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Background/Aims: Regression of liver fibrosis during antiviral therapy in chronic hepatitis B (CHB) patients has been demonstrated, but data on the influence of long-term treatment with tenofovir disoproxil fumarate (TDF) on liver stiffness (LS) measured by transient elastography are scarce. We aimed to investigate the changes in LS values during the 144-week TDF therapy in treatment-naïve CHB patients. Methods: This prospective observational study was conducted from April 2015 to July 2020 at CHA Bundang Medical Center. Laboratory tests and LS measurements were performed at baseline and repeated at weeks 12, 24, 48, 96, and 144. A significant decline in LS was defined as ≥ 30% decrease in LS value at week 96 from baseline. Results: A total of 48 treatment-naïve CHB patients initiating TDF therapy were screened, and 36 patients were included in the final analysis (median age, 46 [interquartile range, 34.5–55.8] years; 19 men [52.8%]). During TDF therapy, the median LS values decreased from 13.8 kPa at baseline to 8.7 kPa, 6.5 kPa, and 6.4 kPa at weeks 48, 96, and 144, respectively (all P < 0.001). At week 96, virological and biochemical responses were achieved in 34 (94.4%) patients and 20 (76.9%) patients, respectively. Moreover, 21 of 36 (58.3%) patients showed a significant decline in LS value. A higher baseline LS value was a single independent predictor for the reduction in LS value at week 96 from baseline (P < 0.001). Conclusions: During the 144-week TDF therapy, LS values declined significantly in treatment-naïve CHB patients. [ABSTRACT FROM AUTHOR]

The distributed energy system (DES) represents an innovative approach to energy generation and distribution that promotes decentralization and diversification of energy sources. DESs can offer numerous benefits, including increased resiliency, reduced transmission losses, improved efficiency, and lower carbon emissions. The optimal design of a DES requires careful consideration of various factors such as geographical location, climate conditions, and energy demand patterns. This paper utilizes a multi-objective genetic algorithm to optimize the combination of technologies and their corresponding sizes in a distributed energy system for three types of commercial buildings—hospitals, large offices, and large hotels across eight different climate zones in the U.S. A range of technologies are considered for integration into the DES. These technologies include photovoltaic systems, wind turbines, combined heat and power systems, solar thermal collectors, and electrical and thermal energy storage. The two objectives considered are maximizing the reduction in carbon dioxide emissions and minimizing the life cycle costs for the DES. The purpose of this study is to optimize and evaluate the multi-objective design of distributed energy systems aimed at decentralizing and diversifying energy sources. The analysis of optimized DES designs across all 24 case scenarios shows that a balance between cost saving and emission reduction has been achieved. Although this study primarily focuses on specific buildings and climate zones, the methods and findings can be adapted for a wider variety of building types across different geographical locations, thus paving the way for more widespread adoption of optimized distributed energy systems. [ABSTRACT FROM AUTHOR]

This paper presents performance prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery based on artificial neural network (ANN). An ANN based prediction model of the ORC system is established with consideration of mean squared error and correlation coefficient. A test bench of combined diesel engine and ORC waste heat recovery system is developed, and the experimental data used to train and test the proposed ANN model are collected. A genetic algorithm (GA) is also considered in this study to increase prediction accuracy, and the ANN model is evaluated with different learning rates, train functions and parameter settings. A prediction accuracy comparison of the ANN model with and without using GA is presented. The effects of seven key operating parameters on the power output of the ORC system are investigated. Finally, a performance prediction and parametric optimization for the ORC system are conducted based on the proposed ANN model. The results show that prediction error of the ANN model with using the GA is lower than that without using GA. Therefore, it is recommended to optimize the weights of the ANN model with GA for a high prediction accuracy. The proposed ANN model shows a strong learning ability and good generalization performance. Compared to the experimental data, the maximum relative error is less than 5%. The experimental results after optimizing the operating parameters are very close to ANN’s predictions, indicating one or more operating parameters can be adjusted to obtain a higher power output during the experiment process. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of existing financial incentive policies in the U.S. for integrated photovoltaic and battery energy storage (PV-BES) systems. A mathematical model of PV-BES system to evaluate annual energy performance is developed in this paper. Four types of buildings (i.e., hospital, large office, large hotel, and secondary school) located in four different states, which each has their own PV and/or BES incentives, are selected and analyzed. Based on the energy performance data for each building type, the simply payback period (PBP) for the PV-BES system in different locations is calculated according to the local incentive policies. The PBP is chosen as an indicator to evaluate the effectiveness of incentive policies for different locations and building types by comparing it to the PBP for the same PV-BES systems without incentive policies. The reduction of carbon dioxide emission (CDE) due to the PV generation is also investigated since it indicates the potential to reduce the PBP for a further step when a high carbon credit is available. Furthermore, a parametric analysis is conducted to determine the sensitivity and contribution of parameters such as the capacity of the PV-BES system, the capital cost of PV module and the battery storage on the performance of the PV-BES system. Results show that for all the evaluated buildings in California and Hawaii, the existing incentive policy could reduce the PBP effectively below 10 years. However, the PBP for most of the evaluated buildings in New Jersey and New York were high even when both the PV and BES incentive policies were taken into account (approximately from 11 to 29 years). [ABSTRACT FROM AUTHOR]

