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This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the "surface image removal" technique and the method of "high dynamic range" photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images. [ABSTRACT FROM AUTHOR]

This note is written to show that the definition of the LA-semihypergroup by V. Amjad, K. Hila and F. Yousafzai "Generalized hyperideals in locally associative left almost semihypergroups, New York J. Math. 2014" should be corrected and that it is not enough to replace the multiplication "·" of an LA-semigroup by the hyperoperation "ο" to pass from an LA-semigroup to an LA-semihypergroup. The two examples of the paper based on the definition of the LA-semihypergroup are wrong that is a further indication that this definition needs correction. According to the last section of the paper, the paper generalizes the results of an LA-semigroup by M. Akram, N. Yaqoob and M. Khan "On (m, n) -ideals of left almost semigroups, Appl. Math. Sci. (Ruse) 2013" while the paper duplicates, without citation, the section 4 of the paper by W. Khan, F. Yousafzai, W. Guo and M. Khan "On (m, n) -ideals of left almost semigroups, J. Semigroup Theory Appl. 2014" with the usual change of "·" to "ο". [ABSTRACT FROM AUTHOR]

Some nomads have their own websites and are on Facebook, which might be worth exploring. There is no typology of these nomads; some have regularity, some on highly ad hoc (just hitting the road) ventures, and some using websites, Facebook, and Internet gatherings to construct a community. Bruder mentions the I RV Daily Report i , the Rubber Tramp Rendezvous, roving GTG (get togethers), FreeCampsites.net, Kampgrounds of America, Amazon's CamperForce, and Craigslist (for job opportunities). [Extracted from the article]

The paper addresses the problem of imaginative resistance in science, that is, why and under what circumstances imagination sometimes resists certain scenarios. In the first part, the paper presents and discusses two accounts concerning the problem and relevant for the main thesis of this study. The first position is that of Gendler (Journal of Philosophy 97:55–81, 2000), (Gendler, in: Nichols (ed) The Architecture of the Imagination: New essays on pretence, possibility and fiction, Oxford University Press, New York, 2006a), (Gendler & Liao, in: Gibson, Carroll (eds) The routledge companion to philosophy of literature, Routledge, New York, 2016), according to which imaginative resistance mainly concerns evaluative scenarios, presenting deviant moral attitudes. The second account examined is that of Kim et al. (in: Cova, Réhault (eds) Advances in experimental philosophy of aesthetics, Bloomsbury, London, 2018), who insisted on the link between imaginative resistance on the one hand and counterfactual and counterdescriptive scenarios on the other. In the light of both theories, this paper discusses the importance of addressing the problem of imaginative resistance in the scientific enterprise in the light of some mechanisms of embodied simulation, based on the activity of mirror neurons and investigated within the framework of the Embodied Simulation Theory. [ABSTRACT FROM AUTHOR]

Focuses on the reliability and power quality at Champion International Corp. paper mill in Deferiet, New York. Numerous process interruptions experienced by Champion; Voltage sags emanating from the Niagara Mohawk Power Corp. transmission system; Causes and location of faults; Mitigation techniques.

BACKGROUND: Following the removal of lead from gasoline, paint and pipes were thought to be the main sources of lead exposure in the United States. However, consumer products, such as certain spices, ceramic and metal cookware, traditional health remedies, and cultural powders, are increasingly recognized as important sources of lead exposure across the United States. OBJECTIVE: This paper reviews data from four US jurisdictions that conduct in-home investigations for children with elevated blood lead levels (BLLs) to examine the prevalence of lead exposures associated with consumer products, in comparison with housing-related sources. METHODS: Authors reviewed investigation data (2010–2021) provided by California, Oregon, New York City, and King County, Washington, and compared the extent of lead exposures associated with housing-related vs. consumer products–related sources. DISCUSSION: The proportion of investigations identifying consumer products–related sources of lead exposure varied by jurisdiction (range: 15%– 38%). A review of US CDC and US FDA alerts and New York City data indicates that these types of lead-containing products are often sourced internationally, with many hand carried into the United States during travel. Based on surveillance data, we believe that US immigrant and refugee communities are at an increased risk for lead exposures associated with these products. To engage health authorities, there is a need for evidentiary data. We recommend implementing a national product surveillance database systematically tracking data on consumer products tested by childhood lead poisoning prevention programs. The data repository should be centralized and accessible to all global stakeholders, including researchers and governmental and nongovernmental agencies, who can use these data to inform investigations. Effectively identifying and addressing the availability of lead-containing consumer products at their source can focus resources on primary prevention, reducing lead exposures for users abroad and in the United States. [ABSTRACT FROM AUTHOR]

