In the era of climate change, global agricultural systems are facing numerous, unprecedented challenges. In order to achieve food security, advanced nano-engineering is a handy tool for boosting crop ...production and assuring sustainability. Nanotechnology helps to improve agricultural production by increasing the efficiency of inputs and minimizing relevant losses. Nanomaterials offer a wider specific surface area to fertilizers and pesticides. In addition, nanomaterials as unique carriers of agrochemicals facilitate the site-targeted controlled delivery of nutrients with increased crop protection. Due to their direct and intended applications in the precise management and control of inputs (fertilizers, pesticides, herbicides), nanotools, such as nanobiosensors, support the development of high-tech agricultural farms. The integration of biology and nanotechnology into nonosensors has greatly increased their potential to sense and identify the environmental conditions or impairments. In this review, we summarize recent attempts at innovative uses of nanotechnologies in agriculture that may help to meet the rising demand for food and environmental sustainability.
Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced ...significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. In this literature, we are proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and Multilayer Perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed, in this literature, to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC as 0.789, 0.934, 0.092, 66.234, and 0.950 respectively which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for the diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset which can lead to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available.
How farmers perceive climate change has an influence on how they adapt to climate change. Climate change perception and vulnerability were assessed based on the household survey information collected ...from randomly selected 118 farmers of Kalapara subdistrict in Bangladesh. This paper identified the socio-economic covariates of climate change perception and vulnerability in relation to agricultural adaptation. It was also determined whether their perception was consistent with meteorological information. Findings revealed that the farmers had a moderate level of perception of and vulnerability to climate change. An overwhelming majority (98%) of the respondents perceived a warmer summer and 96% of them observed a colder winter compared to the past. Among the farmers, 91% believed that rainfall had increased and 97% thought that the timing of rainfall had changed. The belief of increase in soil salinity and associated loss was prevailing among 98 and 99% of them, respectively. Observed climate data were mostly aligned with the farmers' perception with respect to temperature, rainfall, floods, droughts and salinity. Positive correlations were found among the perception of climate change, the perception of vulnerability and the number of adopted adaptation practices. Farmers' level of understanding of climate change, vulnerability and adaptation practices could be improved by involving them in different organizations, such as climate field school and farmer associations. It could accelerate the dissemination of agricultural adaptation practices among them to cope with adverse agricultural impacts of climate change.
•Farmer climate change perceptions mostly tracked with meteorological data.•General farmers and climate field school farmers had the same level of perceptions.•Climate change perception and adaptation practices were positively correlated.•Climate field school farmers had better level of adoption of adaptation practices.
Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of ...emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.
Sulfur (S) metabolism plays a vital role in Cd detoxification, but the collaboration between melatonin biosynthesis and S metabolism under Cd stress remains unaddressed. Using exogenous melatonin, ...melatonin-deficient tomato plants with a silenced caffeic acid
-methyltransferase (
) gene, and
-overexpressing plants with cosuppression of sulfate transporter (
and
genes, we found that melatonin deficiency decreased S accumulation and aggravated Cd phytotoxicity, whereas exogenous melatonin or overexpression of
increased S uptake and assimilation, resulting in an improved plant growth and Cd tolerance. Melatonin deficiency promoted Cd translocation from root to shoot, but
overexpression caused the opposite effect.
overexpression failed to compensate the functional hierarchy of S when its uptake was inhibited by cosilencing of transporter
and
Our study provides genetic evidence that melatonin-mediated tolerance to Cd is closely associated with the efficient regulation of S metabolism, redox homeostasis, and Cd translocation in tomato plants.
Coastal farmers are the first group of people who feel climate-related calamities most severely, such as sea-level rise, salinity intrusion, coastal flooding, tidal surges and tropical cyclones. They ...are operating agricultural activities under these climatic conditions that affect farm productivity. This study explores farmer perceptions of changes in farm productivity and perceptions of causes of decreased farm productivity (if any) over the past 10 years compared with more than 10 years back. We partitioned the causes of decreased farm productivity into climatic and non-climatic based on the primary data collected through household survey in ten coastal subdistricts along the coast of the Bay of Bengal. We visited 381 households during September–October 2018 using a pre-tested structured interview schedule. Average monetary farm productivity in the study area was 1.98. A small proportion (11%) of the sampled farmers mentioned that farm productivity had decreased over the past years. A majority (64%) of them believed that climate change was responsible for such decreases in farm productivity. The farmers who thought that climate change was causing the decreased farm productivity were characterized by greater education, more awareness of climate change, less communication with extension agents, stronger belief in decreased cyclone and salinity, and weaker belief in decreased flood. The farmers perceived that dry period salinity, flood and coastal inundations were the major products of climate change to adversely affect crop productivity. Since agricultural adaptation to climate change requires clear understanding of the climatic impacts on farm productivity, and more than one-third of the farmers failed to identify climatic impacts on decreased farm productivity, their improvement of climate change awareness is essential. Extension organizations and other agents should promote updated climate knowledge among farmers to make them more aware of climate change issues, so that they can adapt to climate change through their agricultural activities.