Decarbonizing the power generation and transportation sectors, responsible for ∼65% of Green House Gas (GHG) emissions globally, constitutes a crucial step to addressing climate change. Accordingly, the energy paradigm is shifting towards carbon-free and low-emission alternative fuels. Even though the current decarbonization using hydrogen is not large since 96% of global hydrogen production is relying on conventional fossil fuels that produce GHGs in the process, hydrogen fuel has been considered a promising fuel for fuel cell and combustion engines. Various renewable approaches utilizing biomass and water have been investigated to produce green hydrogen. With this, recent developments showed viability to achieve deep decarbonization in the power generation and transportation sectors. Hydrogen-powered vehicles are commercially available in many countries, and over 300,000 fuel cell appliances were sold to produce hot water and electricity. This review aims to provide an overview of the potential role of hydrogen in power generation and transportation systems, recent achievements in research development, and technical challenges to successfully applying hydrogen as a primary fuel. Especially this review will focus on the hydrogen application in power generation and transportation sectors using fuel cells, gas turbines, and internal combustion engines (ICEs). • A comprehensive review on hydrogen production and utilization. • AcHievements in hydrogen-powered system are discussed by world-wide projects. • Technical challenges in hydrogen utilization are discussed. • Ideal scenario of hydrogen-powered transportation is suggested. [ABSTRACT FROM AUTHOR]

Building load prediction algorithms are becoming an essential component of building energy technologies as intelligent building technologies are rapidly evolving and require accurate load predictions to make real-time operational decisions. This paper presents a field validation study of an autoregressive with exogenous (ARX) model, indexed with respect to time and temperature, and used for hourly building thermal load prediction with an aim for integration with real time predictive control strategies. Indexing of the ARX model implies that different sets of coefficients are used in the predictive equation depending on different time intervals and temperature ranges. Although many regressive prediction models have been proposed, no field validation has been reported in the literature, which is an essential step before implementation in actual practice. The validation study was carried out using field data from three buildings located in the main campus of Mississippi State University. The proposed model was able to predict hourly thermal load accurately and within the uncertainty bounds of the measured thermal load most of the time. Results also demonstrated that proper indexing of the model allowed it to capture different cooling and heating load profiles and abrupt changes in the load pattern. [ABSTRACT FROM AUTHOR]

In recent years, combined heat and power (CHP) systems have gained more attention from the U.S. government. Both the federal government and the state government have proposed many incentive policies to promote CHP systems. This paper focuses on analyzing and evaluating the effectiveness of existing incentive policies for CHP systems in various U.S. states. In this paper, the existing incentive policies for CHP systems in different states are classified in four categories: capital cost rebate, tax credits, low tax loan, and utility credits. Four types of buildings including hospitals, large offices, large hotels, and primary schools, located in different U.S. regions, are selected and analyzed for the CHP incentives. Using the EnergyPlus simulation software, the energy consumption of each building is obtained. Then the simulation models of a CHP system are established for each building type. From the simulation results, the payback period of the CHP systems in different locations is calculated according to local incentive policies. This payback period is then compared with the one without regard for incentive policies. The results show that most of the incentive policies could obviously shorten the payback period in various U.S. regions, however some of them seem meaningless because some incentives were not effective enough to give a favorable payback period. This paper reveals that the type and level of incentives to promote CHP systems need to be carefully determined because the effectiveness and usefulness of incentive policies are highly dependent on the CHP performance due to the capacity of the power generation unit, the operational strategy of CHP system, the climate location, and the ratio of electricity cost to fuel cost. [ABSTRACT FROM AUTHOR]

