Real Demand Research Articles

An integrated approach for multi-objective differential algorithm based event constrained optimal power flow for power system resiliency enhancement

ABSTRACT Due to climatic changes, the probability of occurrence and the intensity of extreme weather events increase day by day which leads to power outages. The probability of failure events is calculated using Monte Carlo simulation technique which is used to identify the most frequent failure events. The least eigenvalues of the inverse reduced Jacobian matrix decide the sensitive buses for load shedding using V-Q sensitivity analysis. This work presents the multi-objective differential evolution (MODE) algorithm for solving resilient constrained optimal power flow (RCOPF) in a deregulated power system. The control variables namely the real power of generators in a base case and under event, generator bus voltage magnitude, the real and reactive power demand of the selected load buses, tap settings and capacitor bank ratings. This algorithm is validated in IEEE 30 bus test system and the Indian 62 bus utility test system to show the effectiveness in four different cases including tie lines and distributed generation.

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

DESIGN ELEMENTS THAT QUALIFY HOUSING FOR SOCIAL INTEREST: CASE STUDY IN THE MUNICIPALITY OF CURIONÓPOLIS-PA

This article presents a study about the design limitations regarding Social Housing, observing approaches of flexibility, functionality, and adaptability included in the context of project aspects for family residences. These project elements can expand the perspectives for proposals of adapting the residences according to real families' demands and needs. This way, this research aims to carry out an approach about the main project elements to potentialize the quality of social housing from a bibliographical review, which grounded analyses in case studies carried out in Curionópolis-PA. The results comprise a synthesis of the fundamental concepts for the proposition of an alternative that comprehends the performance of these residences directed to the low-income public.

Indoor environmental quality assessment in passively ventilated classrooms in Germany and estimation of ventilation energy losses

In this field study we present an approach for the comprehensive and room-specific assessment of parameters with the overall aim to realize energy-efficient provision of hygienically harmless and thermally comfortable indoor environmental quality in naturally ventilated non-residential buildings. The approach is based on (i) conformity assessment of room design parameters, (ii) empirical determination of theoretically expected occupant-specific supply air flow rates and corresponding air exchange rates, (iii) experimental determination of real occupant-specific supply air flow rates and corresponding air exchange rates, (iv) measurement of indoor environmental exposure conditions of T, RH, cCO2, cPM2.5 and cTVOC, and (v) determination of real energy demands for the prevailing ventilation scheme. Underlying assessment criteria comprise the indoor environmental parameters of category II of EN 16798-1: Temperature T = 20 °C–24 °C, and relative humidity RH = 25 %–60 % as well as the guide values of the German Federal Environment Agency for cCO2cPM2.5 and cTVOC of 1000 ppm, 15 μg m−3, and 1 mg m−3, respectively.Investigation objects are six naturally ventilated classrooms of a German secondary school. Major factors influencing indoor environmental quality in these classrooms are the specific room volume per occupant and the window opening area. It is concluded that the rigorous implementation of ventilation recommendations laid down by the German Federal Environment Agency is ineffective with respect to anticipated indoor environmental parameters and inefficient with respect to ventilation energy losses on the order of about 10 kWh m−2 a−1 to 30 kWh m−2 a−1.

Специфика продвижения на российском рынке недвижимости: анализ лучших практик и потребительских предпочтений

The primary real estate market has shown systematic growth since 2020 due to the stimulating demand by using mortgages with state support, as well as the availability of a project financing instrument that protects the buyer from the risks of losing money invested in shared construction. At the same time, the current unstable economic situation leads to a drop in demand caused by a decrease in purchasing power and rising inflation. This dynamics primarily indicates an increased number of proposals, a high level of competition and the risk of oversaturation for developers. In the current conditions the gap emerging in the real estate market between the existing supply and real demand will only increase, which will require developers to use tools and management solutions aimed at promoting the product in the real estate market and building effective personalized communication with clients. The success of marketing tools depends on companies' focus on consumer sentiment, behavior and preferences. The presented work analyzes the civil construction industry's best practices for interaction with buyers, taking into account their emotions, expectations and concerns.

