Windows Operating Research Articles

A/C usage intensity and adaptive comfort behaviour in residential buildings in South East Queensland, Australia

Occupants’ interaction with building controls has been recognised as a major contributing factor to the often-reported wide discrepancies between actual and predicted energy consumption in buildings. Accordingly, research into actual patterns of occupant behaviour is increasingly being incorporated into building energy performance simulation tools. However, utilizing a single behaviour pattern can also lead to differences between the actual and predicted energy use in a building. This study aims to classify patterns of occupant adaptive comfort behaviours based on longitudinal field observations made in Australian homes. A sample of 41 households was classified into ‘light’, ‘medium’ and ‘heavy’ usage types according to their annual A/C use. Statistical models describing the proportion of a specific adaptive behaviour (incl. A/C and window operation) in relation to outdoor temperature were developed for the three household classifications. The results indicate that more than 30% of the ‘heavy’ usage households operated their A/C for cooling as the outdoor temperature exceeds 30℃, in contrast to 10% for the ‘medium’ and nearly none for the ‘light’ A/C usage groups. In general, the household A/C usage and hours of windows being open in living rooms were found to be inversely proportional. This study also proposes a probability model for classifying the A/C usage levels of a given dwelling into the three groups (i.e. light, medium and heavy) based on simple background information that can be acquired at the design stage (i.e. site and construction characteristics).

SSHAA: A Python Package Index for visualizing features of SSH attacks with text mining in classification

ABSTRACT With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world’s first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant.

CShaperApp: Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

AbstractCaenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development. In this study, we developed a desktop software CShaperApp to segment fluorescence‐labeled images of cell membranes and analyze cellular morphologies interactively during C. elegans embryogenesis. Based on the previously proposed framework CShaper, CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine‐tuned one adapted to their in‐house dataset. Experimental results show that it takes about 30 min to process a three‐dimensional time‐lapse (4D) dataset, which consists of 150 image stacks at a ∼1.5‐min interval and covers C. elegans embryogenesis from the 4‐cell to 350‐cell stages. The robustness of CShaperApp is also validated with the datasets from different laboratories. Furthermore, modularized implementation increases the flexibility in multi‐task applications and promotes its flexibility for future enhancements. As cell morphology over development has emerged as a focus of interest in developmental biology, CShaperApp is anticipated to pave the way for those studies by accelerating the high‐throughput generation of systems‐level quantitative data collection. The software can be freely downloaded from the website of Github (cao13jf/CShaperApp) and is executable on Windows, macOS, and Linux operating systems.

Wagner diagram for modeling O2 pathway—calculation and graphical display by the Helsinki O2 Pathway Tool

Objective. Maximal O2 uptake ( V˙O2max ) reflects the individual’s maximal rate of O2 transport and utilization through the integrated whole-body pathway composed of the lungs, heart, blood, circulation, and metabolically active tissues. As such, V˙O2max is strongly associated with physical capacity as well as overall health and thus acts as one predictor of physical performance and as a vital sign in determination of status and progress of numerous clinical conditions. Quantifying the contribution of single parts of the multistep O2 pathway to V˙O2max provides mechanistic insights into exercise (in)tolerance and into therapy-, training-, or disuse-induced adaptations at individual or group levels. We developed a desktop application (Helsinki O2 Pathway Tool—HO2PT) to model numerical and graphical display of the O2 pathway based on the ‘Wagner diagram’ originally formulated by Peter D. Wagner and his colleagues. Approach. The HO2PT was developed and programmed in Python to integrate the Fick principle and Fick’s law of diffusion into a computational system to import, calculate, graphically display, and export variables of the Wagner diagram. Main results. The HO2PT models O2 pathway both numerically and graphically according to the Wagner diagram and pertains to conditions under which the mitochondrial oxidative capacity of metabolically active tissues exceeds the capacity of the O2 transport system to deliver O2 to the mitochondria. The tool is based on the Python open source code and libraries and freely and publicly available online for Windows, macOS, and Linux operating systems. Significance. The HO2PT offers a novel functional and demonstrative platform for those interested in examining V˙O2max and its determinants by using the Wagner diagram. It will improve access to and usability of Wagner’s and his colleagues’ integrated physiological model and thereby benefit users across the wide spectrum of contexts such as scientific research, education, exercise testing, sports coaching, and clinical medicine.