The role of state administration and its public institutions in certain circumstances represents one of the most significant issues in the last twenty years following the serious security crisis caused by terrorist attacks in New York on 11 September 2001. The accent of public administration reforms was on efficiency and effectiveness of public institutions. Reforms were divided into deregulation and liberalization of public services, territorial decentralization, and regionalization with the strengthening of local government institutions and reform of central government administration. After the security crisis in 2001, financial crisis in 2008 and health crisis in 2020, the role of state institutions and public authorities in governing the state was redefined. The leading motives of general liberalization of public services, deregulation, and various forms of decentralization of public institutions abounded due to the new doctrinal approach of governmental organization. New strengthening of public authorities and public institutions became the new mainstream in the organization and functioning of the state. This paper analyzes how the role of the state can be modified due to the dynamics of social, political, and economic changes in society. New governmental reforms are contrary to earlier political and administrative measures, which include the strengthening of public institutions and concentration of public authorities. The government takes on a regulatory function in many aspects of social, political, and economic life. This situation leads to a new organization of the state, where public authorities take on a significant part of the responsibility to regulate various aspects of economic and social activities. At the focus of this paper will be how the dynamics of specific circumstances in society can affect the developing processes of the state and their public institutions. Those processes have economic, social, and political dimensions. The economic dimension includes regulatory measures on the market which contributes to development of the framework for economic development. The social dimension includes measures of social policy focused on improving the social position of citizens according to the economic development of society. The political dimension includes various aspects of security policy, with various protective measures in the community, which depends on the type of treatment for security of citizens. [ABSTRACT FROM AUTHOR]

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors. [ABSTRACT FROM AUTHOR]

To test alternative explanations for the comorbidity between fibromyalgia (FM), a medically unexplained syndrome involving widespread pain, and posttraumatic stress disorder (PTSD). In contrast to a default “risk factor” hypothesis, tested hypotheses were that: A) The association is due to a sampling bias introduced by the study of care-seeking individuals; B) FM is an additive burden that strains coping resources when confronting life stress; and C) Arousal symptoms of PTSD and FM are confounded. Community-dwelling women in the New York/New Jersey metropolitan area (N=1,312) completed a telephone survey regarding FM-like symptoms prior to September 11, 2001. Approximately 6 months after the World Trade Center terrorist attacks, they again completed the survey, to which questions regarding PTSD symptoms were added. The odds of probable PTSD were more than three times greater in women with FM-like symptoms, both assessed after 9/11. The odds ratio was not reduced by controlling for FM-like symptoms before 9/11 or for the potentially confounded symptoms of PTSD specifically related to arousal. These findings lead us to reject alternate explanations for the comorbidity between FM and PTSD. Speculations that FM and PTSD share psychobiological risk factors remain plausible. [ABSTRACT FROM AUTHOR]

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers. [ABSTRACT FROM AUTHOR]

Does our food for thought come virtually from the internet? When we take a long view, the instant stroke of a keyboard pales in comparison to the centuries-old evolution of real food harvesting and the generation and exchange of ideas, which have resulted in creative capital. The vernacular architecture of the agricultural built environment has almost dematerialized in its transition from the ancient pre-industrial era to the post-truth world, to become almost only an imagined concept. The symbology of the common threshing terrace of a Portuguese remote mountain village can now be found in multiple spaces of the urban realm, including in the metaphor of a community arts incubator's modus operandi in Albany, New York (USA). How has the concept of food for thought developed and materialized? How has it evolved? And what are some of the expected ways it might be utilized in the future? The purpose of the paper is to trace the evolution of this concept and its elements via the BLC Framework. The methodology employs a time analysis of approximately three centuries to distinguish three distinct evolutionary phases: (i) the pre-industrial era, (ii) the industrial era, and (iii) the information-driven era. The key finding is an up-to-date discussion of the food for thought concept in two distinct geographical worlds and three-time eras, as well as a set of lessons learned according to a protest poem and a rock song. The results are presented in the form of five lessons learned with implications for public policy: the first two lessons pertain to issues of procedural justice as encapsulated in the anonymous 'The Goose' poem, while the last three result from a discussion of selected verses in Nick Cave's 'Fable of the Brown Ape' rock song. [ABSTRACT FROM AUTHOR]

