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Abstract Retinal degeneration, a leading cause of irreversible low vision and blindness globally, can be partially addressed by retina prostheses which stimulate remaining neurons in the retina. However, existing electrode-based treatments are invasive, posing substantial risks to patients and healthcare providers. Here, we introduce a completely noninvasive ultrasonic retina prosthesis, featuring a customized ultrasound two-dimensional array which allows for simultaneous imaging and stimulation. With synchronous three-dimensional imaging guidance and auto-alignment technology, ultrasonic retina prosthesis can generate programmed ultrasound waves to dynamically and precisely form arbitrary wave patterns on the retina. Neuron responses in the brain’s visual center mirrored these patterns, evidencing successful artificial vision creation, which was further corroborated in behavior experiments. Quantitative analysis of the spatial-temporal resolution and field of view demonstrated advanced performance of ultrasonic retina prosthesis and elucidated the biophysical mechanism of retinal stimulation. As a noninvasive blindness prosthesis, ultrasonic retina prosthesis could lead to a more effective, widely acceptable treatment for blind patients. Its real-time imaging-guided stimulation strategy with a single ultrasound array, could also benefit ultrasound neurostimulation in other diseases.

Abstract Background Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment. Methods We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date. Results Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 – 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance. Conclusions Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.

[Objective] To meet the training needs of metro drivers and maintenance personnel in studying metro control circuit principles, a MTCCS (metro train control circuit simulation system) is designed to address the issues of high coupling and complex control logic in metro control circuits. [Method] The overall structure of the MTCCS is introduced, and a logical control model of metro train control circuit is established. The depth-first path search algorithm is used to search for all valid paths in the network topology diagram, establishing the mathematical relationship between nodes, branches, valid paths and component states, and cooperating with relay linkage control to establish the logical control model. Taking the control circuit of Shanghai Rail Transit Line 15 as example, simulation testing is conducted on proposed control circuit simulation system. [Result & Conclusion] The proposed control circuit simulation system can meet the learning and training needs of metro drivers and maintenance personnel in understanding the principles of metro control circuits, posing practical value.

Excessive land application of poultry litter (PL) may lead to surface runoff of nitrogen (N) and phosphorus (P), which cause eutrophication, fish death, and water pollution that ultimately have negative effects on humans and animals. Increases in poultry production in the Delmarva Peninsula underscore the need for more efficient, cost-effective, and sustainable disposal technologies for processing PL instead of direct land application. The pyrolysis conversion process can potentially produce nutrient-rich poultry litter biochar (PLB), while the pyrolysis process can change the N and P to a more stable component, thus reducing its runoff. Pyrolysis also kills off any microorganisms that would otherwise trigger negative environmental health effects. This study is to apply an integrated method and investigate the effect of pyrolysis temperature (300 °C, 500 °C), poultry litter source (different feedstock composition), and bedding material mixture (10% pine shavings) on PLB qualities and quantities. Proximate and ultimate analysis showed PL sources and bedding material addition influenced the physicochemical properties of feedstock. The SEM and BET surface results indicate that pyrolysis temperature had a significant effect on changing the PLB morphology and structure, as well as the pH value (7.78 at 300 °C vs. 8.78 at 500 °C), extractable phosphorus (P) (18.73 ppm at 300 °C vs. 11.72 ppm at 500 °C), sulfur (S) (363 ppm at 300 °C vs. 344 ppm at 500 °C), and production yield of PLBs (47.65% at 300 °C vs. 60.62% at 500 °C). The results further suggest that adding a bedding material mixture (10% pine shavings) to PLs improved qualities by reducing the content of extractable P and S, as well as pH values of PLBs. This study also found the increment in both the pore volume and the area of Bethel Farm was higher than that of Sun Farm. Characterization and investigation of qualities and quantities of PLB using the integrated framework suggest that PL from Bethel Farm could produce better-quality PLB at a higher pyrolysis temperature and bedding material mixture to control N and P runoff problems.

Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R2 value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery.