Detecting and Patching Loopholes in Windows Operating System

Widely used in personal and business environments, the Windows operating system is a complex software platform that has historically been vulnerable to various intrusions and vulnerabilities. These breaches can be seen as software vulnerabilities, configuration vulnerabilities, higher validity issues, or architectural flaws, which can pose a serious security risk. This research report provides a comprehensive analysis of Windows vulnerabilities and examines their types, key examples such as EternalBlue and Stuxnet, and the potential impact they can have on individuals and organizations. This white paper examines mitigation strategies and best practices, including patch management, system hardening, vulnerability assessment, and the use of third-party security tools. It also addresses the challenges associated with blockchain, including the complexity of legacy code, zero-day vulnerabilities, targeted attacks, and the growing threat landscape. The paper also reviews future ideas, including secure software development practices, architectural improvements, collaboration and information sharing, automated vulnerability detection and remediation, and continuing education and security awareness. By taking a proactive approach to reducing intrusions, organizations can strengthen the security of their Windows systems, protect sensitive data, and maintain system integrity against threats and crimes.

ModelFLOWs-app: Data-driven post-processing and reduced order modelling tools

This article presents an innovative open-source software named ModelFLOWs-app,1 written in Python, which has been created and tested to generate precise and robust hybrid reduced order models (ROMs) fully data-driven. By integrating modal decomposition and deep learning in diverse ways, the software uncovers the fundamental patterns in dynamic systems. This acquired knowledge is then employed to enrich the comprehension of the underlying physics, reconstruct databases from limited measurements, and forecast the progression of system dynamics. The hybrid ROMs produced by ModelFLOWs-app combine experimental and numerical databases, serving as highly accurate alternatives to numerical simulations. As a result, computational expenses are significantly reduced, and the models become powerful tools for optimization and control in various applications. The exceptional capability of ModelFLOWs-app in developing reliable data-driven hybrid ROMs has been demonstrated across a wide range of applications, making it a valuable resource for understanding complex nonlinear dynamical systems and providing insights in diverse domains. This article presents the mathematical background, as well as a review of some examples of applications. Program summaryProgram title: ModelFLOWs-appCPC Library link to program files:https://doi.org/10.17632/49tzcc8sf3.1Developer's repository link:github.com/modelflows/ModelFLOWs-appLicensing provisions: MIT licenseProgramming language: PythonSupplementary material: Tutorial, example datasets.Nature of problem: ModelFLOWs-app is an open-source Software for data post-processing, patterns identification and development of reduced order models using modal decomposition and deep learning architectures. ModelFLOWs-app provides its users with a user-friendly interface to efficiently identify patterns in data, reconstruct data by repairing or enhancing it, and make predictions based on the available data.Solution method: ModelFLOWs-app methodological framework is formed by two big modules: Module 1 uses modal decomposition methods, and Module 2 is formed by hybrid machine learning tools, which combine modal decomposition with deep learning architectures. Each module solves three different applications: (1) patterns identification, suitable to study the physics behind the data analysed; (2) data reconstruction, capable to reconstruct two- or three- dimensional databases from a set of selected points, using data from sensors, or repairing missing data; (3) data forecasting, which builds reduced order models (ROMs) to predict the spatio-temporal evolution of the signal analysed.Additional comments including restrictions and unusual features: ModelFLOWs-app offers an intuitive and user-friendly interface, making it accessible to users with varying levels of technical expertise. It is compatible with Windows, macOS, and Linux operating systems. Users can import their data in popular formats (.mat, .csv, .npy, .pkl, .h5), and the results can be exported in Matlab “.mat” or Numpy “.npy” for post-processing. The Deep Learning models have been developed using TensorFlow, and users have the option to search for the optimal hyperparameters for their database since hyperparameter optimization has been implemented for all models. See Refs. [1–10].

