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Internet of Things (IoT) technologies (e.g., power-efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users’ privacy concerns. Low-cost and non-intrusive IoT sensors have been proposed for residential houses for use with machine learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their non-intrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This paper proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by non-intrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.

Despite the challenges in treating glioblastomas (GBMs) with immune adjuvants, increasing evidence suggests that targeting the immune cells within the tumor microenvironment (TME) can lead to improved responses. Here, we present a closed-loop controlled, microbubble-enhanced focused ultrasound (MB-FUS) system and test its abilities to safely and effectively treat GBMs using immune checkpoint blockade. The proposed system can fine-tune the exposure settings to promote MB acoustic emission–dependent expression of the proinflammatory marker ICAM-1 and delivery of anti-PD1 in a mouse model of GBM. In addition to enhanced interaction of proinflammatory macrophages within the PD1-expressing TME and significant improvement in survival ( P

Gelatin is a representative hydrophilic protein material with remarkable biocompatibility and biodegradability. From the aspect of materials processing, gelatin also has the advantage that its entire fabrication process can be performed in an aqueous solution. However, practical application of various gelatin materials-in particular gelatin films-has thus far been limited because of their weak mechanical properties and vulnerability under aqueous environments. To overcome these disadvantages, both physical reinforcement approaches and chemical cross-linking agents have been tested. However, little research has been done to make these two roles work at the same time. In this study, cellulose nanocrystals containing aldehyde groups were prepared via a periodate oxidation process and used for cross-linkable reinforcement of gelatin-based bio-composite films. The results revealed that the di-aldehyde cellulose nanocrystal (D-CNC) could react and covalently cross-link with the amine group of the gelatin molecules via Schiff base formation and compared with neat CNC. The gelatin bio-composite film reinforced with the prepared D-CNC exhibited excellent tensile properties and water resistance, and its mechanical and hydrophilic properties could be easily controlled by adjusting the D-CNC content and was greater than addition of same amount in CNC. Therefore, D-CNC will facilitate the widespread use of existing water-soluble polymers, especially natural hydrophilic proteins and can be used in conventional application fields such as the food, pharmaceutical, and biomedical industries.

Macrophages play a key role among the immune cells in host defense. They are also known to be involved in various complex processes like angiogenesis and can infiltrate disease environments like in the case of cancer and atherosclerosis. Hence, imaging macrophages and their trafficking patterns could provide important prognostic and diagnostic markers of various human diseases. Previous attempts at imaging macrophages have been limited in depth of penetration, spatial resolution, and sensitivity. We hypothesize that labelling macrophages with microbubble (MB) contrast agents could render them visible to ultrasound and potentially overcome these limitations to enable imaging of macrophage trafficking at high resolution and sensitivity deep into tissues. Here, we show that macrophages can be labeled with MB to make them echogenic. Subsequently, we demonstrate that there are ultrasound exposure settings where these macrophages can retain high echogenicity and viability without affecting their functionality. We also confirmed that these MB-labeled macrophages can be imaged invivo (7-fold increase in CNR as compared to unlabeled macrophages) following intratumoral injection into subcutaneous tumors. In summary, our research findings demonstrate a highly sensitive approach to study macrophage interactions with disease environments that may lead to novel biomarkers for disease diagnosis.

Brain tumors are particularly challenging malignancies, due to their location in a structurally and functionally distinct part of the human body – the central nervous system (CNS). The CNS is separated and protected by a unique system of brain and blood vessel cells which together prevent most bloodborne therapeutics from entering the brain tumor microenvironment (TME). Recently, great strides have been made through microbubble (MB) ultrasound contrast agents in conjunction with ultrasound energy to locally increase the permeability of brain vessels and modulate the brain TME. As we elaborate in this review, this physical method can effectively deliver a wide range of anticancer agents, including chemotherapeutics, antibodies, and nanoparticle drug conjugates across a range of preclinical brain tumors, including high grade glioma (glioblastoma), diffuse intrinsic pontine gliomas, and brain metastasis. Moreover, recent evidence suggests that this technology can promote the effective delivery of novel immunotherapeutic agents, including immune check-point inhibitors and chimeric antigen receptor T cells, among others. With early clinical studies demonstrating safety, and several Phase I/II trials testing the preclinical findings underway, this technology is making firm steps towards shaping the future treatments of primary and metastatic brain cancer. By elaborating on its key components, including ultrasound systems and MB technology, along with methods for closed-loop spatial and temporal control of MB activity, we highlight how this technology can be tuned to enable new, personalized treatment strategies for primary brain malignancies and brain metastases.

