Window System Research Articles

Electrical and thermal performance of multidimensional semi-transparent CdTe PV window on offshore passenger ships in moored and sailing condition

Utilizing solar energy is an effective method to decrease fossil energy consumption in marine field. Horizontally installed deck photovoltaic (PV) systems have been widely investigated to reduce navigation energy consumption on ocean-going ships. However, energy saving rate is unsatisfactory because of the low energy density of solar energy and the shadow caused by ship body. As for large amount of offshore small ships, it's even harder to utilize the existing technology since deck space is limited. In this paper, semi-transparent CdTe PV window system is proposed for offshore passenger ships without enough deck spaces but with large window area around ship cabin. Multidimensional PV windows can increase PV area and reduce heat gain inside the cabin, shadow effect caused by ship body is also avoided. Energy saving can be both achieved by electrical generation actively and cooling load reduction passively. Dynamic mathematical models of multidimensional PV window under moored and sailing conditions are established. Simulation results indicate that the daily electrical generation and average electrical efficiency are 0.74 kWh & 14.65% in moored condition and 0.83 kWh & 16.24% in sailing condition. Navigation leads to higher received solar radiation (18.2%), higher electrical generation (12.1%) and higher electrical efficiency (10.85%). Electricity created by PV window can support 6 h' continuous operation for auxiliary system on board. With PV windows, the average air temperature in cabin is decreased by 6.5 °C compared with ordinary windows. The average heat fluxes through four directions of PV windows are decreased by 75.44%, 73.64%, 47.52% and 75.33% compared with ordinary windows.

Research on Logic Design of Proton Treatment Control System

The proton treatment control system is the supporting software of the proton therapy device, which specifically coordinates and controls the status and work of each subsystem. In this study, the software architecture and hardware implementation of the proton treatment control system was developed and built a foundation for the overall debugging. Using C# programming language and WPF programming techniques, TCP network communication protocol specified by the proton treatment technical document and MVVM pattern in Windows system, the logic design and implementation of each level were studied. Meanwhile, the communication interface between the subsystems under TCP communication protocol was agreed. The logic design and research of the setup field and treatment field were carried out. And the User Interface was designed and developed using the above technology. The program realizes the communication and interaction between the proton treatment control system and each subsystem, so as to control and monitor the whole treatment process. The proton treatment control system provides a software basis for the remote overall debugging and on-line monitor and control of proton treatment device.

API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques

This paper presents API-MalDetect, a new deep learning-based automated framework for detecting malware attacks in Windows systems. The framework uses an NLP-based encoder for API calls and a hybrid automatic feature extractor based on convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRU) to extract features from raw and long sequences of API calls. The proposed framework is designed to detect unseen malware attacks and prevent performance degradation over time or across different rates of exposure to malware by reducing temporal bias and spatial bias during training and testing. Experimental results show that API-MalDetect outperforms existing state-of-the-art malware detection techniques in terms of accuracy, precision, recall, F1-score, and AUC-ROC on different benchmark datasets of API call sequences. These results demonstrate that the ability to automatically identify unique and highly relevant patterns from raw and long sequences of API calls is effective in distinguishing malware attacks from benign activities in Windows systems using the proposed API-MalDetect framework. API-MalDetect is also able to show cybersecurity experts which API calls were most important in malware identification. Furthermore, we make our dataset available to the research community.

Influence of panels size on the static and dynamic performance of laminated glass panels

Laminated glass is made by joining two or more glass layers together using a polymeric interlayer which can significantly minimize the risk of flying shards and dissipate a great deal of energy after the glass cracking stage. In this paper, tensile tests are conducted on three different polymeric films i.e., PVB, EVA, and SentryGlas (SG) at two different strain rates to characterize the mechanical properties of the interlayer. In addition, a static full-scale water chamber was used to study the effect of panel size on the characteristic failure and deflection of the laminated glass windows experimentally. However, due to the high risk and cost of explosion experiments, dynamic modeling of laminated glass window systems is presented in this paper using ANSYS AUTODYN. The accuracy of the dynamic model is verified with previously published experimental data. Material characterization tests showed that SG film has the highest strength, which appeared significantly also in the static panel test and numerical. In addition, the maximum reduction in panel static strength due to size increase was observed in panels with SG and PVB interlayers with a maximum reduction of 58%. However, SG showed a lower dynamic response under blast loads.

