A hybrid training method for ANNs and its application in multi faults diagnosis of rolling bearing

A hybrid training method with probabilistic adaptive strategy for feedforward artificial neural network was proposed and applied to the problem of multi faults diagnosis of rolling bearing. The traditional training method such as LM shows fast convergence speed, but it’s easy to fall into local minimum. The heuristic method such as DE shows good global continuous optimization ability, but its convergence speed is slow. A hybrid training method of LM and DE is presented, and it overcomes the defects by using the advantages of each other. Probabilistic adaptive strategy which could save the time in some situation is adopted. Finally, this method is applied to the problem of rolling bearing faults diagnosis, and compares to other methods. The results show that, high correct classification rate were achieved by LM, and hybrid training methods still continued to converge while traditional method such as LM stopped the convergence. The probabilistic adaptive strategy strengthened the convergence ability of hybrid method in the latter progress, and achieved higher correct rate

Study on the Effect of 1-Butanol Soluble Lignin on Temperature-Sensitive Gel

A protocol for the fractionation of lignin with 1-butanol as solvent has been proposed in order to improve the utilization of industry alkali lignin. 1-butanol soluble lignin (BSL) was used as a building block for temperature-sensitive hydrogel with N-isopropylacrylamide (NIPAAm) through graft polymerization. The result shows that 1-butanol fractionation is an effective method to improve the molecular weight homogeneity of lignin (PDI, 2.5 to 1.83) and increase the hydroxyl group content (0.585–1.793 mmol/g). The incorporation of BSL into the temperature-sensitive hydrogel can enhance the thermal stability and increase the hydrophobicity of the gel, which leads to a decrease in lower critical solution temperature (LCST). In addition, the compression strength, swelling ratio, and pore size of the gel can be adjusted by the dosage of lignin. This stimuli-responsive gel, with an LCST around 32 °C, is expected to be applied in the agricultural field as a pesticide carrier by stimulating release and absorption properties based on the change in natural environmental temperature

Advances in Application of Cellulose—MOF Composites in Aquatic Environmental Treatment: Remediation and Regeneration

Metal organic frameworks (MOFs) have gained remarkable interest in water treatment due to their fascinating characteristics, such as tunable functionality, large specific surface area, customizable pore size and porosity, and good chemical and thermal stability. However, MOF particles tend to easily agglomerate in nanoscale, thus decreasing their activity and processing convenience. It is necessary to shape MOF nanocrystals into maneuverable structures. The in situ growth or ex situ incorporation of MOFs into inexpensive and abundant cellulose-family materials can be effective strategies for the stabilization of these MOF species, and therefore can make available a range of enhanced properties that expand the industrial application possibilities of cellulose and MOFs. This paper provides a review of studies on recent advances in the application of multi-dimensional MOF–cellulose composites (e.g., aerogels, membranes, and bulk materials) in wastewater remediation (e.g., metals, dyes, drugs, antibiotics, pesticides, and oils) and water regeneration by adsorption, photo- or chemocatalysis, and membrane separation strategies. The advantages brought about by combining MOFs and cellulose are described, and the performance of MOF–cellulose is described and compared to its counterparts. The mechanisms of relative MOF–cellulose materials in processing aquatic pollutants are included. Existing challenges and perspectives for future research are proposed

