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2021 ◽  
Vol 2021 ◽  
pp. 1-9
Sapna Katiyar ◽  
Rijwan Khan ◽  
Santosh Kumar

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Hua Yang ◽  
Jungang Yang ◽  
Wendong Zhao ◽  
Cuntao Liu

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of “exploration-exploitation” into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users’ priority-based satisfaction, users’ weighted satisfaction can be improved by about 47% based on our algorithm in situations where users’ information acquisition is tight time-window constraints. In comparison, UAVs’ energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users’ weighted satisfaction by about 5% with almost the same energy consumption of UAVs.

Biomedicines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1459
Christu Rajan ◽  
Jaya Seema ◽  
Yu-Wen Chen ◽  
Tsai-Chen Chen ◽  
Ming-Huang Lin ◽  

We developed a new probe, Gd-DO3A-Am-PBA, for imaging tumors. Our results showed active targeting of Gd-DO3A-Am-PBA to sialic acid (SA) moieties, with increased cellular labeling in vitro and enhanced tumor accumulation and retention in vivo, compared to the commercial Gadovist. The effectiveness of our newly synthesized probe lies in its adequate retention phase, which is expected to provide a suitable time window for tumor diagnosis and a faster renal clearance, which will reduce toxicity risks when translated to clinics. Hence, this study can be extended to other tumor types that express SA on their surface. Targeting and MR imaging of any type of tumors can also be achieved by conjugating the newly synthesized contrast agent with specific antibodies. This study thus opens new avenues for drug delivery and tumor diagnosis via imaging.

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6678
Artur Sokolovsky ◽  
David Hare ◽  
Jorn Mehnen

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. The analysis often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and often cannot be deployed in IoT devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally efficient, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 s time window compared to the 5 s one. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows might be worth using, consequently making the use of the more cost-efficient sensors feasible. The proposed optimisation approach, as well as the statistically supported findings of the study, allow for an efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the widely accessible IoT/Edge-based vibration analysis.

Jeng-Shyang Pan ◽  
Qing-yong Yang ◽  
Shu-Chuan Chu ◽  
Kuo-Chi Chang

2021 ◽  
Vol 118 (41) ◽  
pp. e2113028118
Xuefeng Kan ◽  
Feng Zhang ◽  
Guanhui Zhou ◽  
Hongxiu Ji ◽  
Wayne Monsky ◽  

The aim of this study was to develop an interventional optical imaging (OI) technique for intraprocedural guidance of complete tumor ablation. Our study employed four strategies: 1) optimizing experimental protocol of various indocyanine green (ICG) concentrations/detection time windows for ICG-based OI of tumor cells (ICG cells); 2) using the optimized OI to evaluate ablation-heat effect on ICG cells; 3) building the interventional OI system and investigating its sensitivity for differentiating residual viable tumors from nonviable tumors; and 4) preclinically validating its technical feasibility for intraprocedural monitoring of radiofrequency ablations (RFAs) using animal models with orthotopic hepatic tumors. OI signal-to-background ratios (SBRs) among preablation tumors, residual, and ablated tumors were statistically compared and confirmed by subsequent pathology. The optimal dose and detection time window for ICG-based OI were 100 μg/mL at 24 h. Interventional OI displayed significantly higher fluorescence signals of viable ICG cells compared with nonviable ICG cells (189.3 ± 7.6 versus 63.7 ± 5.7 au, P < 0.001). The interventional OI could differentiate three definitive zones of tumor, tumor margin, and normal surrounding liver, demonstrating significantly higher average SBR of residual viable tumors compared to ablated nonviable tumors (2.54 ± 0.31 versus 0.57 ± 0.05, P < 0.001). The innovative interventional OI technique permitted operators to instantly detect residual tumors and thereby guide repeated RFAs, ensuring complete tumor eradication, which was confirmed by ex vivo OI and pathology. In conclusion, we present an interventional oncologic technique, which should revolutionize the current ablation technology, leading to a significant advancement in complete treatment of larger or irregular malignancies.

