Window Type Research Articles

Sound insulation of partially open windows - alternative laboratory measurement methods

Sound insulation with partially open windows has had growing attention in recent years. In Denmark, this is primarily caused by the Danish Environmental Protection Agency's guideline from 2007 "Noise from roads", which introduces noise limits with open windows (opening area of 0.35 m2) for situations with a high traffic noise level. The sound insulation of traditional windows in open position have been measured in both the laboratory and external environment, and for certain setups too different results have been observed. To investigate the reasons behind the discrepancies, the traditionally reverberant source room of laboratory measurements was modified to a semi-anechoic room to remove reflections from unwanted reflections from room surfaces and to imitate the external environment during field measurements. Four different window types were tested as part of the research projects "Optimized measurement method for sound reduction of partially open windows part 1 and 2" (MetÅV and MetÅV2) in collaboration with four Danish window manufacturers. The primary focus of the project is to explore alternative laboratory methods to the traditional diffuse field method by studying reference cases from field measurements and taking into consideration different sound source positions and window geometry. This paper describes the results so far.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

Experimental and computational assessment of an energy-saving innovation in a customised testing cabin

The aim of this paper is to assess the performance of an energy-saving device, called ‘Thermocill’ in a customised test cabin, located in a controlled environment. With the UK domestic energy sector responsible for nearly 27 % of the total energy mix in 2022, and massive increases in the cost of energy on homeowners and tenants, simple and low-cost retrofit measures could potentially be of great value, if they prove to be effective at saving energy. The paper describes the experimental and computational methods in detail with the goal of setting an important example/standard for the assessment of similar energy-saving innovations under the UK Standard Assessment Procedure (SAP). This, in turn, would enable the performance of such innovations to be captured in the Energy Performance Certificates (EPCs) for Building Regulation compliance. On that basis, a test cabin was constructed at Salford Energy house 2.0 to investigate Thermocill’s impacts on energy consumption and loss within the cabin. The experimental procedure is subsequently compared against a series of Computational Fluid Dynamics (CFD) simulations conducted for a larger number of scenarios. The performance of Thermocill was explored at different room temperature setpoints (16, 21, and 24 °C), window types (air and argon-filled double-glazed), and with/without blinds. The results were presented for four main scenarios including ‘cabin without Thermocill/blind’, ‘cabin with blind’, ‘cabin with Thermocill’, and ‘cabin with blind/Thermocill’. The results were presented in the form of U-values and heat losses for the windows, while additional results were provided from CFD simulations. A good correlation between the experiments and the CFD was found, showing confidence in both methods. Findings demonstrated that, when Thermocill is in operation, it reduces the average U-value by around 3–4 %, which is considered a significant payback for a passive and economical measure. The methodology presented in this paper is expected to set an important example/standard for the assessment of similar energy-saving innovations under UK SAP. This, in turn, would enable the performance of such innovations to be captured in the EPCs for Building Regulation compliance.

Vehicle routing problem for omnichannel retailing including multiple types of time windows and products

This paper addresses the capacitated vehicle routing problem for omnichannel retailing with multiple types of time windows (hard time windows and soft time windows) and multiple types of products simultaneously. The problem aims to transfer multiple products from a central warehouse to stores and satellites, where split delivery is allowed since the demands of stores or satellites are large and the different product types have different volumes. Based on different characteristics of purchasing channels, the time window of a store is a hard constraint while the time window of a satellite is a soft one. This type of vehicle routing problem exists extensively in omnichannel retail distribution systems in the logistics industry of China, which considers multiple purchasing channels for customers. Since this type of vehicle routing problem is an NP-hard problem and more complicated than the conventional vehicle routing problem with time windows, we design an adaptive large neighborhood search (ALNS) method to solve it. Finally, numerical experiments are conducted to evaluate the effectiveness of the proposed algorithm and reveal some managerial insights. The numerical experiments show that the proposed algorithm can obtain a high-quality solution under a shorter computation time compared with the state-of-the-art MIP solver (Gurobi).

