В световен мащаб над 60% от всички активи са вложени в недвижими имоти, а операциите с недвижими имоти са един от 20-те основни сектора на икономиката в Статистическата класификация на икономическите дейности за Европейската общност. Mатериалната стойност на недвижимите имоти се превръща в ключов елемент от икономиката на България. Общият оборот в сектор „Опеарации с недвижими имоти“ нараства прогресивно, като през 2023 г. достига приблизително 7% от общия оборот на предприятията в страната. Предмет на настоящото изследване е състоянието на пазара за недвижими имоти в България и проследяването на тенденциите в развитието му през последните 20 години. Целта на автора на тази статия е да направи анализ на факторите влияещи на пазара, да разгледа и съпостави различни параметри, да разкрие зависимости между тях и да предложи по-добра осведоменост на участниците в него. Информационните източници, използвани за изследването са български и чуждестранни разработки в областта на операциите с недвижими имоти, статистически данни и първична информация, събрана от автора за периода 2005 – 2024 г.
Genrih Vasil'evich Orekhov, Yuri Alekseevich Balabin, Roman Yur'evich Luzgin
et al.
В практике экспертов, работающих в рамках судебных строительно-технических экспертиз, часто возникают вопросы, связанные с выявлением признаков технического и нормативно-правового характера аварийности многоквартирных жилых домов. Это связано с тем, что число таких домов, по данным Росстата, неуклонно возрастает. Как показал анализ заключений экспертов, выполненных в различных государственных судебно-экспертных учреждениях, единого подхода к подобного рода исследованиям в настоящее время не существует. В этом отношении настоящая статья представляет определенный интерес, поскольку вопросы, связанные с аварийным жилым фондом, являются актуальными. При исследованиях, проводимых экспертами по данным вопросам, часто происходит смысловое объединение понятий «ветхое состояние здания» и «аварийное техническое состояние». Целью данной работы является разработка универсального алгоритма исследований для судебных строительно-технических экспертиз, направленного на разрешение вопросов об аварийности жилых многоквартирных домов, который, в том числе, позволит устранять противоречия между выводами межведомственных комиссий и фактическим техническим состоянием зданий. В статье приводятся конкретные примеры повреждений и аварий (обрушений) многоквартирных домов, их причины. Рассмотрены нормативно-правовые и технические аспекты проблемы. Отмечается, что все описанные аварии не связаны с техногенными или чрезвычайными ситуациями (землетрясения, взрывы, пожары, теракты), а являются результатом ослабления критически важных несущих конструкций.
Chisumbe Sampa, Sinkala Christone, Tembo Chipozya
et al.
The construction industry is one of the most demanding sectors, characterised by high levels of occupational stress among workers. This study investigated the factors contributing to stress and the effectiveness of stress management strategies employed in developing countries, using Zambia as a case study. The methodological approach adopted was quantitative, with data collected using a self-administered structured questionnaire containing closed-ended questions. Using simple random sampling, 69 respondents were sampled and participated in the study. These included skilled and unskilled workers on construction sites drawn from contractors registered with the National Council for Construction in grades 1 to 3. Data collected were analysed using descriptive statistics with relative importance index, as well as exploratory factor analysis. The findings revealed that the leading factors contributing to stress among construction workers included long working hours (RII = 0.748), high workload (RII = 0.707), unrealistic deadlines (RII = 0.696), unsafe working conditions (RII = 0.693), and low salaries (RII = 0.693). Further, results of factor analysis reveal that effective stress management strategies should be centred on improving communication and working hours, as well as on the provision of counselling and social activities for employees. The study recommends enshrining stress management in occupational health and safety regulations as a way of enhancing employee well-being.
