Polarons are electronic excitations dressed by a self-consistent lattice distortion, yet their formation has not been directly resolved in real time. We develop a microscopic lineshape framework that connects the growth of a collective lattice polarization to the population-time evolution of the anti-diagonal linewidth in coherent multidimensional spectroscopy. Within this formalism, the anti-diagonal linewidth directly tracks the decay of lattice frequency-frequency correlations. Underdamped phonon environments produce oscillatory linewidth modulation, whereas overdamped collective polarization dynamics generate monotonic exponential broadening. Applying this framework to multidimensional measurements on perovskite quantum dots, we show that the observed approximately 150 femtosecond exponential anti-diagonal broadening reflects the decay of a collective polarization order parameter. These results establish anti-diagonal linewidth dynamics as a direct real-time signature of Landau polaron formation.
The U.S. residential real estate market represents a significant component of national wealth, yet investment decision-making in this sector remains largely dependent on heuristic judgment rather than systematic, data-driven analysis. This study presents a multi-dimensional analytical framework that integrates multiple Zillow housing market indices to evaluate residential real estate investment opportunities across the United States. Using publicly available data from the Zillow Home Value Index (ZHVI), Zillow Observed Rent Index (ZORI), days-on-market metrics, and regional listing data spanning Q1 2018 through Q1 2023, the research conducts six interconnected analyses: (1) identification of high-growth regions in the single-family home segment, (2) rental market trend analysis across major metropolitan areas, (3) short-term property value forecasting, (4) market liquidity assessment through days-on-market analysis, (5) return on investment (ROI) distribution for rental strategies, and (6) ROI distribution for sale strategies. The framework employs Python-based data manipulation and visualization techniques to synthesize these indices into actionable investment intelligence. Key findings indicate that the top 3% of U.S. regions exhibited disproportionately high single-family home price appreciation between December 2022 and March 2023, rental indices demonstrated a consistent upward trajectory across major metropolitan areas, and property values were projected to increase by approximately 1.6% through March 2024. Furthermore, state-level variation in days-on-market and significant regional disparities in ROI distributions for both rental and sale strategies were identified, with notable outlier regions offering substantially higher returns. The proposed framework demonstrates the practical utility of integrating publicly available housing market indices for systematic investment evaluation, offering a reproducible methodology that can inform both individual and institutional decision-making in the residential real estate sector.
PurposeThis article proposes a Multilayer Network (MLN) model for studying business ecosystems. The model focuses on the flows of products, services and money between buyers and sellers, emphasizing that these flows form both actor-level and emergent system-level ecosystem structures.Design/methodology/approachThe article examines two case studies of real estate owners and their suppliers, using financial transaction data to provide a detailed, data-driven view of business ecosystems.FindingsThe study advances real estate theory by deepening research on the digitalization of real estate owners, especially on their enterprise architectures and supplier networks. Despite size differences, both case firms have similar, complex supplier-network structures. The findings may inform enterprise architecture management and procurement practices in the real estate sector.Originality/valueThe MLN model defines terminology for ecosystem layers and provides methods for establishing ecosystem boundaries. This aligns with the micro-level critique in management and ecosystems research. We conclude by highlighting that event data, when available, can enhance future business ecosystem analysis by enabling the study of broader ecosystem structures with the MLN model.
Jorge Cisneros, Timothy Wojan, Matthew Williams
et al.
Public-use microdata samples (PUMS) from the United States (US) Census Bureau on individuals have been available for decades. However, large increases in computing power and the greater availability of Big Data have dramatically increased the probability of re-identifying anonymized data, potentially violating the pledge of confidentiality given to survey respondents. Data science tools can be used to produce synthetic data that preserve critical moments of the empirical data but do not contain the records of any existing individual respondent or business. Developing public-use firm data from surveys presents unique challenges different from demographic data, because there is a lack of anonymity and certain industries can be easily identified in each geographic area. This paper briefly describes a machine learning model used to construct a synthetic PUMS based on the Annual Business Survey (ABS) and discusses various quality metrics. Although the ABS PUMS is currently being refined and results are confidential, we present two synthetic PUMS developed for the 2007 Survey of Business Owners, similar to the ABS business data. Econometric replication of a high impact analysis published in Small Business Economics demonstrates the verisimilitude of the synthetic data to the true data and motivates discussion of possible ABS use cases.