Delphi method investigation and research on the revision of Diagnosis of Occupational Arsenic Poisoning (GBZ 83-2013)

Objective: To explore the expert opinions on the revision of the Diagnosis of Occupational Arsenic Poisoning (GBZ 83-2013) . Methods: In March 2023, the improved Delphi expert consultation method was adopted, in the first round of consultation, a pre-survey was conducted on 20 experts, and the questionnaire was improved according to the experts' opinions. Then, a second round of expert consultation questionnaire was formed to conduct a questionnaire survey and consultation of 50 experts engaged in occupational disease diagnosis and related work. The feedback of experts was collected and analyzed. Results: The average score for the scientificity and progressiveness of the main technical content of the original standard was 3.33, and the average score for the rationality and operability of the main technical content of the original standard was 3.25. The importance of individual indicators with specific connotations were ranged from 4.20 to 4.45, with coefficients of variation <0.25, and the experts' opinions were relatively concentrated. The experts have provided feedback indicating that the original standard had issues such as lack of continuity in diagnostic gradation, the need to integrate biomarkers with urinary and hair arsenic levels, a scarcity of objective diagnostic indicators, the removal of exposure response from the main text, and a low level of consistency in standard usage. These issues need to be revised urgently. Conclusion: The Diagnosis of Occupational Arsenic Poisoning (GBZ 83-2013) should be revised based on experts' feedback and suggestions to meet the current real demand for occupational arsenic poisoning diagnosis.

Model predictive control of a grid-scale Thermal Energy Storage system in RELAP5-3D

This research delves into the integration and control of a Thermal Energy Storage (TES) system with a Small Modular Reactor (SMR), specifically the NuScale VOYGR SMR module in RELAP5-3D. The research methodology centered on modeling the NuScale VOYGR SMR, a light water pressurized water reactor (LWR) with a power output capacity of 77 MWe per module. The reactor and plant details were sourced from NuScale's final safety analysis report and supplemented by information from the NuScale website. The SMR plays a crucial role in energy generation, and to manage and dispatch the produced energy effectively, a robust storage system is essential. The proposed solution to this challenge is the implementation of the TES system. The selected TES for this research is a two-tank system. The study also employed Model Predictive Control (MPC) to optimize the operation of the TES system in conjunction with the SMR. Various simulations, including accident scenarios, were conducted to assess the system's response and performance. The research leveraged real energy demand data from the California Independent System Operator (CAISO) database and scaled it to reflect the power generation of a single SMR. The findings suggest that while integrating a TES system with an SMR can enhance the performance compared to a standalone SMR, certain scenarios might exacerbate the total power mismatch. The study provides insights into the potential of integrating TES systems with nuclear reactors and the challenges and considerations involved.

Research on maximizing real demand response based on link addition in social networks

The impact of social networks on real-life scenarios is intensifying with the diversification of the information they disseminate. Consequently, the interconnection between social networks and tangible networks is strengthening. Notably, we have observed that messages disseminated on social networks, particularly those soliciting aid, exert a significant influence on the underlying network structure. This study aims to investigate the role and importance of social networks in the information dissemination process, as well as to construct a linear threshold model tailored for the dissemination of emergency information across both real and social networks, leveraging conventional models of information spread. We have developed a model to increase the number of connection edges in social networks in order to enhance their worth. Additionally, we discovered that the objective function possesses submodular features and thus the created problem is NP-hard. As a result, we can use algorithms with approximative assurances of 1−e−1−θ′ to solve our problem and ensures the accuracy of the solution. We also analyze the complexity of the algorithm in solving this problem. Finally we validated our conclusions with three publicly available datasets and one real data set to analysis the results of the solution.

The Integration of Artificial Intelligence in Architectural Visualization Enhances Augmented Realism and Interactivity

The construction industry is an important part of the national economic market of various countries; since 2013, the construction industry's added value in the gross domestic product has been more than 6%, reaching 6.89% in 2022, and is a pillar industry of the national economy. Intelligent construction is the realistic demand to promote the high-quality development of China's construction industry. It is the key focus of transforming and upgrading the traditional construction industry to information, digital, and intelligent. As a new production factor, construction robots have become the key to promoting intelligent construction. Under the guidance of various national and industry policies, thanks to China's huge construction market volume and rich application scenarios, many innovative and entrepreneurial entities have entered the field of construction robots. Architectural visualization is a crucial aspect of architectural design and communication. With the development of science and technology, artificial intelligence (AI) technology is increasingly becoming an essential tool for architectural visualization and communication. The emergence of AI technology has provided architects with more flexible and creative ways to present design ideas. All along, designers who love architecture have been passionate about exploring better solutions for architecture, but there is never an optimal solution for architectural design; AI is the fire of the future era; it brings us more opportunities but also forces us to face new challenges from the future.

