Window Type Research Articles

Deep Spatio-Temporal Attention-Based Recurrent Network From Dynamic Adaptive Functional Connectivity for MCI Identification.

Most existing methods of constructing dynamic functional connectivity (dFC) network obtain the connectivity strength via the sliding window correlation (SWC) method, which estimates the connectivity strength at each time segment, rather than at each time point, and thus is difficult to produce accurate dFC network due to the influence of the window type and window width. Furthermore, the deep learning methods may not capture the discriminative spatio-temporal information that is closely related to disease, thus impacting the performance of mild cognitive impairment (MCI) identification. In this paper, a novel spatio-temporal attention-based bidirectional gated recurrent unit (STA-BiGRU) network is proposed to extract inherent spatio-temporal information from a dynamic adaptive functional connectivity (dAFC) network for MCI diagnosis. Specifically, we adopt a group lasso-based Kalman filter algorithm to obtain the dAFC network with more accurate connectivity strength at each time step. Then a spatial attention module with self-attention and a temporal attention module with multiple temporal attention vectors are incorporated into the BiGRU network to extract more discriminative disease-related spatio-temporal information. Finally, the spatio-temporal regularizations are employed to better guide the attention learning of STA-BiGRU network to enhance the robustness of the deep network. Experimental results show that the proposed framework achieves mean accuracies of 90.2%, 90.0%, and 81.5%, respectively, for three MCI classification tasks. This study provides a more effective deep spatio-temporal attention-based recurrent network and obtains good performance and interpretability of deep learning for psychiatry diagnosis research.

SMART BUILDING ENERGY MODEL USING ARTIFICIAL INTELLIGENCE

This paper presents a Smart Building Energy Model of Residential Building using Artificial Neural Network model (ANN) to assist architects and engineers in selecting the optimum alternative design of building envelope parameters such that external wall and roof insulation material types and window types that minimizes the cost of energy consumption of a residential building to transform it to a green building.

VARIATIONS IN ACCESSIBILITY OF ROUND WINDOW VIA POSTERIOR TYMPANOTOMY APPROACH IN COCHLEAR IMPLANT SURGERY

Objective: To apply the St Thomas’ Hospital (STH) classification of round window type, in a Pakistani pediatric population undergoing cochlear implantation, and rate the inter observer variability of applying this classification.
 Study Design: Cross sectional study.
 Place and Duration of Study: Combined Military Hospital Rawalpindi, from Apr 2019 to Dec 2020.
 Methodology: Patients were examined per-operatively by a panel of four surgeons after "optimal" posterior tympanotomy for round window variations, as per STH classification of approachability of RWM. The observations of the four surgeons were recorded and interobserver variation was assessed and analyzed.
 Results: A total of 100 patients were operated, 45 females and 55 males. Mean age was 3.8 years. There was minimal inter observer variability with regards to round window type and extent of "optimal" posterior tympanotomy. Three patients had type I, 76 had type IIA, 15 had type IIB and 6 patients had type III. Round window insertion/membranous cochleostomy was possible in 70 patients, whereas the rest require extended round window approach or bony cochleostomy.
 Conclusion: The STH classification is a useful predictor of route of CI electrode insertion and most patients can undergo RW insertion with confidence based on minimal variation between surgeons when applying the STH classification as well as when deciding the extent of surgical exposure.

Exploring the impact of balconies on cooling energy demand in an arid climate zone

Abstract This paper explores the impact of balconies on the energy demand required for cooling in the arid climate zone of the city of Adrar, in Algeria. For the purpose to assess several situations of the balconies, we have chosen a parametric method based on a campaign of thermal simulations. The open and eliminated balcony type were selected and characterized by four parameters: the balcony to room ratio, the orientation, the window type, and the balcony position. A set of 100 simulations was selected randomly based on the Monte-Carlo probability technique. The final sample was corrected based on Cook’s distance which gave 85 simulations as a final sample size. A generalized regression model was performed to identify the impact of each parameter. The accuracy of the model is above 97% and the sensitivity analysis shows that the most important factor is the balcony to room ratio which could reduce the energy demand up to 26% followed by the window type (24%), the orientation (8%) and the balcony position (5%). This conclusion stresses the idea of considering the balcony as a passive solution to reduce the cooling energy demand.