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Yu Xu ◽  
Qi Li ◽  
Zhenzhou Tang

Breathing and heartbeat are critical vital signs which reflect the health status of human beings. Aiming to accurately measure the vital sign in short time window, a novel signal processing method for Doppler radar vital sign detection is proposed. Firstly, a two-step I/Q mismatch correction method which, respectively, estimates the time invariant phase imbalance and gain ratio of I/Q channels in the calibration step and the direct-current offsets during normal operation has been proposed. By decreasing the number of estimation parameters from 5 to 2, the parameters can be effectively estimated with data distributed over shorter arc lengths. Then, to solve the discontinuity occurred in arctangent demodulation, the displacement information of chest movement is extracted from the calibrated I/Q signals by extended differentiate and cross multiply algorithm. Finally, instead of Fourier transform-based methods which require long time windows to guarantee sufficient frequency resolution, the optimal parameters of respiration and heartbeat are found by the intelligent search of the differential evolution algorithm. The experimental results show that the proposed method can accurately measure respiratory rate and heartbeat rate with a short time window. For the 8 s time window, the mean absolute errors of respiration and heartbeat were 0.52 bpm and 0.79 bpm, respectively, demonstrating its promise in real-time applications.

2021 ◽  
Jiefan Ling ◽  
Xuanyi Lin ◽  
Xiao Li ◽  
Ngan Yin Chan ◽  
Jihui Zhang ◽  

Abstract Study Objectives Insomnia and depression are common comorbid conditions in youths. Emerging evidence suggests that disrupted reward processing may be implicated in the association between insomnia and the increased risk for depression. Reduced reward positivity (RewP) as measured by event-related potential (ERP) has been linked to depression, but has not been tested in youths with insomnia. Methods Twenty-eight participants with insomnia disorder and without any comorbid psychiatric disorders and 29 healthy sleepers aged between 15-24 completed a monetary reward task, the Cued Door task, whilst electroencephalographic activity was recorded. RewP (reward minus non-reward difference waves) was calculated as the mean amplitudes within 200ms to 300ms time window at FCz. Two analyses of covariance (ANCOVAs) were conducted with age as a covariate on RewP amplitude and latency, respectively. Results Participants with insomnia had a significantly lower RewP amplitude regardless of cue types (Gain, Control, and Loss) than healthy sleepers, F (1, 51) = 4.95, p = .031, indicating blunted reward processing. On the behavioural level, healthy sleepers were more prudential (slower reaction time) in decision making towards Loss/Gain cues than their insomnia counterparts. Trial-by-trial behavioural adjustment analyses showed that, compared with healthy sleepers, participants with insomnia were less likely to dynamically change their choices in response to Loss cues. Conclusions Dysfunctional reward processing, coupled with inflexibility of behavioural adjustment in decision-making, is associated with insomnia disorder among youth, independent of mood disorders. Future studies with long-term follow-up are needed to further delineate the developmental trajectory of insomnia-related reward dysfunctions in youth.

2021 ◽  
Henrik M. Bette ◽  
Edgar Jungblut ◽  
Thomas Guhr

Abstract. Modern utility-scale wind turbines are equipped with a Supervisory Control And Data Acquisition (SCADA) system gathering vast amounts of operational data that can be used for failure analysis and prediction to improve operation and maintenance of turbines. We analyse high freqeuency SCADA-data from the Thanet offshore windpark in the UK and evaluate Pearson correlation matrices for a variety of observables with a moving time window. This renders possible an asessment of non-stationarity in mutual dependcies of different types of data. Drawing from our experience in other complex systems, such as financial markets and traffic, we show this by employing a hierarchichal k-means clustering algorithm on the correlation matrices. The different clusters exhibit distinct typical correlation structures to which we refer as states. Looking first at only one and later at multiple turbines, the main dependence of these states is shown to be on wind speed. In accordance, we identify them as operational states arising from different settings in the turbine control system based on the available wind speed. We model the boundary wind speeds seperating the states based on the clustering solution. This allows the usage of our methodology for failure analysis or prediction by sorting new data based on wind speed and comparing it to the respective operational state, thereby taking the non-stationarity into account.

2021 ◽  
pp. 107957
Marouene Chaieb ◽  
Dhekra Ben Sassi

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