Gypsy: AI-Powered Virtual Assistant for Windows OS

The proposed virtual assistant software, powered by AI, is specifically crafted for users operating Windows computers to enhance the efficiency and safety of their tasks with minimal input. This innovative design allows users to swiftly and effortlessly accomplish various everyday computer-based activities. Examples include streamlined online ordering, accessing desktop and web applications, conducting web searches, setting reminders, obtaining live directions, reading notes, checking weather forecasts, translating words, performing mathematical calculations, setting alarms, receiving the latest news, and sending emails and WhatsApp messages, among other functionalities. Furthermore, the project incorporates the ability to recognize both voice and text input commands. Users can interact with the system through voice commands captured by the computer's microphone, which are then converted into text output. This conversion facilitates the system in comprehending the input and executing the requested task. The tool is seamlessly integrated with the Internet and the Windows operating system, ensuring the delivery of the desired functions and results in response to user queries.

Creation of a Program for Demonstration and use in the Educational Process of Material on the Discipline Physics

Introduction. The article presents a description of the program developed by the authors, which demonstrates the dependencies in Ohm’s law for a section of a chain and Ohm’s law for a complete chain. This program demonstrates the possibilities of using information technology in the educational process at universities. The program was created in the Clickteam Fusion 2.5 environment for the Windows operating system, to create demonstrations at laboratory and practical classes in the discipline of physics for higher educational institutions. The purpose of the article is to study and consider the application of information technology in the study of the basics in the physics course. The scientific novelty of the work lies in the fact that it describes a program that was created as a prototype to demonstrate the possibilities of creating and conducting laboratory and practical work in physics and other technical disciplines. Methodology and methods of research. Search for forms of visual and at the same time correct presentation of the taught material using the developed program. The results of the study. The development of original algorithmic demonstration programs at the university helps to attract students to work on the material. The article analyzes the necessity and relevance of using information technologies in the educational process of the university, as well as discusses ways to implement them in practice. Conclusion. This concept can be used in the development of laboratory work and recommendations so that students can independently gain knowledge in various disciplines, and the program is also provided to further stimulate students’ interest in their professional activities.

Direct Optical Convolution Computing Based on Arrayed Waveguide Grating Router

AbstractOptical convolution computing is gaining traction owing to its inherent parallelism, multi‐dimensional processing, and energy efficiency. To handle input dimensions of N, conventional implementations necessitate N2 optical elements, such as Mach–Zehnder interferometers or micro‐ring resonators, to process multiply‐accumulate (MAC) operations, limiting scalability and resulting in elevated power consumption. Here, a direct convolution computing method based on wavelength routing, utilizing the unique sliding property of an arrayed waveguide grating router (AWGR) to perform the sliding window operation of the convolution in the wavelength–space domains is proposed. With two input vectors directly loaded onto two modulator arrays, the convolution result is instantaneously produced at a photodetector array. The entire convolution computation is executed within a single clock cycle without the need for preprocessing or decomposition into elementary MAC operations. The number of active elements is minimal, only needed for input/output. The proposed optical convolution unit has striking advantages of high scalability, high speed, and processing simplicity compared to those based on optical matrix‐vector multipliers. In the first experimental demonstration, a remarkable classification accuracy of up to 98.2% in handwritten digit recognition tasks using a LeNet‐5 neural network is achieved.

A hybrid learning approach to model the diversity of window-opening behavior

The diverse window-opening behaviors of individuals can result in significant differences in indoor thermal environments, air quality, and energy utilization. However, the majority of existing studies focus on constructing an average window operation model, thus overlooking the diversity of behaviors. Current methods for addressing behavioral diversity face challenges with integration into building performance simulation software and are highly dependent on data scale. To address these limitations, this study proposes a novel approach that combines unsupervised learning (K-Means) and supervised learning (Light Gradient Boosting Machine, LightGBM) for modeling the diverse window-opening behaviors. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to interpret the predictive model. This study yielded four key findings: 1) There were 12 different window-opening behavior patterns. Interestingly, 65 % of the residents’ window-opening behaviors were not influenced by environmental factors but were instead a matter of personal habit. 2) Using random sampling to divide the dataset may pose a risk of data leakage. The time series cross-validation method is more suitable for evaluating the performance of the window state prediction model. 3) Under the time series sampling strategy, the LightGBM model incorporating behavioral diversity improved the prediction accuracy by 1.3%–10.4 % compared to the standalone LightGBM model. Notably, when the daily average window opening time was used as a clustering feature in the LightGBM model (Cluster(T)-LightGBM), the accuracy reached 87.1 %. 4) The SHAP feature analysis highlighted high-intensity window-opening categories, outdoor temperature, and indoor CO2 concentration as the most pivotal predictors.

