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While postdoctoral research (postdoc) training is a common step toward academic careers in science, technology, engineering and mathematics (STEM) fields, the role of postdoc training in social sciences is less clear. An increasing number of social science PhDs are pursuing postdocs. This paper aims to identify factors associated with participation in postdoc training and examines the relationship between postdoc training and subsequent career outcomes, including attainment of tenure-track faculty positions and early career salaries.

Using data from the National Science Foundation Survey of Earned Doctorates and Survey of Doctorate Recipients, this study applies propensity score matching, regression and decomposition analyses to identify the role of postdoc training on the employment outcomes of PhDs in the social science and STEM fields.

Results from the regression analyses indicate that participation in postdoc training is associated with greater PhD research experience, higher departmental research ranking and departmental job placement norms. When the postdocs and non-postdocs groups are balanced on observable characteristics, postdoc training is associated with a higher likelihood of attaining tenure-track faculty positions 7 to 9 years after PhD completion. The salaries of social science tenure-track faculty with postdoc experience eventually surpass the salaries of non-postdoc PhDs, primarily via placement at institutions that offer relatively higher salaries. This pattern, however, does not apply to STEM PhDs.

This study leverages comprehensive, nationally representative data to investigate the role of postdoc training in the career outcomes of social sciences PhDs, in comparison to STEM PhDs. Research findings suggest that for social sciences PhDs interested in academic careers, postdoc training can contribute to the attainment of tenure-track faculty positions and toward earning relatively higher salaries over time. Research findings provide prospective and current PhDs with information helpful in career planning and decision-making. Academic institutions, administrators, faculty and stakeholders can apply these research findings toward developing programs and interventions to provide doctoral students with career guidance and greater career transparency.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

This study aims to analyze the development direction of train speed, density and weight in China.

The development of China's railway in the past 40 years can be divided into 3 stages. At the stage of potential tapping and capacity expansion, it is important to improve the train weight and density by upgrading the existing lines, and improving transportation capacity rapidly. At the stage of railway speed increase, the first priority is to increase train speed, reduce the travel time of passenger train, and synchronously take into account the increase of train density and weight. At the stage of developing high-speed railway, train speed, density and weight are co-developing on demand.

The train speed of high-speed railway will be 400 km h⁻¹, the interval time of train tracking will be 3 min, and the traffic density will be more than 190 pairs per day. The running speed of high-speed freight EMU will reach 200 km h⁻¹ and above. The maximum speed of passenger train on mixed passenger and freight railway can reach 200 km h⁻¹. The minimum interval time of train tracking can be compressed to 5 min. The freight train weight of 850 m series arrival-departure track railway can be increased to 4,500–5,000 t and that of 1,050 m series to 5,500–6,400 t. EMU trains should gradually replace ordinary passenger trains to improve the quality of railway passenger service. Small formation trains will operate more in intercity railway, suburban railway and short-distance passenger transportation.

The research can provide new connotations and requirements of railway train speed, density and weight in the new railway stage.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

The purpose of this paper is to examine the effect of the intensive lifestyle modification program on weight and metabolic syndrome risk reduction in rural obese women who have no underlying non-communicable diseases in Thailand.

A randomized controlled trial was conducted. In total, 60 healthy obese women aged 30-50 years were recruited and randomly assigned to either the intervention (n=30) or control (n=30) group after health screening. Tailored nutritional counseling, health education and exercise training were included in the lifestyle modification program. Behavioral modification techniques were also incorporated. The intervention was conducted weekly for the first eight weeks, then biweekly until week 16.

The student’s t-test was used to compare mean difference between groups. The total weight loss in the intervention group (n=29) was significantly higher, 7.6±2.9 kg, compared with the control group (n=30) who lost 0.7±1.4 kg (p<0.001). The intervention group lost weight 10.2 percent from baseline which was significantly higher than that in the control group (p<0.001). Systolic and diastolic blood pressures, fasting blood sugar, and waist circumference were significantly improved. Triglyceride levels slightly improved while high density lipoprotein cholesterol was slightly lowered. The intervention group showed a statistical reduction in abnormal components of metabolic syndrome compared with the control group, with the relative risk=0.24, 95% confidence interval=0.072-0.791, and p=0.018.

Compatibility of the program activities conducted by a health professional who had achieved healthy weight loss and accepted as a role model was a key to achieving effective weight loss and metabolic syndrome risk reduction in obese women in rural areas. The program should be integrated into the conventional practice of health care centers.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

The study’s purpose was to determine Extension agents’ (n= 111) perceived level of importance, knowledge, and training needs for leadership skills. Mean Weighted Discrepancy Scores were calculated to determine training needs. Participants’ perceived responses were average to above average importance for all skills; however, the participants’ perceived responses were varied concerning knowledge for most skills. The five highest rated training needs were resolve conflict, efficiently manage time, assess community needs, effectively lead a team, and prioritize tasks. The only common training need by Agriculture & Natural Resources (ANR), Family & Consumer Sciences (FCS), and 4-H agents was resolve conflict. Create vision was a training need only identified by FCS agents. The 4-H role needs were handle emotions and handle criticism.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

Obesity and associated morbidity and mortality are major challenges for people with severe mental illness, particularly in secure (forensic) mental health care (patients who have committed a crime or have threatening behaviour). This study aims to explore experiences of weight management in secure mental health settings.

This study used a mixed-methods approach, involving thematic analysis. A survey was delivered to secure mental health-care staff in a National Health Service (NHS) mental health trust in Northern England. Focus groups were conducted with current and former patients, carers and staff in the same trust and semi-structured interviews were undertaken with staff in a second NHS mental health trust.

The survey received 79 responses and nine focus groups and 11 interviews were undertaken. Two overarching topics were identified: the contrasting perspectives expressed by different stakeholder groups, and the importance of a whole system approach. In addition, seven themes were highlighted, namely: medication, sedentary behaviour, patient motivation, catered food and alternatives, role of staff, and service delivery.

Secure care delivers a potentially “obesogenic environment", conducive to excessive weight gain. In future, complex interventions engaging wide-ranging stakeholders are likely to be needed, with linked longitudinal studies to evaluate feasibility and impact.

To the best of the authors’ knowledge, this is the first study to involve current patients, former patients, carers and multidisciplinary staff across two large NHS trusts, in a mixed-methods approach investigating weight management in secure mental health services. People with lived experience of secure services are under-represented in research and their contribution is therefore of particular importance.