This paper presents a review on combined cooling, heating, and power (CCHP) systems. This work summarizes the methods used to perform energetic and exergetic analyses, system optimization, performance improvement studies, and development and analysis of CCHP systems, as reported in existing literature. In addition, this work highlights the most current research and emerging trends in CCHP technologies. It is envisioned that the information collected in this review paper will be a valuable source of information, for researchers, designers, and engineers, and provides direction and guidance for future research in CCHP technology. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-objective optimization model for combined heat and power is proposed. [•] The model minimizes operational cost, primary energy consumption and CO2 emissions. [•] The proposed stochastic model guarantees the optimized system operation. [•] An incentive model is presented to assist the multi-objective decision analysis. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a power generation and heat recovery model for reciprocating internal combustion engines (ICEs). The purpose of the proposed model is to provide realistic estimates of performance/efficiency maps for both electrical power output and useful thermal output for various capacities of engines for use in a preliminary CHP design/simulation process. The proposed model will serve as an alternative to constant engine efficiencies or empirical efficiency curves commonly used in the current literature for simulations of CHP systems. The engine performance/efficiency calculation algorithm has been coded to a publicly distributed FORTRAN Dynamic Link Library (DLL), and a user friendly tool has been developed using Visual Basic programming. Simulation results using the proposed model are validated against manufacturer’s technical data. [Copyright &y& Elsevier]

Abstract: Optimization of combined cooling, heating, and power (CCHP) systems operation commonly focuses only on energy cost. Different algorithms have been developed to attain optimal utilization of CCHP units by minimizing the energy cost in CCHP systems operation. However, other outcomes resulting from CCHP operation such as primary energy consumption and emission of pollutants should also be considered during CCHP systems evaluation as one would expect these outcomes can be subject to regulation. This paper presents an optimization of the operation of CCHP systems for different climate conditions based on operational cost, primary energy consumption (PEC), and carbon dioxide emissions (CDE) using an optimal energy dispatch algorithm. The results for the selected cities demonstrate that in general there is not a common trend among the three optimization modes presented in this paper since optimizing one parameter may reduce or increase the other two parameters. The only cities that show reduction of PEC while also reducing the CDE are Columbus, MS; Minneapolis, MN; and Miami, FL. For these cities the operational cost always increases when compared to the reference case consisting of using a vapor/compression cycle for cooling and natural gas for heating. On the other hand, for San Francisco and Boston, CCHP systems increase the CDE. In general, if CCHP systems increase the cost of operation, as long as energy savings and reduction of emissions are guaranteed, the implementation of these systems should be considered. [Copyright &y& Elsevier]

Abstract: The Cooling, Heating, and Power (CHP) systems have been widely recognized as a key alternative for thermal and electric energy generation because of the outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. Nevertheless, the total energy cost of CHP systems can be highly dependent on the operation of individual components. This paper presents an energy dispatch algorithm that minimizes the cost of energy (e.g., cost of electricity from the grid and cost of natural gas into the engine and boiler) based on energy efficiency constrains for each component. A deterministic network flow model of a typical CHP system is developed as part of the algorithm. The advantage of using a network flow model is that the electric and thermal energy flows through the CHP equipment can be readily visualized allowing for easier interpretation of the results. This algorithm has been used in simulations of a case study on the operation of an existing micro-CHP system. The results from the simulation are presented in the paper to demonstrate the economical advantages resulting from optimal operation. [Copyright &y& Elsevier]

Homeowners across the globe are continually seeking methods of improving energy efficiency for financial benefit and personal satisfaction. From small contributions such as swapping light bulbs to large capital investment initiatives like upgraded heating and cooling systems, homeowner decisions require accurate data and confidence in potential outcomes. This study models the path to net-zero energy for two separate heating, ventilation and air conditioning (HVAC) plus solar photovoltaic (PV) systems in residential buildings across 12 Unites States climate zones, and determines the optimal combination for each climate zone. An existing, traditional air-conditioning system with natural gas furnace is paired with a PV array. The net-zero results are compared to the same residence upgraded to a climate-customized geothermal heat pump HVAC system paired with a PV array. Results confidently favor the geothermal HVAC system + PV for climates with a significant heating demand in winter, and the baseline + PV system for cooling-dominant climates, exceeding $3,900 and $3,500 annual household savings, respectively. A major contribution is the introduction of a template that predicts the net-zero system option for climate zones outside the 12 studied. Patterns in local soil properties, sun intensity and financial incentives allow for net-zero system predictions nationwide. For example, all locations resulting in a baseline + PV net-zero system have strong sun intensity ratings. This research delivers climate-specific recommendations for the preferred net-zero HVAC + PV system through analysis of accurate energy performance and financial forecasting. Recommendations provide homeowners with valuable expectations on two HVAC + PV options along the path to a net-zero energy home. • Baseline electrical cooling/gas heating compared to geothermal heat pump system. • Climate-specific net-zero energy residential building recommendations. • Massive savings potential and short payback period in cold climates. • Net-zero energy system prediction method introduced through critical factors. • Consumer decision driver analysis for energy-efficiency projects. [ABSTRACT FROM AUTHOR]

