database contains data on more than twenty thousand events, from fiscal scuffles to urban revolts involving thousands of individuals. For every event, we provide information on the date, location, type of conflict, and, when possible, number of participants, participation of women, and a summary of events.

Recommendation of SLM Process Parameters Based on Analytic Hierarchy Process and Weighted Particle Swarm Optimization for High-Temperature Alloys.

Selective laser melting (SLM) of high-temperature alloys involves intricate interdependencies among key process parameters, such as laser power and scanning speed, affecting properties such as density and tensile strength. However, relying solely on experiential knowledge for process parameter design often hampers the precise attainment of target requirements. To address this challenge, we propose an innovative approach that integrates the analytic hierarchy process (AHP) and weighted particle swarm optimization (WPSO) to recommend SLM process parameters for high-temperature alloy fabrication. Our proposed AHP-WPSO model consists of three main steps. First, a comprehensive historical database is established, capturing the process parameters and performance metrics of high-temperature alloy SLM parts. Utilizing an AHP framework, we compute the performance similarity between target and historical cases, applying rational thresholds to identify analogous cases. When suitable analogs are elusive, the model seamlessly transitions to the second step. Here, the WPSO model optimizes and recommends process parameters according to target specifications. Lastly, our experimental validation of the GH4169 high-temperature alloy through SLM experiments corroborates the effectiveness of our AHP-WPSO model in making process parameter recommendations. The outcomes underscore the model's high accuracy, attaining a recommendation precision of 99.81% and 96.32% when historical analogs are present and absent, respectively. This innovative approach offers a robust and reliable solution to the challenges posed in SLM process parameter optimization for high-temperature alloy applications.

Bone Marrow Stimulation for Osteochondral Lesions of the Tibial Plafond Yields Good Patient-Reported Outcomes in Daily Living but Moderate Outcomes in Sports Activities at 2- to 22–Years Follow-Up

To assess the patient-reported outcomes, as well as the revision and complication rates, of patients who underwent arthroscopic bone marrow stimulation (BMS) for an osteochondral lesion of the tibial plafond (OLTP). Patients with an OLTP treated with arthroscopic BMS at a minimum follow-up of 2-years were cross-sectionally included from a historical database. The primary outcome was the Numeric Rating Scale (NRS) during walking. Secondary outcomes included the NRS in rest and during running, and the Foot and Ankle Outcome Score. Additionally, the association of baseline patient and lesion demographics with follow-up patient-reported outcome measures (PROMs) was assessed with the Spearman rank correlation test. A subanalysis was performed for PROMs in patients with or without a coexisting talar (i.e., bipolar) lesion. Finally, the revision surgery (i.e., repeat surgery for the OLTP) and complication rates were assessed. Fifty-one patients were included at a mean 8.8 (standard deviation [SD]= 5.7, range, 2-22) years follow-up. Seventy-three percent of patients had a solitary OLTP, and 27% had a coexisting talar (bipolar) lesion. Males had a significantly higher rate of bipolar lesions compared to females (P ≤ .01), and patients with a bipolar lesion had a significantly larger OLTP lesion diameter (P= .02) and volume (P= .04). At final follow-up, the mean NRS during walking was 1.9 (SD= 2.3) out of 10. Anterior-posterior OLTP size (r= 0.36; P ≤ .01) was significantly associated with a higher NRS pain score during walking, although the presence of bipolar lesions did not result in inferior clinical outcomes. At final follow-up, 6% of patients underwent revision surgery. Minor complications were observed in 12% of patients. Arthroscopic BMS for OLTP results in favorable patient-reported outcomes at mid- to long-term follow-up, although moderate outcomes were observed in sports activities. Lesion size was associated with increased pain scores, although bipolar lesions did not result in inferior patient-reported outcomes. Six percent of patients required revision surgery, and 12% of patients had minor complications after surgery. Level III, retrospective cohort study.