Impacts of thermo-optical properties on the seasonal operation of thermochromic smart window

A type of smart window using thermochromic glazing (TCG) is a promising technology for green buildings owing to the self-regulating feature and low-maintenance need. Its most important feature, thermo-optical properties that regulate the blockage of solar heat, is directly linked to the variation of surface temperatures. However, challenges from the inhomogeneity of thermo-optical properties, the coupled solar radiation and natural convection, and varying outdoor conditions all seriously hinder the understanding of its mechanism. In this paper, a validated Computational Fluid Dynamics (CFD) model achieves the simulation of inhomogeneous tinting of TCG by defining the thermo-optical properties of each finite volume according to the surface temperature. Solar radiation and natural convection at outdoor, indoor and the cavity are solved to reflect glazing temperature more accurately. The case studies compared six different switching temperatures in the range of 20 ∼ 42.5 °C with a transition gradient of 10 °C. Averaged meteorological data for both summer and winter, sunny days and cloudy days are selected to present realistic climate impacts. The result reveals the overall saving in transmitted solar radiation in summer and heating penalties in winter. It suggests the best switching temperatures for each climate condition. With the seasonal operation, the highest saving in solar heat gain is 20.9% when adopting a switching temperature of 25–35 °C, while the lowest saving can be negative, meaning TCG is not suitable for those climate zones. The proposed evaluation criteria help to quantify the applicability of TCG with the input of the summer/winter day ratio and sunny/cloudy ratio. The best region to apply TCG is where summer days are longer and winter solar radiation is significantly lower. The in-depth understanding of this temperature-sensitive process benefits the optimization of TCG in buildings, especially for its seasonal operation needs.

An unusual case of aortopulmonary window with crossed pulmonary arteries

We report a case of a 4-year-old boy with a distal type of aortopulmonary window with crossed arrangement of the pulmonary arteries. This case highlights the potential clinical implications of this variant pulmonary arterial anatomy in the setting of the aortopulmonary window and the role of computed tomography angiography in accurately defining vascular relationships in patients with complex congenital cardiac defects.

How will future climate impact the design and performance of nearly zero energy buildings (NZEBs)?

Climate change, driven by greenhouse gas emissions, is a growing global concern, threatening world-wide environment, health and economy. Energy needs for buildings are a large source of greenhouse gas emissions. As the energy needs of buildings strongly depends on weather patterns, this paper investigates how climate change may impact building heating and cooling loads, cost-optimal efficiency measures, and renewable energy production. Eight locations (Stockholm, Milan, Vienna, Madrid, Paris, Munich, Lisbon, and Rome) highlight differences among European climates. Weather datasets, commonly used in building energy simulations, are evaluated to see how climatic parameters have changed over recent decades. A future climate change scenario (with uncertainties) is analyzed for the year 2060. Weather files are used to drive building energy simulations for a standard baseline and a (Nearly Zero Energy Building) NZEB residential building whose design is improved using a cost-optimization approach.The analysis indicates most currently available weather datasets cannot assure reliable results with building simulations. We find the energy balance in European buildings will significantly change under future conditions: heating will decrease by 38%–57%, while cooling will increase by +99%–380% depending on location. In future NZEBs, efficiency measures to reduce cooling needs and overheating will be favored (e.g. roof insulation, window type, solar shading, envelope finishes), illustrating how improving energy efficiency will be more crucial within climate change scenarios. Compared to the baseline, more efficient NZEBs will enable renewable energy to much better cover building needs. There will also be advantages from reducing winter and summer peak demand, particularly when coupled to short-term electrical storage. When solar resource is limited in winter, more airtight, better-insulated NZEBs improve PV self-consumption.

