Search Results (3,426)

Due to the increasing number of vehicles and the limited land supply, old residential areas generally face parking difficulties. An intelligent parking service is a critical study direction to address parking difficulty since it can achieve the automatic management of parking processes and planning of parking spaces. However, the existing intelligent parking service systems have shortcomings such as low information quality, low management efficiency, and single service mode. To address the shortcomings, in this paper, we conduct a systematic study on utilizing digital twin (DT) technology to improve the intelligent parking service system. The main contributions are threefold: (1) We analyze the function requirements of the intelligent parking service for old residential areas, such as visual monitoring, refined management, and simulation optimization. (2) We design a DT-based intelligent parking service system by collecting data on physical parking space, constructing the corresponding virtual parking space, and building the user interaction platform. An old residential area in Guangzhou, China is used as a use case to show that the designed parking service system can meet the function requirements. (3) Through mathematical modeling and simulation evaluation, we utilize two typical intelligent parking services including dynamic parking planning and driving safety assessment to demonstrate the effectiveness of the proposed system. This study provides innovative solutions for parking management in old residential areas, utilizing DT technology to not only improve information quality and management efficiency, but also provide a theoretical basis and practical reference for the intelligent transformation of urban parking services. Full article

Zero-emission buildings, which do not emit CO₂ or other greenhouse gases throughout their entire life cycle, play a crucial role in sustainable development and the fight against climate change. Achieving carbon neutrality in construction requires considering emissions associated with material production, construction, operation, as well as demolition and disposal. These buildings utilize energy-efficient technologies, renewable energy sources, and low-carbon materials, minimizing their environmental impact. The building sector accounts for a significant percentage of global greenhouse gas emissions, making it a key area for climate action. In Poland, where aging and energy-inefficient buildings prevail, the need for a transition towards zero-emission buildings is particularly urgent. This paper assesses the feasibility and hurdles of retrofitting existing buildings to achieve zero emissions by utilizing renewable energy systems like solar photovoltaic and heat pump technologies. The publication discusses the technical, economic, and legal aspects of this transformation, with particular emphasis on the Polish context and available support programs. The purpose of this publication is to disseminate practical knowledge and foster innovation among architects, investors, and decision-makers engaged in the development of a sustainable built environment. A key example is Net Zero Energy Buildings (NZEBs), which generate as much energy as they consume over a year through technologies such as photovoltaic panels, solar collectors, and heat pumps. NZEBs combine effective insulation, energy-efficient systems, and smart energy management to minimize consumption, and may even produce excess energy that feeds back into the grid. Despite challenges in construction and maintenance, the increasing adoption of zero-emission and NZEBs worldwide reflects their long-term ecological, economic, and health benefits. The focus of this publication is to analyze the potential for transforming standard buildings, as defined by current regulations, into zero-emission buildings powered entirely by renewable energy sources. This case study analyzes the energy potential of a residential building located in Krakow, Poland. The building’s energy efficiency potential was assessed through computer simulations using Audytor OZC software (version 7.0 Pro, Sankom), taking into account local climate conditions and building standards. The study analyzed the impact of various strategies, such as upgrading thermal insulation, using energy-efficient windows, and installing photovoltaic panels, on energy consumption and CO₂ emissions. Full article

Determining ventilation rates in commercial animal buildings has been technically challenging. This study aimed to develop an innovative method and a ventilation model and provide new insights into animal building ventilation. A layer house with 46 fans was studied over six months. A full-size and fast-response portable fan tester was developed for on-site fan ventilation measurement. Results indicated that the house differential pressures (dP) varied from +10.4 to <−100.0 Pa but remained between −10 and −30 Pa for 75.7% of the time. The mean house dP was −18.1 ± 8.9 Pa (mean ± standard deviation). Fan rotational speeds ranged from 495 to 580 rpm, with an average of 555 ± 14 rpm. Daily mean house ventilation rates ranged from 1800 to 22,142 m³ min⁻¹, averaging 4.68 m³ h⁻¹ per hen. This study concluded that house dP can be greatly affected by strong winds in addition to fan operations and air inlet openings. Fan rotational speeds are influenced by pulley sizes and fan belt maintenance. On-site fan tests with the fan tester can generate reliable data for fan ventilation characterization. Fan models that incorporate both fan rotational speeds and differential pressures considerably improve ventilation rate calculations. Full article