Prediction of NOx Concentration at SCR Inlet Based on BMIFS-LSTM

As the main energy source for thermal power generation, coal generates a large amount of NOx during its incineration in boilers, and excessive NOx emissions can cause serious pollution to the air environment. Selective catalytic reduction denitrification (SCR) selects the optimal amount of ammonia to be injected for denitrification based on the measurement of NOx concentration by the automatic flue gas monitoring system. Since the automatic flue gas monitoring system has a large delay in measurement, it cannot accurately reflect the real-time changes of NOx concentration at the SCR inlet when the unit load fluctuates, leading to problems such as ammonia escape and NOx emission exceeding the standard. In response to these problems, this paper proposes an SCR inlet NOx concentration prediction algorithm based on BMIFS-LSTM. An improved mutual information feature selection algorithm (BMIFS) is used to filter out the auxiliary variables with maximum correlation and minimum redundancy with NOx concentration, and reduce the coupling and dimensionality among the variables in the data set. The dominant and auxiliary variables are then fed together into a long short-term memory neural network (LSTM) to build a prognostic model. Simulation experiments are conducted using historical operation data of a 300 MW thermal power unit. The experimental results show that the algorithm in this paper reduces the average relative error by 3.45% and the root mean square error by 1.50 compared with the algorithm without auxiliary variable extraction, which can accurately reflect the real-time changes of NOx concentration at the SCR inlet, solve the problem of delay in NOx concentration measurement, and reduce the occurrence of atmospheric pollution caused by excessive NOx emissions

Dosimetric Impact of a Tumor Treating Fields Device for Glioblastoma Patients Undergoing Simultaneous Radiation Therapy

PurposeA recent randomized phase III clinical trial in patients with glioblastoma demonstrated the efficacy of tumor treating fields (TTFields), in which alternating electric fields are applied via transducer arrays to a patient’s scalp. This treatment, when added to standard of care therapy, was shown to increase overall survival from 16 to 20.9 months. These results have generated significant interest in incorporating the use of TTFields during postoperative concurrent chemoradiation. However, the dosimetric impact of high-density electrodes on the scalp, within the radiation field, is unknown.MethodsThe dosimetric impact of TTFields electrodes in the radiation field was quantified in two ways: (1) dose calculated in a treatment planning system and (2) physical measurements of surface and deep doses. In the dose calculation comparison, a volumetric-modulated-arc-therapy (VMAT) radiation plan was developed on a CT scan without electrodes and then recalculated with electrodes. For physical measurements, the surface dose underneath TTFields electrodes were measured using a parallel plate ionization chamber and compared to measurements without electrodes for various incident beam angles and for 12 VMAT arc deliveries. Deep dose measurements were conducted for five VMAT plans using Scandidos Delta4 diode array: measured doses on two orthogonal diode arrays were compared.ResultsIn the treatment planning system, the presence of the TTFields device caused mean reduction of PTV dose of 0.5–1%, and a mean increase in scalp dose of 0.5–1 Gy. Physical measurement showed increases of surface dose directly underneath by 30–110% for open fields with varying beam angles and by 70–160% for VMAT deliveries. Deep dose measurement by diode array showed dose decrease of 1–2% in most areas shadowed by the electrodes (max decrease 2.54%).ConclusionThe skin dose in patients being treating with cranial irradiation for glioblastoma may increase substantially (130–260%) with the addition of concurrent TTFields electrodes on the scalp. However, the impact of dose attenuation by the electrodes on deep dose during VMAT treatment is of much smaller, but measureable, magnitude (1–2%). Clinical trials exploring concurrent TTFields with cranial irradiation for glioblastoma may utilize scalp-sparing techniques to mitigate any potential increase in skin toxicity

Going Nano with Confined Effects to Construct Pomegranate-like Cathode for High-Energy and High-Power Lithium-Ion Batteries

Pomegranate-like Li3V2(PO4)(3)@C (LVP@C) cathode materials are fabricated through confined effect helped by the vacuum-assisted capillary action. The performance of LixV2(PO4)(3) (x = 0-5) at an extended working voltage of 1.2-4.8 V has been studied by operando X-ray powder diffraction and hybrid functional density functional theory (DFT) calculation. The DFT calculation results suggest that Li3V2(PO4)(3) can be intercalated with another two Li+ with a stable crystalline structure, which improves the specific capacity of LVP significantly. The cathode exhibits a specific capacity of 320 mAh g(-1) with an energy density of 736 Wh kg(-1), which is one of the best performances for intercalation cathode materials for Li-ion batteries to our knowledge. Besides, the cathode showed excellent rate capability. In the working potential of 3.0-4.8 V, it exhibits a high specific capacity of 195 mAh g(-1) at 0.2 C, and even at a high rate of 30 C, it still delivers the specific capacity of 145 mAh g(-1) with a power density of 15.93 kW kg(-1). The good performance is mainly attributed to the unique pomegranate structure, which can provide continuous three-dimensional conductive networks for fast electron and Li-ion transfer. This paper provides a new strategy for synthesizing other cathode or anode materials with high energy and power density

A programmable epidermal microfluidic valving system for wearable biofluid management and contextual biomarker analysis.