Geothermal residential heating and cooling systems have undeniable potential savings. The possibilities of the energy savings with a geothermal heat pump system is well-established in the commercial and residential sectors. Building location has a critical impact on the performance of geothermal heat pump systems and magnitude of savings. An important contribution of this paper takes the step past technological optimization to investigate 12 climate zones across the contiguous United States. Residential homes within common neighborhoods are thoroughly analyzed by considering soil characteristics and home construction features. Within these climate zones, federal and all local incentive programs are quantified to determine an accurate expectation for capital investment payback period, a critical factor for system attractability. Ultimately, a climate zone is classified as either a promising or poor candidate for residential geothermal technology based on data from previously conducted human interest polls regarding payback period on energy savings investments. With such lasting potential delivered to the hands of consumers, geothermal energy use still experiences slow implementation. This paper conducts a study integrating data on technology, finances, and human nature to identify the prevailing barrier to widespread geothermal execution. Solid evidence on energy and monetary savings reveals the dominant barriers are initial capital investment and long payback period. This paper highlights the immense positive impact that local incentives have on affecting these two prevailing deterrents. • United States nationwide investigation of current state of residential geothermal technology deployment. • Climate-specific geothermal heat pump energy and financial analysis. • Barriers to widespread residential geothermal technology implementation. • Template for neighborhood customizable geothermal energy potential and policy status. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are an effective medium for thermal management of avionics because of their ability to absorb, store, and release high heat loads while operating within narrow temperature range in confined spaces. Despite hosting several benefits, PCM systems suffer from low thermal conductivity issue. Strategically placed fins in a volume filled with PCM has the potential to significantly improve the transient thermal performance of PCMs by improving the overall thermal conductivity and providing enhanced heat transfer surface area. Furthermore, avionics can be subjected to varying gravity conditions during flight that can have significant influence on the overall thermal performance of PCMs. This emphasizes the need to characterize the PCM performance under various gravity conditions that can impart different buoyancy induced effects in a confined system. To this end, numerical investigation to study the melting characteristics of PCM-fin configuration subjected to three different gravity conditions, i.e., (a) microgravity, (b) terrestrial gravity, and (c) hypergravity, is conducted. The influence of these three conditions on the performance of PCM-fin combination configuration is investigated and finally the temperature field, melt fraction, and melting time is reported. The performance of PCM-fin system is compared with the corresponding PCM-only configuration to highlight the benefits of adding fins under each investigated gravity condition. [ABSTRACT FROM AUTHOR]

Fibrous air filters are common devices used to remove airborne particles. Their performance is typically measured through their resistance to airflow and captured particle mass. Models describing the evolution of filter performance have been heavily researched; however, the need for improvement remains. Experimental work is irreplaceable in the development of high-fidelity models, yet the estimation of necessary variables is not trivial and may be influenced by selected measurement instruments and analysis methodologies. Therefore, the purpose of this work is to propose a framework to investigate the response of common aerosol measurement instruments, their corresponding analysis methodologies, and the application of their data. A Scanning Mobility Particle Sizer (SMPS) and Laser Aerosol Spectrometer (LAS) were selected for consideration, and their recorded data were compared against baseline measurements. The results of the experiments indicated that the SMPS and LAS yielded a ratio of estimated mass concentrations to the baseline mass concentrations of approximately 1.175 and 0.749, respectively. Regarding the SMPS, it was suggested that the measurable size range, application of a coverage factor, dynamic shape factor, and association between the curve fits and histograms were influential in the final estimates. For the LAS, the application of a curve fit, its association to the histograms, and the selection of the sampling periods were influential. Considering the results, the impact of these factors may not be considered negligible and may skew reproducibility between studies and fossilize confounding factors. Therefore, the proposed methodologies are useful in addressing potential errors in data collection and analysis. [ABSTRACT FROM AUTHOR]

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial model is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. The whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period. [ABSTRACT FROM AUTHOR]