Real estate business, Regional economics. Space in economics
The construction industry plays an important part in economic growth but notably contributes to environmental deterioration owing to excessive resource use and waste generation. The implementation of circular economy (CE) principles provides a revolutionary strategy for reducing these unfavourable consequences by encouraging resource efficiency, material reuse, and waste reduction. The article assesses the economic and environmental benefits of principles of CE in civil construction, with a specific emphasis on their use in Sri Lanka. The research contains an analytical examination of current trends, an assessment of economic implications, and an evaluation of environmental sustainability improvements provided by CE adoption. It incorporates global and local case studies to find best practices and obstacles in circular economy implementation. The study technique comprises statistical analysis, including Pearson correlation analysis, to explore links between CE methods such as material recycling, waste management, and energy efficiency. The results demonstrate that CE principles greatly cut operating costs, boost energy efficiency, and minimize carbon footprints. These observations emphasize the significance of concerted efforts among policymakers, industry stakeholders, and regulatory agencies to promote the adoption of sustainable construction practices. The article continues by underlining the significance of organized legislative frameworks and technical advancements to assist the circular transition within the construction industry.
Real estate business, Regional economics. Space in economics
Oghenetejiri Okporokpo, Funminiyi Olajide, Nemitari Ajienka
et al.
As the frequency and complexity of Distributed Denial-of-Service (DDoS) attacks continue to increase, the level of threats posed to Smart Internet of Things (SIoT) business environments have also increased. These environments generally have several interconnected SIoT systems and devices that are integral to daily operations, usually depending on cloud infrastructure and real-time data analytics, which require continuous availability and secure data exchange. Conventional detection mechanisms, while useful in static or traditional network environments, often are inadequate in responding to the needs of these dynamic and diverse SIoT networks. In this paper, we introduce a novel trust-based DDoS detection model tailored to meet the unique requirements of smart business environments. The proposed model incorporates a trust evaluation engine that continuously monitors node behaviour, calculating trust scores based on packet delivery ratio, response time, and anomaly detection. These trust metrics are then aggregated by a central trust-based repository that uses inherent trust values to identify traffic patterns indicative of DDoS attacks. By integrating both trust scores and central trust-based outputs, the trust calculation is enhanced, ensuring that threats are accurately identified and addressed in real-time. The model demonstrated a significant improvement in detection accuracy, and a low false-positive rate with enhanced scalability and adaptability under TCP SYN, Ping Flood, and UDP Flood attacks. The results show that a trust-based approach provides an effective, lightweight alternative for securing resource-constrained business IoT environments.
Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model transparency, build trust in the predictions, and gain a deeper understanding of business processes, it is crucial to explain the decisions made by these models. Existing explainability methods for PBPM decisions are typically *post-hoc*, meaning they provide explanations only after the model has been trained. Unfortunately, these post-hoc approaches have shown to face various challenges, including lack of faithfulness, high computational costs and a significant sensitivity to out-of-distribution samples. In this work, we introduce, to the best of our knowledge, the first *self-explaining neural network* architecture for predictive process monitoring. Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction, while adapting the optimization objective to improve the reliability of the explanation. We first demonstrate that incorporating explainability into the training process does not hurt model performance, and in some cases, actually improves it. Additionally, we show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency.
Modern firms face a flood of dense, unstructured reports. Turning these documents into usable insights takes heavy effort and is far from agile when quick answers are needed. VTS-AI tackles this gap. It integrates Visual Thinking Strategies, which emphasize evidence-based observation, linking, and thinking, into AI agents, so the agents can extract business insights from unstructured text, tables, and images at scale. The system works in three tiers (micro, meso, macro). It tags issues, links them to source pages, and rolls them into clear action levers stored in a searchable YAML file. In tests on an 18-page business report, VTS-AI matched the speed of a one-shot ChatGPT prompt yet produced richer findings: page locations, verbatim excerpts, severity scores, and causal links. Analysts can accept or adjust these outputs in the same IDE, keeping human judgment in the loop. Early results show VTS-AI spots the direction of key metrics and flags where deeper number-crunching is needed. Next steps include mapping narrative tags to financial ratios, adding finance-tuned language models through a Model-Context Protocol, and building a Risk & Safety Layer to stress-test models and secure data. These upgrades aim to make VTS-AI a production-ready, audit-friendly tool for rapid business analysis.
Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.
Ashutosh Pradhan, Daniele Ottaviano, Yi Jiang
et al.