Natural Ventilation Performance for Schools During a Pandemic and the Post-Pandemic COVID 19

Natural ventilation is applied to most educational buildings in Indonesia. Natural ventilation is one of the strategies to achieve the physical comfort of residents and an effort to save energy. During a pandemic, natural ventilation requires because it is expected to make the air circulation in the room. The presence of good air circulation will make the spread of the virus can be avoided. One indicator of the success of space for good and healthy air circulation is the achievement of Air Change per Hours (ACH) values according to standards. The study aims to find out and identify the performance of natural ventilation as an element that determines indoor air circulation against the spread of the covid 19 viruses, comfortable air velocity in a room and user capacity. The study used simulation methods to determine the value of ACH and air velocity. The research main goal was to look at the possibility of reopening schools as safely and quickly as possible. The results showed that the school buildings studied could be used for Pandemic and postpandemic times, indicated by ACH grades > 8 and air velocity in room 0.2-0.8 m/s. The building has an opening area of approximately 30% with an opening type that is able 45% wind flow. With the simulation obtained, to achieve health and comfort standards with 50% users, with certain air velocity conditions, the minimum opening area is 20% with 45% aperture type and 90% type.Keywords: ACH; natural ventilation; performance; window types

Natural Ventilation Performance for Schools During a Pandemic and the Post-Pandemic COVID 19

Natural ventilation is applied to most educational buildings in Indonesia. Natural ventilation is one of the strategies to achieve the physical comfort of residents and an effort to save energy. During a pandemic, natural ventilation requires because it is expected to make the air circulation in the room. The presence of good air circulation will make the spread of the virus can be avoided. One indicator of the success of space for good and healthy air circulation is the achievement of Air Change per Hours (ACH) values according to standards. The study aims to find out and identify the performance of natural ventilation as an element that determines indoor air circulation against the spread of the covid 19 viruses, comfortable air velocity in a room and user capacity. The study used simulation methods to determine the value of ACH and air velocity. The research main goal was to look at the possibility of reopening schools as safely and quickly as possible. The results showed that the school buildings studied could be used for Pandemic and postpandemic times, indicated by ACH grades > 8 and air velocity in room 0.2-0.8 m/s. The building has an opening area of approximately 30% with an opening type that is able 45% wind flow. With the simulation obtained, to achieve health and comfort standards with 50% users, with certain air velocity conditions, the minimum opening area is 20% with 45% aperture type and 90% type.Keywords: ACH; natural ventilation; performance; window types

Towards window state detection using image processing in residential and office building facades

Although building energy simulation is an essential tool for designers, numerous studies have proven the existence of a performance gap between the estimated and measured energy use. In response, researchers are exploring ways to collect authentic behavioural data to improve existing inaccurate data-driven occupancy behaviour models. An important occupant behaviour currently being studied is window operation behaviour as it results in large consequences on heating/cooling loads and ventilation rates of a building. Ex-situ camera-based window operation monitoring has been proposed as a solution. This study used image processing technologies that automatically identified windows on a façade and determined their individual state (i.e. open, partially open, or closed). The algorithm developed through this study yielded an 89% accuracy rate over all the windows tested. This algorithm was developed to specifically target punched façades with awning windows. Factors that affected the accuracy of ex-situ camera-based window operation monitoring included environmental conditions such as lighting, obstructions, and reflections. Furthermore, there were challenges in determining threshold values used to isolate important image data that defined the window state and location and identifying the significant peaks for window angle image data. The next steps for this research should determine appropriate threshold values that can be used universally through additional testing and to explore new image processing techniques for other window types.