The consumption and production of energy are more dynamic as distributed energy resources (DERs) such as solar photovoltaics (PV) are deployed within the electric distribution system. The existing techniques for bulk generation do not take full advantage of DERs and can lead to wasted energy and higher costs for both utility companies and consumers. Commercial and residential building energy management systems are usually on a fixed schedule and are not able to respond to changes in energy price instantaneously. There is a need for a real-time pricing structure that can accommodate the fluctuating cost of energy based on supply and demand, and for an energy management system that is able to respond to the dynamic utility rate. As such, there is a need for a robust energy management control strategy and methodology to validate new approaches. To address this gap, a strategy to control heating, ventilation, and air conditioning (HVAC) systems in a residential house was developed along with a validation methodology. A model of predictive control was implemented to optimize the thermostat setpoints and minimize energy cost for an individual residential house while maintaining thermal comfort of residents. This model was integrated with EnergyPlus simulation via an open source co-simulation platform previously developed at the National Institute of Standards and Technology (NIST). Total energy consumption and cost for consumers were compared between a case with the proposed model and a baseline case that used fixed-temperature setpoint control. The simple dynamic pricing model used in simulations was proportional to the demand of energy at that time of day. This work will contribute to the development of dynamic utility pricing models and residential control strategies for grid-interactive buildings and homes. The outcome of this research can be expanded to different building models or locations in future work.

Transcranial focused ultrasound (tFUS) therapy shows great potential in several applications, including blood brain barrier (BBB) opening. In these applications two important aspects are the registration of the skull to CT images for aberration correction and monitoring the microbubble (MB) activity using methods like passive acoustic mapping (PAM). Currently the image registration is performed using MRI as the tFUS ultrasound arrays with piezoelectric transducers are not optimized as wideband transducers for higher resolution imaging rather they are tuned to MB excitation and detection frequencies. The potential of capacitive micromachined ultrasonic transducer (CMUT) as wideband and low noise receiver for tFUS has been already realized. However, CMUT has some unique characteristics such as control of operation mode which in turn can change its frequency in a very broad range, from MB excitation, to detection and skull imaging for image registration. In this work, we demonstrate the feasibility of multi-mode operation of CMUT operation for tFUS, where the same commercially available CMUT is used for both MB activity monitoring for PAM as well as skull imaging. Furthermore, experiments show that the same array can potentially be used for both MB activity excitation/detection in addition to skull imaging. This is achieved by using the same CMUT in conventional and collapsed mode effectively utilizing a frequency range of 0.5 MHz to 5 MHz.

In applications such as liver or transcranial imaging, contrast-enhanced ultrasound (CEUS) imaging is challenged by high levels of patient-specific attenuation and physiological motion. To generate high contrast images of targets below highly attenuating tissues, we proposed and investigated the method of using very low frequency (100 kHz) ultrasound to modulate and image microbubbles at 2 or 5 MHz. Results of free field of imaging microbubbles in water indicate that it is feasible to use 100 kHz ultrasound to modulate the scattering amplitude of microbubbles. Images acquired in a phantom indicate that the proposed method provides significantly higher contrast-to-tissue ratio (22.7 to 24.6 dB) than amplitude modulation pulse inversion (10.4 to 11.9 dB). In addition, contrast degradation due to tissue motion was overcome by singular value decomposition-based clutter filter. Transcranial images obtained using 2 MHz imaging pulses show significant contrast enhancement (17.9 to 23.1 dB) at a depth of 3 cm when imaging through the temporal bone of the human skull.