Particulate matter air pollution and volatile organic compounds released into the air from the incomplete combustion of fossil fuels and wildfires creates significant damage to human health and the environment. Advances in air filtration and purification technology are needed to mitigate aerosol hazards. This article details an effort to explore the potential benefits of new materials and methods for the production of nonwoven air filtration media through electrospinning and stabilizing polyacrylonitrile fibers. The investigated production methods include electrospinning fibrous matting onto a stainless steel wire mesh and stabilizing the nonwoven media in a chamber furnace. The media is then tested for air filtration penetration and airflow resistance, and the fiber size distribution is measured using scanning electron microscopy. The experimental results show that the electrospun media approaches the performance criteria for airflow resistance and particle capture efficiency of high-efficiency particulate air (HEPA) filter media. Furthermore, performance estimations for electrospun media of increased thickness and for a decreased filtration velocity show potential to exceed the HEPA media resistance and efficiency criteria. Thus, it is suggested that electrospun and stabilized nonwoven fibrous media are candidates as alternatives to traditionally manufactured HEPA media and may potentially benefit modern air filtration technology and reduce hazards associated with particulate matter. Additionally, the authors recommend future exploration into the carbonization and activation of electrospun filter media for the adsorption and mitigation of volatile organic compounds as a secondary benefit, while maintaining high efficiency and low airflow resistance in the removal of particulate matter from aerosol streams. [ABSTRACT FROM AUTHOR]

Fibrous filters are commonly utilized to remove aerosols from the air to capture contaminants and pollutants prior to atmospheric release, or to clean atmospheric air itself. An ideal filter achieves the highest capture efficiency possible while providing the least airflow resistance possible. From this, the ability to predict how a filter operates based upon its functional parameters, such as filtration velocity and particle size and type, and characteristic parameters, such as its fiber size and media porosity, are integral to not only properly applying and estimating filter performance, but also facilitating the improvement and development of filtration technology. The purpose of this work is to review both classical and contemporary particle loading models to provide a comprehensive discussion built upon a foundation of first principles. The different relevant particle capture mechanisms are presented and discussed in terms of fibrous air filtration, along with the separate loading regimes. Specific loading models are then introduced considering these concepts and are separated into three categories: 1) analytical models, 2) computational models, and 3) experimental models. In short summary, analytical models have been well-covered and benefit from their ease of use but suffer from a difficulty in describing the stochastic nature of realistic particle loading. Computational models benefit from their inherent incorporation of first principles thereby addressing the dynamic nature of particle loading, however they may be difficult to apply outside of research environments due to their complexity and computationally demanding nature. Experimental models benefit from the avoidance of simplifying assumptions and the ability to correlate data describing phenomena that are difficult to quantify but are limited to applications covered by their experimental parameters and have difficulty addressing anomalies that present themselves. Suggested future work from identified research gaps focus on the lack of available data and reliable methods to measure media characteristics as well as developing models that predict the full spectrum of the loading process based upon media and particle characteristics. • Particle loading models predict filter performance from factors such as loaded mass. • Prevalent work is categorized as analytical, computational, or experimental. • Included models are presented in terms of foundational particle capture mechanisms. • Strengths and weaknesses of the model categories are presented and discussed. • Future work is identified through current research gaps. [ABSTRACT FROM AUTHOR]

• Bertrand's paradox for chords on a circle can be extended to fibers on a square, with many methods of fiber placement. • A combination approach of fiber placement methods can arrive at an acceptable level of uniformity. • Homogeneous fiber layout requires invariance to Euclidean transformations: rotation, scale, and translation. • Fiber "line density" and "fragment size" are defined and used to evaluate methods of uniform random fiber placement. • The proposed exact solution creates homogeneous uniform random fiber placement for the creation of digital twin geometry. This paper considers the random placement of fibers on a square to achieve a desired homogeneous uniform distribution, as required in production of digital twin geometry for computational analysis of composite materials. Six methods of random fiber placement were demonstrated. Bertrand's paradox for placement of random chords on a circle provided the framework to compare, contrast, and evaluate similar methods for placing fibers on a square. Simulations were performed with chord placement on a circle and fiber placement on a square. Results were evaluated using uniformity of areal line density and fragment size distributions. A combined approach for placement of fibers on a square was developed that achieved homogeneous uniform distribution. A proposed exact solution to achieve the desired uniform distribution was demonstrated. A statistical analysis and data regression demonstrated the homogeneous results achieved by the combined approach and the proposed exact solution. The objectives of this paper were achieved through demonstration of both a combined approach and a proposed exact solution to random fiber placement on a square to achieve homogeneous, uniform distribution of fibers for use in digital twin geometry for computational testing of composite materials. [ABSTRACT FROM AUTHOR]

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems' performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI's and Elsevier's databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields. [ABSTRACT FROM AUTHOR]