The window opening behavior of infant families: A case study during transition season in the cold region of China

Window opening behavior is a common and effective way for occupants to improve indoor thermal comfort and air quality. There are many studies on the window opening behavior in residential buildings, but research on infant families is still lacking. This paper examines the window opening behavior of four infant families in Beijing and Tianjin that are located in a cold region of China during the transition season. The Kendall Tau coefficient is used to measure the correlation between behavior and environmental factors for the first time. Two types of windows opening behavior were found: i) related to environmental factors and ii) independent of environmental factors. The study reveals that the window opening probability in the living room of infant families is 70.6%, and in the bedroom is 57.2%, which is obviously higher than the existing research on ordinary residents (24.3 ∼ 43.2%). The window opening probability of infant families shows a more obvious downward trend with increasing outdoor PM2.5 concentration compared to ordinary families. Non-environmental factors, such as time of day and age of infant, also affect behavior. Based on the proposed concept of indoor CO2 driving strength (DSCO2_In), it was concluded that the variation of the indoor CO2 concentration is the result of the window opening behavior rather than a driving factor. The logistic regression model shows that outdoor temperature and PM2.5 concentration are the most important factors. This study can help us gain a thorough understanding of the window opening behavior in infant families and it can also provide data and theoretical support for of the simulation of energy consumption in infant families.

Design of a Robotic Arm Window Cleaner at the Heights to Avoid Accidents Due to Falls

Working at height is extremely dangerous because there is not much safety for the worker, for that reason there are accidents of falls from scaffolding. According to the Ministerio de Trabajo y Promoción de Empleo (MTPE) 5.49% are accidents caused in work at heights. It is important to bear in mind that the higher the height there is more possibility of suffering a fall, therefore, there are many factors in the height, the main factor is the air since it does not allow the scaffold to remain still. That is why, this research work proposes the design of a robotic arm with 5 degrees of freedom with Arduino Mega 2560 and a link with Radio Frequency (RF), to replace the worker and reduce the risk of accident, also consider that the robotic arm will be manipulated wirelessly, being ideal to prevent a fall of personnel and controllable from a safe area. It was obtained as a result, that the robotic arm with 5 degrees of freedom is more than enough to perform a cleaning of three types of windows, and the programming for the robotic arm is quite easy and can be programmed very quickly, in addition the SolidWorks software helps to design and layout the robotic arm.

On the invariance of energy influential design parameters in a cold climate – a meta-level sensitivity analysis based on the energy, economy, and building characteristics

ABSTRACT Building performance simulation (BPS) applied at earlier stages of design has the potential to assist design parameter decisions that significantly impact a building’s life cycle. Since at such early stages, numerous design variables are still undetermined (e.g. window type, insulations, etc.) the scenarios to be simulated through BPS will be vast and will require extensive time and computational power. Previous studies have tested the sensitivity of a building’s energy consumption during its operation, to design parameters. Most of those studies, however, have used a single case study in their analysis. Thus, the objective of this paper is to evaluate the dependency of results of such sensitivity analyses, on the case study being used. To accomplish that, a hybrid method combining one-parameter-at-a-time (OAT) and global samplings was used. Within the cold climate scope of Québec, Canada, multiple buildings were used to investigate the sensitivity of energy and economy performance to design parameters (architectural, electrical, and mechanical systems); as well as the sensitivity of parameters’ impact on building models. Results indicate that architectural and electrical parameters are sensitive to the model. To expand on the understanding of the root cause of this sensitive behaviour, hypotheses were developed and evaluated through global sampling.

Ventilation Characteristics and Performance Evaluation of Different Window-Opening Forms in a Typical Office Room

As most existing office buildings in China lack fresh air systems for ventilation, natural ventilation with windows remains the main means of improving indoor air quality and adjusting indoor thermal comfort. However, knowledge of the ventilation characteristics of various window-opening forms in actual buildings is limited and current methods for evaluating ventilation performance lack a comprehensive consideration of ventilation rate and thermal comfort. In this study, the ventilation characteristics of different window-opening forms were systematically compared by conducting computational fluid dynamics (CFD) simulations. A full-scale experiment was conducted in a typical office room in a university in Tianjin to validate the CFD simulation. Two ventilation modes (wind-driven cross-ventilation and temperature-driven single-sided ventilation), three window-opening angles, and seven window types were investigated. Additionally, the ratio of the ventilation rate to the absolute value of thermal sensation was used to quantify the indoor natural-ventilation performance. The results showed that a sliding window with a full opening has the highest discharge coefficients of 0.68 and 0.52 under wind-driven cross-ventilation and temperature-driven single-sided ventilation, respectively, and top-hung windows opening both inwards and outwards have better ventilation performance than other window types under the two ventilation modes. This study is applicable to the design and practice of natural ventilation.