Conventional thermoelectric energy harvester design strategies often focus on improving the material property thermoelectric figure of merit (ZT); however, enhanced performance through improved materials cannot be achieved without optimized integration of subcomponents. The low voltage output from a thermoelectric module makes it challenging to design a practically usable wearable energy harvester. In addition to consideration of effective heat dissipation along with matching module geometry, a voltage needs to be boosted at a usable voltage value to utilize produced power. Using a DC to DC voltage step-up converter adds additional design complexity as the power cannot be harnessed at maximum power point of thermoelectric module due to input impedance requirement of the power conditioning circuit. Moreover, additional power loss occurs due to inherent voltage conversion inefficiency, which also depends on the harvesting voltage value. A recent design framework on wearable thermoelectric energy harvester assumed that a Maximum Power Point Tracking (MPPT) boost converter can be utilized for thermoelectric energy harvester systems, however it is unable to accurately determine the maximum power point during operation because of the transient nature of thermoelectric systems. Additionally, an MPPT circuit consumes power and adds complexity to the system design, which may not economically justify the use of such a circuit. This work proposes an encompassing thermoelectric system design framework for small scale energy harvesting using only state-of-the-art commercial products. Particularly, thermoelectric module geometry design is examined with the incorporation of a DC to DC voltage step-up converter without the use of MPPT. The framework can provide guidance for further development of subcomponents and materials, as well as system integration. An operational wearable energy harvester system was built using off-the-shelf components and demonstrated a usable power output which provides experimental evidence for the proposed design strategy.

Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand response (DR) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs, but it is also important for HEMS to account for occupant preferences to ensure occupant satisfaction. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foresee™ HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact on energy consumption and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while also maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful in controlling the electrical grid and developing HEMS solutions.

Focused ultrasound–mediated delivery of RNA-loaded nanoparticles increases brain tumor cell apoptosis through oncogene targeting., RNA-based therapies offer unique advantages for treating brain tumors. However, tumor penetrance and uptake are hampered by RNA therapeutic size, charge, and need to be “packaged” in large carriers to improve bioavailability. Here, we have examined delivery of siRNA, packaged in 50-nm cationic lipid-polymer hybrid nanoparticles (LPHs:siRNA), combined with microbubble-enhanced focused ultrasound (MB-FUS) in pediatric and adult preclinical brain tumor models. Using single-cell image analysis, we show that MB-FUS in combination with LPHs:siRNA leads to more than 10-fold improvement in siRNA delivery into brain tumor microenvironments of the two models. MB-FUS delivery of Smoothened (SMO) targeting siRNAs reduces SMO protein production and markedly increases tumor cell death in the SMO-activated medulloblastoma model. Moreover, our analysis reveals that MB-FUS and nanoparticle properties can be optimized to maximize delivery in the brain tumor microenvironment, thereby serving as a platform for developing next-generation tunable delivery systems for RNA-based therapy in brain tumors.

As the interest in the recycling of byproducts and resources derived from agriculture has increased, research on the application of sericin abandoned in the silk industry has been continuously carried out, but there is a limit due to the weak mechanical properties. Recently, nanocellulose has been attracting attention as an optimal reinforcing material to improve the deficient physical properties of natural polymers. In the present study, bamboo-derived cellulose nanofibrils (B-CNFs) were prepared and used as a facile reinforcer to improve the mechanical properties of the sericin film. B-CNF was prepared via simple and eco-friendly ultrasonic treatment and whole bio-nanocomposite film fabrication process conducted under aqueous solution condition. The prepared B-CNF was well distributed into the glycerol-plasticized sericin matrix without agglomeration until 10 wt% B-CNF loading, while agglomerated cellulose nanofibrils appeared after 20 wt% B-CNF loading. This uniform distribution of B-CNF not only greatly improved the properties of the sericin film but also affected the hydrophilic properties of bio-nanocomposite film. Moreover, the prepared B-CNF reinforced bio-nanocomposite films had notable antioxidant activity without any additional antioxidant ingredients. These findings support the potential use of B-CNF reinforced sericin films in active food packaging, drug delivery carriers, and in wound dressing materials. Silk and bamboo-derived natural polymeric raw materials and aqueous solvent-based film manufacturing processes will strongly provide cost efficiency and environmental friendliness and can be a suitable replacement of petroleum-based polymer industry and its related application fields.

Limited heat transfer between thermoelectric modules and external heat reservoirs reduces the temperature difference imposed on thermoelectric materials, which reduces the power output of thermoelectric generators. In this study, the addition of macroscopic pores into thermoelectric materials is proposed as one way for resolving the issue. A semi-empirical model that relates the conductivities to the level of porosity is used for modeling the effect of porosity. The maximum power and other relevant parameters are compared between the generators with and without porosity at a realistic condition. An analytic model for evaluating economic performance is utilized to study the economic benefits of the implementation of porosity in thermoelectric elements. We demonstrate that the use of macroporous thermoelectric elements can effectively decrease the thermal conductance of the thermoelectric module, resulting in improved performance. The amount of raw materials needed to produce thermoeletric modules can be reduced simultaneously, resulting in economic benefits.