The increasing complexity of embedded hardware platforms poses significant challenges for real-time workloads. Architectural features such as Intel RDT, Arm QoS, and Arm MPAM are either unavailable on commercial embedded platforms or designed primarily for server environments optimized for average-case performance and might fail to deliver the expected real-time guarantees. Arm DynamIQ Shared Unit (DSU) includes isolation features-among others, hardware per-way cache partitioning-that can improve the real-time guarantees of complex embedded multicore systems and facilitate real-time analysis. However, the DSU also targets average cases, and its real-time capabilities have not yet been evaluated. This paper presents the first comprehensive analysis of three real-world deployments of the Arm DSU on Rockchip RK3568, Rockchip RK3588, and NVIDIA Orin platforms. We integrate support for the DSU at the operating system and hypervisor level and conduct a large-scale evaluation using both synthetic and real-world benchmarks with varying types and intensities of interference. Our results make extensive use of performance counters and indicate that, although effective, the quality of partitioning and isolation provided by the DSU depends on the type and the intensity of the interfering workloads. In addition, we uncover and analyze in detail the correlation between benchmarks and different types and intensities of interference.
Weerawardhana Savindika, Weerakoon Thilina Ganganath, Wimalasena Sulaksha
et al.
The notion of smart buildings is becoming a global trend. The smart concept is spread not only via buildings but also through cities, transportation, and communication. Many difficulties human beings face can be solved by smart building technology. For example, environmental contamination and resource depletion, such as water and fossil fuels. In Sri Lanka, smart building adoption was at a low level. As a result, the purpose of this research is to assess user comprehension of smart building service preferences and adoption from a Sri Lankan viewpoint. A questionnaire survey is used to obtain data from the community as part of the data collection approach. To achieve the objectives stated above, the data will be analysed using principal component analysis, multiple regression analysis, and Pareto analysis. Results indicated that the majority of Sri Lankans do not grasp smart building technologies. Using principal component analysis, eleven major service preferences were determined. Multiple regression analysis is used to identify the factors that influence service preference. The most essential elements impacting smart building adoption are competency to utilize new technology, preference for smart building attributes, and user satisfaction. This study would be an excellent resource for the future adoption of smart building technologies in Sri Lanka.
Real estate business, Regional economics. Space in economics
The analysis of the development of the international nuclear power construction market makes it possible to identify and assess the problems of economic reliability and risks in the implementation of international projects of the State Corporation Rosatom. For many years, Rosatom has been consistently conducting international cooperation and support for various projects abroad, developing the company's and peaceful atom's competencies in other countries. The main directions in the field of international cooperation are the maintenance and implementation of nuclear power plants, as well as the development of a new promising area of international business development for the construction of nuclear research and technology centres. The article considers an example of the implementation of the centre for nuclear research and technology in the multinational state of Bolivia. The Centre for Nuclear Research and Technology is a complex innovative technical project that embodies a complex of nuclear technologies for the development of various fields at the state level (research, agricultural development, advanced training, medicine, expertise, etc.). The project is being implemented within the framework of an agreement between the Government of the Russian Federation and the Government of Bolivia, which also makes it possible to obtain a new contract in the Latin American market in the future. Examples of international cooperation in the field of international activities of the Rosatom are considered. An expert risk analysis of the project in Bolivia was carried out. Thus, in the future, several large pilot innovative high-tech projects will be implemented in Latin America, as well as further implementation of similar projects in other parts of the world (Africa and Asia).
Business Process Management (BPM) is gaining increasing attention as it has the potential to cut costs while boosting output and quality. Business process document generation is a crucial stage in BPM. However, due to a shortage of datasets, data-driven deep learning techniques struggle to deliver the expected results. We propose an approach to transform Conditional Process Trees (CPTs) into Business Process Text Sketches (BPTSs) using Large Language Models (LLMs). The traditional prompting approach (Few-shot In-Context Learning) tries to get the correct answer in one go, and it can find the pattern of transforming simple CPTs into BPTSs, but for close-domain and CPTs with complex hierarchy, the traditional prompts perform weakly and with low correctness. We suggest using this technique to break down a difficult CPT into a number of basic CPTs and then solve each one in turn, drawing inspiration from the divide-and-conquer strategy. We chose 100 process trees with depths ranging from 2 to 5 at random, as well as CPTs with many nodes, many degrees of selection, and cyclic nesting. Experiments show that our method can achieve a correct rate of 93.42%, which is 45.17% better than traditional prompting methods. Our proposed method provides a solution for business process document generation in the absence of datasets, and secondly, it becomes potentially possible to provide a large number of datasets for the process model extraction (PME) domain.