Konversi Pada Bank Perkreditan Rakyat (BPR) Menjadi Bank Perkreditan Rakyat Syariah (BPRS) dan Pengaruh Terhadap Kinerja Keuangan Pada PT. BPR Jam Gadang

The Sharia law can work in Indonesia because the Indonesian banking system still adheres to a 2 window system. According to Law No. 21 of 2008 concerning Sharia banking, the 2 window banking principle in Indonesia will end no later than 2023, whereby banks that have been running Sharia business using the Sharia law will be forced to spin off or convert. One of the reasons for the conversion of Conventional Banks into Islamic Banks is the large number of Islamic financial institutions which show enormous potential, as shown by the number of Conventional Banks which have formed sharia divisions. The Islamic banking industry also applies principles that comply with Islamic law and Sharia recommendations. This study aims to see how the Conversion of Rural Banks (BPR) to Sharia Rural Banks (BPRS) and the Effect on the Financial Performance of Jam Gadang. This study uses a qualitative method with a case study approach. With the researcher can get a complete picture of the problems that are formulated by focusing on the process and the search for meaning behind the phenomena that arise in the research process. The method that can be used for this time series analysis is the Least Square Method of linear line equations from time series analysis.

A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort

AbstractThe energy performance of a building has a significant impact on the environment as well as the comfort of its occupants. Given that window systems and control elements such as shading devices play a critical role in a building's energy consumption and comfort, this article presents an investigation into the optimization of windows in four different orientations, along the four cardinal directions, for a typical office room in Tehran. The objective is to reduce cooling, heating, and lighting energy consumption while improving occupant comfort simultaneously, using the NSGA‐II algorithm as a multi‐objective optimization method. To this end, first, an investigation into the effects of various parameters on total building energy consumption and occupant comfort is conducted. Subsequently, each parameter is evaluated in detail concerning its impact on each façade. As the final step, for each façade, the optimal solutions and the most undesirable scenarios are determined. The results, in which the possibility of glare is also considered, can be used by architects and designers for making informed design decisions. The results show that, in all orientations, the number of slats and their distance from the wall are the most effective parameters of shading configurations. Although the eastern and western windows are larger in this study, visual discomfort can be controlled using suitable shadings to achieve acceptable levels of visual comfort. Furthermore, north façades provide the least amount of thermal comfort and consume the least amount of energy while experiencing glare for longer periods.

On the detection of lateral movement through supervised machine learning and an open-source tool to create turnkey datasets from Sysmon logs

Lateral movement (LM) is a principal, increasingly common, tactic in the arsenal of advanced persistent threat (APT) groups and other less or more powerful threat actors. It concerns techniques that enable a cyberattacker, after establishing a foothold, to maintain ongoing access and penetrate further into a network in quest of prized booty. This is done by moving through the infiltrated network and gaining elevated privileges using an assortment of tools. Concentrating on the MS Windows platform, this work provides the first to our knowledge holistic methodology supported by an abundance of experimental results towards the detection of LM via supervised machine learning (ML) techniques. We specifically detail feature selection, data preprocessing, and feature importance processes, and elaborate on the configuration of the ML models used. A plethora of ML techniques are assessed, including 10 base estimators, one ensemble meta-estimator, and five deep learning models. Vis-à-vis the relevant literature, and by considering a highly unbalanced dataset and a multiclass classification problem, we report superior scores in terms of the F1 and AUC metrics, 99.41% and 99.84%, respectively. Last but not least, as a side contribution, we offer a publicly available, open-source tool, which can convert Windows system monitor logs to turnkey datasets, ready to be fed into ML models.

MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing.

In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation.

BugTracker: Software for Tracking and Measuring Arthropod Activity

The automated video tracking of the activity/movement of an experimental organism is essential for reliable, repeatable quantitative analyses in behavioral ecology and also in other disciplines. There are only some open-access, open-source automated tracking software applications that can track unmarked organisms. Moreover, several of these software applications are substantially affected by brightness and differences in the lighting conditions of the video recording. Our Python-based software, called BugTracker, uses the latest innovations in computer vision technologies to solve these problems. By analyzing videos with considerably different lighting conditions with BugTracker and other available software, we demonstrate that our software could reliably track the studied organisms of any size and speed. Additionally, the results provide accurate measures of the organism’s movements. BugTracker is the most reliable currently available, easy-to-use, and automated tracking software compatible with the Windows, Linux, and MacOS operating systems.