This paper presents a co-simulation platform which combines a building simulation tool with a cyber-physical systems (CPS) approach. Residential buildings have a great potential of energy reduction by controlling home equipment based on usage information. A CPS can eliminate unnecessary energy usage on a small, local scale by autonomously optimizing equipment activity, based on sensor measurements from the home. It can also allow peak shaving from the grid if a collection of homes are connected. However, lack of verification tools limits effective development of CPS products. The present work integrates EnergyPlus, which is a widely adopted building simulation tool, into an open-source development environment for CPS released by the National Institute of Standards and Technology (NIST). The NIST environment utilizes the IEEE high-level architecture (HLA) standard for data exchange and logical timing control to integrate a suite of simulators into a common platform. A simple CPS model, which controls local heating, ventilation, and cooling (HVAC) temperature set-point based on environmental conditions, was tested with the developed co-simulation platform. The proposed platform can be expanded to integrate various simulation tools and various home simulations, thereby allowing for cosimulation of more intricate building energy systems.

RNA-based therapies offer unique advantages for treating pediatric brain tumors. However, the systemic delivery remains a major problem due to degradation of unmodified RNA in biological fluids, poor brain accumulation, and poor cancer cell uptake or escape from the endosomal lipid bilayer barrier. While nanoparticle encapsulation can prolong circulation time and facilitate cellular uptake, their accumulation in brain tumor remains particularly poor due to their low permeability across the blood-brain barrier and limited intratumoral penetration. Focused ultrasound, when combined with circulating microbubbles (MB-FUS) provides a physical method to transiently modulate the brain tumor microenvironment (TME) and improve nanoparticle delivery. Here, we have examined the delivery of siRNA targeting the Smoothened (SMO) pathway, packaged in 50 nm cationic lipid-polymer hybrid nanoparticles (cLPH:siRNA-SMO), combined with MB-FUS in murine SmoA2 sonic hedgehog (SHH) medulloblastoma. At 30 hours after treatment, we observed the depletion of the SMO protein target, responsible for driving SHH medulloblastoma formation and growth, in mice that had received treatment with MB-FUS and cLPH:siRNA-SMO, but not with cLPH:siRNA-SMO alone. We also confirmed that SMO protein depletion was spatially achieved in the tumor regions with detected cLPH:siRNA-SMO using FISH assay, and that there was 15 fold induction of tumor cell apoptosis compared to tumors in mice that had received cLPH:siRNA-SMO alone. The limited induction of apoptosis was observed with either cLPH:siRNA (non-targeting) or MB-FUS and cLPH:siRNA (non-targeting), suggest that the observed apoptosis induction in the SmoA2 model was the direct result of SMO depletion rather than nonspecific effects of MB-FUS or cLPH:siRNA. Our findings provide a paradigm shift in drug delivery in brain tumors, where physical methods and nanotechnology are tuned together to develop rational strategies for the effective delivery of nucleic acids in brain tumors.

Based on the data from U.S. Energy Information Administration (EIA), the total annual energy consumed by buildings in the United States has increased by 325% over the past 70 years. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control heating, ventilation, and air conditioning (HVAC) systems. However, the complex and costly installation process of occupancy sensors prolongs the return on investment for the residential sector. This paper presents a cost-effective approach to occupancy detection utilizing a two-layer detection scheme based on data obtained from multiple non-intrusive sensors (temperature and motion). The sensor data were consumed by multiple heuristic models (lower layer) for recognizing a set of human activities (door handle touch, water usage, and motion near the door area). As non-intrusive sensors, such as temperature sensors, may lead to less accurate occupancy information, a data fusion scheme using machine learning (upper layer) is utilized to holistically validate any individual sensor. The proposed two-layer methodology enhances the validity and reliability of occupancy detection. The human activities data was used to train and test four machine learning models (Random Forest, Decision Tree, K-Nearest Neighbor, and Support Vector Machine). The proposed occupancy detection system was installed in a 62 m2 living lab. Four temperature sensors and one motion sensor were used to collect the environmental information for 54 days. The validity of the proposed detection system was verified by the accuracy and the F1-score of each model. In all machine learning models, the two-layer detection system showed significant improvements to the accuracy and the F1-score over the current state-of-the-art approach with the same data. As such, the proposed work demonstrated similar or improved level of the accuracy (95%) and F1-score (95%) over other works, while using reduced sensor density.