[Objective] The objective is to accurately assess whether individuals can safely evacuate during a metro station fire, promptly identify and prevent people from entering hazardous areas, providing effective measures and suggestions for personnel safe evacuation. [Method] Building on a comprehensive analysis of ASET (available safe evacuation time) and RSET (required safe evacuation time), a method for analyzing personnel safe evacuation based on real-time key indicators is proposed. Taking a metro station of Shanghai Rail Transit Line 15 as example, PyroSim fire simulation software is employed to obtain fire simulation results, determining the ASET at critical locations in the metro station. Simultaneously, Pathfinder personnel evacuation simulation software is used to obtain personnel evacuation results, determining the RSET at critical locations. By comparing the ASET and RSET at each critical location, the situation of personnel evacuation is preliminarily assessed. Additionally, by measuring three key indicators of real-time temperature, visibility and FED (fractional effective dose) at selected locations throughout the evacuation process, the safety of personnel evacuation is verified. [Result & Conclusion] By the criterion that RSET is less than ASET at critical locations, it can be determined that there is an error in personnel evacuation safety. The method based on real-time key indicator validation provides a more accurate judgment. This approach allows for timely identification of individuals entering hazardous areas, and takes into account the entire evacuation process, offering instructive measures and suggestions for personnel safe evacuation in metro station fire.

Objective. Retinal degeneration involving progressive deterioration and loss of function of photoreceptors is a major cause of permanent vision loss worldwide. Strategies to treat these incurable conditions incorporate retinal prostheses via electrically stimulating surviving retinal neurons with implanted devices in the eye, optogenetic therapy, and sonogenetic therapy. Existing challenges of these strategies include invasive manner, complex implantation surgeries, and risky gene therapy. Methods and Results. Here, we show that direct ultrasound stimulation on the retina can evoke neuron activities from the visual centers including the superior colliculus and the primary visual cortex (V1), in either normal-sighted or retinal degenerated blind rats in vivo. The neuron activities induced by the customized spherically focused 3.1 MHz ultrasound transducer have shown both good spatial resolution of 250 μm and temporal resolution of 5 Hz in the rat visual centers. An additional customized 4.4 MHz helical transducer was further implemented to generate a static stimulation pattern of letter forms. Conclusion. Our findings demonstrate that ultrasound stimulation of the retina in vivo is a safe and effective approach with high spatiotemporal resolution, indicating a promising future of ultrasound stimulation as a novel and noninvasive visual prosthesis for translational applications in blind patients.

The prediction and pre-evaluation of the thermal properties and combustion-related problems (e.g., emissions and ash-related problems) are critical to reducing emissions and improving combustion efficiency during the agricultural crop residues combustion process. This study integrated the higher heating value (HHV) model, specific heat model, and fuel indices as a new systematic approach to characterize the agricultural crop residues. Sixteen linear and non-linear regression models were developed from three main compositions of the ultimate analysis (e.g., C, H, and O) to predict the HHV of the agricultural crop residues. Newly developed HHV models have been validated with lower estimation errors and a higher degree of accuracy than the existing models. The specific heat of flue gas during the combustion process was estimated from the concentrations of C, H, O, S, and ash content under various excess air (EA) ratios and flue gas temperatures. The specific heat of agricultural crop residues was between 1.033 to 1.327 kJ/kg·K, while it was increased by decreasing the EA ratios and elevating the temperature of the flue gas. Combustion-related problems, namely corrosions, PM1.0 emissions, SOx, HCl, and ash-related problems were predicted using the fuel indices along with S and Cl concentrations, and ash compositions. Results showed that agricultural crop residues pose a severe corrosion risk and lower ash sintering temperature. This integrated approach can be applied to a wide range of biomass before the actual combustion process which may predict thermal-chemical properties and reduce the potential combustion-related emissions.

Abstract In clinical decision making, in addition to anatomical information, biomechanical properties of soft tissues may provide additional clues for disease diagnosis. Given the fact that most of diseases are originated from micron sized structures, an elastography imaging system of fine resolution (~100 µm) and deep penetration depth capable of providing both qualitative and quantitative measurements of biomechanical properties is desired. Here, we report a newly developed multi-functional ultrasonic micro-elastography imaging system in which acoustic radiation force impulse imaging (ARFI) and shear wave elasticity imaging (SWEI) are implemented. To accomplish this, the 4.5 MHz/40 MHz transducer were used as the excitation/detection source, respectively. The imaging system was tested with tissue-mimicking phantoms and an ex vivo chicken liver through 2D/3D imaging. The measured lateral/axial elastography resolution and field of view are 223.7 ± 20.1/109.8 ± 6.9 µm and 1.5 mm for ARFI, 543.6 ± 39.3/117.6 ± 8.7 µm and 2 mm for SWEI, respectively. These results demonstrate that the promising capability of this high resolution elastography imaging system for characterizing tissue biomechanical properties at microscale level and its translational potential into clinical practice.