Deep learning for predictive window operation modeling in open-plan offices

This study explores how the past, both short and long-term, affects the predictive window operation modeling in open-plan offices. To achieve this, the study proposes a deep learning method that uses long short-term memory (LSTM) artificial neural networks. The proposed model is an ensemble of LSTM networks that utilize relevant indoor environmental data from multiple sensory systems and weather data from a nearby weather station to predict the window state with different predictive horizons. The drawbacks of such networks are extensive training time and the lack of interpretability. Hence, Bayesian optimization is proposed for efficient hyperparameter tuning of the network. Furthermore, explainability through a model agnostic approach is utilized for feature importance ranking.The performance of the LSTM networks was compared to a dense feedforward neural network (DFNN) on an imbalanced dataset to evaluate the effect of the long-term past. Handling imbalanced window operation data was improved by employing class weight and early stopping, leading to a higher true positive rate, marginally better F1-score, and fewer network training epochs. The results indicate that a stateful bi-directional LSTM (Bi-LSTM) network models the data sequence more efficiently in aggregated comparison than other LSTM types and DFNN. However, the DFNN slightly outperformed LSTM networks in interval-by-interval comparison, with an F1-score of 0.88 when predicting the window state 10 min ahead, shifting to 0.82 for a 60-minute predictive horizon. Additionally, the study showed that the ensemble model performed better in terms of F1-score than the individual models.

Teaching the principles of least-cost poultry feed formulation utilizing the Solver function within a computer software workbook

A Microsoft Excel workbook, User-Friendly Feed Formulation with Data from Australia (UffdAu.xlsm), has been developed for teaching feed formulation techniques to tertiary level, university students. It runs under both Microsoft Windows and Apple iOS operating systems. The example ingredient composition matrix is based on the Australian Feed Ingredient Database to illustrate the biological and econometric principles of least-cost feed formulation. The nutrient data are based roughly on recent primary breeder company recommendations. The workbook is easily adapted to appropriate ingredients, nutrients, and prices most relevant to the students, wherever it is used. The workbook uses the linear routines of Excel's Solver add-in under the Data heading in the header Ribbon. There is a worksheet illustrating how to adapt non-linear responses such as exogenous enzymes to typical linear models using a step function. Additional worksheets illustrate how proximate analysis can be interpreted in modern analytical chemistry terms and, how various feed energy measures are related to feed composition. UffdAu.xlsm is available free of charge from the Poultry Hub Australia website (https://www.poultryhub.org).

Detection Techniques for DBI Environment in Windows

Dynamic binary instrumentation (DBI) is a technique that enables the monitoring and analysis of software, providing enhanced performance compared to other analysis tools. However, to provide the robust dynamic analysis capabilities, it commonly requires the setup of separate environments for analysis, thereby increasing the contrast with normal execution and the distinctive features that may reveal the presence of the DBI environment. Malware adapts to detect the presence of DBI environments, and it consequently leads to the expansion of the attack surface. In this paper, we provide an in-depth exploration of anti-instrumentation techniques that can be exploited by malware, with a specific focus on the Windows operating system. Leveraging the unique features of the DBI environment, we introduce and categorize DBI detection techniques. Additionally, we conduct a comprehensive analysis of the techniques through the implementation algorithms with bypassing methods for the techniques. Our experiments showcase the effectiveness of these techniques on the latest versions of several DBI frameworks. Furthermore, we address associated concerns with the aim of contributing to the development of enhanced tools to combat malicious activities exploiting DBI and propose directions for future research.

Integrated characterization of deep karsted carbonates in the Tahe Oilfield, Tarim Basin

Abstract As transport channels of oil and gas, fracture networks can greatly improve reservoir seepage, which is of great significance to the hydraulic fracturing and hydrocarbon deposit exploitation in petroleum science and engineering. In this paper, our target reservoirs are deep karsted carbonates at depths of >6000 m and highly heterogeneous, leading to complex seismic responses with weak energy and low resolution. Therefore, it is challenging to predict the spatial distribution of a carbonate fracture-cavern reservoir and to characterize its delicate structure. We present a characterization method for an excellent fracture description by integrating several attribute results on 3D seismic field data. First, we use a noise elimination method to remove the noise interference in seismic data without damaging the fault structure characteristics. Next, we propose a novel spatially windowed 2D Hilbert transform-based operator to perform volumetric edge detection on 3D seismic field data. Then, the volumetric edge results are co-rendered with other seismic geometric attributes to generate multi-attribute fusion results for a comprehensive prediction that can excellently delineate geologic anomalies at different scales in deep carbonates. The results indicate that integrating several scale attributes can obtain more rich geological discontinuity and reveal more subtle fractures than using single attribute. The multi-attribute fusion results can effectively delineate some small-medium-sized faults, and they provide practical support for the exploration and production of Tahe carbonate fracture-cavern reservoirs.