The rapid and accurate detection of fire scenes in various environments is crucial for effective disaster management and mitigation. Fire scene classification is a critical aspect of modern fire detection systems that directly affects public safety and property preservation. This research introduced a novel hybrid deep learning model designed to enhance the accuracy and efficiency of fire scene classification across diverse environments. The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. By leveraging the strengths of pre-trained networks such as ResNet50, VGG16, and EfficientNet-B3, the model captures detailed features at multiple scales, ensuring robust detection capabilities. Including spatial and channel attention mechanisms further refines the focus on critical areas within the input images, reducing false positives and improving detection precision. Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. The model also exhibited reduced computational complexity and enhanced inference speed, making it suitable for deployment in real-time applications on various hardware platforms. This study sets a new benchmark for fire detection and offers a powerful tool for early warning systems and emergency response initiatives. Full article

In this work, we describe the implementation of an infrastructure of conversational chatbots by using natural language processing and training within the Rasa framework. We use this infrastructure to create a chatbot assistant for the users of a bioinformatics suite. This suite provides a customized interface solution for omic pipelines and workflows, and it is named GPRO. The infrastructure has also been used to build another chatbot for a Laboratory Information Management System (LIMS). The two chatbots (namely, Genie and Abu) have been built on an open framework that uses natural language understanding (NLU) and machine learning techniques to understand user queries and respond to them. Users can seamlessly interact with the chatbot to receive support on navigating the GPRO pipelines and workflows. The chatbot provides a bridge between users and the wealth of bioinformatics knowledge available online. Full article

Background/Objectives: Spinal cord injury (SCI) is a disabling condition prevalent worldwide, requiring rehabilitation services from injury through community living. This study, conducted by representatives of the World Rehabilitation Alliance (WRA), aims to identify strategies for strengthening SCI rehabilitation services globally, with particular attention to settings where resources are limited. Methods: Three focus groups were held between 2023 and 2024 with WRA representatives specializing in SCI rehabilitation. Discussions focused on four key areas: workforce and education, health policy and systems research, primary care, and emergency response. Perspectives were developed taking into account frameworks from the World Health Organization (WHO). Results: Key insights into SCI rehabilitation services emphasize workforce and education as critical areas, underscoring the importance of specialized training, certification, and ongoing support to build capacity. In health systems and policy research, significant gaps in evidence-based practices were highlighted, emphasizing the need for comprehensive data collection and national registries to guide policy and align SCI care with global standards. The integration into primary care systems is recommended to improve access and address common complications in low- and middle-income countries (LMICs). For emergency response, this study stresses the importance of preparedness and establishing multi-disciplinary teams capable of managing SCI cases in resource-limited settings, reducing preventable complications, and improving patient outcomes. Conclusions: SCI rehabilitation services are essential to global health, with a need for workforce development, research, national registries, and integration into primary and emergency care. Such efforts should improve accessibility and align with global best practices, ensuring comprehensive and accessible rehabilitation for all. Full article

(1) Background: Osteoarthritis (OA) is a serious chronic disease mostly affecting the knee joint. Despite the many efforts for developing strategies to predict and control Knee Osteoarthritis (KOA), the disease is on the rise. This paper describes the process for the creation of a simulation model, the Dynamic Knee Osteoarthritis Simulation (DYNAMIKOS) model, that captures the complex interrelationships of the risk factors for the development of KOA; (2) Methods: The DYNAMIKOS model will be based on the System Dynamics approach. The first step will be to develop a Causal Loop Diagram (CLD) model for the risk factors involved incorporating a series of Group Modeling Building (GMB) workshops with experts and stakeholders. Using data from a representative sample of KOA patients, the statistical approaches Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling (SEM) will be carried out. (3) Results: This study will develop a simulation System Dynamics model for the risk factors of KOA based on the results of CLD and SEM; (4) Conclusions: The proposed DYNAMIKOS model could be used for effectively analyzing the complex interrelationships among the multiple factors that constitute the spread of KOA. In this way, plausible prevention strategies could be implemented for effectively managing and leading the potential eradication of KOA. Full article

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMSs) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMSs often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMSs can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and a real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMSs significantly boosts energy efficiency and safety in smart buildings. Simulation results reveal an hourly average power savings of 36.8 kw with the integrated failsafe model for all scenarios. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management. Full article