Recent studies on improving the thermoelectric figure of merit (ZT) have advanced research into self-powered, wearable technologies using thermoelectric generators. However, previous design approaches do not consider structurally practical heat sink and module geometries, the use of a boost converter, or the size constraint of the generator due to aesthetic appeal, all of which lower the overall power output. Additionally, the reduced efficiency in using a boost converter changes the electrical and thermal load matching conditions for maximum power. In this study, the limitations of practicality were considered for a wearable thermoelectric generator that utilizes a state-of-the-art boost converter and an optimized heat sink. Heat sink fin geometries and thermoelectric module geometries were explored to maximize the power output within a 42.0 cm2 area and a 1.0 cm total height, in order to justify the wearability of the energy harvester. With optimized values of fin and module heights, the system was designed to produce 0.48 mW of electrical power at a boosted output voltage of 3.0 V, enough to power a small heart-rate monitor.

This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, machine learning structure modeling, and so forth.

A two-year-old castrated male Pomeranian dog was referred with the chief complaints of coughing and subcutaneous emphysema. On physical examination, the crepitant areas were palpable. When auscultated, the right chest was absent of respiratory sound, while the sound of the opposite side was enhanced. Radiographs presented pneumothorax and pneumomediastinum. On computed tomography, hypoattenuated bulla-like lesion at right middle lung lobe and trapped air in mediastinum were shown. After patient stabilization, surgery for excision of affected lobe was performed. During follow-up period, there were no recurrence and complication on radiographic examination. Based on clinical and pathological findings, the dog was diagnosed as congenital lobar emphysema.

Heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Since most people do not have the expertise to control the HVAC system efficiently, unnecessary energy consumption is often caused by wasteful behaviors (overheating/overcooling and operation without occupancy). New Internet of Things (IoT) products for a smart home have the potential for energy saving by reducing the unnecessary operation of the energy system in a residential house. However, many people still hesitate to adopt this product with uncertain economic benefit. This work explores a co-simulation platform to assess energy saving impact and economic benefits of occupancy driven thermostat in a residential house. An occupancy simulator was devised and utilized to consider the random nature of the occupancy in a typical single family residential house. Six HVAC system control strategies were explored based on three types of thermostats (always on; schedule based; and occupancy driven) as well as two setpoint control algorithms (fixed setpoint; and adaptive control). Energy-plus was integrated into the co-simulation platform, which evaluated energy consumption and indoor temperature of a residential house in five climate conditions. User's comfort level was evaluated using the adaptive model for the six different control strategies in each location. The result showed that the occupancy information-based control algorithm can save about between 11% and 34% of energy without significantly risking the occupant's comfort level. The work also suggests that the adaptive control model has a more tangible saving impact as IoT products can easily integrate outside temperature. Depending on the locations, adaptive control can save up to 54% of energy consumption, and the occupancy information can add additional energy saving impact by 20%. Payback period varies for different control strategies and depends on the rate of utility as well as the amount of energy saving. Compared with the most wasteful control strategy (fixed setpoint - always on), adding a thermostat with adaptive – occupancy driven control strategy can lead to less than one year of payback period regardless of location.

Nanocellulose can be categorized into cellulose nanofibers (CNFs) and cellulose nanocrystals (CNCs). CNFs and CNCs are oxidized by periodate oxidation to introduce dialdehyde groups, which can act as a crosslinking agent. Polyvinyl alcohol (PVA) is widely used in food packaging, however, it is vulnerable to aqueous environments. In this study, dialdehyde nanocellulose-reinforced PVA nanocomposite films were fabricated to enhance the water resistance. CNF and CNC were oxidized using varying amounts of sodium periodate and the selected fillers were designated as DCNF and DCNC, respectively. In the case of DCNF, physicochemical networking effect was induced to PVA, while DCNC showed only chemical network reinforcement effect. However, both PVA/DCNF and PVA/DCNC composite films have significantly improved vulnerabilities in moisture environment compared to PVA films. These results indicate that while physical network structures are important, the chemically formed network structures can play a crucial role in enhancing the water resistance of PVA films.