Mouse Escape Behaviors and mPFC-BLA Activity Dataset: Understanding Flexible Defensive Strategies Under Threat

Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence.

The impact of market competitors on the demand for reinsurance

PurposeAccording to the market competition theory, a firm’s decision-making is influenced by the behaviors or strategies of its competitors. The repercussions of competition include market-stealing and spillover effects. Relatively few studies in the reinsurance literature discuss the effect of competitors on an insurer’s decision-making. This study aims to fill a gap in the reinsurance literature by comparing insurers' reinsurance demand to their competitors' reinsurance purchases.Design/methodology/approachThis study uses unbalanced panel data for the US property-liability insurance industry from 2006 to 2017 to determine the impact of competitors' reinsurance purchases on insurers' reinsurance demand. This study employs the Mixed Effect Model and the Quantile Regression to test the proposed hypotheses.FindingsThe evidence suggests that the affiliated reinsurance purchases of competitors have a positive and substantial effect on the affiliated reinsurance demand of insurers, crediting mimicking the reinsurance strategy. Interestingly, the market-stealing effect is supported while the non-affiliated reinsurance metric is used. Remarkably, given insurers with low non-affiliated reinsurance purchases, the finding sustains the mimicking reinsurance strategy. Nevertheless, the market-stealing effect remains a concern for insurers with a high non-affiliated reinsurance purchase.Originality/valueThe new findings concerning competitor effects analysis fill a void in the reinsurance literature. Risk diversification, capital substitution, and real services demand may play a crucial role in determining the market-stealing effect, leading to a decrease in market share. Insurers can mitigate the market-stealing effect of competitors by accessing expertise and capital substitution through non-affiliated reinsurance purchases.

Screening biotoxin aptamer and their application of optical aptasensor in food stuff: a review.

Biotoxins are ranges of toxic substances produced by animals, plants, and microorganisms, which could contaminate foods during their production, processing, transportation, or storage, thus leading to foodborne illness, even food terrorism. Therefore, proposing simple, rapid, and effective detection methods for ensuring food free from biotoxin contamination shows a highly realistic demand. Aptamers are single-stranded oligonucleotides obtained from the systematic evolution of ligands by performing exponential enrichment (SELEX). They can specifically bind to wide ranges of targets with high affinity; thus, they have become important recognizing units in safety monitoring in food control and anti-terrorism. In this paper, we reviewed the technical points and difficulties of typical aptamer screening processes for biotoxins. For promoting the understanding of food control in the food supply chain, the latest progresses in rapid optical detection of biotoxins based on aptamers were summarized. In the end, we outlined some challenges and prospects in this field. We hope this paper could stimulate widespread interest in developing advanced sensing systems for ensuring food safety.

A Lightweight and Efficient Multi-Type Defect Detection Method for Transmission Lines Based on DCP-YOLOv8.

Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model's high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3's detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability.

Share pledge and accounting conservatism in share-pledging firms: Evidence from a natural experiment in China.

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment-China's enactment of provisions on share reductions that restrict pledge creditors' cashing-out behavior-we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors' liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors' concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms.

The Demand for Real Money Balances in Zimbabwe: An Error Correction Estimation

Purpose: The purpose of the study is to examine the stability of the money demand function in Zimbabwe. Understanding, the money demand function is a prerequisite for effective monetary policy formulation and understanding the monetary transmission process and shocks propagation in the economy. Materials and Methods: The study employs the error correction modeling methodology to investigate the money demand function for Zimbabwe using quarterly data from 2017q2 – 2023q2. The analysis is expanded to characterize the monetary transmission mechanism following a shock to the price level and how the demand for real money balances responds to both single period and multiple shocks. Findings: The findings confirm a stable long run money demand function that is subject to short run dynamics. In addition, real money demand responds positively to real GDP (Scale factor) and inversely related to the price level (inflation). Short term dynamics (particularly inflation expectations) compound real money demand collapse in response to rising inflation. Implications to Theory, Practice and Policy: Monetary Policy must aim to collapse inflation expectations through a tight monetary control program and a functioning interbank market for foreign exchange to avoid surging parallel market activity.

