Proper Accounting is Vital for Sustainable Business Growth

This study explains the role of accounting in business growth. In addition, this study clarifies how accounting offers support for business process. This paper demonstrates the types of services that perform by accounting. The research indicates how accounting information can be used in order to meet the needs of a business, make right decisions, and improve the company’s profitability. This article also examines why business organization often needs a way to keep score when conducting business operations. How accounting usually fits this need because it allows to company to create financial reports that enable business owners and managers to review the efficiency of operations. The conclusion of this study shows the importance of using accounting as a sophisticated financial management system for business organization's performance, growth, and expansion

Factors Impacting Employee Job Satisfaction

This study explains the factors that have an affect on job satisfaction between employees within an organization. In addition, this study clarifies the relationship between the work environment and job satisfaction within an organization. This paper demonstrates the importance of attaining job satisfaction through creating a positive workplace. Also, the research illustrates the role of job satisfaction in an organization's performance. The conclusion of this study shows that working conditions, salary and compensations, fairness, respect from co-workers, and the relationship with supervisors have an overall impact on job satisfaction amongst an organization's employees

Investigating the impact of adaptive facades on energy performance using simulation and machine learning

Buildings consume approximately 40% of the world's primary energy, and half of this energy demand stems from space cooling and heating. To meet the targets of designing high performance buildings, intelligent solutions need to be integrated into the design process of buildings to achieve indoor environmental comfort and minimize energy consumption. In particular, the building façade plays a crucial role, as it acts as a separator element that can control the indoor environment and energy performance. This is even more important in buildings with extensive glazing systems particularly in harsh, hot climates. As stated in the literature, buildings are exposed to dynamic environmental factors that change continuously throughout the day and the year. Nonetheless, regardless of the climatic variations, building skins have been typically designed as static envelopes, which are limited in terms of their responsiveness to indoor or outdoor environmental conditions. In contrast, adaptive façades (AFs) are flexible regarding the adaptability of the system to climatic conditions enabling them to respond to short-term changes in the environment. In practice, assessing the performance of AFs during the early stages of the design is still a challenging task due to their time-varying dynamic behaviour. Most current building performance tools (BPS) were originally developed to assess fixed façades where changes to the geometry of the façade are not taken into consideration during simulation. To that end, adaptive systems require a more complex workflow that can correctly predict their performance. This research is intended to assist architects and façade specialists in two main aspects; firstly, an algorithmic framework was developed to predict the energy performance of AFs in the early design stages. The algorithmic workflow creates a link between plug-ins including the Ladybug and Honeybee tools, and EnergyPlus for running the simulation with the built-in tool energy management system (EMS) to program a code to actuate the AF system in an hourly time step (Figure 1). The workflow considers the time-varying dynamic behaviour of AFs based on different environmental parameters. The aim is to accurately evaluate the potential of AFs in energy performance in an office tower. Secondly, by exploring the complexity and limitation of current tools, a novel method is proposed to assess the energy performance of AFs using machine learning (ML) techniques. Two different ML models, namely, an artificial neural network (ANN) and a decision tree (DT), were developed to predict the energy performance of AFs in the early design stages in a significantly faster time compared to simulation. The surrogate models were trained, tested, and validated using the generated synthetic database by simulation (hourly cooling loads of AF and hourly solar radiation). During the training phase, a hyperparameters tuning procedure was carried out to select the most suitable surrogate model (Figure 2). By comparing the static shading system with AFs in terms of energy consumption, the results confirmed that the AFs were more effective in terms of cooling load reductions compared to static façades where cooling loads were reduced by 34.6%. The findings also revealed that the control scenario that triggered both incident solar radiation and operative temperature in a closed loop mechanism performed better than other control scenarios. Regarding the surrogate models, this research found that ML techniques can predict the hourly cooling loads of AFs with a high accuracy in the range of 85% to 95%. In particular, the DT model showed a 17% improvement in R2 accuracy over the ANN model in predicting the hourly cooling loads of AFs

Predicting cooling energy demands of adaptive facades using artificial neural network

Adaptive Façades (AFs) have proven to be effective as a building envelope that can enhance energy effi- ciency and thermal comfort. However, evaluating the performance of these AFs using the current building performance simulation (BPS) tools is complex, time-consuming, and computationally intensive. These limitations can be overcome by using a machine learning (ML) model as a method to assess the AF system efficiently during the early design stage. This study presents an alternative approach using an Artificial Neural Network (ANN) model that can predict the hourly cooling loads of AF in significantly less time compared to BPS. To construct the model, a generative parametric simulation of office tower spaces with an AF shading system were simulated in terms of energy consumption using Honeybee add-on in Grass- hopper which are linked to EnergyPlus for training the ANN model. The prediction results showed a highly accurate model that can estimate cooling loads within seconds