Classification and online clustering of zero-day malware

AbstractA large amount of new malware is constantly being generated, which must not only be distinguished from benign samples, but also classified into malware families. For this purpose, investigating how existing malware families are developed and examining emerging families need to be explored. This paper focuses on the online processing of incoming malicious samples to assign them to existing families or, in the case of samples from new families, to cluster them. We experimented with seven prevalent malware families from the EMBER dataset, four in the training set and three additional new families in the test set. The features were extracted by static analysis of portable executable files for the Windows operating system. Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families. We classified 97.21% of streaming data with a balanced accuracy of 95.33%. Then, we clustered the remaining data using a self-organizing map, achieving a purity from 47.61% for four clusters to 77.68% for ten clusters. These results indicate that our approach has the potential to be applied to the classification and clustering of zero-day malware into malware families.

TreeViewer: Flexible, modular software to visualise and manipulate phylogenetic trees.

Phylogenetic trees illustrate evolutionary relationships between taxa or genes. Tree figures are crucial when presenting results and data, and by creating clear and effective plots, researchers can describe many kinds of evolutionary patterns. However, producing tree plots can be a time-consuming task, especially as multiple different programs are often needed to adjust and illustrate all data associated with a tree. We present TreeViewer, a new software to draw phylogenetic trees. TreeViewer is flexible, modular, and user-friendly. Plots are produced as the result of a user-defined pipeline, which can be finely customised and easily applied to different trees. Every feature of the program is documented and easily accessible, either in the online manual or within the program's interface. We show how TreeViewer can be used to produce publication-ready figures, saving time by not requiring additional graphical post-processing tools. TreeViewer is freely available for Windows, macOS, and Linux operating systems and distributed under an AGPLv3 licence from https://treeviewer.org. It has a graphical user interface (GUI), as well as a command-line interface, which is useful to work with very large trees and for automated pipelines. A detailed user manual with examples and tutorials is also available. TreeViewer is mainly aimed at users wishing to produce highly customised, publication-quality tree figures using a single GUI software tool. Compared to other GUI tools, TreeViewer offers a richer feature set and a finer degree of customisation. Compared to command-line-based tools and software libraries, TreeViewer's graphical interface is more accessible. The flexibility of TreeViewer's approach to phylogenetic tree plotting enables the program to produce a wide variety of publication-ready figures. Users are encouraged to create their own custom modules to expand the functionalities of the program. This sets the scene for an ever-expanding and ever-adapting software framework that can easily adjust to respond to new challenges.

Smart Garden on Chili Plants Based on IoT

Abstract:Cultivating chili plants requires special attention such as temperature and soil humidity. Internet of Things (IoT) technology is a technology that allows objects around us to be connected to the internet network. The widespread adoption of various internet of things technologies has a very pronounced influence in the domestic sector such as home and smart car applications, and the agricultural sector is no exception.Temperature monitoring system, humidityIoT-based soil pan on chili plants (smart garden) is an IoT-based monitoring system design to control temperature and soil moisture on chili plants which can make it easier for farmers to measure soil and plant conditions and to make it easier for farmers to monitor the quality of their agricultural land. The process of creating a monitoring system design begins with interviews to collect primary data and literature studies to collect secondary data. Then carry out system design, tool assembly and implementation. The system for monitoring and controlling temperature and soil moisture using IoT can be run using Windows operating system software. The hardware used is the DHT11 temperature sensor, Moisture Sensor, Arduino, and Nodemcu ESP8266. This system utilizes a pump and fan for its hardware, using an automatic system implementation from a microcontroller so that it can make the hardware run without user intervention.If the humidity value displayed on the LCD is <70% then the pump will be on and the plants will be watered, and if the humidity is >80% then the pump will be off and stop watering.