The increasing and widespread use of BPMN business processes, also embodying DMN tables, requires tools and methodologies to verify their correctness. However, most commonly used frameworks to build BPMN+DMN models only allow designers to detect syntactical errors, thus ignoring semantic (behavioural) faults. This forces business processes designers to manually run single executions of their BPMN+DMN processes using proprietary tools in order to detect failures. Furthermore, how proprietary tools translate a BPMN+DMN process to a computer simulation is left unspecified. In this paper, we advance this state of the art by designing a tool, named BDTransTest providing: i) a translation from a BPMN + DMN process B to a Java program P ; ii) the synthesis and execution of a testing plan for B, that may require the business designer to disambiguate some input domain; iii) the analysis of the coverage achieved by the testing plan in terms of nodes and edges of B. Finally, we provide an experimental evaluation of our methodology on BPMN+DMN processes from the literature.
In the world of science new technology have opened up the possibility to rely on advanced computational methods and models to conduct and produce scientific research. An important aspect of scientific and business workflows is provenance - which refers to the information describing the production, history or lineage of an end product, which can also be data, digitalized processes and other not tangible artifacts. While there are already systems, tools and standards to capture provenance of data and workflows the provenance of adaptations/changes in workflows has not been addressed yet. In this paper we carry out a literature review to establish the state of the art on this topic and present our methodology and findings. Our findings confirm that provenance of adaptation has not been addressed adequately in the fields of business and scientific workflows. The two fields also have different motivation for recording the lineage of data or processes. While scientific workflows are interested in reproducibility and visualization, business workflows solutions are indirectly connected to compliance, exception handling and analysis. The adaptive nature of workflows in both fields is not reflected in the research on process provenance yet, as our results show. The use of standard provenance standards is also not wide spread.
Historically low interest rates and active stimulation of national economies through the large-scale use of quantitative easing techniques by central banks have led to a rapid increase in financial assets value on world markets. The Russian economy is experiencing rapid growth in real estate values, prices for durable goods, and general inflation. All these factors have led to a large-scale increase in brokerage accounts and active growth of private investors on the Moscow Interbank Currency Exchange. As the number of the latter grows, their savings, which are redirected into investments in the financial market, have an increasing weight in shaping trends in the Russian market. At the same time, there is a need to understand the existing risks and methods of dealing with them. Consequently, there is a growing need for a systematic and strategic approach, methods, and criteria for assessing the investment attractiveness of companies. In addition, the struggle among public companies on the Russian stock market is intensifying, and the issue of improving investment attractiveness and business efficiency and developing tools to implement these processes is becoming more and more urgent.
В статье предлагается рассмотреть не коммерческую составляющую девелопмента ТЦ, а его роль в создании комфортной среды при реализации комплексного развития территорий (КРТ). Появившаяся концепция предполагает создание удобных районов для жизни с продуманным размещением социальных, общественно-деловых объектов. Важная роль в создании комфортной городской среды отводится центрам притяжения в новых или существующих районах, такую функцию во многом могут реализовать районные или окружные ТЦ. Приведена статистика ввода торговых площадей, которая подтверждает популярность строительства новых районных ТЦ в различных городах России. Автором выделены новые элементы, выполняющие социальную функцию, привлекающие посетителей и удовлетворяющие потребности живущих по соседству горожан. Исследуются рекомендации в Стандарте КРТ в отношении недвижимости торгового назначения для малоэтажной, среднеэтажной и центральной моделей при новой застройке. Для девелоперов предложены варианты организации внешней среды, которые нужно предусмотреть при разработке концепции микрорайона и выборе местоположения для ТЦ, связанные с парковкой, благоустройством. В качестве примера развития уже существующих районов рассмотрен проект создания соседских центров в результате редевелопмента советских кинотеатров одной из девелоперских компаний Москвы. При разработке концепции основное внимание уделяется взаимодействию с жителями района, поддержанию программы лояльности. Благодаря созданию оптимального набора арендаторов увеличивается посещение ТЦ в различное время, развлекательные мероприятия или скидки становятся поводом для дополнительных визитов. Статья помогает оценить, насколько концепция КРТ помогает сблизить интересы бизнеса и общества, какие инструменты и факторы этому способствуют.
