In recent years, pandemic outbreaks have raised concerns about the spread of respiratory infections and their impact on public health. Since the pathogen emission during human respiration is recognized as the primary source, characterizing the physical properties of exhaled particles and airflow has become a crucial focus of attention. This article critically reviews experimental studies in exhaled particles and airflow, examines the uncertainty introduced by different measurement methods, analyzes how it is reflected in measurement outcomes, and provides an in-depth understanding of particle size distribution and airflow behaviors of human respiration. The measurement techniques assessment highlights the variability among particle sizing techniques in detection size range, collection efficiency, hydration status of captured particles, and experimental protocols. A combination of sampling-based instruments and laser imaging systems is recommended for particle sizing to cover a wider detection range, with refined setups in thermal conditions, sampling distance, volume, and duration. Meanwhile, it identifies the complementary nature of qualitative and quantitative measurements of airflow characterization techniques. Image recording systems plus data reconstruction programs are suggested to capture dynamic airflow features while accuracy validation by other techniques is required at the same time. Subsequent analysis of the measurement data showed that the various experimental measurements provided substantial information, but they also revealed disagreements and challenges in quantification. The dominance of submicron aerosols in exhaled particles and jet-like transport in exhaled airflow is obvious. More efforts should be made to measure particles larger than 20 μm, capture airflow dynamics in a high temporal and spatial resolution, and quantify the impact of face coverings to improve the understanding of human respiratory emissions.

This article investigates the efficacy of temperature-controlled airflow systems in modern operating rooms for contaminant control, a critical factor in preventing surgical site infections. We have conducted experimental measurements in an operating room equipped with temperature-controlled ventilation to map the airflow field and contaminant dispersion (airborne particles with diameters ranging from 0.5 to 1 μm). The results were used to validate the computational fluid dynamics code, which was then employed to simulate and examine different conditions, including contaminant release locations and air supply rates. Realizable k-epsilon and passive scalar models were utilized to simulate airflow and airborne particle phases. We assessed the airflow distribution and contaminant dispersion, utilizing indices such as ventilation and air change efficiency scales. The analysis provided quantitative insights into the distribution and removal of contaminants, as well as the speed at which the room air was replaced. Contamination was found to be effectively reduced when contaminants were released near exhaust outlets or under central unidirectional inlets. The presence of the operating table caused a big distortion of the central downward airflow, forming a horizontal air barrier at the periphery. Under this unique interior configuration, an appropriate air supply ratio between central and periphery zones was required to achieve optimal overall ventilation performance.

This article aims to support the targeted worldwide green transition process by introducing and thoroughly analyzing a low-temperature heating and high-temperature cooling, smart building system. This concept allows for greater use of renewable energy while utilizing less input energy than conventional heating and cooling techniques. The proposed system consists of a reversible water-to-water heat pump driven by low-temperature geothermal energy. A rule-based control strategy is developed to establish an intelligent connection with the regional energy grids for peak shaving and compensating for the building's energy costs over the year. The dynamic simulation is carried out for a multi-family building complex in Stockholm, Sweden, using TRNSYS. The most favorable operating condition is determined via an artificial neural network-assisted tri-objective optimizer based on the grey wolf algorithm in MATLAB. The comparison of the proposed smart model with the conven-tional system in Sweden results in 332%, 203%, and 190% primary energy reduction, cost saving, and carbon dioxide emission mitigation, respectively. As indicated by the parametric results, the conflicting fluctuation between desirable and unfavorable indicators highlights the importance of multi-objective optimization. The grey wolf optimizer obtains 12% higher efficiency, 1.2 MWh lower annual bought energy, 24 $/MWh lower unit cost, and 5.1 MWh more yearly sold energy than the design condition. The scattered distribution reveals that tank volume and subcooling degree are sensitive parameters. According to the transient results, the suggested smart system can independently satisfy the building's heating, cooling, and electricity demands for more than 81% of the year, thanks to the two-way connection with the electricity and heating networks via the rule-based controller.

As natural resources of sand and fine material used in the concrete industry are becoming more scarce, crushed materials are being used to a greater extent. Shape and texture of machine crushed materials differ from aggregates naturally ground, layered and sorted under the course of millions of years. It is assumed that crushed aggregates are more angular and of less smooth texture than natural aggregates, which will affect the flow behaviour of a suspension containing crushed material. In the mix design process of cementitous suspensions, an adequate rheology of the micro mortar (all constituents in the concrete being able to pass a 0.125 mm sieve, including the cement) is crucial. In this paper, the shape of fine particles is linked to the micro mortar plastic viscosity of the filler suspension including cement. The plastic viscosity here serves as an important quality assessment of the filler, since the micro mortar workability features are vital for the final mix design quality of the concrete workability.

Photovoltaic (PV) systems are recently the most used sustainable energy source to fit with the energy demand growth. Generally, batteries, as storage systems, are installed along with PV modules. When it comes to an optimal power management of PV/battery hybrid systems, the uncertain and intermittent behavior of PV power production can provoke some challenges, with which, the real-time operation of the hybrid system can be degraded, therefore, PV power forecast is highly needed. Data-driven models are became nowadays very efficient methods to build regression models for the purpose of PV power forecast. In this paper, Adaptive Neuro-Fuzzy Inference (ANFIS) is chosen as a data-driven technique, to build up forecasting models. Standard ANFIS, which uses only weather data, cannot avoid the confusing scenarios like PV modules covered by the snow in clear-sky days. This work proposes an improved ANFIS model taking historical generated power into account. The developed model is validated on a real case, using the PV system of the institute of energy system technologies in Offenburg. When adding the average of produced power of the last 72 hours as additional input, the model was able to follow the rapid changes in weather conditions and overcome the unexceptional situations like the problem of snow on the PV modules.          

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic(PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltageprofiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes intoaccount the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions arerequired. In this context, the grid status and weather data need to be transmitted continuously, via wirelesscommunication technology, to an edge computer. Based on the transmitted data, and using the system mathe-matical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpointfor each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWERIEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the gridwith a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordinationof PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In thiscontext, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. Animprovement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved.Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.

Indoor air quality in office rooms directly influences occupants' comfort and productivity. Higher air change rates in offices compared to residential buildings increase the heating demand considerably in cold climate countries. Reducing the thermal losses from exterior walls and windows provides the possibility of using low-temperature heating systems in order to reduce the required heating and enhance the occupants' perceived thermal comfort. In the present study, the indoor air quality in an office room under Nordic climate has been investigated using computational fluid dynamics method. A well-insulated window (so-called energy-active window) is installed at the exterior wall. The effect of using two heating emission systems, floor heating, and radiator, on the perceived thermal comfort of the occupants has been studied and compared.

Concrete is a composite building material which long term function can be modified for example by changing the water to cement ratio (w/c) or by adding in other chemical admixtures to change the fresh and hardened properties of the concrete. The overall goal of this study is to reduce the water absorption capacity of the cement paste/microstructure by at least 85 %. This is achieved by using bulk hydrophobic agents in the mixing phase rather than post hardened surface application. Numerous commercial agents and vegetable oils were tested and showed promising results at a dosage equal to 3% of cement weight. This though affected compressive strengths negatively. As these concretes will be exposed to Nordic winter conditions, the concrete should perform well under repeated salt water freezing and thawing. This continued study will show how a selection of these bulk hydrophobic concretes performed during this part of the study. The concrete has a w/c = 0.4 with a cement content (CEM I) of 430 kg/m3.