Energy Savings in Thermal Insulations for Sustainable Buildings

Energy use is second to staffing in building operating costs. Sustainable technology in the energy sector is based on utilizing renewable sources of energy such as solar, wind, glazing systems, insulation. Other areas of focus include heating, ventilation and air conditioning; novel materials and construction methods; improved sensors and monitoring systems; and advanced simulation tools that can help building designers make more energy efficient choices. The objective of this research is studying the effect of insulations on energy consumption of buildings in Iraq and identifying the amount of energy savings from application the insulations in buildings. HAP (Hourly Analysis Program) is used to calculate the thermal loads and the amount of energy needed. It is concluded that the use of the thermal insulation in the roof, walls, floors, and double glazing system for windows in building effectively reduces the energy required for air conditioning.

Eternal Blue Vulnerability

Abstract: Many organizations have experienced the damage caused by cyberattacks exploiting Windows vulnerabilities. For operational reasons, the parameters of Windows are still used, especially in the enterprise management system (ICS). In this case, attackers can torture them to spread the disease. Specifically, the vulnerability in MS17-010 was used in attacks to spread malware such as WannaCry ransomware and other malware. Many systems for example, electronic newspapers, payment centres and car manufacturers are used around the world and there is a security vulnerability in Windows that causes serious problems. Since tools like Eternal Blue or Eternal Romance are published on the internet, attackers can easily exploit these vulnerabilities. This tool attacks legitimate processes running on Windows systems. It can be difficult for employees to see the signs of a struggle. Attacks can be mitigated using security updates; however, security updates are sometimes difficult to implement due to their long lifetime and stringent requirements. There are many ways to identify attacks that cause vulnerabilities, such as intrusion detection systems (IDS), but they are sometimes difficult to use because they require prior service. In this research, we propose a method to identify the attack that exploited the vulnerability in MS17-010 by analysing Windows built-in event Logs. This method can detect attacks against almost all supported versions of Windows. It can also be easily integrated into the production environment as it only uses the standard Windows operating system.

Comparison of shading design between the northern and southern hemispheres: using the NSGA-II algorithm to reduce building energy consumption and improve occupants' comfort

PurposeThis paper aims to investigate the optimization of window and shading designs to reduce the building energy consumption of a standard office room while improving occupants' comfort in Tehran and Auckland.Design/methodology/approachThe NSGA-II algorithm, as a multi-objective optimization method, is applied in this study. First, a comparison of the effects of each variable on all objectives in both cities is conducted. Afterwards, the optimal solutions and the most undesirable scenarios for each city are presented for architects and decision-makers to select or avoid.FindingsThe results indicate that, in both cities, the number of slats and their distance from the wall are the most influential variables for shading configurations. Additionally, occupants' thermal comfort in Auckland is much better than in Tehran, while the latter city can receive more daylight. Furthermore, the annual energy use in Tehran can be significantly reduced by using a proper shading device and window-to-wall ratio (WWR), while building energy consumption, especially heating, is negligible in Auckland.Originality/valueTo the best of the authors' knowledge, this is the first study that compares the differences in window and shading design between two cities, Tehran and Auckland, with similar latitudes but located in different hemispheres. The outcomes of this study can benefit two groups: firstly, architects and decision-makers can choose an appropriate WWR and shading to enhance building energy efficiency and occupants' comfort. Secondly, researchers who want to study window and shading systems can implement this approach for different climates.

GUIDE: A GUI for automated quantum chemistry calculations.

The accuracy of quantum mechanics (QM) simulations depends heavily on the quality of initial input files. Despite the popularity of QM simulation packages, achieving precise results still heavily relies on the user's proficiency in preparing the QM simulation systems. In this work, we present an easy-to-use tool called GUIDE, a YASARA plugin to assist researchers in quantum chemistry workflow automation using ORCA and MOPAC simulation packages. GUIDE lets users compute complex QM calculation workflows via an automated graphical window system. It allows for a more integrated and streamlined research process, as researchers can easily access all the necessary tools within one software without switching between multiple programs. This tool can save time and increase efficiency in computational chemistry methods. GUIDE is written in Python and is freely available for download at https://github.com/YAMACS-SML/GUIDE. The plugin is released under a GPL-3.0 license and is supported on Windows and Linux.