In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits users, drivers, and the provider. We propose an intuitive mean to improve idle ride-sharing vehicles' repositioning: repositioning heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, current and expected future fleet distribution, and the location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution in the future, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on the previously learned policy in every iteration. We then update the policy based on the simulation's outcome and use it in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations and therefore, revenue loss for the platform and drivers significantly while leading to a better service level for the users and to a fairer treatment of drivers. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) [Grant 494812908]. M. W. Ulmer's work is funded by the German Research Foundation Emmy Noether Programme [Grant 444657906]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1202. [ABSTRACT FROM AUTHOR]

Focuses on two exhibitions of surrealist art works in New York in October 1942. Definition of surrealism; Key examples of the avant-garde's forays into installation art; Details of the two exhibitions.

Background: To guide and provide orientation to research on student suicide, this study aimed to analyze the research status on student suicide with the help of bibliometric analysis. Utilising bibliometric methodology, this study complements existing literature by giving a comprehensive, reliable thematic representation of this topic. Method: A Scopus search was done and all published research papers were reviewed. A total of 187 papers published between 1959 and 2021 were analyzed. We identified the most influential journals in this field, the most prolific nations and institutions, as well as the years with the most publications, the most cited papers, notable authors, and most researched areas, It was followed by use of the VOSviewer software (version 1.6.18) to construct and visualize the bibliometric networks like co-authorship network map, inter-country co-authorship network map, and keywords co-occurrences network maps. Results: The analysis reveals the current research trend where the number of publications on 'student suicide' reflects an overall upward trend over the years, suggesting that research on student suicidology is fast evolving but the pace is slow. The maximum publications were in the year 2020. The United States made the largest contribution (n = 85). The most productive institution was the University of Rochester, New York (n = 10). Allan J Schwartz was the most prolific author, with nine research papers. Journal of College Student Psychotherapy (n = 13) was the journal and Medicine (n = 118) was the discipline with the highest number of publications. Conclusion: The study potentially has important implications for designing more comprehensive screening and assessment tools for suicide risk. Future work should pay more attention to developing effective intervention programs for students. The results also highlight the need for more research work on student suicidology. More research in this area is needed to create awareness among students. Consequently, this study can be a big critique and torch bearer at the same time. [ABSTRACT FROM AUTHOR]

Focuses on a DNA science and technology course which taught at the University of Albany for high school students during the summer of 1998. What was learnt during the course; Details on the use of Escherichia coli in experiments; Information on plasmids; In-depth look at the course.

Accurate power load forecasting could provide a scientific basis for the rapid response and stable operation of a modern power system. To take advantage of the meteorological big data to improve short-term forecasting accuracy, while considering the nonlinear and spatiotemporal correlation characteristics of the power load data, this paper proposes a short-term power load forecasting method based on meteorological data dimensionality reduction and a hybrid deep neural network. First, the elastic network is used to reduce the dimensions of high-dimensional meteorological big data, eliminate irrelevant meteorological factors, and improve the quality of input data. Then, taking the dimension-reduced meteorological data and historical load data as input, a load forecasting model based on a novel deep neural network is established. This model uses a convolution neural network (CNN) and a bi-directional long short-term memory (BiLSTM) neural network to extract the spatial and temporal correlation features of power load related data, and combines the attention mechanism to enhance the learning weight of the load series in important periods, and adopts residual connection (RC) to optimize the network training speed and alleviate the overfitting problem. Finally, taking the open data set of the New York Independent System Operating Agency (NYISO) as an example, single-step and multi-step advance prediction experiments are carried out to verify the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Worldwide, the recent SARS-CoV-2 virus disease outbreak has infected more than 691,000,000 people and killed more than 6,900,000. Surprisingly, Sub-Saharan Africa has suffered the least from the SARS-CoV-2 pandemic. Factors that are inherent to developing countries and that contrast with their counterparts in developed countries have been associated with these disease burden differences. In this paper, we developed data-driven COVID-19 mathematical models of two 'extreme': Cameroon, a developing country, and New York State (NYS) located in a developed country. We then identified critical parameters that could be used to explain the lower-than-expected COVID-19 disease burden in Cameroon versus NYS and to help mitigate future major disease outbreaks. Through the introduction of a 'disease burden' function, we found that COVID-19 could have been much more severe in Cameroon than in NYS if the vaccination rate had remained very low in Cameroon and the pandemic had not ended. [ABSTRACT FROM AUTHOR]