The electrically controlled dimming film of thiol-vinyl ether system with low-voltage and high contrast ratio for smart windows

As a type of smart window, polymer dispersed liquid crystal (PDLC) electrically controlled dimming film can respond to external electrical stimuli to regulate the optical flux of sunlight, reducing energy consumption. Herein, a PDLC film was prepared by systematically studying the functionalities and structural compatibility of vinyl ether and thiol compounds, and the optimal PDLC film possesses a superior contrast ratio (CR) of 196.9. When the film thickness is adjusted to 8 μm, the film saturation voltage is reduced to 6.7 V, still maintaining a high CR of 48.4. The well-designed PDLC films exhibited excellent modulation of light. The temperature difference between PDLC film and bare ITO glass can reach 4 °C under the simulated sunlight test, demonstrating the good thermal insulation performance. This smart films with low power consumption and high CR can meet the demand for active regulation of optical flux on smart windows.

Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations

AbstractThe modeling and forecasting of return volatility for the top three cryptocurrencies, which are identified by the highest trading volumes, is the main focus of the study. Eleven different GARCH-type models were analyzed using a comprehensive methodology in six different distributions, and deep learning algorithms were used to rigorously assess each model’s forecasting performance. Additionally, the study investigates the impact of selecting dynamic parameters for the forecasting performance of these models. This study investigates if there are any appreciable differences in forecast outcomes between the two different realized variance calculations and variations in training size. Further investigation focuses on how the use of expanding and rolling windows affects the optimal window type for forecasting. Finally, the importance of choosing different error measurements is emphasized in the framework of comparing forecasting performances. Our results indicate that in GARCH-type models, 5-minute realized variance shows the best forecasting performance, while in deep learning models, median realized variance (MedRV) has the best performance. Moreover, it has been determined that an increase in the training/test ratio and the selection of the rolling window approach both play important roles in achieving better forecast accuracy. Finally, our results show that deep learning models outperform GARCH-type models in volatility forecasts.

Visualization and Segmentation of Lung Tissue in DICOM-format CT Images

Abstract In this project, two tools were developed as a preliminary step towards the creation of future Computer-Aided Diagnosis systems for the automatic detection of lung diseases. The first methodology aimed to extract information from DICOM-format files using Python and the Jupyter Notebook programming environment. The implemented process enables the generation of images in various anatomical planes, using the pixel matrix in Hounsfield units. Additionally, a custom tool was developed for anonymizing and visualizing file information. The techniques employed are independent of the grayscale image generated and are not reliant on the type of window used. The second tool introduces a lung segmentation technique for chest CT images. The segmentation uses the pixel matrix in Hounsfield units to initially identify lung tissue based on its linear X-ray attenuation. Subsequently, the Watershed algorithm based on markers is employed as the primary foundation of the technique. Various morphological operations and transformations on axial slices are applied, resulting in successful lung segmentation.

Research on natural ventilation of a community library related to epidemiological insight by using CFD simulation method

Abstract Spreading through airflow is one of the most common routes for the transmission of viruses, which may result in serious public health problems. To acquire a healthy space for the community library, designing a reasonable ventilation system is a vital measure which can reduce the risk of transmission of the virus effectively. The purpose of this research is to study natural ventilation methods in an epidemiological insight for the design of a healthy community library by using computational fluid dynamics (CFD) software SOLIDWORKS Flow Simulation. Five typical opening types of windows were selected and studied in the research on the ventilation of the yoga room of a community library, with airflow evaluated by the ACH method, so as to search for the most suitable window type to avoid airborne virus transmission. The results acquired by SOLIDWORKS simulation showed that 75%, 90%, and 100% openings corresponding to Jalousie, casement, and double casement window types respectively are optimal in community space. The natural ventilation design by proper windows can be applied in the community library and other public spaces.