Thermal performance study of multiple thermal insulating glazings with polycarbonate films as interval layers

With the development of global economy, the proportion of energy consumption in buildings is increasing to approximately 40% total energy consumption. The glazed window system, which is the weakest part in the building envelopes, remarkably contributes to energy loss. Scholars and manufacturers are constantly working to improve the thermal properties of glass, such as Low-E glass, aerogel glass, vacuum glass, and so on. However, the advanced glazing either shades the sunlight in winter, or is too expensive to be widely used. In this paper, polycarbonate films with high transparency are used as the interval layers of double glazing, which not only have better thermal properties and visible light transmittance in winter, but also are less expensive to be used. The structures and thermal properties of multiple thermal insulating glazing with polycarbonate films (MTIGP) are systematically investigated. The U-values of MTIGP can be achieved from 1.5 to 2.0 W/m2·K with high transmittance of visible light (Tvis, 60–70%), when compared with a typical Low-E double glazing system (U-value, around 2.0 W/m2·K; TVis, around 60%), even lower when argon (Ar) or krypton (Kr) is used instead of air. The Solar Heat Gain Coefficient (SHGC) of MTIGP is higher than that of a typical Low-E double glazing system, which means more sunlight transmitting into the glazing system in winter to keep the rooms warmer. MTIGP and double silver Low-E pane are simulated with EnergyPlus, the results shows that energy consumption of MTIGP is better than that of double silver Low-E pane. The developed glazing system is expected to be used in severe cold and cold climate zones to improve thermal insulation and sunlight transmittance to reduce energy consumption.

Field-Effect Passivation of the Cu2O/ZnO Transparent Heterojunction Photovoltaic Device Using Ga2O3 Thin Film

Photovoltaics (PV) would be more promising if light could generate electric power invisibly. This will endow more degrees of freedom to PV cells for wide-range deployment. Transparent photovoltaic (TPV) devices are the groundwork of the building-integrated photovoltaic (BIPV) systems that provide aesthetics to the buildings and solar energy convertor modules. TPV is adequate for the BIPV system of windows. Heterojunctions between p-type Cu2O and n-type ZnO have emerged as one of the most promising TPV devices. However, their inadequate transparency and a deficit of open-circuit voltage (VOC) and short-circuit current density (JSC) remained an open challenge. In this work, we have fabricated a thin Ga2O3 buffer layer embedded transparent ZnO/Cu2O heterojunction photovoltaic device, showing an average visible transmittance of more than 50%. The insertion of the Ga2O3 buffer layer remarkably increases the JSC and VOC to ∼860% and ∼41%, respectively. The TPV device also demonstrated a high JSC of 3.41 mA/cm2 under UV light. The Ga2O3 layer provides a graded conduction band alignment and passivates the interface by the field-effect passivation mechanism. The Ga2O3 buffer layer embedded device also demonstrated a broadband photodetection with remarkably high responsivity and detectivity of 250 mA/W and 5 × 1011 Jones at self-biased conditions.

Dynamic malware detection based on CNN-LSTM network

As cyberspace continues to evolve, so does malicious behavior across the network. Attacks in cyberspace, such as the spread of malware, bring harm and risks to users. Malware will attempt to steal user information or impair system resources. Therefore, to protect against malware, a series of detection software such as firewalls have been developed to protect users information and systems from damage. As malware continues to evolve and develop, and the forms of attack are increasing, the corresponding malware and detection techniques should also be evolving issues. The commonly used raw detection technique is based on signature and behavior specification, which has the advantages of fast speed and less computation. However, this method is limited to known software, the target software which is an unknown software will not be applicable. Therefore, one obvious shortage of traditional malware detection techniques is that new or unknown malware are not likely to be detected. To solve this problem, this paper uses machine learning to analyze different features of malware. By this way, new malware can be detected by the model. Based on the API call of the Windows system, this paper proposed a convolutional neural network for the classification of malware types. On this basis, this paper used CNN to extract the features, considering the text characteristics of the API sequence. After subsequence debonding and meaningless suffixes deletion of the original API sequence, word vectors were obtained through training as the input of the subsequent machine model. Then, different convolution kernels were used to process contextual semantic information. Then, the LSTM model is used to extract the long-distance features from the features.