What constitutes a traumatic event and how trauma victims receive treatment has evolved. Individuals can now access treatment through a more person-centered approach. In contrast, the client is viewed as a person and not a diagnosis and receives treatment in less restrictive environments with the client being more involved in their care plan. This trauma-informed care approach has proven beneficial in clinical settings (Bloom in Natl Abandon Infants Assist Resour Center 16:12–17, 2007; Bloom in Levin and Becker (eds), A public health perspective of women's mental health, Springer, New York, 2010; Fallot RD, Harris M. Creating cultures of trauma-informed care: a self-assessment and planning protocol. Community Connections. 2011. https://www.researchgate.net/publication/272167009%5fCreating%5fCultures%5fof%5fTrauma-Informed%5fCare%5fA%5fSelf-Assessment%5fand%5fPlanning%5fProtocol.; Isobel et al. in Int J Ment Health Nurs 30:495–505, 2021). However, this way of thinking could be valuable in non-clinical settings. The purpose of this qualitative conceptual review was to introduce how trauma-informed care principles could be integrated into the workplace. Tools and guidelines shown to be beneficial for introducing a more trauma-informed approach in clinical settings are reviewed. Recommendations are then made on how said tools and procedures can be manipulated and used in various workplaces. This paper is a starting point for the conversations that need to occur at all levels of an organization interested in applying a trauma-informed care approach to a non-clinical work environment. [ABSTRACT FROM AUTHOR]

Data clustering is a thoroughly studied data mining issue. As the amount of information being analyzed grows exponentially, there are several problems with clustering diagnostic large datasets like the monitoring, microbiology, and end results (SEER) carcinoma feature sets. These traditional clustering methods are severely constrained in terms of speed, productivity, and adaptability. This paper summarizes the most modern distributed clustering algorithms, organized according to the computing platforms used to process vast volumes of data. The purpose of this work was to offer an optimized distributed clustering strategy for reducing the algorithm's total execution time. We obtained, preprocessed, and analyzed clinical SEER data on liver cancer, respiratory cancer, human immunodeficiency virus (HIV)-related lymphoma, and lung cancer for large-scale data clustering analysis. Three major contributions and their effects were covered in this paper: To begin, three current Pyspark distributed clustering algorithms were evaluated on SEER clinical data using a simulated New York cancer dataset. Second, systemic inflammatory response syndrome (SIRS) model inference was done and described using three SEER cancer datasets. Third, employing lung cancer data, we suggested an optimized distributed bisecting k -means method. We have shown the outcomes of our suggested optimized distributed clustering technique, demonstrating the performance enhancement. [ABSTRACT FROM AUTHOR]

Digital platforms such as Uber, Rappi or Airbnb impose a labor model where workers are not recognized as such and do not have minimum rights. They are part of platform capitalism, a form of capital that uses digital technologies to impose a precarious employment relationship. However, faced with this model, application workers are beginning to organize and demand rights. In this article, I examine the ideology that platforms promote and the resistances of app distributors. I argue that, in the face of the imagined individualism of digital platforms, delivery workers are recreating forms of community. They have not been passive or totally subaltern but are displaying different forms of antagonism and autonomy. This paper is based on qualitative research with interviews of key informants and analysis of the documents of collectives and groups of Mexican delivery workers, both in Mexico City and in New York. [ABSTRACT FROM AUTHOR]

In Spring 2020, the New York Army and Air National Guard (NYNG) rapidly deployed to New York City (NYC) to assist in the recovery, processing, and transport of COVID-19 decedents. This study reports on a survey conducted by NYNG service members three to six months post-mission (n = 177). Data showed that there was a dose–response relationship between mission stress exposure and decremented mental health, but certain activities were associated with better mental health outcomes. The paper also reviews resources provided by behavioral health personnel to support service members during the mission and lessons learned to inform future decedent recovery missions. [ABSTRACT FROM AUTHOR]

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on using machine learning (ML) classifiers, most notably random forest and gradient tree boosting, for estimating the inventory level from available features including historical data. However, while similar approaches adopted in the context of bike sharing assume the data to be well-balanced, this assumption is not met in the case of the inventory problem. Indeed, as the demand for bike sharing is sparse, datasets become biased toward low demand values, and systematic errors emerge. Thus, we propose to include a new iterative resampling procedure in the classification problem to deal with imbalanced datasets. The proposed model, tested on the real-world data of the Citi Bike operator in New York, allows to (i) provide upper-bound and lower-bound values for the bike-sharing inventory problem, accurately predicting both predominant and rare demand values; (ii) capture the main features that characterize the different demand classes; and (iii) work in a day-to-day framework. Finally, successful bike-sharing systems grow rapidly, opening new stations every year. In addition to changes in the mobility demand, an additional problem is that we cannot use historical information to predict inventory levels for new stations. Therefore, we test the capability of our model to predict inventory levels when historical data is not available, with a specific focus on stations that were not available for training. [ABSTRACT FROM AUTHOR]

Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties. [ABSTRACT FROM AUTHOR]

Violence involving firearms in the USA is a very important problem. As a consequence, a large number of crimes of this type are recorded every year. However, the solutions proposed have not managed to reduce the number of this type of crime. One of the cities with a large number of violent crimes is New York City. The number of crimes is not homogeneous and depends on the district where they occur. This paper proposes to study the information about the crimes in which firearms are involved with the aim of characterizing the factors on which the occurrence of this type of crime depends, such as the levels of poverty and culture. Since the districts are not homogeneous, the information has been analyzed at the district level. For this, data from the open data portal of the city of New York have been used and machine-learning techniques have been used. The results have shown that the variables on which they depend are different in each district. [ABSTRACT FROM AUTHOR]

In early 2020, IAAO President Amy Rasmussen created the Artificial Intelligence Task Force with the goal of developing a white paper describing the impact and uses of artificial intelligence (AI) in government valuation offices. The COVID-19 pandemic in early 2020 forced government valuation offices to adapt overnight. Many jurisdictions rapidly virtualized tasks and duties, which accelerated ongoing efforts to utilize office automation and implement intelligent software solutions. More and more, workflows incorporating digital information and multiple sources of data are processed and analyzed using software and integrated applications. The fully integrated workflows facilitate the increased usage of AI in operations, assessment, and valuation. This white paper delivers an introduction and overview of AI through case and pilot studies and review of relevant analytic methods while touching on possible organizational impacts. The paper looks at the changing role of valuers and assessment administrators and the evolution of valuation offices where AI will be used to improve operations, value estimates, and administration. It provides illustrative examples of AI use in the conduct of tax assessment, including the administrative aspects not directly involved in valuation. While there is substantial fanfare around valuation with AI, many of the benefits to be realized from the technology are in areas of administration, validation, and oversight. This is reflected in the case studies included, with more than half involving AI applications outside of the explicit valuation function. The introduction provides a definition and brief history of AI. It also helps disentangle the raft of AI methods with how they are used and provides a concrete list of which assessment activities may benefit from those general classes of algorithms. More importantly, the first section helps put into context why AI is becoming more widespread and what that means for organizations from both staffing and administration standpoints. After the introduction's overview of what AI is, why it has captured professional imagination, and the organizational changes it portends, we provide examples of current uses by assessing organizations and their partners. The first case study is about the Property Valuation Services Corporation's (PVSC) foray as the first organization in Canada to publish a tax assessment roll using AI-based valuations. This case study highlights the multiyear process leading the organization to that accomplishment and the lessons it learned along the way. The second use case is a pilot study by PVSC. The section discusses the success of AI, particularly machine-learning methods, for the valuation of residential properties in the Netherlands. The third use case is from BC Assessment (BCA) and describes how valuations of manufactured homes were conducted using AI methods. For successful adoption of AI in an assessment office, this case study highlights the importance of communication and feedback from appraisers and integration of AI-modeled values with the computerassisted mass appraisal (CAMA) system. The fourth case study comes from the City of New York and showcases applications using AI to better manage form intake and processing. Using optical character recognition (OCR), it is possible to process the volumes of senior exemption applications and condominium declaration forms received in paper and PDF formats. As with the other case examples, the results still require human oversight but provide a significant improvement over the existing process. The fifth use case also comes from the City of New York. This section discusses how geospatial data and AI methods are being integrated and leveraged to determine land use, detect building changes, and extract parcel data from images and may be used to automate data collection. This section also gives background on the geospatial data required to leverage land use and building change detection applications, which are growing increasingly familiar and important to tax assessment organizations. The sixth application involves integrating AI-powered valuation as a feature within CAMA. To illustrate the potential of AI to automate sales-based valuation models, this study examines Tyler Technologies' experience trying to provide an AI-powered valuation option for its users. It also clarifies the technology's perceived limitations, which create headwinds for widespread adoption. This section ends with a discussion of international adoption of AI in property assessment offices in four African nations: Rwanda, Nigeria, Uganda, and Zambia. The full digitalization of their records and workflows using imagery and modern technology allows them to modernize their systems without going through and updating legacy records and operational processes found in more established assessment jurisdictions. Following the case studies, the reader will find a section delving deeper into the core machine learning (ML) and AI methods underpinning these applications. ML is covered in the first part of this section. Other methods discussed cover key concepts in artificial neural networks and search and optimization, which underpin virtually every AI application. Finally, the paper closes with recommendations. Key takeaways are that some tax assessment organizations and their partners are already cautiously adopting AI. The technology's adoption will grow more widespread and touch every tax assessment organization. As such, familiarity with how it is being used, a basic understanding of what is driving these changes, and what they mean for your organization are important. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse a corpus of 137 texts published between 1888 and 1899. These texts are included in one of the most successful Spanish-language newspapers published in the United States during the second half of the nineteenth century: Las Novedades (New York, 1876-1918). In the analysis I will determine the linguistic ideologies and the discursive strategies used for the promotion of Hispanism and Spanish language in a sociopolitical context in which the Spanish hegemony was losing all its power at an international level. Thus, the ideologies found are divided in two different categories: the linguistic ideologies that give the language a cultural value and the linguistic ideologies that make reference to the economic and commercial value of the language. The results obtained from this analysis will contribute to understand the role of Spanish language in the United States. Furthermore, this paper presents an unpublished corpus of clear historiographic interest that contributes to clarify the panorama of Hispanism in a country like the United States. [ABSTRACT FROM AUTHOR]

