Hasil untuk "Real estate business"

Lokuliyana Chathrika Kawmadi, Zvirgzdins Janis

The construction sector is one of the most distinct economic sectors in both developed and developing nations worldwide. Energy and raw material usage in emerging countries is rapidly increasing. Sri Lanka is coping with major environmental issues because of the continued development of the construction industry. Sri Lankan construction industry issues have occurred with the unsustainable construction practices due to the linear economy. As a result, Sri Lanka is focusing on sustainability by incorporating circular economy principles into the construction and built environment sectors. Therefore, the aim of the study is to evaluate the current application of the circular economy principle for sustainable building performance in the Sri Lankan construction industry and to provide recommendations for developing circular economy strategies to improve sustainable building performance in the Sri Lankan construction industry. The research used a quantitative research approach. The questionnaire survey was targeted to identify the prevailing issues and the level of applicability of circular economy principles and the sustainability of the Sri Lankan construction industry. Based on the findings, it can be concluded that the lack of awareness, education, and training in sustainable practices during construction projects, the absence of a proper legal framework and enforcement, and the generation of high waste volumes and carbon dioxide emissions are the most prevalent issues in the Sri Lankan construction industry. A sustainable building industry in Sri Lanka can be achieved through promoting green building technologies, improving the legal framework to strengthen the circular economy implementation, recycling of waste, and utilisation of locally sourced eco-friendly materials.

Ilya Alexandrovich Zvonov, Natalia Vladimirovna Kashirina

В настоящей статье рассмотрены основные изменения в нормативно-техническом регулировании строительной отрасли, связанные с началом реализации реестрового принципа, цифровой трансформацией и поэтапным переходом к параметрическому нормированию. Большинство происходящих изменений направлено на сокращение сроков и стоимости изысканий, проектирования и строительства, повышение качества проектной и строительной продукции. Они становятся повседневной профессиональной практикой. Особое внимание в статье уделено реестровому принципу, предусматривающему формирование целостной системы нормативных документов, содержащих общие обязательные требования, применительно ко всем этапам жизненного цикла объектов капитального строительства. Такие ре­естры могут и должны содержать полный объем информации и необходимые для работы с ней инструменты, требующиеся специалисту. В связи с этим реестры все чаще называют цифровой технологией. Кратко проанализированы результаты первого года работы цифрового Реестра требований, подлежащих применению при выполнении инженерных изысканий, проектировании, строительстве и сносе. Рассмотрены перспективы перевода данного Реестра требований в машиночитаемый и машинопонимаемый форматы, перечни решаемых задач и условия дальнейшего развития. Проведено сравнение с реестровыми инструментами, применяемыми в России и зарубежных странах. Подняты вопросы актуализации требований в рамках существующих и создаваемых реестров. Предложено обоснование применения и проведена оценка возможностей корпоративных реестров требований в составе информационных систем или стандартов организаций. В статье авторы коснулись перспективных вопросов формирования автоматизированных систем управления требованиями. В продолжение предыдущих публикаций авторы развивают вопросы системной интеграции реестров требований и технологий параметрического нормирования.

Binqi Sun, Zhihang Wei, Andrea Bastoni et al.

Memory bandwidth regulation and cache partitioning are widely used techniques for achieving predictable timing in real-time computing systems. Combined with partitioned scheduling, these methods require careful co-allocation of tasks and resources to cores, as task execution times strongly depend on available allocated resources. To address this challenge, this paper presents a 0-1 linear program for task-resource co-allocation, along with a multi-objective heuristic designed to minimize resource usage while guaranteeing schedulability under a preemptive EDF scheduling policy. Our heuristic employs a multi-layer framework, where an outer layer explores resource allocations using Pareto-pruned search, and an inner layer optimizes task allocation by solving a knapsack problem using dynamic programming. To evaluate the performance of the proposed optimization algorithm, we profile real-world benchmarks on an embedded AMD UltraScale+ ZCU102 platform, with fine-grained resource partitioning enabled by the Jailhouse hypervisor, leveraging cache set partitioning and MemGuard for memory bandwidth regulation. Experiments based on the benchmarking results show that the proposed 0-1 linear program outperforms existing mixed-integer programs by finding more optimal solutions within the same time limit. Moreover, the proposed multi-objective multi-layer heuristic performs consistently better than the state-of-the-art multi-resource-task co-allocation algorithm in terms of schedulability, resource usage, number of non-dominated solutions, and computational efficiency.