Fuzzy logic control implementation on arduino uno based automatic window system

The window is the most important unit in building construction as a place for light and air circulation from inside and outside the building. Technological advances greatly affect everyday human life, humans are greatly facilitated by the presence of technology, one of which is the merging of windows with technology into Automatic Windows that can open and close automatically using Photodiode as input with Arduino Uno as a control system. Using fuzzy logic as a basis for decision makers in different circumstances at one time is very suitable as a method in this research.

Reliability optimization of multi-state consecutive sliding window systems under different activation strategies

The Consecutive-sliding window system (CSWS) is a recently developed version of the sliding window system (SWS), a structure inspired by a consecutive k- out - of -r- from -n: F system. In the CSWS, if the performance of any window of at least m-consecutive ones known by a set is lower than a predetermined demand value, the system fails. The present study proposes a more general version of the CSWS in which multi-state components of the system are constructed by binary elements with the possibility of using different active, standby, or K-mixed activation strategies for the elements in a component. Each strategy leads to a unique performance of that component in a multi-state mode. The proper allocation of elements to the components and the application of appropriate activation strategies have a significant effect on the system's reliability and cost. Therefore, a model is proposed to identify the optimal structure of components. A novel probabilistic algorithm is also developed for evaluating the performance of each adopted strategy. Moreover, a universal generating function-based approach is employed to calculate the system's reliability. To solve the proposed mathematical model, an evolutionary algorithm is developed. Finally, a numerical example and an industrial case study are analyzed to verify the importance of the proposed model.

Study on the impact of photovoltaic electrochromic modular smart window on indoor environment

Both electrochromic window (EC) and photovoltaic (PV) shading technology can control the indoor lighting environment. However, their combination form has great impact on the performance. The innovative smart windows with photovoltaic electrochromic (PV-EC) modules are designed, and consisted with photovoltaic areas, EC areas and transparent areas. Because the position of different modules can be adjusted freely, the system can select the most suitable combination mode for different building applied conditions. The PV-EC modular smart window test rig was established to learn the influence of different combinations of the windows on lighting condition. The combination(d) has the largest useful daylighting illumination among them, and which reaches 75.26%. PV area is installed at the lower layer, EC area is installed at the middle layer, and transparent area is installed at the upper layer in combination (d). Compared with ordinary windows, the window system can save up to 15.79% energy. And for different floors, radiation areas and space types, the system can select the corresponding combinations to adjust the lighting environment. It shows that the window system could improve the lighting environment and energy saving of the building, and effectively copes with different building condition.

Open-Source Real-Time Closed-Loop Electrical Threshold Tracking for Translational Pain Research.

Nociceptors are a class of primary afferent neurons that signal potentially harmful noxious stimuli. An increase in nociceptor excitability occurs in acute and chronic pain conditions. This produces abnormal ongoing activity or reduced activation thresholds to noxious stimuli. Identifying the cause of this increased excitability is required for the development and validation of mechanism-based treatments. Single-neuron electrical threshold tracking can quantify nociceptor excitability. Therefore, we have developed an application to allow such measurements and demonstrate its use in humans and rodents. APTrack provides real-time data visualization and action potential identification using a temporal raster plot. Algorithms detect action potentials by threshold crossing and monitor their latency after electrical stimulation. The plugin then modulates the electrical stimulation amplitude using an up-down method to estimate the electrical threshold of the nociceptors. The software was built upon the Open Ephys system (V0.54) and coded in C++ using the JUCE framework. It runs on Windows, Linux, and Mac operating systems. The open-source code is available (https://github.com/Microneurography/APTrack). The electrophysiological recordings were taken from nociceptors in both a mouse skin-nerve preparation using the teased fiber method in the saphenous nerve and in healthy human volunteers using microneurography in the superficial peroneal nerve. Nociceptors were classified by their response to thermal and mechanical stimuli, as well as by monitoring the activity-dependent slowing of the conduction velocity. The software facilitated the experiment by simplifying the action potential identification through the temporal raster plot. We demonstrate real-time closed-loop electrical threshold tracking of single-neuron action potentials duringin vivohuman microneurography, for the first time, and duringex vivomouse electrophysiological recordings of C-fibers and Aδ-fibers. We establish proof of principle by showing that the electrical threshold of a human heat-sensitive C-fiber nociceptor is reduced by heating the receptive field. This plugin enables the electrical threshold tracking of single-neuron action potentials and allows the quantification of changes in nociceptor excitability.