• Experimental investigation on additively manufactured lattices with four different topologies, namely, (a) Octet, (b) FD-Cube, (c) Tetrakaidecahedron, and (d) Cube, was conducted to analyze their periodic heat transfer characteristics. • The flow became thermally fully developed after travelling five or six rows of unit cells in the streamwise direction and heat transfer coefficient levels of Octet, FD-Cube, and TKD in this periodic heat transfer zone were similar with values between ∼216 W / m 2 K and 415 W / m 2 K. • Lattices were capable of providing ∼ 10 to 20 times higher heat transfer than the smooth channels and also yielded overall thermal-hydraulic performance value of ∼ 3 at the lowest representative Reynolds number investigated. In the present study, the flow and heat transfer characteristics of lattices with four different unit cell topologies, namely, (a) Octet, (b) FD-Cube, (c) Tetrakaidecahedron (TKD), and (d) Cube were investigated with air as the working fluid. All the samples had the designed porosity of ∼ 0.88 and unit cell size of 10 mm. The distribution of local heat transfer coefficient revealed that depending on the unit cell topology, the flow became thermally developed after travelling over five or six rows of unit cells in streamwise direction. The heat transfer coefficient values in thermally developed zones were similar for Octet, FD-Cube and TKD. Overall heat transfer coefficient provided by Octet, FD-Cube, and TKD was in the range of ∼216 W / m 2 K to 415 W / m 2 K and Cube yielded the lowest heat transfer coefficient values between 167 W / m 2 K and 290 W / m 2 K. Three different characteristic length scales, viz. (a) channel hydraulic diameter, (b) pore-diameter, and (c) fiber-diameter were used to define the Nusselt number. The usage of pore-scale dimensions such as pore-diameter and fiber-diameter provided a very coherent data set for Nusselt number of all the topologies which helped in deriving a single correlation for predicting the thermal performance. The lattices provided heat transfer augmentation of 10-20 times higher than the smooth channel counterparts. Amongst all the investigated topologies, FD-Cube incurred the highest and Cube yielded the lowest pressure drop. It was also observed that convective heat transfer coefficients obtained for different lattices can be conveniently reported in Nusselt number forms with pore diameter and/or fiber diameters as reference length scales, where such Nusselt numbers for different lattice configurations converges to a single trendline when plotted against Reynolds numbers (also based on same reference length scales). We have provided a single correlation for Nusselt numbers as a function of Reynolds numbers which fits all the lattice configurations studied here. Finally, the thermal-hydraulic performance of lattices is reported which had similar magnitude levels for all the investigated topologies and a single correlation has been proposed for THP covering all the configurations of present study. The investigated topologies provided THP of ∼ 3 at the lowest investigated Reynolds number. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infection remains a threat to global public health. Serum hepatitis B surface antigen (HBsAg) has been used in screening for HBV infection. Quantitative HBsAg assays are useful for monitoring the natural history of HBV infection and its response to therapy. The aim of this study was to determine the relationship between quantitative (qHBsAg; IU/mL) and semi-quantitative (sqHBsAg; signal-to-cutoff ratio [S/Co]) HBsAg titers in patients with chronic hepatitis B (CHB). Methods: We retrospectively included 284 samples with HBV DNA < 20 IU/mL from patients who had simultaneous qHBsAg (using electrochemiluminescence assay) and sqHBsAg tests. Patients were grouped according to their serum HBV-envelope antigen (HBeAg) status (HBeAg-negative, n = 239 and HBeAg-positive, n = 45). The Spearman test was used to analyze the correlation between the quantitative and semi-quantitative assays. Results: There was a significant linear correlation between sqHBsAg and qHBsAg in the HBeAg-negative patients (qHBsAg [IU/mL] = 0.0094 × sqHBsAg [S/Co]1.323; adjusted R2 = 0.8445; p < 0.001). There was a substantial hook effect in the assays from the HBeAg-positive patients, so we performed a stratified analysis according to qHBsAg <1000 IU/mL or ≥1000 IU/mL and found a significant positive linear correlation between sqHBsAg S/Co and qHBsAg (qHBsAg [IU/mL] = 0.072 × sqHBsAg [S/Co]1.331; adjusted R2 = 0.7878; p < 0.001) in HBeAg-positive patients with qHBsAg titers of <1000 IU/mL and a significant negative correlation in HBeAg-positive patients with qHBsAg titers of ≥1000 IU/mL (qHBsAg [IU/mL] = 8.987 × 1014 × sqHBsAg [S/Co]−3.175; adjusted R2 = 0.6350; p < 0.001). Conclusions: There was a highly linear, positive correlation between qHBsAg and sqHBsAg in HBeAg-negative CHB patients. The hook effect led to a negative correlation in HBeAg-positive CHB patients with qHBsAg titers ≥1000 IU/mL. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest contributors to the world's total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people's daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields. [ABSTRACT FROM AUTHOR]

