The Internet of Things (IoT) has tremendous success in health care, smart city, industrial production and so on. Protected agriculture is one of the fields which has broad application prospects of IoT. Protected agriculture is a mode of highly efficient development of modern agriculture that uses artificial techniques to change climatic factors such as temperature, to create environmental conditions suitable for the growth of animals and plants. This review aims to gain insight into the state-of-the-art of IoT applications in protected agriculture and to identify the system structure and key technologies. Therefore, we completed a systematic literature review of IoT research and deployments in protected agriculture over the past 10 years and evaluated the contributions made by different academicians and organizations. Selected references were clustered into three application domains corresponding to plant management, animal farming and food/agricultural product supply traceability. Furthermore, we discussed the challenges along with future research prospects, to help new researchers of this domain understand the current research progress of IoT in protected agriculture and to propose more novel and innovative ideas in the future.
Background
Prior research has demonstrated the efficacy of internet-based cognitive behavioral therapy (ICBT) for social anxiety disorder (SAD). However, it is unclear how shame influences the efficacy of this treatment.
Objective
This study aimed to investigate the role shame played in the ICBT treatment process for participants with SAD.
Methods
A total of 104 Chinese participants (73 females; age: mean 24.92, SD 4.59 years) were randomly assigned to self-help ICBT, guided ICBT, or wait list control groups. For the guided ICBT group, half of the participants were assigned to the group at a time due to resource constraints. This led to a time difference among the three groups. Participants were assessed before and immediately after the intervention using the Social Interaction Anxiety Scale (SIAS), Social Phobia Scale (SPS), and Experience of Shame Scale (ESS).
Results
Participants’ social anxiety symptoms (self-help: differences between pre- and posttreatment SIAS=−12.71; Cohen d=1.01; 95% CI 9.08 to 16.32; P<.001 and differences between pre- and posttreatment SPS=11.13; Cohen d=0.89; 95% CI 6.98 to 15.28; P<.001; guided: SIAS=19.45; Cohen d=1.20; 95% CI 14.67 to 24.24; P<.001 and SPS=13.45; Cohen d=0.96; 95% CI 8.26 to 18.64; P<.001) and shame proneness (self-help: differences between pre- and posttreatment ESS=7.34; Cohen d=0.75; 95% CI 3.99 to 10.69; P<.001 and guided: differences between pre- and posttreatment ESS=9.97; Cohen d=0.88; 95% CI 5.36 to 14.57; P<.001) in both the self-help and guided ICBT groups reduced significantly after treatment, with no significant differences between the two intervention groups. Across all the ICBT sessions, the only significant predictors of reductions in shame proneness were the average number of words participants wrote in the exposure module (β=.222; SE 0.175; t96=2.317; P=.02) and gender (β=−.33; SE 0.002; t77=−3.13; P=.002). We also found a mediation effect, wherein reductions in shame fully mediated the relationship between the average number of words participants wrote in the exposure module and reductions in social anxiety symptoms (SIAS: β=−.0049; SE 0.0016; 95% CI −0.0085 to −0.0019 and SPS: β=−.0039; SE 0.0015; 95% CI −0.0075 to −0.0012).
Conclusions
The findings of this study suggest that participants’ engagement in the exposure module in ICBT alleviates social anxiety symptoms by reducing the levels of shame proneness. Our study provides a new perspective for understanding the role of shame in the treatment of social anxiety. The possible mechanisms of the mediation effect and clinical implications are discussed.
Trial Registration
Chinese Clinical Trial Registry ChiCTR1900021952; http://www.chictr.org.cn/showproj.aspx?proj=36977
The aim of the present study is to investigate the efficacy of internet-based cognitive behavior therapy (ICBT) for individuals with social anxiety (SA) and different levels of Taijin Kyofusho (TKS) in China. The ICBT program was translated into Chinese with some specific contents adapted for Chinese culture. Participants ( N = 80) with SA were assigned either to a treatment ( n = 55) or control group ( n = 25). Both groups were further divided into subgroups, based on their Taijin Kyofusho Scale (TKSS) scores. Participants in the ICBT treatment group reported significant posttreatment reductions in Social Interaction Anxiety Scale and Social Phobia Scale scores, relative to participants in the control group. In addition, participants in the treatment group with higher pretreatment TKS levels showed significantly greater reductions in TKSS scores. Results suggest that ICBT is a promising approach for the treatment of individuals with SA both with and without features of TKS. Clinical and cross-cultural implications, mechanisms of change, limitations, and future directions are discussed.
In this work, an electrochemiluminescence (ECL) aptasensor based on silver nanoparticles (AgNPs) for detection of profenofos was presented. The aptasensor was based on the affinity of the aptamer (Apt) with the target profenofos. When the Apt contacted with the profenofos, the ECL detection signals were weakened, indicating the successful construction of the aptasensor. In order to improve the aptasensor performance, the platinum electrode (PE) was modified by AgNPs due to their good conductivity and catalytic property. Meanwhile luminol-hydrogen peroxide was selected as a luminescence system. Through the standard curve experiment and specificity experiment, it was proved that the sensor had the advantages of low detection limit and good specificity. Under the optimum conditions, the aptasensor had a good linear response to profenofos from 0.5 μg/mL to 100 μg/mL (1.34 μM to 267.6 μM ), with a low detection limit of 0.13 ng/mL and a correlation coefficient (R 2 ) of 0.9903. The aptasensor was also successfully applied for detection of profenofos in real vegetables. Because the developed aptasensor had the following advantages of simple preparation, good stability, high selectivity and sensitivity, it would show great potential for other small molecule contamination detection.
In this article, a new optimized method for diagnosing and analyzing breast cancer from the mammography images is presented. In this regard, preprocessing is used to remove the Gaussian noises that are used to happen in the mammography images and also to remove the additional areas. Then, image segmentation is performed on the images to determine the areas where the contrast material is perceptible. Afterward, combined feature extraction based on a discrete wavelet transform and gray‐level co‐occurrence matrix is proposed for extracting the important information fromthe images. Finally, a new classification model based on an improved Elman neural network (ENN) has been proposed. The ENN is optimized by an improvedversion of the collective animal behavior algorithm. Simulation results areimplemented to the Mammographic Image Analysis Society database and the resultsare compared with three different methods.
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