Image segmentation is deemed an important task in biomedicine, often required for proper diagnosis and prognosis of many diseases. Deep learning (DL) based segmentation methods have received ...considerable attention in recent years due to the increasing availability of clinical datasets. Many novel ideas have been proposed over the years driving progress in the field of automatic segmentation research. Contrary to the theme of contemporary literature, we demonstrate that considering the background tissue segmentation task alongside the main foreground task can improve overall segmentation performance when considered from a general medical image segmentation perspective. We, therefore, propose a DL framework called Twin Segmentation Network (Twin-SegNet) that ties together two streams (foreground and background) through an image reconstruction task. A boxed Mean Squared Error loss is proposed to complement the dice losses from both streams. We furthermore propose a Wavelet Convolutional Block (WCB) to enhance the edge information extracting capabilities of both streams and also a Partial Channel Recalibration (PCR) block to allow mutual feature exchange between the two streams so that each stream can emphasize more on channels with more discriminative and relevant features. We present experimental results on five public datasets: BUSI, GLAS, ISIC-2018, MoNuSeg, and CVC-ClinicDB. Unlike conventional baselines that demonstrate convincing performance in some datasets and poor performance in others, Twin-SegNet is able to consistently achieve state-of-the-art results with impressive F1 scores of 88.46%, 93.11%, 91.61%, 81.78% and 94.69% on each dataset respectively, showing its great potential as a general segmentation framework.
•A dual-stream dynamically coupled segmentation framework, Twin-SegNet is proposed for achieving general-purpose medical image segmentation.•An image reconstruction task is formulated in order to tie together both segmentation streams and a novel boxed MSE loss is used to guide the reconstruction task.•A wavelet convolutional block (WCB) is used to enhance the edge-preserving capabilities of both foreground and background segmentation streams.•A partial channel recalibration (PCR) scheme is adopted to allow both segmentation streams to exchange feature information to encourage better segmentation performance via mutual learning.•Experiments on five different segmentation datasets, including breast tumor segmentation, gland segmentation, skin lesion segmentation, nuclei segmentation, and polyp segmentation demonstrate the efficacy of the proposed method.
Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization of autonomous vehicle technology. The importance of this task has led to vast amount of ...research efforts and many promising methods have been proposed in the existing literature. However, most of these methods have been evaluated on clean and challenge-free datasets and overlooked the performance deterioration associated with different challenging conditions (CCs) that obscure the traffic-sign images captured in the wild. In this paper, we look at the TSDR problem under CCs and focus on the performance degradation associated with them. To this end, we propose a Convolutional Neural Network (CNN) based prior enhancement focused TSDR framework. Our modular approach consists of a CNN-based challenge classifier, Enhance-Net-an encoder-decoder CNN architecture for image enhancement, and two separate CNN architectures for sign-detection and classification. We propose a novel training pipeline for Enhance-Net that focuses on the enhancement of the traffic sign regions (instead of the whole image) in the challenging images subject to their accurate detection. We used CURE-TSD dataset consisting of traffic videos captured under different CCs to evaluate the efficacy of our approach. We experimentally show that our method obtains an overall precision and recall of 91.1% and 70.71% that is 7.58% and 35.90% improvement in precision and recall, respectively, compared to the current benchmark. Furthermore, we compare our approach with different CNN-based TSDR methods and show that our approach outperforms them by a large margin.
Due to limited motor capabilities, people with upper limb disabilities have trouble utilizing a typical mouse while operating a computer. Different wearable Assistive Mouse Controllers (AMCs) have ...been developed to overcome their challenges. However, these people may not be able to realize the importance, ease of use, and social approval of these AMCs due to their fear of new technology, lack of confidence, and lack of ingenuity. These may negatively affect their attitude and intention toward accepting AMCs for equitable human-computer interaction. This study presents the development of a sensor-based head-mounted AMC, followed by an empirical analysis of its acceptance using the Technology Acceptance Model (TAM) from the socioeconomic perspective of Bangladesh. In a similar vein, we examined the effects of three additional psychological constructs-technology anxiety, confidence, and innovation, on its acceptance along with the original components of the TAM. A total of 150 individuals with stroke-induced upper limb disability participated in an online survey, and their responses were analyzed using confirmatory factor analysis and structural equation modeling, following the general least square method. Analysis revealed, about 96.44% of the participants had positive attitude towards the AMC, and almost 88.56% of them had positive intentions to accept it. Furthermore, about 68.61% of them expressed signs of anxiety, 96.35% were confident, and 94.16% of them had an innovative mindset in terms of device usage. The findings imply that individuals with an innovative mentality are more capable of comprehending the practical implications of a new technology than those without one. It is also feasible to reduce technological anxiety and boost a user's confidence while using an AMC by combining an innovative mentality with straightforward device interaction techniques. Additionally, peer encouragement and motivation can significantly enhance their positive attitude towards accepting the AMC for facilitating their interaction with a computer.
Goal: Although photoplethysmographic (PPG) signals can monitor heart rate (HR) quite conveniently in hospital environments, trying to incorporate them during fitness programs poses a great challenge, ...since in these cases, the signals are heavily corrupted by motion artifacts. Methods: In this paper, we present a novel signal processing framework which utilizes two channel PPG signals and estimates HR in two stages. The first stage eliminates any chances of a runaway error by resorting to an absolute criterion condition based on ensemble empirical mode decomposition. This stage enables the algorithm to depend very little on the previously estimated HR values and to discard the need of an initial resting phase. The second stage, on the other hand, increases the algorithm's robustness against offtrack errors by using recursive least squares filters complemented with an additional novel technique, namely time-domain extraction. Results: Using this framework, an average absolute error of 1.02 beat per minute (BPM) and standard deviation of 1.79 BPM are recorded for 12 subjects performing a run with peak velocities reaching as high as 15 km/h. Conclusion: The performance of this algorithm is found to be better than the other recently reported algorithms in this field such as TROIKA and JOSS. Significance: This method is expected to greatly facilitate the presently available wearable gadgets in HR computation during various physical activities.
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