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed. Full article

The integration of mental health (MH) services into tuberculosis (TB) and HIV care remains a significant challenge in South Africa’s Free State province. This study seeks to understand the perspectives of public health programme managers on the barriers to such integration and to identify potential strategies to overcome these challenges. Data were collected between February and October 2021 using qualitative methods including four individual semi-structured interviews and two focus group discussions with a total of 15 managers responsible for the MH, primary healthcare, TB, and HIV programmes. Thematic data analysis was guided by an adapted version of the World Health Organization’s “building blocks” framework encompassing “service delivery”, “workforce”, “health information”, “essential medicines”, “financing”, and “leadership/governance”. Additionally, the analysis underscored the crucial role of “people”, acknowledging their significant contributions as both caregivers and recipients of care. Managers highlighted significant concerns regarding the insufficient integration of MH services, identifying structural barriers such as inadequate MH management structures and staff training, as well as social barriers, notably stigma and a lack of family treatment adherence support. Conversely, they recognised strong management structures, integrated screening, and social interventions, including family involvement, as key facilitators of successful MH integration. The findings emphasise the need for a whole-system approach that addresses all building blocks while prioritising the role of “people” in overcoming challenges with integrating MH services into TB and HIV care. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

This study proposes a portable and IoT-based electrochemical point-of-care sensing device for detecting zopiclone in cocktails. The system utilizes an electrochemical laccase biosensor and a potentiostat, offering a low-cost and portable device for detecting this sedative drug in cocktails. The sensor characterization experiments demonstrated the linear behavior of the oxidation and reduction currents for each of the targeted concentrations of zopiclone, enabling their detection and quantification even when mixed with an interfering substance. The proposed system could be used for the in situ analysis of cocktails, providing a valuable tool for monitoring the presence of hypnotic drugs in various social and clinical settings. The study utilized materials and reagents, including zopiclone, lab-made lemon juice, lab-made tequila, and lab-made triple sec, all prepared with reactants obtained in Bogotá, Colombia. The potentiostat used in the system was designed to manage cyclic voltammetry measurements. The electrochemical cells’ durability and longevity were also tested and characterized, with all electrodes undergoing 200 tests and their performance degradation varying according to the molecule used. The study concludes that the proposed system offers a valuable tool for detecting and monitoring pharmaceutical substances in various interfering ingredients that build up cocktails. Further research and application of this system can help address the global concern surrounding the administration of hypnotic substances to unknowing consumers through food or drinks to enable robbery and sexual assault. Full article

In the context of digital construction, responsibility management in smart city building information modeling (BIM) projects spans the entire building lifecycle. The involvement of numerous BIM designers in project management and frequent data exchanges pose significant challenges for the traceability, immutability, and responsibility attribution of BIM models. To address these issues, this study proposes a blockchain-based responsibility management and collaboration framework for BIM projects using non-fungible tokens (NFTs), aiming to enhance the management of responsibilities and accountability in BIM projects. This research adopts a design science methodology, strictly adhering to scientific research procedures to ensure rigor. First, NFTs based on blockchain technology were developed to generate corresponding digital signatures for BIM model files. This approach ensures that each BIM model file has a unique digital identity, enhancing transparency and traceability in responsibility management. Next, the interplanetary file system (IPFS) was used to generate digital fingerprints, with the content identifier generated by IPFS uploaded to the blockchain to ensure the immutability of BIM model files. This method guarantees the integrity and security of BIM model files throughout their lifecycle. Finally, the proposed methods were validated through a blockchain network. The experimental results indicate that the proposed framework is theoretically highly feasible and demonstrates good applicability and efficiency in practical production. The constructed blockchain network meets the actual needs of responsibility management in smart city BIM projects, enhancing the transparency and reliability of project management. Full article

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational costs. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system. Full article

The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption and CO₂ emissions released into the atmosphere, making a notable contribution to climate change. It is therefore imperative that energy efficiency in buildings is prioritized in order to reduce environmental impacts and meet the targets set out in the European 2030 Agenda. In this context, renewable energy communities (RECs) have the potential to play an important role, promoting the use of renewable energy at the local level, optimizing energy management, and reducing consumption by sharing resources and advanced technologies. This paper introduces an open tool (OT) designed for the configuration of energy systems dedicated to RECs. The OT considers several inputs, including thermal and electrical loads, energy consumption, the type of building, surface area, and population size. The OT employs artificial intelligence (AI) algorithms and machine learning (ML) techniques to generate forecast optimized scenarios for the sizing of photovoltaic systems, thermal, and electrical storage, and the estimation of CO₂ emission reductions. The OT features a user-friendly interface, enabling even non-experts to obtain comprehensive configurations for RECs, aiming to accelerate the transition toward sustainable and efficient district energy systems, driving positive environmental impact and fostering a greener future for communities and cities. Full article