Thibault Douzon, Stefan Duffner, Christophe Garcia
et al.
Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Extraction in business documents. However, most pre-training tasks proposed in the literature for business documents are too generic and not sufficient to learn more complex structures. In this paper, we use LayoutLM, a language model pre-trained on a collection of business documents, and introduce two new pre-training tasks that further improve its capacity to extract relevant information. The first is aimed at better understanding the complex layout of documents, and the second focuses on numeric values and their order of magnitude. These tasks force the model to learn better-contextualized representations of the scanned documents. We further introduce a new post-processing algorithm to decode BIESO tags in Information Extraction that performs better with complex entities. Our method significantly improves extraction performance on both public (from 93.88 to 95.50 F1 score) and private (from 84.35 to 84.84 F1 score) datasets composed of expense receipts, invoices, and purchase orders.
Latņikovs Sergejs, Malahova Jeļena, Jemeļjanovs Vladimirs
The study is related to the improvement of the safety methodology for the application of dangerous chemical substances and the development of binding action and response algorithms for officials of the State Fire and Rescue Service, who first arrive at the sites of the event with the presence of dangerous chemical, biological, radioactive and explosive substances. The safety methodology is intended to quickly provide the primary information needed to the rescue team in the context of action at the site, which will allow for the possible negative effects and risks to the environment to be minimised as soon as possible and more effectively. The examination of the regulatory enactments determining the responsibility of the services and the regulatory documents governing action revealed the limits of the cooperation and responsibility of the services, as well as the division of functions in response to events with the hazardous substances as well as the assessment of the internal regulatory enactments of the services involved in cooperation, allowing for a better assessment of their resources and capacity in the event of an emergency. Also, the manuals, instructions and action algorithms of the European services were evaluated, which were taken into account, tested and used in cooperation between different services in the event of an emergency with dangerous substances. Based on the results of the study, an instruction was developed which included primary information necessary for the head of rescue works, in the context of action at the site, which would allow for the earliest and more efficient reduction of the potential adverse effects and risks to the environment caused by the event. The instruction will be included in the training course programme for the preparedness of fire rescuers and future rescue managers, which will significantly facilitate not only the implementation of the work process but also improve the quality of the training process of the College of Fire Safety and Civil Protection according to modern competencies.
Real estate business, Regional economics. Space in economics
Chun-Chang Lee, Chih-Min Liang, Wen-Chih Yeh
et al.
This study explored the impacts of urban renewal projects on neighboring housing prices. Hierarchical linear modeling (HLM) was employed to analyze urban renewal projects in Taipei City. The Level 1 independent variables pertained to a house itself (19,157 pieces of data), such as its structure and neighborhood attributes. The Level 2 variable pertained to an urban renewal project (23 cases of urban renewal), and the explanatory variable was the scale of each urban renewal project. The study examined whether differences exist between the impacts of various urban renewal projects on neighboring housing prices, and analyzed the extent to which the differences in neighboring housing prices are caused by the differences between urban renewal projects. The empirical results showed that the mean housing price varies significantly between each urban renewal project. In regard to the variance in the mean house price, 31.46% was caused by the differences between the urban renewal projects. The estimated coefficient of the grand floor area of urban renewal (FLAREA) had a positive value and attained a 1% level of significance. This indicates that the larger the scale of an urban renewal project, the larger its effects on neighboring housing prices. The empirical results of this study could better explain the impacts of the scale of an urban renewal project on the externalities of urban renewal.