Applied Hedge Algebra Approach with Multilingual Large Language Models to Extract Hidden Rules in Datasets for Improvement of Generative AI Applications

Generative AI applications have played an increasingly significant role in real-time tracking applications in many domains including, for example, healthcare, consultancy, dialog boxes (common types of window in a graphical user interface of operating systems), monitoring systems, and emergency response. This paper considers generative AI and presents an approach which combines hedge algebra and a multilingual large language model to find hidden rules in big data for ChatGPT. We present a novel method for extracting natural language knowledge from large datasets by leveraging fuzzy sets and hedge algebra to extract these rules, presented in meta data for ChatGPT and generative AI applications. The proposed model has been developed to minimize the computational and staff costs for medium-sized enterprises which are typically resource and time limited. The proposed model has been designed to automate question–response interactions for rules extracted from large data in a multiplicity of domains. The experimental results show that the proposed model performs well using datasets associated with specific domains in healthcare to validate the effectiveness of the proposed model. The ChatGPT application in case studies of healthcare is tested using datasets for English and Vietnamese languages. In comparative experimental testing, the proposed model outperformed the state of the art, achieving in the range of 96.70–97.50% performance using a heart dataset.

The effect of early postnatal auditory stimulation on outcomes in preterm infants.

Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both of which may affect postnatal brain maturation. Therefore, fostering a nurturing postnatal auditory environment during hospitalization may have a significant impact on related outcomes of preterm infants. Studies using a range of postnatal auditory stimulations have suggested that exposure to sounds or lack thereof can have a significant impact on outcomes. However, studies are inconsistent with sound levels, duration of exposure to auditory stimuli, and the gestational age at which infants are exposed. IMPACT: Auditory stimulation can provide a low cost and low risk intervention to stabilize respiration, improve neuronal maturation and reduce long-term sequelae in preterm infants. The potential benefits of auditory stimulation are dependent on the type of sound, the duration of exposure and age at time of exposure. Future studies should focus on the optimal type and duration of sound exposure and postnatal developmental window to improve outcomes.

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

Evaluation of cooling energy saving and ventilation renovations in office buildings by combining bioclimatic design strategies with CFD-BEM coupled simulation

Bioclimatic design knowledge and measures, including passive techniques and natural ventilation cooling, are urgently for building renovation to mitigate global warming. The performance of indoor airflow, energy, economic, and environmental aspects has been researched separately. However, developing a comprehensive framework to investigate bioclimatic strategies under the impact of climate change is challenging. This study aims to integrate bioclimatic design strategies with a coupled simulation approach using Fluent and EnergyPlus to evaluate retrofitting techniques for a single-room office in a university building in a hot summer and cold winter climate. A bioclimatic analysis was conducted to recommend cost-effective passive cooling strategies, including natural ventilation and window shading systems. Two passive measures, changing window types and furniture layouts, were included in renovation packages. Results indicated that the empty model is not valid compared to the fully furnished model when calculating room air change per hour (ACH). Natural ventilation was the most cost-effective strategy to mitigate climate change, with a net present value (NPV) ranging from USD 135.09 to 402.81 over 30 years. However, a higher ACH did not ensure a longer natural ventilation period. By changing the top-hung window to a vertical sash and implementing a movable furniture layout, can achieve better ventilation efficiency. The installation of window vertical shadings had the highest annual emission savings by 2080, while it performed negative economic viability in both the present and the 2050 climates. This research demonstrated how combining bioclimatic measures with a co-simulation approach can offer a thorough evaluation of building energy retrofitting. The results could support architectural and engineering design by integrating bioclimate measures to identify the most effective strategies.

Successful one-stage surgical repair in a rare adult patient with berry syndrome.