Background/aim: In a randomized controlled trial, lenvatinib was non-inferior to sorafenib in overall survival (OS) of patients with unresectable hepatocellular carcinoma (uHCC). This study aimed to compare the effects of sorafenib and lenvatinib as first-line systemic therapy against uHCC with real-world data in chronic hepatitis B patients.Methods: This retrospective single-center study involved 132 patients with HBV-related uHCC. Propensity score matching (PSM) was used to balance the baseline characteristics, including age, sex, serum alpha-fetoprotein levels, Child-Pugh class, tumor size, and tumor stage. The primary endpoint was overall survival (OS), and the secondary endpoints included progression-free survival (PFS), time to progression (TTP), and tumor response.Results: After PSM, the final analysis included 44 patients treated with lenvatinib and 88 with sorafenib. The OS (7.0 vs 9.2 months, p = 0.070) and PFS (4.6 vs 2.4 months, p = 0.134) were comparable between the two drugs. Multivariable analysis showed that lenvatinib and sorafenib were not independent prognostic factors of OS (adjusted hazard ratio = 1.41, 95% confidence interval = 0.96-2.08, p = 0.077) after adjustment for baseline alpha-fetoprotein levels, total bilirubin levels, alanine aminotransferase level, performance status, tumor stage, and tumor size. However, the lenvatinib group had a significantly prolonged TTP (5.2 vs 2.5 months, p = 0.018) and a higher objective response rate (18.2% vs 4.5%, p = 0.020) and disease control rate (77.3% vs 47.7%, p = 0.001) than the sorafenib group.Conclusions: Our study demonstrated that lenvatinib had a comparable OS and PFS but longer TTP and better tumor response compared to sorafenib in patients with HBV-related uHCC. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of the long and short-term (LSTM) implementation with a particular emphasis on assessing the impact of transfer learning techniques in improving prediction accuracy for building energy demand and on-site power outputs using empirical data from real-world building environments. The initial study utilized simulated data from a single-family prototype building model, employing cluster analysis to segment the training and testing datasets based on distinct cooling and heating periods. Subsequently, real-world data from an existing residential building was incorporated by utilizing LSTM-based transfer learning to improve the prediction accuracy of building energy demand and on-site power generation within a target domain. The training and testing phases involved pre-processed datasets with distinct time-series datasets for environmental, electricity demand, and on-site power generation data. The input variables in the architecture of the machine learning model included environmental, time-related data, and past-day energy datasets. This study also implemented interquartile range (IQR) analysis during the data pre-processing phase to effectively bridge the gap between the source and target domain feature and label spaces to minimize discrepancies and improve the accuracy of prediction performance. The results showed the LSTM model initially developed for a source domain effectively predicted energy demand and on-site power generation across summer and winter. Within target tasks, while initial transfer learning enhancements in prediction accuracy were modest due to each domain's low relevancies in their features and labels, significant improvements were achieved following strategic data pre-processing. The results underscored the importance of detailed pre-processing analysis in LSTM-based transfer learning models for accurate energy demand forecasting in real-world settings. The integration of transfer learning with IQR analysis refined the prediction capabilities of the models under practical conditions. • LSTM-based model enhances energy prediction accuracy. • Transfer learning enhances accuracy in on-site power predictions. • Cluster analysis segments training/testing in simulated data. • Real-world data integration improves LSTM predictions. • IQR analysis bridges gaps, enhancing prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present a performance-based approach to building configuration design to improve the urban ventilation potential at the conceptual design stage, and we demonstrate its application through a case study. The target performance optimized was the ventilation potential of a district, including a region of interest at a spatial scale of hundreds of meters. To estimate this performance, we used computational fluid dynamics (CFD), coupled with an evolutionary algorithm, to optimize the design alternatives to produce the building configuration most suitable for a given set of site conditions. Three calculation components must be assembled for a CFD-based design optimization: an optimizer, a geometry/mesh generator, and a CFD solver. To provide links between the calculation components, we utilized an in-house parametric design program. A case study was conducted to test the applicability of the proposed design method to identify the optimal solutions that minimize adverse effects on the ventilation potential of the surrounding area. For a configuration of buildings in a dense urban area, the proposed design method successfully improved the design alternatives. The results show that the urban ventilation potential in the case of the optimized building configuration is 16% greater than that of the initial building configuration. [ABSTRACT FROM AUTHOR]