PQLyCar: Peptide-based dynamic query-driven sample rescaling strategy for identifying carboxylation sites combined with KNN and SVM

Lysine carboxylation is one of the most crucial type of post-translation modification, which plays a significant role in catalytic mechanisms. Therefore, it is essential to study lysine carboxylation and explore its biological mechanism. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are much more convenience and faster. Therefore, it is urgent to establish an accurate carboxylation identification model. Herein we proposed a method, named pQLyCar for identification of lysine carboxylation using SVM as classifier. In pQLyCar, a peptide-based dynamic query-driven sample rescaling strategy (pDQD-SR) is proposed to address the class imbalance of training data, which builds a specific prediction model for each query sample. KNN algorithm calculates distance between samples according to original sequences instead of feature vectors. Information entropy is applied to select optimal size of sliding window and various types of sequence- and position-based features are incorporated for construction of feature space, including residues composition (RC), K-space and position-special amino acid propensity (PSAAP). Finally, the performance of pQLyCar is measured with a specificity of 96.49% and a sensibility of 99.59% using jackknife test method, which indicated that pQLyCar method can be a useful tool for prediction of lysine carboxylation sites.

Resonance structures in kink-antikink collisions in a deformed sine-Gordon model

We study kink-antikink collisions in a model which interpolates smoothly between the completely integrable sine-Gordon theory, the ϕ4 model, and a ϕ6-like model with three degenerate vacua. We find a rich variety of behaviours, including integrability breaking, resonance windows with increasingly irregular patterns, and new types of windows near the ϕ6-like regime. False vacua, extra kink modes and kink fragmentation play important roles in the explanations of these phenomena. Our numerical studies are backed up by detailed analytical considerations.

Abstract P122: Leveraging Machine Learning Models For Dynamic Prediction Of Bleeding And Ischemic Risk After Drug-eluting Stent Implantation

Background: Contemporary risk scores for ischemic or bleeding endpoint prediction after drug-eluting stent (DES) implantation have limited predictive accuracy and fixed prediction windows. Objective: This study aimed to dynamically predict the ischemic and bleeding risks in different follow-up windows for patients with DES leveraging cutting-edge machine learning models. Methods: Using the Cerner Health Facts, we identified 98,236 adult patients who received DES with more than three months of follow-up after discharge. Three traditional machine learning models (LR, RF, and LGBM), three RNN-based deep learning models (GRU, LSTM, RETAIN), and one transformer-based model (Med-BERT) were applied for the risk stratification. The total follow-up window covered two years after discharge. ML models were tested in three types of prediction windows with varying lengths (i.e., one year, six months, and three months). Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC) and precision-recall curve. Results: For bleeding event prediction, the best AUC for the three-month window was 77.02% (GRU), for the six-month window was 81.11% (BiGRU + MedBert), and for the one-year window is 71.86% (LGBM). For ischemic event prediction, the best AUC for the three-month window was 71.09% (GRU), for the six-month window was 68.97% (LGBM), for the one-year window was 69.41% (LSTM). The transformer-based model and RNN-based models generally performed better than the traditional ML in both bleeding and ischemic risk prediction. Conclusions: Our machine learning model-based approaches for dynamically predicting the ischemic and bleeding risks after stenting is feasible and effective. The best-performing models show good discriminative capabilities in different prediction scenarios and have the potential to guide the clinical decision-making of the optimum DAPT duration in a personalized way.