Berry syndrome is a rare congenital cardiac malformation, herein we report an adult male patient who was successfully repaired by one-stage surgery. 18-year-old male patient presenting symptoms of chest tightness and shortness for over a year presented to outpatient clinic in our department to have corrective procedure heart. CTA revealed an Aortopulmonary Window (APW) type III, measuring 4.6 cm in maximum diameter. The right pulmonary artery originated from the ascending aorta, and the pulmonary trunk exhibited dilation with dimensions of 8.3 cm × 5.7 cm × 5.9 cm. Additional findings included Interrupted Aortic Arch (IAA) type A, intact ventricular septum, and Patent Ductus Arteriosus (PDA). Echocardiography showed bidirectional shunt at the level of APW in severe pulmonary hypertension. The right heart catheterization indicated a mean pulmonary artery pressure of 70mmHg and a pulmonary artery resistance of 5 Wood units. We evaluated after two weeks of treatment with epoprostenol at a rate of 20 ng/(kg. min) and found a significant improvement in pulmonary-artery pressure. Finally, we communicated with the patient's family and decided to proceed with the procedure. For complex cardiovascular malformations, the ideal treatment strategy must be tailored to the characteristics of the patient to provide maximum efficacy and safety.

Karst hyporheic zone and its aquatic environmental effects: a preliminary study in South China

ABSTRACT Groundwater (GW)–surface water (SW) interactions profoundly impact aquatic systems. In karst regions characterized by the coexistence of various media, the hyporheic zone (HZ) remains poorly understood. This study explores the HZ by examining karst development and various hydrological, hydrochemical, and biological processes. The research reveals high development of karst and difference in water environment may suggest a diversity of water sources beyond GW, leading to the formation of a karst hyporheic zone (KHZ). Four common KHZ types are proposed based on karst development, with a focus on spring outlet and karst window types. Interactions in these zones are driven by river backflow and GW abstraction in high- and low-water periods, respectively. KHZs may have water quality purification capabilities, but further research on biogeochemical processes is needed. This study provides preliminary insight into KHZs in karst water systems and suggests key scientific questions for future research in South China.

Energy and economic performance optimization of a window with variable transparency shape-stabilized PCM in hot summer and cold winter climate zone

Improving the thermal performance of windows is important for building energy efficiency. Filling windows with variable transparency shape-stabilized phase change materials (VTSS-PCM) improves the thermal inertia of windows while avoiding the leakage of PCM. In this paper, a new type of VTSS-PCM window was proposed, tested, simulated and optimized in hot summer and cold winter climate zone. A numerical model of the VTSS-PCM window was built, and the model was validated using experimental tests. On this basis, three key parameters of VTSS-PCM were investigated and optimized. Finally, the energy and economic performance of the optimized VTSS-PCM window were compared with a typical hollow glass window. The results showed that the total annual unfavourable heat transfer (TAHT) and the annual investment cost (AIC) of the VTSS-PCM window obtained from the optimization were 118.16 kWh/m2 and 8.53 CNY/m2, respectively. Compared with the hollow glass window, the VTSS-PCM window reduced the TAHT by 30.14% and the total annual cost by 28.39%. The VTSS-PCM window produced a better development potential in terms of energy performance and economic performance. This study provided a reference for the application of the VTSS-PCM window in hot summer and cold winter regions of China.

Experimental investigation of a photovoltaic solar air conditioning system and comparison with conventional unit in the context of the state of Piaui, Brazil

Globally the building sector is responsible for a big share of electric energy consumption and emissions. To alleviate these impacts it is necessary to improve the energy efficiency besides implementing renewable energy sources for building sector sustainability. PV panels for generating electric energy are most preferred by the sector. In the present study, two similar rooms were prepared one with a conventional air conditioner of the window type powered from the grid and the other is powered from PV panels. The heat load was calculated and used to define the conventional air conditioner and also to define the area of photovoltaic solar panels and the associated equipments to install a PV circuit for supplying electric energy to the PV air conditioner. Both rooms were equipped with calibrated instrumentation including thermocouples to measure the temperature distribution in rooms as well as electric energy consumption during the experiments. Ambient temperature, wind speed, temperature in the vicinity of the photovoltaic panels, humidity, solar radiation, voltage, current, flow of energy consumed by the systems were measured and registered by data acquisition system. Experiments were carried out with the systems working simultaneously at pre-defined times. The solar PV-based air conditioner consumed approximately 342 kWh during 30 days of experiments, while the air conditioner connected to the grid, consumed about 330 kWh, which is 5 % less than the solar PV air conditioner confirming that solar PV air conditioner is viable. The COP for the solar-based air conditioner is about 2.6 and the COP for conventional air conditioner is 2.9. The incident solar radiation is high and stable in Teresina, Piauí, Brazil which ensures viability of the solar PV air conditioner. The power supply from CC showed better quality and costs an AC system, and that PV System can save about $700.00 in energy consumption and the payback occurs in 3.7 years.

Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

This study explores the behavior of machine-learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set, we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, and 120) and temporal sizes (5, 8, 11, 14, 17, and 20 months) were tested. To our surprise, we found that, for a window of 20 months, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Finally, a moderately strong positive correlation was found to exist between a model’s false-positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting.

Balancing ensemble averaging and signal stationarity when processing acoustic data recorded during a rocket launch

There are many important details to consider when collecting acoustical data during a rocket launch including system noise floor, dynamic range and transducer sensitivity. In this talk, the choice of signal processing parameters will also be shown to be important. Since the pressures are created by turbulence, ensemble averaging is key to reducing random variation. As such, the use of a Hanning window with 50% overlap would be customary for performing this type of processing. However, as the rocket lifts off, the source location moves, and the data can only be considered stationary for short blocks of time. Therefore, the ability to average is limited by non-stationarity. To balance these competing phenomena, an adjustable windowing function such as the Tukey window may be more appropriate than Hanning. Noise data collected at two different launches will be used to compare of the Hanning window with 50% overlap to that using of the Tukey window with a taper of α = 0.25, which filters 12.5% of the signal at each extremity, with 87.5% overlap. The use of both window types will be compared when estimating auto spectral densities and integrated overall levels. The effect of blocksize will also be determined.

Decreased dynamic variability of the cerebellum in the euthymic patients with bipolar disorder

BackgroundBipolar disorder (BD) is a complex mental illness characterized by different mood states, including depression, mania/hypomania, and euthymia. This study aimed to comprehensively evaluate dynamic changes in intrinsic brain activity by using dynamic fractional amplitude of low-frequency fluctuations (dfALFF) and dynamic degree centrality (dDC) in patients with BD euthymia or depression and healthy individuals.MethodsThe resting-state functional magnetic resonance imaging data were analyzed from 37 euthymic and 28 depressed patients with BD, as well as 85 healthy individuals. Using the sliding-window method, the dfALFF and dDC were calculated for each participant. These values were compared between the 3 groups using one-way analysis of variance (ANOVA). Additional analyses were conducted using different window lengths, step width, and window type to ensure the reliability of the results.ResultsThe euthymic group showed significantly lower dfALFF and dDC values of the left and right cerebellum posterior lobe compared with the depressed and control groups (cluster level PFWE < 0.05), while the latter two groups were comparable. Brain regions showing significant group differences in the dfALFF analysis overlapped with those with significant differences in the dDC analysis. These results were consistent across different window lengths, step width, and window type.ConclusionsThese findings suggested that patients with euthymic BD exhibit less flexibility of temporal functional activities in the cerebellum posterior lobes compared to either depressed patients or healthy individuals. These results could contribute to the development of neuropathological models of BD, ultimately leading to improved diagnosis and treatment of this complex illness.

Exponential synchronization of nonlinear dynamical systems via an aperiodic hybrid control with actuator saturation and impulse time windows

AbstractConsidering impulse time windows and the actuator saturation, we put forward a novel aperiodic hybrid control scheme to achieve the exponential synchronization of two nonlinear dynamical systems (NDSs). Different from the existing research, the actuator saturation of two types of control signals and impulse time windows are both considered in the exponential synchronization analysis. Some sufficient conditions are found with designing the hybrid controller, and the attraction domain (AD) of the error system is also estimated. On the other hand, the conditions which can ensure the exponential synchronization between two NDSs with this hybrid control without saturation limit are also given. Finally, experimental results for the synchronization of three dimensional Hopfield‐type neural networks and the image reproduction based on the synchronization of neural networks demonstrate the feasibility of our theoretical results.