In the absence of experimental air filtration test data, it is difficult to predict the air filtration efficiency and airflow resistance of filter media. In this novel work, an effort is undertaken to predict the air filtration efficiency and airflow resistance of filter media based solely on scanning electron microscopy (SEM) images of the filter fabric. The analytical single fiber efficiency (SFE) model is incorporated into a machine-learning model designed to analyze digital imagery. A convolutional neural network (CNN) is developed and optimized using synthetic training data produced from digital replication of air filter media. Simulated air filter media were created to replicate the physical characteristics of actual nonwoven nanofibrous air filter media. Digital grayscale images of the simulated media provided the input data for the CNN. Analytical calculations of efficiency and resistance based on the SFE model provided target data for the CNN. The regression model included seven convolution layers and two hidden fully connected layers along with width reduction and depth expansion methods. Twelve model parameters were optimized using training and validation data of 2100 iterations of simulated media. The model was then employed to predict the air filtration behavior of actual air filter media based on an SEM image of the media. The resulting predictions of air filtration efficiency and airflow resistance based solely on an SEM image suggests the potential viability of using synthetic data derived from simulated media to train machine learning models for real-world applications. To the knowledge of the authors, this is the first such application of a CNN to predict air filtration efficiency and airflow resistance using an SEM image, synthetic data created from simulated media, and the SFE analytical model. • Convolutional neural networks enable prediction of air filtration efficiency. • Analytical single fiber efficiency models are applied to simulated media. • Computer vision with CNNs enables prediction of airflow pressure resistance. • Machine learning modeling with CNNs will advance the science of air filtration. [ABSTRACT FROM AUTHOR]

The objective of this paper is to design and optimize the high temperature metalized thin-film polymer capacitor by a combined computational and experimental method. A finite-element based thermal model is developed to incorporate Joule heating and anisotropic heat conduction arising from anisotropic geometric structures of the capacitor. The anisotropic thermal conductivity and temperature dependent electrical conductivity required by the thermal model are measured from the experiments. The polymer represented by thermally crosslinking benzocyclobutene (BCB) in the presence of boron nitride nanosheets (BNNSs) is selected for high temperature capacitor design based on the results of highest internal temperature (HIT) and the time to achieve thermal equilibrium. The c -BCB/BNNS-based capacitor aiming at the operating temperature of 250 °C is geometrically optimized with respect to its shape and volume. “Safe line” plot is also presented to reveal the influence of the cooling strength on capacitor geometry design. [ABSTRACT FROM AUTHOR]

Highlights: [•] The use of increased thermal capacitance (ITC) to reduce building cooling loads. [•] Water is circulated through a piping system in building walls or ceiling. [•] A building with and without ITC is simulated in TRNSYS. [•] ITC reduces the cooling load, especially when used in the ceiling. [Copyright &y& Elsevier]

COVID-19 forced the human population to rethink its way of living. The threat posed by the potential spread of the virus via an airborne transmission mode through ventilation systems in buildings and enclosed spaces has been recognized as a major concern. To mitigate this threat, researchers have explored different technologies and methods that can remove or decrease the concentration of the virus in ventilation systems and enclosed spaces. Although many technologies and methods have already been researched, some are currently available on the market, but their effectiveness and safety concerns have not been fully investigated. To acquire a broader view and collective perspective of the current research and development status, this paper discusses a comprehensive review of various workable technologies and methods to combat airborne viruses, e.g., COVID-19, in ventilation systems and enclosed spaces. These technologies and methods include an increase in ventilation, high-efficiency air filtration, ionization of the air, environmental condition control, ultraviolet germicidal irradiation, non-thermal plasma and reactive oxygen species, filter coatings, chemical disinfectants, and heat inactivation. Research gaps have been identified and discussed, and recommendations for applying such technologies and methods have also been provided in this article. • Comprehensive review of COVID-19 mitigation methods focusing on HVAC systems. • Investigated methods include ventilation, filtration, and air ionization. • Other featured methods include environmental conditioning and ultraviolet light. • Developing technologies include heat, nanoparticles, chemical, and plasma methods. • Identified research gaps provide paths for future work and development. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces an easily implementable and computationally efficient, ARX (autoregressive with exogenous, i.e., external, inputs) time and temperature indexed model for 1h ahead building thermal load prediction. Time and temperature indexing implies that different sets of coefficients are used in the predictive equation depending on the time of the day or the ambient temperature. The indexing and proposed structure of the model follows physically motivated interpretations of the loading conditions and thermal response of the building. One of the main contributions of the proposed model is that it allows determining the dominant factors that affect the thermal load at a given time. A free and widely adopted building energy and thermal load simulation program from the U.S. Department of Energy is used to determine the prediction accuracy of the proposed model on several different benchmark-building types: a small office building, a medium office building, a midrise apartment, and a high-rise apartment. [Copyright &y& Elsevier]