The Impact of Covid-19 on Real Estate Demand Based on Green Spaces: Case Study of Türkiye (Ankara)

The Impact of Covid-19 on Real Estate Demand Based on Green Spaces: Case Study of Türkiye (Ankara)

거시경제 및 인구특성의 변화가 수도권 및 비수도권 주택 매매시장에 미치는 영향에 관한 연구

This study attempted to compare and derive the impact on the housing sales market in the metropolitan area and local cities and to present implications in terms of the real estate brokerage market. The Vector Error Correction Model was used as the analysis method in consideration of the time series characteristics and cointegration relationship of the used macroeconomic and demographic variables. The analysis results and implications are summarized as follows. In the case of the metropolitan area model, interest rates, consumer price indices, population growth rates, and fertility rates were the main factors influencing the housing sales market. The five metropolitan city models are the main factors influencing interest rates, economic growth rates, and consumer price indices, while the explanatory power of the fertility rate is very low. Considering these results, housing can be classified as an investment product in the metropolitan area, and local cities can be classified as a housing sales market with strong characteristics as a consumer product. Long-term housing sales are not expected to increase significantly due to the recent decline in investment and real demand due to the stabilization of housing prices due to high-interest rates. The housing brokerage market needs to be developed into a convergence field by combining highly relevant management and consulting.

Reflections on AIGC Empowering the Development of Guizhou's Intangible Cultural Heritage

Under the background of rural vitalization strategy, it is the mission of the times as well as the realistic demand to realize the vitalization and dissemination of intangible cultural heritage, and to transform the cultural empowerment advantage into economic development momentum. Based on AIGC technology, this paper discusses the era context of the development of intangible cultural heritage in Guizhou Province, and analyzes the digital development path of intangible cultural heritage in Guizhou from the four aspects of reconstructing intangible cultural heritage preservation mode, innovating intangible cultural heritage inheritance dimension, developing intangible cultural heritage products and expanding intangible cultural heritage dissemination channels.

Sustainability of the Housing Market: Post-COVID-19 Effects on Housing Preferences in Cyprus

After the announcement of the outbreak of the COVID-19 pandemic, people around the world had to adopt online education, working from home and social-distancing policies in an effort to stop the spread of COVID-19. The lockdowns and home isolation brought to the surface the current limitations of the existing housing stock as people needed to re-organize their home space to host various daytime functionalities. Therefore, one could question whether the priorities of potential homebuyers have been changed due to technology, health, and socialization alteration? This study explores the preferences for housing attributes and trends in Cyprus post-COVID-19 pandemic and compares these against the views of real estate professionals who represent the Cypriot residential demand prior the pandemic. Data were gathered via qualitative surveys from 117 potential homebuyers and 36 real estate professionals from across Cyprus. A survey methodology with questionnaires was used to analyze the changes in the real estate demand between the pre- and post-COVID-19 periods. Descriptive statistics were used to determine the mean importance of the housing attributes and to compare the results from the pre- and post-COVID-19 pandemic periods. The analysis found a shift toward houses rather than apartments, and the results suggest that new housing should contain more capacity for private use spaces, and at the same time, it should be designed with flexible and adaptive layouts to accommodate more activities. This study discusses whether these shifts are likely to be temporary or permanent and the implications for real estate professionals.

Preparing England's further education system for zero-carbon construction skills

Skills issues for craft trades in building decarbonisation often focus on improving technical knowledge but known problems of quality assurance in construction also include ‘soft’ employability skills and the application of sustainability values in practice. New course content is a necessary but not sufficient component of changing practice. The real demand for skills and actual use of skills in the workplace is just as important as new skills supply. A deliberative workshop with 40 expert stakeholders investigated the preparedness of the education and training system for zero-carbon construction skills. Several challenges emerge from this research. The market context for jobs and skills training shows how the over-narrow focus on new courses leads to waste and failures. Policy has an important role to play in developing minimum vocational standards through licensing, allied to consistent, long-term industrial strategy. The low prestige of further education and vocational training hinders real skills development and recruitment and retention of the workforce. Colleges operate with limited resources and competing priorities, creating real constraints on important activities like teacher training. Short courses and continuing professional development have no common standards or link with initial education and training, leading to piecemeal learning of unknown quality.