Embodied artificial intelligence emphasizes the role of an agent's body in generating human-like behaviors. The recent efforts on EmbodiedAI pay a lot of attention to building up machine learning models to possess perceiving, planning, and acting abilities, thereby enabling real-time interaction with the world. However, most works focus on bounded indoor environments, such as navigation in a room or manipulating a device, with limited exploration of embodying the agents in open-world scenarios. That is, embodied intelligence in the open and outdoor environment is less explored, for which one potential reason is the lack of high-quality simulators, benchmarks, and datasets. To address it, in this paper, we construct a benchmark platform for embodied intelligence evaluation in real-world city environments. Specifically, we first construct a highly realistic 3D simulation environment based on the real buildings, roads, and other elements in a real city. In this environment, we combine historically collected data and simulation algorithms to conduct simulations of pedestrian and vehicle flows with high fidelity. Further, we designed a set of evaluation tasks covering different EmbodiedAI abilities. Moreover, we provide a complete set of input and output interfaces for access, enabling embodied agents to easily take task requirements and current environmental observations as input and then make decisions and obtain performance evaluations. On the one hand, it expands the capability of existing embodied intelligence to higher levels. On the other hand, it has a higher practical value in the real world and can support more potential applications for artificial general intelligence. Based on this platform, we evaluate some popular large language models for embodied intelligence capabilities of different dimensions and difficulties.
Gerhard Zeisler, Tim Tobias Braunauer, Albert Fleischmann
et al.
The widely adopted Business Process Model and Notation (BPMN) is a cornerstone of industry standards for business process modeling. However, its ambiguous execution semantics often result in inconsistent interpretations, depending on the software used for implementation. In response, the Process Specification Language (PASS) provides formally defined semantics to overcome these interpretational challenges. Despite its clear advantages, PASS has not reached the same level of industry penetration as BPMN. This feasibility study proposes using PASS as an intermediary framework to translate and execute BPMN models. It describes the development of a prototype translator that converts specific BPMN elements into a format compatible with PASS. These models are then transformed into source code and executed in a bespoke workflow environment, marking a departure from traditional BPMN implementations. Our findings suggest that integrating PASS enhances compatibility across different modeling and execution tools and offers a more robust methodology for implementing business processes across organizations. This study lays the groundwork for more accurate and unified business process model executions, potentially transforming industry standards for process modeling and execution.
Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative models often lack authentic material properties. Manual assignment of materials using graphic software is a tedious and time-consuming task. In this paper, we exploit advancements in Multimodal Large Language Models (MLLMs), particularly GPT-4V, to present a novel approach, Make-it-Real: 1) We demonstrate that GPT-4V can effectively recognize and describe materials, allowing the construction of a detailed material library. 2) Utilizing a combination of visual cues and hierarchical text prompts, GPT-4V precisely identifies and aligns materials with the corresponding components of 3D objects. 3) The correctly matched materials are then meticulously applied as reference for the new SVBRDF material generation according to the original albedo map, significantly enhancing their visual authenticity. Make-it-Real offers a streamlined integration into the 3D content creation workflow, showcasing its utility as an essential tool for developers of 3D assets.
We explore the nonlinear dynamics of a macroeconomic model with resource constraints. The dynamics is derived from a production function that considers capital and a generalized form of energy as inputs. Energy, the new variable, is depleted during the production process and has to be renewed, whereas capital grows with production and decreases from depreciation. Dependent on time scales and energy related control parameters, we obtain steady states of high or low production, but also sustained oscillations that show properties of business cycles. We also find conditions for the coexistence of stable fixed points and limit cycles. Our model allows to specify investment and saving functions for Kaldor's model of business cycles. We provide evidence for an endogenous origin of business cycles if depleting resources are taken into account.