René Bohnsack, Mickie de Wet

This article develops the concept of Autonomous Business Models (ABMs) as a distinct managerial and strategic logic in the age of agentic AI. While most firms still operate within human-driven or AI-augmented models, we argue that we are now entering a phase where agentic AI (systems capable of initiating, coordinating, and adapting actions autonomously) can increasingly execute the core mechanisms of value creation, delivery, and capture. This shift reframes AI not as a tool to support strategy, but as the strategy itself. Using two illustrative cases, getswan.ai, an Israeli startup pursuing autonomy by design, and a hypothetical reconfiguration of Ryanair as an AI-driven incumbent, we depict the evolution from augmented to autonomous business models. We show how ABMs reshape competitive advantage through agentic execution, continuous adaptation, and the gradual offloading of human decision-making. This transition introduces new forms of competition between AI-led firms, which we term synthetic competition, where strategic interactions occur at rapid, machine-level speed and scale. It also challenges foundational assumptions in strategy, organizational design, and governance. By positioning agentic AI as the central actor in business model execution, the article invites us to rethink strategic management in an era where firms increasingly run themselves.

Manuel Alberto M. Ferreira

Given the busy period and busy cycle major importance in queuing systems, it is crucial the knowledge of the respective distribution functions that is what allows the calculation of the important probabilities. For the M|G|$\infty$ queue system, there are no round form formulae for those distribution functions. But, for the M|D|$\infty$ queue, due the fact that its busy period and busy cycle have both Laplace transform expression round forms, what does not happen for any other M|G|$\infty$ queue system, with an algorithm created by Platzman, Ammons and Bartholdi III, that allows the tail probabilities computation since the correspondent Laplace transform in round form is known, those distribution functions calculations are possible. Here, we will implement the algorithm through a FORTRAN program.

Robin D. Pesl

Modern enterprise computing systems integrate numerous subsystems to resolve a common task by yielding emergent behavior. A widespread approach is using services implemented with Web technologies like REST or OpenAPI, which offer an interaction mechanism and service documentation standard, respectively. Each service represents a specific business functionality, allowing encapsulation and easier maintenance. Despite the reduced maintenance costs on an individual service level, increased integration complexity arises. Consequently, automated service composition approaches have arisen to mitigate this issue. Nevertheless, these approaches have not achieved high acceptance in practice due to their reliance on complex formal modeling. Within this Ph.D. thesis, we analyze the application of Large Language Models (LLMs) to automatically integrate the services based on a natural language input. The result is a reusable service composition, e.g., as program code. While not always generating entirely correct results, the result can still be helpful by providing integration engineers with a close approximation of a suitable solution, which requires little effort to become operational. Our research involves (i) introducing a software architecture for automated service composition using LLMs, (ii) analyzing Retrieval Augmented Generation (RAG) for service discovery, (iii) proposing a novel natural language query-based benchmark for service discovery, and (iv) extending the benchmark to complete service composition scenarios. We have presented our software architecture as Compositio Prompto, the analysis of RAG for service discovery, and submitted a proposal for the service discovery benchmark. Open topics are primarily the extension of the service discovery benchmark to service composition scenarios and the improvements of the service composition generation, e.g., using fine-tuning or LLM agents.

Nan Chen, Xiaotian Dai, Tong Cheng et al.

Emerging real-time applications have driven the transition to multicore embedded systems, where tasks must share resources due to functional demands and limited availability. These resources, whether local or global, are protected within critical sections to prevent race conditions, with locking protocols ensuring both exclusive access and timing requirements. However, transient faults occurring within critical sections can disrupt execution and propagate errors across multiple tasks. Conventional locking protocols fail to address such faults, and integrating traditional fault tolerance techniques often increases blocking. Recent approaches improve fault recovery through parallel replica execution; however, challenges remain due to sequential accessing, coordination overhead, and susceptibility to common-mode faults. In this paper, we propose a Lock-frEe Fault-Tolerant Resource Sharing (LEFT-RS) protocol for multicore real-time systems. LEFT-RS allows tasks to concurrently access and read global resources while entering their critical sections in parallel. Each task can complete its access earlier upon successful execution if other tasks experience faults, thereby improving the efficiency of resource usage. Our design also limits the overhead and enhances fault resilience. We present a comprehensive worst-case response time analysis to ensure timing guarantees. Extensive evaluation results demonstrate that our method significantly outperforms existing approaches, achieving up to an 84.5% improvement in schedulability on average.

J. Reid Cummings

The Dream Team Approach is a time-tested training and teaching tool for teaching advanced commercial real estate finance and investment analysis. Instructors can implement the Dream Team Approach immediately by applying its included curriculum design. It also offers crucial guidance for incorporating the Dream Team Event, a unique, engaging, and potentially highly visible industry interactive learning experience, as a capstone project. Detailed guidance is provided for organizing, preparing, and delivering the Dream Team Event. The Dream Team Approach has been delivering outstanding student learning outcomes since 2008. It is now offered in the hope that its adoption and use will motivate, encourage, and educate the next generation of commercial real estate professionals.