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption in United States, encompassing about 25% of total residential energy usage. A significant portion of energy is wasted by unnecessary operation, such as overheating/overcooling or operation without occupants. Wasteful behaviors will consume twice the amount of energy compared to energy conscious behaviors. Many market programmable thermostats exist to address this problem, however, difficulties in persistent programming of such products and lack of understanding of underlying physics prevent users from achieving tangible impact. Hence, fully autonomous energy control system is desirable to engage as many people into energy conscious behaviors as possible. Occupancy measurement is necessary components to enable fully autonomous control. Occupancy information can save energy by automatically turn off the HVAC system when the building is not occupied, or floats to a more energy-efficient setback temperature when the activity level is low. A number of existing sensor solutions available on the market include Passive Infrared (PIR), ultrasonic, Bluetooth/GPS, and CO2 sensors, but these are either too expensive, not user-friendly, or limited in detection scope. These sensors are also incapable of detecting whether or not the occupant is an animal or a human. The work in this paper proposes an economical, reliable, non-invasive package to both detect human presence in a residence of a wide variety of geometries at the time and predict future occupancy pattern, by utilizing temperature sensors. To accomplish this, thermal sensors will be attached to both ends of door handles to collect the temperature data. This data will allow us to create a schedule to identify human activity leaving and exiting the space. At the same time, we will be collecting the skin temperature to determine the human activity level for better identification of the thermal comfort zone for occupants. The prediction model for occupancy pattern will be developed from previous data by using machine learning algorithm. For verification, experimental setup was built to verify our model by comparing actual human presence data from a house with the measured and predicted occupancy pattern from the temperature sensors. Future steps include implementing a data fusion scheme into the model to combine information from multiple types of sensors.

Recently, thermoelectric modules have been considered as possible replacement to solar photovoltaic system due to its potential for combined heat and power. In the designs of solar thermoelectric cogeneration systems, a careful compromise has to be made between thermal energy and electrical power. For practical purposes, electrical power is preferred over heat energy. However, due to the current low conversion efficiencies of thermoelectric materials, increasing electrical power generation causes the overall combined efficiency to suffer. This study proposes an exergetic analysis of combined heat and power solar thermoelectric systems to maximize exergetic efficiency. The modeling is based on the previously investigated design of a solar thermoelectric generator for residential combined heat and power generation. The working conditions (cold side reservoir temperature and solar concentration) are varied to maximize exergetic efficiency without sacrificing too much electricity generation. The module geometry for thermal load matching is also suggested.

Recent reports on improved efficiencies of solar thermoelectric generation (STEG) systems have generated interest in STEGs as a competitive power generation system. In this paper, the design of a combined cooling and power utilizing concentrated solar power is discussed. Solar radiation is concentrated into a receiver connected to thermoelectric modules, which are used as a topping cycle to generate power and high grade heat necessary to run an absorption chiller. Modeling of the overall system is discussed with experimental data to validate modeling results. A numerical modeling approach is presented which considers temperature variation of the source and sink temperatures and is used to maximize combined efficiency. A system is built with a demonstrated combined efficiency of 32% in actual working conditions with power generation of 3.1 W. Modeling results fell within 3% of the experimental results verifying the approach. An optimization study is performed on the mirror concentration ration and number of modules for thermal load matching and is shown to improve power generation to 26.8 W.

Water flow rate sensors in residential homes have the capacity to revolutionize energy conservation by providing real time, user specific water usage data to the Internet of Things. Water heaters typically heat water to a constant set-temperature throughout the day. This constant heating contributes to about 18% of total home energy usage, making water heaters an especially effective target for potential energy savings. By harnessing the knowledge of hot water usage and flow rate, machine learning processes can determine an optimized water heating schedule for individual users. However, current methods to determine water flow rate involve either a complicated installation process or use of expensive equipment. The work in this paper proposes an economical, non-invasive package to both detect hot water usage and measure the flow rate, by utilizing three temperature sensors. Processing of the data quantitatively correlates temperature change of the incoming/outgoing water pipes to the water flow rate through the pipes. To accomplish this, the principle of energy conservation was applied using transient temperature measurements taken from the outer surface of both the cold inlet and hot outlet pipes. In the process of formulating energy conservation equations, there exist unknowns which will be determined by different self-testing algorithms. Developing transient and steady state equations for the inlet and outlet pipes allowed for calculations of the flow rate through a water heater to be performed. Specified conservation equations applied to both cold inlet and hot outlet pipes will enhance the accuracy and reliability of the proposed method. For verification, experimental setup was built to verify our model by comparing actual usage and flow rate measurements from a household water heater with the calculated usage and flow rate from the temperature change rate.