Abstract Despite renewed regulatory attention, shadow banking across the globe is still a nonnegligible part of economic life. This paper researches China’s shadow banking during 2020–2022, a stage marked by COVID-19 and strengthened global regulation on Non-Bank Financial Intermediation (NBFI). Its business model surprisingly resembles its Western peers, funding underserved sectors and having similar exposure to balance sheet mismatch. Uninsured interbank funds and wealth management products support massive holding of bond investment (36.6% of the total assets), making risk contagion easier. This paper re-summarizes growth dynamics in a “Pull-Push” framework and proposes the concept of reintermediation corresponding to disintermediation. Consecutive regulation on NBFI and the real estate sector kept dragging on growth, rendering it in liquidity surplus. We provide empirical evidence on the relationship of China’s shadow banking with macro-finance and note several breakdowns of pre-pandemic relations among economic and financial indicators. The most remarkable breakdown is the weakened functionality of the monetary policy transmission channel. Besides, it continued to twist financial regulatory indicators to a lesser extent.
History of scholarship and learning. The humanities, Social Sciences
В статье рассматриваются и исследуются возможности использования открытой части информационной системы ГИС ЖКХ, раздела «Аналитика и отчеты» как источника больших достоверных данных и инструментов работы с ними. Применение таких данных и инструментов работы важно и актуально при подготовке будущих специалистов, формировании у них навыков аналитической работы с информацией. Целью исследования является возможность постановки и реализации аналитических задач для образовательных целей на базе реальных достоверных данных ГИС ЖКХ. В ходе исследования были изучены основные особенности системы и предложены постановки аналитических задач для учебных целей. Использованы разделы системы «Техническое состояние многоквартирных домов» и «Привлечение управляющих организаций к административной ответственности». Одна из задач решается полностью с использованием встроенных инструментов платформы, другая — регрессионный анализ — с привлечением инструментальных средств табличного процессора. Встроенные инструменты открытой части системы ГИС ЖКХ «Аналитика и отчеты» позволяют сортировать данные, переходить от агрегированных данных к детализированным и обратно, дополняют графическими моделями табличные данные, что делает работу с информацией простой и понятной. Отсутствие возможности экспорта данных из системы затрудняет возможность использования сторонних приложений для последующей обработки данных. Решение такого класса аналитических задач позволяет обучающимся получить навыки постановки задачи, решения и интерпретации полученных результатов. Рассмотренные примеры аналитических задач могут быть расширены другими задачами, предложенными непосредственно самими обучающимися. Данное исследование имеет практическое значение при подготовке будущих специалистов в области экономики, менеджмента, управления недвижимостью и ЖКХ.
This research aims to test whether capital intensity, leverage, liquidity, tax to book ratio and business risk have an effect on financial performance (empirical study of property and real estate companies listed on the Indonesia Stock Exchange for the 2019-2022 period). This data uses secondary data. The sample for this research is property and real estate companies listed on the Indonesia Stock Exchange (BEI) in 2019-2020 using the purposive sampling method. There are 72 companies that meet the criteria as research samples. This research uses quantitative methods. This research uses quantitative methods. The variables used are capital intensity, leverage, liquidity, tax to book ratio, business risk and financial performance. The test used is multiple linear regression analysis using the SPSS 27 program application. The results of this research show that the variables capital intensity, leverage, liquidity have a significant effect on financial performance, and tax to book ratio, business risk has a significant effect on financial performance.
Yifeng Philip Chen, Edward J. Oughton, Jakub Zagdanski
et al.
Broadband connectivity is regarded as generally having a positive macroeconomic effect, but we lack evidence as to how it affects key economic activity metrics, such as firm creation, at a very local level. This analysis models the impact of broadband Next Generation Access (NGA) on new business creation at the local level over the 2011-2015 period in England, United Kingdom, using high-resolution panel data. After controlling for a range of factors, we find that faster broadband speeds brought by NGA technologies have a positive effect on the rate of business growth. We find that in England between 2011-2015, on average a one percentage increase in download speeds is associated with a 0.0574 percentage point increase in the annual growth rate of business establishments. The primary hypothesised mechanism behind the estimated relationship is the enabling effect that faster broadband speeds have on innovative business models based on new digital technologies and services. Entrepreneurs either sought appropriate locations that offer high quality broadband infrastructure (contributing to new business establishment growth), or potentially enjoyed a competitive advantage (resulting in a higher survival rate). The findings of this study suggest that aspiring to reach universal high capacity broadband connectivity is economically desirable, especially as the costs of delivering such service decline.
