The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve ...spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.
Polymeric g‐C3N4 is a promising visible‐light‐responsive photocatalyst; however, the fast recombination of charge carriers and moderate oxidation ability remarkably restrict its photocatalytic ...oxidation efficiency towards organic pollutants. To overcome these drawbacks, a self‐modification strategy of one‐step formaldehyde‐assisted thermal polycondensation of molten urea to prepare carbon‐deficient and oxygen‐doped g‐C3N4 (VC‐OCN) is developed, and the carbon vacancy concentration is well‐controlled by changing formaldehyde dosage. The VC‐OCN catalysts exhibit interesting carbon vacancy concentration‐dependent photocatalytic removal efficiency to p‐nitrophenol (PNP) and atrazine (ATN), in which VC‐OCN15 with appropriate carbon vacancy concentration displays significantly higher pollutant removal efficiency than bulk g‐C3N4. The apparent first‐order rate constant of VC‐OCN15 for PNP and ATN removal is 4.4 and 5.2 times higher than that of bulk g‐C3N4. A combination of the experimental results and theoretic calculations confirm that the synergetic effect of carbon vacancies and oxygen doping sites can not only delay the recombination of charge carriers but also facilitate adsorption of oxygen molecules on the carbon vacancies, which leads to the generation of plentiful active oxygen species including not only superoxide anion radicals but also indirectly formed hydroxyl radicals and singlet oxygen. These active oxygen species play a dominant role in the removal of target pollutants.
A strategy of one‐step formaldehyde‐assisted thermal polycondensation of molten urea to prepare carbon‐deficient and oxygen‐doped g‐C3N4 (VC‐OCN) is developed, in which carbon vacancy concentration is controllable. At a suitable carbon vacancy concentration, the VC‐OCN exhibits a significantly higher photocatalytic oxidation capacity to organic pollutants than bulk g‐C3N4, attributed to the synergetic effect of carbon vacancies and oxygen doping sites.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it ...possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.
Field surveys and questionnaires are a cornerstone of rural socioeconomic research, providing invaluable firsthand data regarding on-the-ground situations. However, cost-effective and efficient ...methods for validating the accuracy of self-reported data in such questionnaires are lacking. Biased data are likely to lead to incorrect conclusions. In this study, we propose a new index, the survey bias index (SBI), for evaluating the degree of survey bias in field surveys. This index was obtained by comparing the data recorded in questionnaires with those from portable unmanned aerial vehicles (UAVs). In a case study, we employed SBI to reveal the degree of survey bias of questionnaires in field surveys on rural homesteads. The SBI of self-reported areas of rural homesteads reached 0.439, implying that 43.9% of data were significantly different from those collected using UAVs. A greater SBI was obtained in the pre-urban zone (0.515) than in the pure rural zone (0.258). These results indicate that homestead areas in the pre-urban zone have more incentive to expand than those in the pure rural zone. UAV remote sensing can strongly support research in the field of social economy, which reveals key information hidden in field surveys and questionnaires.
China’s rural population has declined markedly with the acceleration of urbanization and industrialization, but the area under rural homesteads has continued to expand. Proper rural land use and ...management require large-scale, efficient, and low-cost rural residential surveys; however, such surveys are time-consuming and difficult to accomplish. Unmanned aerial vehicle (UAV) technology coupled with a deep learning architecture and 3D modelling can provide a potential alternative to traditional surveys for gathering rural homestead information. In this study, a method to estimate the village-level homestead area, a 3D-based building height model (BHM), and the number of building floors based on UAV imagery and the U-net algorithm was developed, and the respective estimation accuracies were found to be 0.92, 0.99, and 0.89. This method is rapid and inexpensive compared to the traditional time-consuming and costly household surveys, and, thus, it is of great significance to the ongoing use and management of rural homestead information, especially with regards to the confirmation of homestead property rights in China. Further, the proposed combination of UAV imagery and U-net technology may have a broader application in rural household surveys, as it can provide more information for decision-makers to grasp the current state of the rural socio-economic environment.
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation ...and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
A pair of chiral binaphthyl enantiomers ( S -/ R -6) incorporating a tetraphenylethene (TPE) moiety as an aggregation-induced emission (AIE) active group exhibits bright yellow circularly polarized ...electroluminescence (CP-EL) emission with a remarkable g EL value, low turn-on voltage, and high brightness in the nondoped CP organic light emitting diodes (CP-OLEDs). This work provides a new strategy to develop doping-free CP-OLED materials.
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IJS, KILJ, NUK, PNG, UL, UM
BACKGROUND During February 2020, the coronavirus disease 2019 (COVID-19) epidemic in Hubei Province, China, was at its height, requiring isolation of the population. This study aimed to compare the ...emotional state, somatic responses, sleep quality, and behavior of people in Hubei Province with non-endemic provinces in China during two weeks in February 2020. MATERIAL AND METHODS Questionnaires were completed by 939 individuals (357 men; 582 women), including 33 from Hubei and 906 from non-endemic provinces. The Stress Response Questionnaire (SRQ) determined the emotional state, somatic responses, and behavior. The Pittsburgh Sleep Quality Index (PSQI) was used to measure the duration of sleep and sleep quality. RESULTS There were 939 study participants, aged 18-24 years (35.89%) and 25-39 years (35.57%); 65.92% were university students. During a two week period in February 2020, the emotional state and behavior of participants in Hubei improved, but the quality of sleep did not. Health workers and business people became increasingly anxious, but other professionals became less anxious. The data showed that most people in Hubei Province developed a more positive attitude regarding their risk of infection and the chances of surviving the COVID-19 epidemic. CONCLUSIONS During a two-week period, front-line health workers and people in Hubei Province became less anxious about the COVID-19 epidemic, but sleep quality did not improve. Despite public awareness, levels of anxiety exist that affect the quality of life during epidemics, including periods of population quarantine. Therefore, health education should be combined with psychological counseling for vulnerable individuals.
Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis. However, machine ...learning methods generally require extra preprocessing or feature engineering, and handling large-scale data using these methods is challenging. In this study, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognise drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix. The best recognition result for drugs in urine with an accuracy of 98.05% in the prediction set was obtained using CNN with spectra as input in the matrix form. The optimal quantitation for pirimiphos-methyl was obtained using FCN with spectra in the matrix form, and the analysis was accomplished with a determination coefficient of 0.9997 and a root mean square error of 0.1574 in the prediction set. These networks performed better than the common machine learning methods. Overall, the deep learning networks provide feasible alternatives for the recognition and quantitation of SERS.
Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.
Two pairs of CPL-active binaphthyl-pyrene emitters with good photophysical properties and excellent chiroptical performances can emit high brightness blue CP-EL. The theoretical calculation results ...show that
R
-/
S
-5
can form rigid helix π-π stacking in the aggregation state which may greatly contribute to
g
EL
of CP-OLEDs.
Two pairs of CPL-active enantiomers can emit high brightness blue CP-EL. The CP-OLEDs of chiral emitters
R
-/
S
-5
showed high
g
EL
values.
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