창호의 단열성능 향상을 위한 고단열 프레임 개발

In order to promote a zero-energy school, it is necessary to improve the energy performance of the building and minimize energy loss. The aim of this study is to develop high-insulation windows to save energy in school buildings. A Insulation block applied to window frames was developed. A property test of the insulation block was performed for 7 items. Simulations and experiments were conducted to evaluate the insulation performance of windows to which insulation blocks were applied. As a result of the window performance evaluation, in the case of the simulation, The U-value of the entire window was improved by 3.08 % in Case 1(window without insulation block) compared to Case 2(window with insulation block). In the case of the physical test of KS F 2278, the U-value of Case 2(window with insulation block) was calculated to be 0.935 W/㎡·K. This window prototype will need to be supplemented. Insulation blocks will be applicable to various types of windows. It will also be effective in reducing building energy.

Analytical investigation into error propagation of power spectral density transmissibility (PSDT) based on coherence function

Power spectral density transmissibility (PSDT) has attracted widespread interests because it is insensitive to the nature of excitation and has no demand for measuring the input. The classical spectral estimation procedure is usually subject to measurement and spectral leakage errors, which might cause distortion in the estimation of PSDT. This study investigates how the error of PSD estimation propagates into PSDT estimation based on analytical approximation formulas. Based on perturbation theory in tandem with statistical moments of the ratio of random variables, both first- and second-order asymptotic expressions of the mean and variance of PSDT estimation are derived in terms of coherence functions, PSDT measurements, and the number of averages. Given these theoretical findings, it is further revealed that the variance of PSDT estimation around the system poles approaches zero, and the variances of the magnitude and phase of the mode shape decrease to local minima. The performance of the analytical error propagation formulas is validated through numerical and field test data. Parametric studies into the effects of recording durations, window types, and reference outputs on the statistics of PSDT estimation and mode shape estimation are also conducted.

Evidence for Transgenerational Effects on Autism Spectrum Disorder Using Multigenerational Space-time Cluster Detection

BACKGROUND AND AIM: Space-time cluster analyses are commonly used in spatial epidemiology. There is evidence of transgenerational factors associated with ASD. Space-time clusters of parents and grandparents of ASD cases may provide evidence of transgenerational effects and generate hypotheses regarding ancestral conditions associated with ASD. The Aim of this study is to identify and describe space-time clusters of ancestors of individuals with a clinical diagnosis of ASD. METHODS: Parents and grandparents of diagnosed ASD individuals were identified using the Utah Registry of Autism Developmental Disabilities (URADD) linked to their pedigrees in the Utah Population Database (UPDB). Inclusion criteria for ASD case ancestors included (1) three generations of continuous Utah residency, and (2) first reported case of ASD in the family. Ancestors of individuals without ASD were selected based on a 2:1 random sample matched by age, time-period, and sex to the ASD cases. Residential addresses of the ancestors for their perinatal, childhood and adolescent time periods were compiled from administrative databases linked to the UPDB and geocoded. Bernoulli space-time scan statistic was used to identify clusters for each type of ancestor and developmental window. RESULTS:The study included 7,914 parents and 15,828 grandparents generated from 3,957 ASD cases. Over 90% of the addresses were geocoded to street address. Twenty statistically significant space-time clusters (p-value 0.05) were identified (13 increased relative risk (RR) and 7 with RR1). The four strongest clusters (RR = 2.74 – 2.96) were from paternal grandparents at the perinatal time-period spanning 1 to 10 years and covering 8 to 132 km2 in predominantly urban residential areas. Of the 13 clusters with RR1 (range=1.27 – 2.96) 11 began between 1946 and 1960, nine were grandparents. CONCLUSIONS:Multiple very high relative risk space-time clusters of ASD ancestors during early life provide evidence of potential transgenerational effects on ASD in grandchildren. KEYWORDS: Space-time clustering, Multigenerational, Autism Spectrum Disorder, Transgenerational Effects