M.N. Rahman, K.S. Akter, M.I. Faridatul

Urban expansion is not only a driving force for economic growth and development but also a perilous element for anthropogenic carbon emission coinciding with population growth, urban expansion, land use pattern, urban sprawl. The study compares urban expansion and its impact on the carbon emission pattern for Rajshahi, a city of Bangladesh where land cover data is an important determinant. At first, the study aims to evaluate the pattern of urban expansion and the second step quantifies the carbon emission trend. The satellite images are used for land cover detection of urban expansion and Support Vector Machine (SVM) algorithm is applied for its classification. Similar carbon emission data are examined to relate carbon emission quantification rate by remote sensing algorithm. It indicates that urban expansion significantly explains carbon emission by comparing with similar secondary data to extract carbon emission and energy consumption. After accentuating urban expansion and carbon emission rate, the spatiotemporal pattern is analyzed in 16 different directions revealing that the urban expansion and emission trend expands to north-western and north-eastern directions of Rajshahi, turning it into a low carbon absorbed city. The responsible landmarks are real estate housing complex, educational institutions, student dormitories, industrial areas, brickfields and mango wholesale business. In order to understand the effects of uncontrolled urban expansion, this study will assist policymakers and urban planners in developing a comprehensive and well-organized strategy.

Vahid Ashrafimoghari, Necdet Gürkan, Jordan W. Suchow

The rapid evolution of artificial intelligence (AI), especially in the domain of Large Language Models (LLMs) and generative AI, has opened new avenues for application across various fields, yet its role in business education remains underexplored. This study introduces the first benchmark to assess the performance of seven major LLMs, OpenAI's models (GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo), Google's models (PaLM 2, Gemini 1.0 Pro), and Anthropic's models (Claude 2 and Claude 2.1), on the GMAT, which is a key exam in the admission process for graduate business programs. Our analysis shows that most LLMs outperform human candidates, with GPT-4 Turbo not only outperforming the other models but also surpassing the average scores of graduate students at top business schools. Through a case study, this research examines GPT-4 Turbo's ability to explain answers, evaluate responses, identify errors, tailor instructions, and generate alternative scenarios. The latest LLM versions, GPT-4 Turbo, Claude 2.1, and Gemini 1.0 Pro, show marked improvements in reasoning tasks compared to their predecessors, underscoring their potential for complex problem-solving. While AI's promise in education, assessment, and tutoring is clear, challenges remain. Our study not only sheds light on LLMs' academic potential but also emphasizes the need for careful development and application of AI in education. As AI technology advances, it is imperative to establish frameworks and protocols for AI interaction, verify the accuracy of AI-generated content, ensure worldwide access for diverse learners, and create an educational environment where AI supports human expertise. This research sets the stage for further exploration into the responsible use of AI to enrich educational experiences and improve exam preparation and assessment methods.

Wei Guan, Jian Cao, Jianqi Gao et al.

Detecting anomalies in business processes is crucial for ensuring operational success. While many existing methods rely on statistical frequency to detect anomalies, it's important to note that infrequent behavior doesn't necessarily imply undesirability. To address this challenge, detecting anomalies from a semantic viewpoint proves to be a more effective approach. However, current semantic anomaly detection methods treat a trace (i.e., process instance) as multiple event pairs, disrupting long-distance dependencies. In this paper, we introduce DABL, a novel approach for detecting semantic anomalies in business processes using large language models (LLMs). We collect 143,137 real-world process models from various domains. By generating normal traces through the playout of these process models and simulating both ordering and exclusion anomalies, we fine-tune Llama 2 using the resulting log. Through extensive experiments, we demonstrate that DABL surpasses existing state-of-the-art semantic anomaly detection methods in terms of both generalization ability and learning of given processes. Users can directly apply DABL to detect semantic anomalies in their own datasets without the need for additional training. Furthermore, DABL offers the capability to interpret the causes of anomalies in natural language, providing valuable insights into the detected anomalies.

Gyan Wickremasinghe, Siofra Frost, Karen Rafferty et al.