The traditional approach to determine an optimum current for thermoelectric cooling assumes that a refrigeration chamber is insulated and has no thermal resistance to a thermoelectric module. As a result, minimum temperature occurs when Peltier cooling matches with parasitic heat transfer and Joule heating. In practical application, minimum temperature happens when heat addition from the environment is matched with heat extracted by a thermoelectric module, and the optimum current differs from that anticipated by the traditional approach. Hence, consideration for insulation and thermal resistances via thermoelectric module should be made to achieve desirable cooling performance/refrigeration temperature. This paper presents a modeling approach to determine the optimum current as well as the optimum geometry to power a small thermoelectric vaccine delivery system for developing communities under the World Health Organization requirements. The model is derived from three energy conservation equations for temperatures at both ends of the thermoelectric materials within a module, as well as the refrigeration chamber temperature. A prototype was built and demonstrated a minimum temperature of 3.4°C. With optimized module geometry, the system is estimated to reduce power consumption by over 50% while achieving twice the temperature difference.

The latent heat of fusion of paraffin-based nanofluids has been examined to investigate the use of enhanced phase change materials (PCMs) for thermal energy storage (TES) applications. The nanofluid approach has often been exploited to enhance thermal conductivity of PCMs, but the effects of particle addition on other thermal properties affecting TES are relatively ignored. An experimental study of paraffin-based nanofluids containing various particle sizes of multi-walled carbon nanotubes has been conducted to investigate the effect of nanoparticles on latent heat of fusion. Results demonstrated that the magnitude of nanofluid latent heat reduction increases for smaller diameter particles in suspension. Three possible mechanisms – interfacial liquid layering, Brownian motion, and particle clustering – were examined to explain further reduction in latent heat, through the weakening of molecular bond structures. Although additional research is required to explore detailed mechanisms, experimental evidence suggests that interfacial liquid layering and Brownian motion cannot explain the degree of latent heat reduction observed. A finite element model is also presented as a method of quantifying nanofluid PCM energy storage performance. Thermal properties based on modified effective medium theory and an empirical relation for latent heat of fusion were applied as model parameters to determine energy stored and extracted over a given period of time. The model results show that while micro-scale particle inclusions exhibit some performance enhancement, nanoparticles in PCMs provide no significant improvement in TES performance. With smaller particles, the enhancement in thermal conductivity is not significant enough to overcome the reduction in latent heat of fusion, and less energy is stored over the PCM charge period. Therefore, the nanofluid approach may not be justifiable for energy storage applications. However, since the model parameters are dependent on the material properties of the system observed, storage performance may vary for differing nanofluid materials.

In this paper, a thermoelectric generator (TEG) energy harvesting system with a temperature-sensor-based maximum power point tracking (MPPT) method is presented. Conventional MPPT algorithms for photovoltaic cells may not be suitable for thermoelectric power generation because a significant amount of time is required for TEG systems to reach a steady state. Moreover, complexity and additional power consumption in conventional circuits and periodic disconnection of power source are not desirable for low-power energy harvesting applications. The proposed system can track the varying maximum power point (MPP) with a simple and inexpensive temperature-sensor-based circuit without instantaneous power measurement or TEG disconnection. This system uses TEG’s open circuit voltage (OCV) characteristic with respect to temperature gradient to generate a proper reference voltage signal, i.e., half of the TEG’s OCV. The power converter controller maintains the TEG output voltage at the reference level so that the maximum power can be extracted for the given temperature condition. This feedforward MPPT scheme is inherently stable and can be implemented without any complex microcontroller circuit. The proposed system has been validated analytically and experimentally, and shows a maximum power tracking error of 1.15%.