Active biofluid management is central to the realization of wearable bioanalytical platforms that are poised to autonomously provide frequent, real-time, and accurate measures of biomarkers in epidermally-retrievable biofluids (e.g., sweat). Accordingly, here, a programmable epidermal microfluidic valving system is devised, which is capable of biofluid sampling, routing, and compartmentalization for biomarker analysis. At its core, the system is a network of individually-addressable microheater-controlled thermo-responsive hydrogel valves, augmented with a pressure regulation mechanism to accommodate pressure built-up, when interfacing sweat glands. The active biofluid control achieved by this system is harnessed to create unprecedented wearable bioanalytical capabilities at both the sensor level (decoupling the confounding influence of flow rate variability on sensor response) and the system level (facilitating context-based sensor selection/protection). Through integration with a wireless flexible printed circuit board and seamless bilateral communication with consumer electronics (e.g., smartwatch), contextually-relevant (scheduled/on-demand) on-body biomarker data acquisition/display was achieved

A touch-based multimodal and cryptographic bio-human-machine interface.

The awareness of individuals’ biological status is critical for creating interactive and adaptive environments that can actively assist the users to achieve optimal outcomes. Accordingly, specialized human–machine interfaces—equipped with bioperception and interpretation capabilities—are required. To this end, we devised a multimodal cryptographic bio-human–machine interface (CB-HMI), which seamlessly translates the user’s touch-based entries into encrypted biochemical, biophysical, and biometric indices. As its central component, the CB-HMI features thin hydrogel-coated chemical sensors and inference algorithms to noninvasively and inconspicuously acquire biochemical indices such as circulating molecules that partition onto the skin (here, ethanol and acetaminophen). Additionally, the CB-HMI hosts physical sensors and associated algorithms to simultaneously acquire the user’s heart rate, blood oxygen level, and fingerprint minutiae pattern. Supported by human subject studies, we demonstrated the CB-HMI’s capability in terms of acquiring physiologically relevant readouts of target bioindices, as well as user-identifying and biometrically encrypting/decrypting these indices in situ (leveraging the fingerprint feature). By upgrading the common surrounding objects with the CB-HMI, we created interactive solutions for driving safety and medication use. Specifically, we demonstrated a vehicle-activation system and a medication-dispensing system, where the integrated CB-HMI uniquely enabled user bioauthentication (on the basis of the user’s biological state and identity) prior to rendering the intended services. Harnessing the levels of bioperception achieved by the CB-HMI and other intelligent HMIs, we can equip our surroundings with a comprehensive and deep awareness of individuals’ psychophysiological state and needs

A wearable freestanding electrochemical sensing system.

To render high-fidelity wearable biomarker data, understanding and engineering the information delivery pathway from epidermally retrieved biofluid to a readout unit are critical. By examining the biomarker information delivery pathway and recognizing near-zero strained regions within a microfluidic device, a strain-isolated pathway to preserve biomarker data fidelity is engineered. Accordingly, a generalizable and disposable freestanding electrochemical sensing system (FESS) is devised, which simultaneously facilitates sensing and out-of-plane signal interconnection with the aid of double-sided adhesion. The FESS serves as a foundation to realize a system-level design strategy, addressing the challenges of wearable biosensing, in the presence of motion, and integration with consumer electronics. To this end, a FESS-enabled smartwatch was developed, featuring sweat sampling, electrochemical sensing, and data display/transmission, all within a self-contained wearable platform. The FESS-enabled smartwatch was used to monitor the sweat metabolite profiles of individuals in sedentary and high-intensity exercise settings