Industry 4.0 and beyond will rely heavily on sustainable Business Decision Modelling (BDM) that can be accelerated by blockchain and Digital Twin (DT) solutions. BDM is built on models and frameworks refined by key identification factors, data analysis, and mathematical or computational aspects applicable to complex business scenarios. Gaining actionable intelligence from collected data for BDM requires a carefully considered infrastructure to ensure data transparency, security, accessibility and sustainability. Organisations should consider social, economic and environmental factors (based on the triple bottom line approach) to ensure sustainability when integrating such an infrastructure. These sustainability features directly impact BDM concerning resource optimisation, stakeholder engagement, regulatory compliance and environmental impacts. To further understand these segments, taxonomies are defined to evaluate blockchain and DT sustainability features based on an in-depth review of the current state-of-the-art research. Detailed comparative evaluations provide insight into the reachability of the sustainable solution in terms of ideologies, access control and performance overheads. Several research questions are put forward to motivate further research that significantly impacts BDM. Finally, a case study based on an exemplary supply chain management system is presented to show the interoperability of blockchain and DT with BDM.

Yilun Zhao, Yan Rong, Yiyi Liu et al.

Adverse environmental impacts in the watershed are driven by urbanization, which is reflected by land use and land cover (LULC) transitions, such as increased impervious surfaces, industrial land expansion, and green space reduction. Some adverse impacts on the water environment include urban flooding and water quality degradation. Our study area, the Rouge River Watershed, has been susceptible to accelerated urbanization and degradation of ecosystems. Employing the Land Change Modeler (LCM), we designed four alternative urban development scenarios for 2023. Subsequently, leveraging the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), we utilized two models—Nutrient Delivery Ratio (NDR) and Flood Risk Mitigation (UFRM)—to evaluate and compare the performance of these scenarios, as well as the situation in 2019, in terms of nutrient export and urban flooding. After simulating these scenarios, we determined that prioritizing the medium- and high-intensity development scenario to protect open space outperforms other scenarios in nutrient export. However, the four scenarios could not exhibit significant differences in urban flooding mitigation. Thus, we propose balanced and integrative strategies, such as planning green infrastructure and compact development, to foster ecological and economic growth, and enhance the Rouge River Watershed’s resilience against natural disasters for a sustainable future.

Larisa Igorevna Zaitseva

In the current conditions of sanctions pressure and crisis phenomena, state regulation measures in the investment and construction sector should be aimed at preventing and eliminating contradictions and disputes between business entities, that is, the effectiveness of such measures must be assessed based on the ability to ensure conditions for conflict-free construction. At the same time, the concept of “conflict-free” does not mean the absence of disputes as such, but refers to the most constructive approaches to resolving disagreements, taking into account the interests of the participants in the relationship and the development priorities of the system as a whole. Of particular importance in achieving this goal is the activity of the judiciary in the field of forming a uniform judicial practice, focused on development priorities, as well as the application of judicial assistance technologies in the use of conciliation procedures in relation to construction disputes. Thus, the most significant recently developed positions relate to the extension of framework that is absent in the legislation, the interpretation of contract terms, the use of support measures and anti-sanction regulations, the peculiarities of bringing to responsibility for violation of urban planning standards and the consequences of non-compliance with mandatory requirements at the starting phase of the project. Analysis of approaches developed in judicial practice allows to identify risks associated with the implementation by public bodies (the prosecutor's office) of control and supervision functions. Assistance in the use of conciliation procedures, in turn, allows parties to use more flexible formats aimed at finding a mutually acceptable solution to a conflict situation within the court. At the same time, the problem of participation of public bodies in conciliation procedures remains relevant, significantly reducing their effectiveness.

Chun-Chang Lee, Wen-Chih Yeh, Zheng Yu et al.

In Taiwan, many housing transactions are special relationship-based transactions that involve family and friends, debt relations, urgent purchases and sales, and government agencies. As such, the prices in such transactions should differ from those in what we consider to be normal arm’s length transactions. In this regard, social capital theory can be used to analyze these transactions. The empirical data on housing transactions conducted in Taipei City from January 1, 2012 to December 31, 2018 were collected for this study. The empirical results showed that the prices in transactions involving debt relations and urgent purchases and sales were 22.6% lower than those in normal arm’s length transactions. The prices in transactions with government agencies were 48.9% lower than those in normal arm’s length transactions. The prices in transactions with first-degree, second-degree, and third-degree relatives were respectively, 57.3%, 53.1%, and 50.3% lower than those in normal arm’s length transactions. The prices in transactions involving friends were 28.0% lower than those in normal arm’s length transactions. The empirical results highlight the importance of the impacts of personal relationships or social relations on housing prices in special relationship-based transactions. The results also supported the social capital hypothesis.

Fabiana Fournier, Lior Limonad, Inna Skarbovsky et al.