With the rapid development of short-term and spot trade of liquefied natural gas (LNG), the natural gas market is gradually evolving from regionalization to globalization. At the same time, the existence and rationality of long-term LNG contracts have become increasingly controversial. To explore the value of long-term LNG contracts in the process of natural gas market globalization, this article constructs a two-stage game model and applies China’s LNG trade data in 2018 to the model. The study shows that, compared with complete import of short-term LNG, even if the long-term LNG contracts do not have price advantages, importing an appropriate amount of long-term LNG may help to increase the total LNG imports, reduce the price of LNG, and thus improve import benefits. Besides, a moderate amount of long-term LNG contracts is conducive to the establishment of a stable and flexible natural gas supply system and the security of natural gas imports. Therefore, natural gas importers should not underestimate or even ignore the value of long-term LNG contracts while actively participating in short-term and spot trade of natural gas.
The rental market in Poland is underdeveloped, not subject to any statutory registers, and based primarily on small, private owners. The article estimates the duration of residential rental properties that have been reported to the listing exchange system using duration methods. Duration methods also allow the analysis to include those properties that were not rented at the time the analysis was completed, but their owners continued to report their willingness to rent. The article presents the results of the analysis of how long it takes to rent an apartment over the entire analysis period and in each year separately. The presented study is the first to examine the duration of residential real estate offerings for rent in the Polish market. Considering the entire analysis period, 25% of the reported apartments were rented in 7.3 weeks. The fastest 25% of rental offers were successful (rented) in 2019 – 4.86 weeks. The longest apartment owners waited for a tenant was in 2020 – more than 8 weeks, which was undoubtedly influenced by the lockdown of the entire country, and the change in the organization of work and study (remote work and studies, which did not require being in the city).
Summary: Electricity sector is the largest CO2 emitter and water user in China’s industrial sectors. The low-carbon transition of China’s electricity sector reduces its cooling water consumption. Here we firstly quantify CO2 emission and virtual water embodied in electricity trade with Quasi-Input-Output model. Then, we analyze the impacts of energy substitution, efficiency improvement, and electricity trade on water-saving co-benefits of CO2 reduction with the differences between the baseline scenario and counterfactual scenario. Results show that the low-carbon transition contributes to water-saving in China’s electricity sector. Virtual water and embodied CO2 have relatively decoupled from electricity trade since 2012. Water-saving (+10.4% yr−1) outweighed CO2 reduction (+8.4% yr−1) through energy substitution and efficiency improvement in the ‘new normal’ stage. Our work emphasizes the need to integrate water-saving co-benefits of CO2 reduction into electricity system planning and highlights the challenges to facilitate coordinated development of the electricity-water nexus in China.
The potential of energy saving can be effectively realized by combining its tools with the methods of information modelling of the life cycle of a capital construction object, which determined the problem statement of this study. In this paper, the dependence of peak and average power of heating systems necessary to maintain comfortable temperature in the building on the climatic characteristics of the construction region is determined. For the dome part, the surface of which is realized in the form of a periodic sequence of elementary cells, the dependences of heat losses through the dome part of the building on the characteristics of the cells are calculated. The parameter describing the energy efficiency of space-planning solutions is determined, and its dependences on the geometric and thermophysical properties of enclosing structures are calculated. The total energy efficiency of the space-planning solution for the heating period is calculated. It is proved that energy optimization of the life cycle is possible only on the basis of heat flows through the surface of enclosing structures, linear and point thermally stressed elements (TSE) arising at the boundary of the dome part, the boundaries of the elementary cells filling the dome and on the structural elements of the triangular hemisphere or stratodesic dome. The formulated algorithms for describing all energy flows through the enclosing structures of the building made it possible to build an information model of the energy and economic efficiency of the life cycle of a capital construction object. The constructed vector information model of the energy and economic efficiency of the life cycle of a capital construction object allows solving the following practical tasks: to determine the dependence of energy costs on climatic parameters; to optimize the composition, terms of implementation and sequence of technical measures aimed at increasing the heat-insulating properties of enclosing structures and thermally stressed elements; to determine the profitability of these measures and their payback period.