RI−Calc: A user friendly software and web server for refractive index calculation

The refractive index of an optical medium is essential for studying a variety of physical phenomena. One useful method for determining the refractive index of scalar materials (i.e., materials which are characterized by a scalar dielectric function) is to employ the Kramers−Kronig (K−K) relations. The K−K method is particularly useful in cases where ellipsometric measurements are unavailable, a situation that frequently occurs in many laboratories. Although some packages can perform this calculation, they usually lack a graphical interface and are complex to implement and use. Those deficiencies inhibit their utilization by a plethora of researchers unfamiliar with programming languages. To address the aforementioned gap, we have developed the Refractive Index Calculator (RI−Calc) program that provides an intuitive and user−friendly interface. The RI−Calc program allows users to input the absorption coefficient spectrum and then easily calculate the complex refractive index and the complex relative permittivity of a broad range of thin films, including of molecules, polymers, blends, and perovskites. The program has been thoroughly tested, taking into account the Lorentz oscillator model and experimental data from a materials' refractive index database, demonstrating consistent outcomes. It is compatible with Windows, Unix, and macOS operating systems. You can download the RI−Calc binaries from our GitHub repository or conveniently access the program through our dedicated web server at nanocalc.org.

Pericardial Window Operation in Oncology Patients: Analysis of Long-Term Survival and Prognostic Factors.

Pericardial effusion (PE) is a serious condition in cancer patients, primarily arising from malignant dissemination. Pericardial window formation is a surgical intervention for refractory PE. However, the long-term outcomes and factors associated with postoperative survival remain unclear. We retrospectively analyzed data from 166 oncology patients who underwent pericardial window formation at Samsung Medical Center between 2011 and 2023. We analyzed survival and PE recurrence regarding surgical approach, cancer type, and cytopathological findings. To identify factors associated with survival, we utilized Cox proportional-hazards regression. All patients had tumors documented in accordance with the American Joint Committee on Cancer staging manual, including lung (61.4%), breast (9.6%), gastrointestinal (9.0%), hematologic (3.6%), and other cancers (16.4%). Surgical approaches included mini-thoracotomy (67.5%) and thoracoscopy (32.5%). Postsurgical cytopathology confirmed malignancy in 94 cases (56.6%). Over a median follow-up duration of 50.0 months, 142 deaths and 16 PE recurrences occurred. The 1-year overall and PE recurrence-free survival rates were 31.4% and 28.6%, respectively. One-year survival rates were significantly higher for thoracoscopy recipients (43.7% vs. 25.6%, p=0.031) and patients with negative cytopathology results (45.1% vs. 20.6%, p<0.001). No significant survival difference was observed between lung cancer and other types (p=0.129). Multivariate analysis identified New York Heart Association class, cancer stage, and cytopathology as independent prognostic factors. This series is the largest to date concerning window formation among cancer patients with PE. Patients' long-term survival after surgery was generally unfavorable. However, cases with negative cytopathology or earlier tumor stage demonstrated comparatively high survival rates.

Changes in Concentration of Carbon Dioxide and Temperature in University Classrooms

This study aims to observe variations in temperature, relative humidity, and carbon dioxide concentrations in university classrooms without mechanical ventilation throughout the day. Over three days, two classrooms in Riga Stradiņš University, equipped solely with natural ventilation, were evaluated for variations in indoor air quality. Observations were recorded using the Aranet4 sensor during actual study sessions with varying numbers of occupants, and ventilation periods marked by window operations. The recorded parameters were compared with established indoor air quality guidelines. The results showed that the relative humidity remained within the ideal range (40%-60%) throughout the observation period. Indoor temperature varied between 18 °C to 26 °C, with noticeable increases during practical study sessions and decreases during ventilation periods. The carbon dioxide levels consistently exceeded the safe limit of 1000 ppm during all study sessions, with the time taken to exceed this threshold being directly proportional to the number of occupants. The findings underline the need for strategic ventilation interventions and classroom arrangements, considering occupancy and room size to promote healthier learning environments. Further research incorporating occupant surveys and including diverse educational settings is recommended. Keywords: indoor air quality; natural ventilation; classroom environment; carbon dioxide; relative humidity; temperature.