Unraveling the causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions and making informed decisions regarding process improvements. Process discovery algorithms exploit time precedence as their main source of model derivation. Hence, a causal view can supplement process discovery, being a new perspective in which relations reflect genuine cause-effect dependencies among the tasks. This calls for faithful new techniques to discover the causal execution dependencies among the tasks in the process. To this end, our work offers a systematic approach to the unveiling of the causal business process by leveraging an existing causal discovery algorithm over activity timing. In addition, this work delves into a set of conditions under which process mining discovery algorithms generate a model that is incongruent with the causal business process model, and shows how the latter model can be methodologically employed for a sound analysis of the process. Our methodology searches for such discrepancies between the two models in the context of three causal patterns, and derives a new view in which these inconsistencies are annotated over the mined process model. We demonstrate our methodology employing two open process mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery technique. We apply it to a synthesized dataset and two open benchmark datasets.

Vitaly Alexandrovich Lukinov

В статье проанализированы показатели обеспеченности населения жильем, основные параметры жилой среды, отражающие ее комфортность, и разработаны критерии комфортабельности условий проживания населения в жилых помещениях. Рассмотрены действующие законодательные документы и рекомендации общественных организаций о классификации уровней комфортности жилых помещений. Проведен сравнительный анализ российских и европейских основных показателей, отражающих энергопотребление и тепловой комфорт для проживания населения. Автором разработаны основополагающие принципы, характеризующие благоприятные условия проживания населения: комфортность жилого помещения и его местоположение, нормативы обеспеченности каждого члена семьи комнатами и общей площадью квартиры (индивидуального жилого дома), состояние микроклимата в жилом помещении (в первую очередь теплового комфорта), наличие и качество функционирования инфраструктуры на окружающей жилой дом территории (в микрорайоне или квартале). Автор считает, что комфортабельные условия проживания населения достижимы при одновременном функционировании двух главных составляющих жилой среды: наличия комфортного жилого помещения и необходимой инфраструктуры (инженерной, социальной и транспортной). Сформулированы авторские определения комфортности жилой среды, жилого помещения и комфортабельных условий проживания семьи, одиноко проживающего человека. Автором разработаны основные критерии, отражающие комфортабельные условия проживания семьи. К ним относятся: проживание семьи в собственном комфортном жилом помещении (или по договору социального найма), обеспеченность каждого члена семьи общей площадью квартиры (дома) в размере не менее 18 м2 и одной комнатой и, хотя бы, еще одной общей комнатой, наличие в жилом помещении, как минимум, двух санузлов. Автором проведена классификация условий проживания населения в жилых помещениях (индивидуальных (одноквартирных) жилых домах и в квартирах многоквартирных домов), которые могут быть комфортабельными, дискомфортабельными и некомфортабельными. Всего в статье выделено шесть уровней условий проживания российских граждан в жилых помещениях и приведены критерии, в соответствии с которыми рекомендуется определять класс комфортабельности проживания семьи или одиноко проживающего человека. Положения данной статьи дополняют методологию комплексного управления жилищным фондом страны.

Marc Schmitt

The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards automated decisions in business analytics. Finally, AutoML has the potential to foster human empowerment in a world that is rapidly becoming more automated and digital.

Larionov Arkady, Nekrasova Tatiana

The paper substantiates that the development of ecological construction in Russia is currently progressing at an insufficiently high rate, which is primarily due to the lack of effective systemic support from the state at the legislative and executive levels. The author emphasizes that without the formation of a proper legislative base and state programs that stimulate ecological construction, all attempts to build by introducing innovative ecological solutions will not give the expected effect. The need for state incentives for business entities in the residential real estate market, the creation of legal and economic mechanisms that will be the engine of ecological housing construction in modern Russia is argued as a development perspective for the segment of the national economy studied in the work.

Ahamad Reza Mir Mohammadi Kooshknow, Rien Herber, Franco Ruzzenenti

Electricity storage systems (ESS) are hailed by many scholars and practitioners as a key element of the future electricity systems and a key step toward the transition to renewables . Nonetheless, the global speed of ESS implementation is relatively slow, and among possible reasons is the lack of viable business models. We developed an agent-based model to simulate the behavior of ESS within the Dutch electricity market. We adopted an exploratory modeling analysis (EMA) approach to investigate the effects of two specific business models on the value of ESS from the perspective of both investors and the government under uncertainties in the ESS technical and economics characteristics, and uncertainties in market conditions and regulations. Our results show ESS is not profitable in most scenarios, and generally wholesale arbitrage business model leads to more profit than reserve capacity. In addition, ESS economic and technical characteristics play more important roles in the value of ESS than market conditions, and carbon pricing.