The aim of this article is to analyze Angie Cruz's novel Dominicana from a multicultural and gender perspective focusing on how Cruz introduces the female body as a metaphor for the immigrant experience lived by Dominican Women during the 1960s in the United States. Also, this paper studies how the female body becomes a metaphorical border in the diasporic experience for the central character as a way to depict an essentially female in-between-space. Thus, Cruz rewrites and recreates from the female body the diasporic experience of Dominican women immigrants in New York from an intersectional perspective. [ABSTRACT FROM AUTHOR]

The construction introduced by Gross, Hacking and Keel in (Several Complex Variables (Springer, New York, NY, 1976))allows one to construct a formal mirror family to a pair (S,D) where S is a smooth rational projective surface and D a certain type of Weil divisor supporting an ample or anti-ample class. In that paper, they proved two convergence results when the intersection matrix of D is not negative semi-definite and when the matrix is negative definite. In the original version of that paper, they claimed that if the intersection matrix were negative semi-definite, then family extends over an analytic neighbourhood of the origin but gave an incorrect proof. In this paper, we correct this error. We reduce the construction of the mirror to such a surface to calculating certain log Gromov--Witten invariants. We then relate these invariants to the invariants of a new space where we can find explicit formulae for the invariants. From this we deduce analytic convergence of the mirror family, at least when the original surface has an I4 fibre. [ABSTRACT FROM AUTHOR]

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID‐19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in‐sample and out‐of‐sample. [ABSTRACT FROM AUTHOR]

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week's data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week's New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making. [ABSTRACT FROM AUTHOR]

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention. [ABSTRACT FROM AUTHOR]

In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to occur quickly depending on the number of lags included in the test statistic, and a significant increase in statistical power is also observed relative to some recently proposed permutation tests. An example application is presented by fitting spatial autoregressive models to the distribution of COVID-19 cases across counties in New York state. [ABSTRACT FROM AUTHOR]

Wildfires are a significant source of PM2.5 that adversely affect local and regional air quality. However, tracking wildfire smoke and their impacts are difficult. This study explores how a ground‐based Doppler lidar network can improve monitoring of long‐range transported wildfire smoke. As a case study, this paper reviews the transported wildfire smoke and its impact on air quality across New York State (NYS) during two events: 18–21 and 25–27 July 2021. Observations of wildfire smoke and enhancement of PM2.5 across NYS during those events were consistent across ground‐based sensors, satellites, back trajectory analyses, and model forecasts. However, for some of the days, the model over/under‐forecasted smoke plumes. Overall, this study highlights the value of the NYS Mesonet Profiler Network to monitor wildfire smoke with high spatiotemporal resolution. Such a relatively dense network can be a valuable observational tool for evaluating PM2.5, and aiding satellite measurements and air quality forecasting models. Plain Language Summary: Anthropogenic emissions of PM2.5 have declined across New York State (NYS) and nationally over the last two decades. However, increasing frequency and intensity of wildfire activity threatens to reverse this long‐term PM2.5 trend. During July 2021, wildfire smoke from Southern Canada and Northwestern US significantly elevated PM2.5 (in exceedance of the US EPA standards) 5,000 km away in NYS. Yet, air quality forecasting models performed poorly in the presence of the wildfire smoke. Thus, it has become increasingly important to monitor transported wildfire smoke and understand its impact on surface air‐quality. The New York State Mesonet Profiler Network is one such tool that can be used to monitor long‐range transported wildfire smoke, track its vertical and spatial extent across NYS in real‐time, assess its impact on air quality and complement/augment air quality forecasting models and satellite observations. Key Points: A long‐range transported wildfire smoke event significantly enhanced PM2.5 across New York State in July 2021Multi‐platform observations are used to understand the details of the eventA lidar network can help to monitor smoke event, assess impacts on surface PM2.5, aid satellite measurements and improve forecasting models [ABSTRACT FROM AUTHOR]