Building energy efficiency estimations with random forest for single and multi-zones

Surrogate models (SM) present an opportunity for rapid assessment of a building's performance, surpassing the pace of simulation-based methods. Setting up a simulation for a single concept involves defining numerous parameters, disrupting the architect's creative flow due to extended simulation run times. Therefore, this research explores integrating building energy analysis with advanced machine learning techniques to predict heating and cooling loads (KWh/m2) for single and multi-zones in buildings. To generate the dataset, the study adopts a parametric generative workflow, building upon Chou and Bui's (2014) methodology. This dataset encompasses multiple building forms, each with unique topological connections and attributes, ensuring a thorough analysis across varied building scenarios. These scenarios undergo thermal simulation to generate data for machine learning analysis. The study primarily utilizes Random Forest (RF) as a new technique to estimate the heating and cooling loads in buildings, a critical factor in building energy efficiency. Following that, A random search approach is utilized to optimize the hyperparameters, enhancing the robustness and accuracy of the machine learning models employed later in the research. The RF algorithms demonstrate high performance in predicting heating and cooling loads (KWh/m2), contributing to enhanced building energy efficiency. The study underscores the potential of machine learning in optimizing building designs for energy efficiency

Predicting incident solar radiation on building’s envelope using machine learning

The assessment of the impact of solar radiation on building envelopes has typically been achieved by using simulation software, which is time consuming and requires advanced computational knowledge. Given the increased complexity of large scale-projects and the demand for performative buildings, new innovative methods are required to assess the design efficiently. In this paper, we present an alternative and innovative approach to assessing solar radiation intensity on an office building envelope using two machine-learning (ML) models: Artificial Neural Network (ANN) and Decision Tree (DT). The experimental workflow of this paper consists of two stages. In the first stage, a generative parametric office tower and its urban context were designed and simulated using Grasshopper software to create a large synthetic dataset of the solar radiation that strikes the office room envelope with several types of analyses. In the second stage, the generated datasets were imported into two ML algorithms (ANN and DT) to create a model for training and testing. The comparison of these two ML models proved that input data types have a significant impact on the accuracy of the prediction and model selection. DT was found to be more accurate than ANN because the data is mostly categorical, which is the most suitable learning background for DT algorithms

Generation of a large synthetic database of office tower’s energy demand using simulation and machine learning

Machine learning (ML) has proven to be an effective technique serving as a predictive surrogate model for evaluating the performance of buildings. This approach provides considerable benefits such as reduced processing time, simplified predictions and computational efficiency. This study presents an alternative approach using a decision tree (DT) model to predict the hourly cooling loads of adaptive façade (AF) in significantly less time than when applying building performance simulation (BPS). Due to the absence of real-world data, generative parametric modelling of a prototypical office tower with an adaptive façade shading system situated in an urban setting was carried out along with simulation of its energy demand using the Honeybee add-on for Rhino/Grasshopper software. The generated large synthetic datasets were fed in so as to train and test the decision tree model. The prediction results revealed an extremely accurate model capable of estimating cooling loads in a matter of seconds. The paper concludes by arguing that decision tree surrogate models can be effectively used by researchers and designers to assess their future adaptive façade design

Safety of prolonged use of metoclopramide and domperidone as treatment for chronic gastrointestinal dysmotility disorders in patients with systemic sclerosis

Background: Metoclopramide and domperidone are prokinetic agents commonly used to treat gastrointestinal dysmotility disorders. This study aimed to evaluate the safety and associated side effects of prolonged-use metoclopramide and domperidone as treatment for chronic gastrointestinal dysmotility disorders in patients with systemic sclerosis (SSc). Methods: A quantitative observational survey was conducted by interview questionnaire in rheumatology outpatients at a tertiary teaching hospital in Riyadh, Saudi Arabia. The study included all patients aged 25–80 years diagnosed with SSc. All patients were on metoclopramide or domperidone for the treatment of chronic gastrointestinal dysmotility symptoms over at least 12 weeks. Results: Eighteen eligible patients were included. Most study participants were diagnosed with SSc complicated by interstitial lung disease (n = 13; 72.2 %). The most frequently reported side effect that occurred while taking prokinetic drugs was shortness of breath (n = 12; 66.7 %). None of the participants reported experiencing depression, galactorrhea, or syncope. CNS side effects were reported in 5.6 %. There were no differences in side effects based on the type and dosage of prokinetic drug used. Conclusions: Use of metoclopramide and domperidone for the treatment of chronic gastrointestinal dysmotility in SSc patients for 12 weeks or longer was not associated with any troublesome side effects. Further studies with more participants are needed to confirm our findings