Electricity generation and emission characteristics during the poultry litter and natural gas co-combustion process has rarely been studied. In this study, a Stirling engine was successfully integrated into the existing lab-scale swirling fluidized bed combustion system in order to further investigate the poultry litter and natural gas co-combustion process. Electricity, gaseous emissions, particulate matter (PM), and fly ash composition were analyzed under various operating conditions. Results indicated that the electricity reached 905 W under a water flow rate of 13.1 L/min and an engine head temperature of 584 °C. It was found that excess air (EA) ratios between 0.79 and 1.08 can relatively produce more electricity with lower emissions. At a secondary air (SA) height of 850 mm, secondary air/total air (SA/TA) ratios between 0.22 and 0.44 may significantly reduce NOx and CO emissions. By increasing the mixing ratio (MR), SO2 was reduced while NOx increased at the beginning of co-combustion process but then decreased again. Additionally, PM results were lower than Maryland emissions standards. The fly ash results showed a higher nutrient content (close to 16%). This study shows the possibility of using poultry litter as a sustainable energy source for energy production while emitting lower emissions in the small decentralized combustion system.

Piezoelectric arrays are widely used in non-destructive detecting, medical imaging and therapy. However, limited by traditional manufacturing methods, the array’s element is usually designed in simple geometry such as a cube or rectangle, restricting potential applications of the array. This work demonstrates an annular piezoelectric array consisting of different concentric elements printed by Mask-Image-Projection-based Stereolithography (MIP-SL) technology. The printed array displays stable piezoelectric and dielectric properties. Compared to a traditional single element transducer, the ultrasonic transducer with printed array successfully modifies the acoustic beam and significantly improves spatial resolution.

Improper land application of excess poultry waste (PW) causes environmental issues and other problems. Meanwhile there is an increasing trend of using PW as an alternative energy resource. The Higher Heating Value (HHV) is critical for designing and analyzing the PW conversion process. Several proximate-based mathematical models have been proposed to estimate the HHV of biomass, coal, and other solid fuels. Nevertheless, only a small number of studies have focused on a subclass of fuels, especially for PW. The aim of this study is to develop proximate-based regression models for an HHV prediction of PW. Sample data of PW were collected from open literature to develop regression models. The resulting models were then validated by additional PW samples and other published models. Results indicate that the most accurate model contains linear (all proximate components), polynomial terms (quadratic and cubic of volatile matter), and interaction effect (fixed carbon and ash). Moreover, results show that best-fit regression model has a higher R2 (91.62%) and lower estimation errors than the existing proximate-based models. Therefore, this new regression model can be an excellent tool for predicting the HHV of PW and does not require any expensive equipment that measures HHV or elemental compositions.

Iron cyanide complexes are common contaminants at former manufactured gas plant sites. Although combined forms of cyanide have low toxicity, iron cyanide complexes can be decomposed to HCN through photolysis. This experiment was designed to evaluate the efficiency of phytoremediation and investigate potential toxic effect for iron cyanide complexes (i.e., ferric ferrocyanide) using the cyanogenic and non-cyanogenic plants in soil. Results showed that Sorghum (a cyanogenic plant) had the highest cyanide degradation in the root zone with the removal of approximately 32% while Switchgrass (a non-cyanogenic plant) and Flax (a cyanogenic plant) had 21% and 17% removal, respectively, with 4% dissipation in unvegetated soil. It was found that roots had higher cyanide concentrations than plant shoots. Water-soluble cyanide and the weak acid dissociable (WAD) fraction in the soil had increased at the end of the experiment, suggesting that plant may enhance the mobility and bioavailability of cyanide by root exudation. The risk associated with the leaching of cyanide compounds was assessed with the toxicity assay (Microtox). However, results indicated that the leachate from the pots was not toxic. [ABSTRACT FROM AUTHOR]