Light emitted from atoms during transitions of electrons from higher to lower discrete states has the form of photons carrying energy and angular momentum. This paper considers the process of emission of a single photon from the hydrogen atom by using quantum theory and Maxwell's equations [W. Gough, Eur. J. Phys. 17, 208, 1996; L. D. Landau and E. M. Lifshitz, Quantum Mechanics (Pergamon Press, Oxford, 1965); J. D. Jackson, Classical Electrodynamics (John Wiley & Son, New York, 1975, 1982); P. M. Morse and H. Feshbach, Methods of Theoretical Physics (McGraw-Hill Book Company, Inc., New York, 1953)]. The electric and magnetic fields of a photon arise from the time-dependent quantum probability densities of the orbit and the spin current. This paper is an extension of the semiclassical description of photon emission published by the author earlier in 1999 [M. Kowalski, Phys. Essays 12, 312 (1999)]. In the semiclassical approach, the Coulomb force and a radiation resistance force have been taken into account to get time-dependent emission of the photon. In both the quantum and semiclassical cases, the transition takes place within a time interval equal to one period of the photon's wave. The creation of a one-wavelength-long photon is supported by the results of experiments using ultrafast (ultrashort) laser pulses to generate excited atoms, which emit light pulses shorter than two photon wavelengths [F. Krausz and M. Ivanov, Rev. Mod. Phys. 81, 163 (2009); H. Kapteyn and M. Murnane, Phys. World 12, 31 (1999)]. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show an application of variety engineering in the social realm (Beer in The heart of enterprise. Wiley, Chichester, 1979, in Brain of the firm. Wiley, Chichester 1981, in diagnosing the system for organizations. Wiley, Chichester, 1985). It focuses on reducing environmental complexity by catalysing self-organizing processes (Espejo and Reyes in Organizational systems: managing complexity with the VSM. Springer, London, 2011). This catalysis is based on the use of Sen and Nussbaum's capabilities approach (Sen in Development as freedom. Oxford University Press, New York, 1999; Nusbaum in Women and human development: the capabilities approach. Cambridge University Press, New York, 2000). By doing this an organization may improve the quality of the relations with their clients by transforming environmental agents into new suppliers. This approach opens a new dimension of social responsibility for organizations. A particular case is presented in which a regional university in Colombia faces the challenge of attending low-income students coming from small municipalities. They designed a strategy to offer low-cost housing and a daily balanced meal without using the university's own resources. Instead, they managed to involve some agents of the community by developing their capabilities. This kind of variety engineering shows the possibility of new forms of social responsibility in universities by incorporating the capabilities approach into their managerial practices. This is something that is currently under study in other universities (Boni et al. in Science and engineering ethics. Springer, London, 2015). [ABSTRACT FROM AUTHOR]