First published online 17 January 2022
Alexander Nikolaevich Dmitriev, Ildar Gazinurovich Mustafin
В данной статье рассматривается проблема цифровизации девелоперских структур как организация цифровой деятельности и управление ресурсами в строительстве, включающее оцифрованную программу производства и осуществления строительной продукции, которая, в том числе, предусматривает оцифровку внешних взаимосвязей (кооперационных цепочек) и внутренних бизнес-процессов в каждой строительной компании. Это значит, что все процессы должны быть оцифрованы. Основное направление — внедрение ERP систем. Итоги исследования показали, что многие российские строительные компании существенно озабочены цифровой трансформацией и необходимостью цифровизации строительных технологий для перехода на новый технологический этап. Проведенное исследование окажет поддержку руководству строительных компаний в понимании текущей ситуации, их места в глобальной цифровой трансформации и направлений, которым надо уделять особое внимание. Оценка эффективности дает не только возможность рассчитать бюджет будущего проекта и реально оценить все ресурсы, которые потребуются для его реализации, но и успешно реорганизовать бизнес-процессы организации и усилить контроль за функциональностью системы — определения вовлечения бизнеса и детального описания требуемых функций для решения поставленных целей и задач. Реализация мероприятия по автоматизации системы управления позволит согласовать цели и задачи, стоящие перед компанией, с целями и задачами, стоящими перед отдельными подразделениями и топ-менеджерами, четко понять функции, ответственность и требования к сотрудникам для построения эффективной системы управления компанией, сформировать целевую бизнес-модель компании для внедрения корпоративной информационной системы. Сложность применения ERP-систем в данной индустрии обусловлена такими особенностями строительного производства, как наличие весьма сложных взаимоотношений: инвестор – заказчик строительства – генеральный подрядчик – субподрядчики, и вытекающими отсюда особенностями календарного планирования.
The relevance of the topic is confirmed by the interest of the owners in increasing the value of the enterprise by means of the most effective way of using its assets. A review of the sources on the research topic confirmed its relevance and showed that in order to improve the work with assets in the corporate governance system, it is necessary to provide arguments for the mandatory application of the highest and best use (HBU) principle when evaluating assets and businesses and to develop recommendations for evaluating the basic components of the analysis, offering a single algorithm for selecting HBU assets of the enterprise. The purpose of the study is to improve the methodological tools for choosing the most effective use of enterprise assets in the corporate governance system, ensuring the detail and validity of the analysis results. The analysis employs general scientific methods of comparative analysis of modern economic and mathematical models and methods of choosing the most effective option for using the assets of an enterprise, systematization of data and generalization of domestic and foreign experience in the field of enterprise value management concepts and company changes. The principle of choosing the HBU and its application in practice is disclosed, the argumentation of the mandatory application of this principle when assessing the assets of an enterprise is given. Recommendations on the evaluation of the basic components of the analysis is developed and the effectiveness of the application of these methods in practice is evaluated. Despite the complexity of the methods of discounting cash flows, the paper shows that the results of a carefully conducted analysis of the choice of alternative options for the use of assets are applicable to the final approval of the evaluation results determined by standard approaches. The paper shows that when choosing the most effective option for using investment assets, it is necessary to determine the optimal number of floors for legally permitted and physically possible options, justify in detail the cash flows, their change, and the rate of return on capital, take into account the uncertainty of the initial data and estimate the relative error of the expected value. When evaluating a business, choosing an effective option, special attention should be paid to the possibilities of optimizing cash flows and the use of non-core real estate. The analysis carried out in order to choose the most effective option for using the assets of the enterprise should induce an increase in the value of the assets of the enterprise at acceptable risks for the investor.
Tien-Cuong Dinh, Cécile Gachet, Hsueh-Yung Lin
et al.
The aim of this paper is twofold. First of all, we confirm a few basic criteria of the finiteness of real forms of a given smooth complex projective variety, in terms of the Galois cohomology set of the discrete part of the automorphism group, the cone conjecture and the topological entropy. We then apply them to show that a smooth complex projective surface has at most finitely many non-isomorphic real forms unless it is either rational or a non-minimal surface birational to either a K3 surface or an Enriques surface. In the second part of the paper, we construct an Enriques surface whose blow-up at one point admits infinitely many non-isomorphic real forms. This answers a question of Kondo to us and also shows the three exceptional cases really occur.
Hanan Shteingart, Gerben Oostra, Ohad Levinkron
et al.
Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.