A Multi-Level Grid Database for Protecting and Sharing Historical Geographic Urban Data: A Case Study of Shanghai

Historical geographic data play an important supporting role in the study of long-term geographic studies, such as climate change, urban expansion and land-use and land-cover change. These data vary in source, format and accuracy and are widely found in historical documents, old maps, produced vector data, aerial photographs, old photographs, etc. The complex nature of data makes it difficult for researchers to organize, store and manage in a unified manner. Thus, GIS practitioners and social scientists will collectively face the challenge of integrating historical data into spatial databases. Herein, we introduced the concept of a multi-level spatial grid, selecting Shanghai as the study area, to construct the Shanghai historical geographic database and give the conceptual model and processing method. The experiment was performed using the China Historical Geographic Information System (CHGIS), which showed the historical evolution of Shanghai more conveniently. Meanwhile, we simulated one million rows of historical geographic data in Shanghai and compared the retrieval efficiency of the encoding method with the latitude–longitude and geometric object indexing methods, which demonstrated that our method was very effective. This research is important for the construction of a historical urban database, which can better preserve historical resources and promote urban culture with information science and technology.

The service sector in the classical world: focus on entertainment and well-being

PurposePredicting effects of artificial intelligence on service occupations can be supported by a long historical perspective. Historical databases and archaeology help reconstructing the service sector in ancient societies. Here, the purpose of this paper is to analyse occupational specialization within services in cities of ancient Greece and the Roman Empire, as well as how the service sector is reflected in architectural remains, to identify differences and similarities with today’s Europe.Design/methodology/approachOccupational titles are traced in epigraphical and literary sources, sorted according to ISCO-08. Secondary sources are used for the architectural evidence of service activities, as well as for the role of contests and entertainment in antiquity.FindingsCompared to current European service employment, professionals were fewer in classical Athens and imperial Rome, which had a greater proportion of specialized salespersons. There were few office buildings and no civic hospitals, but heavy investment in facilities for entertainment and well-being. Quality assessments for goods were little developed; contests for cultural and sports activities assessed entertainment service quality.Research limitations/implicationsThis study covers two periods in classical antiquity and is restricted to Mediterranean cultures, although findings may help understanding the service sector in poor countries with informal employment.Originality/valueWhile particular services provided in ancient cities have been studied, there has been no broad comparative overview of their service occupations. Services in earlier societies with primitive information and communication technologies can provide clues for current developments.

Critical Factors Affecting the Bid Cost of Building Construction Projects

A cost database is essential for contractors to estimate accurate bid costs, and most contractors use historical cost databases for this purpose. Factors that influence bid cost should be investigated so that they are accurate. From the existing literature, this study identifies 30 factors that affect bid costs, and a survey was sent to 80 prime and subcontractors to determine the critical factors. The intraclass correlation test was conducted to determine the consistency of the responses. Then the Relative Importance Index (RII) was determined to identify the critical factors inhibiting the bid cost. In addition, the Mann-Whitney test was performed to determine whether the critical factors were highly ranked. Using RII and Mann-Whitney tests, the study determined seven critical factors that affect the bid cost of building projects: failure to track labor productivity; failure to report site-specific project conditions; failure to report complexity of the project; lacking documentation of union restrictions, regulations, local building code, and safety requirements; failure to report change order impact on labor productivity; failure to incorporate change orders into a cost control program; and failure to report project description. These findings will assist contractors to accurately bid building projects.

Historical Control Background Incidence of Spontaneous Neoplastic Lesions of Sprague-Dawley Rats in 104-Week Toxicity Studies.

Data collected from approximately 1800 male and 1800 female Sprague-Dawley (SD) rats used in 104-week carcinogenicity studies were archived in a historical control database at Labcorp Early Development, Inc, and the neoplastic microscopic observation data from these rats were retrospectively evaluated. Historical control data can provide useful information on the range and incidence of spontaneously occurring background neoplasms in the species and strain of the test animal used in different types of toxicity studies, including studies of differing lengths, delivery of test article, and test animal. Some of the most common malignant findings noted included fibrosarcoma of skin/subcutis and thyroid C-cell carcinoma in males (2.1% each) while mammary gland carcinoma and pituitary carcinoma (25% and 2.6%) were most common in females. Pituitary adenoma of pars distalis was found to be the most prevalent benign neoplasm in both males and females (56.4% and 77.1%). Fibroadenoma of mammary gland (35.6%) and thyroid C-cell adenoma (8.5%) were the second and third most common benign tumors in female SD rats. In males, the thyroid C-cell adenoma (10.9%) and benign pheochromocytoma (8.9%) were the second and third most common tumors.