Objectives: This paper aimed to identify the technology frontiers of artificial intelligence-assisted pathology based on patent citation network. Methods: Patents related to artificial intelligence-assisted pathology were searched and collected from the Derwent Innovation Index (DII), which were imported into Derwent Data Analyzer (DDA, Clarivate Derwent, New York, NY, USA) for authority control, and imported into the freely available computer program Ucinet 6 for drawing the patent citation network. The patent citation network according to the citation relationship could describe the technology development context in the field of artificial intelligence-assisted pathology. The patent citations were extracted from the collected patent data, selected highly cited patents to form a co-occurrence matrix, and built a patent citation network based on the co-occurrence matrix in each period. Text clustering is an unsupervised learning method, an important method in text mining, where similar documents are grouped into clusters. The similarity between documents are determined by calculating the distance between them, and the two documents with the closest distance are combined. The method of text clustering was used to identify the technology frontiers based on the patent citation network, which was according to co-word analysis of the title and abstract of the patents in this field. Results: 1704 patents were obtained in the field of artificial intelligence-assisted pathology, which had been currently undergoing three stages, namely the budding period (1992–2000), the development period (2001–2015), and the rapid growth period (2016–2021). There were two technology frontiers in the budding period (1992–2000), namely systems and methods for image data processing in computerized tomography (CT), and immunohistochemistry (IHC), five technology frontiers in the development period (2001–2015), namely spectral analysis methods of biomacromolecules, pathological information system, diagnostic biomarkers, molecular pathology diagnosis, and pathological diagnosis antibody, and six technology frontiers in the rapid growth period (2016–2021), namely digital pathology (DP), deep learning (DL) algorithms—convolutional neural networks (CNN), disease prediction models, computational pathology, pathological image analysis method, and intelligent pathological system. Conclusions: Artificial intelligence-assisted pathology was currently in a rapid development period, and computational pathology, DL and other technologies in this period all involved the study of algorithms. Future research hotspots in this field would focus on algorithm improvement and intelligent diagnosis in order to realize the precise diagnosis. The results of this study presented an overview of the characteristics of research status and development trends in the field of artificial intelligence-assisted pathology, which could help readers broaden innovative ideas and discover new technological opportunities, and also served as important indicators for government policymaking. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. [ABSTRACT FROM AUTHOR]

The mutual development of airport groups and regions is gradually going global with the economic globalization. There is a contradiction between Beijing-Tianjin-Hebei (Jing-Jin-Ji) Airport Group and regional development, which leads to the unbalanced development of aviation enterprises in the region. Therefore, it is of major theoretical and practical significance to analyze the coordinated development relationship between Jing-Jin-Ji Airport Group and the region. This paper selects representative index system, establishes the adaptability evaluation model of airport group and regional development, and quantitatively analyzes the adaptability level of Jing-Jin-Ji Airport Group and radiation area. Taking New York Airport Group as a reference, the synergy degree between Jing-Jin-Ji Airport Group and the region is evaluated by the synergy evaluation model. Analyze the reasons for the development of New York Airport Group, and put forward the measures for the coordinated development of Jing-Jin-Ji Airport Group and the region in the future. The results show that the synergy index (SD) of Jing-Jin-Ji Airport Group is 0.5381, which is lower than that of New York Airport Group (0.7507), and Jing-Jin-Ji Airport Group is barely synergetic. It is necessary to optimize airspace, allocate flight resources, speed up the development of airport economy, and optimize the structure of airport groups. Jing-Jin-Ji Airport Group will be accelerated to enter a high-quality collaborative stage from the aspects of system and resource integration and government leadership. [ABSTRACT FROM AUTHOR]

While enrollment in Catholic schools is rising in the global south, it is declining in many Western countries. Providing a quality and holistic education experience remains essential in both developed and developing countries, especially for disadvantaged groups. But in addition, in developed countries, trade-offs may have to be confronted to stem the decline in enrollment that affects the financial sustainability of schools. Building on a presentation made at the closing plenary of OIEC's World Congress in New York, this paper focuses on the United States where enrollment in Catholic schools has been dropping for more than 50 years. In the absence of state support for Catholic schools and in a context of rising operating costs and therefore tuition, the decline in enrollment is due in part to a lack of affordability of schools given high out-of-pocket costs paid by parents. But other factors related to perceptions about Catholic schools also play a role. To stem the decline in enrollment, market research can help, including to assess how favorably Catholic and other types of schools are perceived in the population. The paper relies on data collected for the National Catholic Education Association to compare favorability ratings by type of schools and assess factors associated with these perceptions. [ABSTRACT FROM AUTHOR]

Building on a presentation made at the closing plenary of OIEC's World Congress in New York, this paper considers two trends affecting Catholic education globally and their potential implications for university research and programs in support of K12 schools. The first trend is the shift of enrollment in K12 Catholic education towards the global south and especially Africa in terms of the sheer number of students enrolled in schools. The second trend relates to the deepening learning crisis in K12 education affecting much of the developing world, and again especially Africa. These two trends have implications for university research and programs in support of K12 Catholic schools on the continent. The paper argues that apart from discussions related to Catholic identity, which are of course important, more attention should be placed by universities, including in the West, on the related but distinct issue of how to improve basic learning for students enrolled in K12 Catholic schools in the global south. [ABSTRACT FROM AUTHOR]