Thaan Vuzha Nilam Tharisu: the land without a farmer becomes barren

This report forms a part of the international research project on policy and sustainable agriculture, Policies that Work for Sustainable Agriculture and Regenerated Rural Economies. The report details the findings of one of the constituent studies, undertaken by an Indian NGO, the Society for People's Education and Economic Change (SPEECH). The Importance of this project is that it concentrated on rainfed rather than irrigated agriculture - i.e. the sharp end of rural development in India, and that the focus was very much on the micro-level, looking at policy as seen from the ground. One of the recurring themes throughout the research was the importance of appreciating people as individuals, and in this spirit some of the personal qualities of the research team are shared. The research covers events in sites in the Virudhunagar district of Tamilnadu: Tiruchuli Panchayat Union and the Villur chain of tanks. This is an essentially rural area, where the need for sustainable forms of agriculture and rural livelihoods is clear. The political landscape is fractured and complex (§2.3), and the officials with the responsibility of implementing policy face significant obstacles and disincentives in doing so in response to the needs of local communities

A Conceptual Model Of Foreign Student Profiling

The proliferation of the foreign student’s enrolment is projected at 7.2 million in the year 2025 from 1.8 million in 2000. Malaysian institutions are driven by foreign student’s recruitment in line with Ministry of Higher Education vision of recruiting 200,000 foreign students by the year 2020. Although Malaysia emerging as one of top ten foreign students’ recruiter, the knowledge of the extent of foreign students’ intention to study in Malaysia is still modest. Yet, there is no precise profiling system that could infer whether these foreign students are genuinely coming into Malaysia for education purpose. To fill this study gap, we utilize the immense of information which available through the various types of social media, we adopt the user profiling method using social network analysis for the detection purpose of foreign students in Malaysian public universities

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions

Significance of 18S rDNA specific primers in the identification of genus Dunaliella

The cells survive in extreme marine environments has received significant interest due to their high valuable compounds. In the present attempt, a total of six different isolates of Dunaliella isolated from the salt pans of Andhra Pradesh, India were identified based on their morphology and cultural characteristics. Besides, the isolates were subjected to molecular identification using 18S rDNA specific primers. Out of the six isolate one was never amplified with the any of species specific primers used hence it was partially sequenced and submitted in GenBank. This study obviously describes the incidence of non carotenogenic strains (never turn from green to red) of Dunaliella bardawil and Dunaliella parva in natural environment

The key role of nitric oxide in hypoxia: hypoxic vasodilation and energy supply-demand matching

Significance: a mismatch between energy supply and demand induces tissue hypoxia with the potential to cause cell death and organ failure. Whenever arterial oxygen concentration is reduced, increases in blood flow - 'hypoxic vasodilation' - occur in an attempt to restore oxygen supply. Nitric oxide is a major signalling and effector molecule mediating the body's response to hypoxia, given its unique characteristics of vasodilation (improving blood flow and oxygen supply) and modulation of energetic metabolism (reducing oxygen consumption and promoting utilization of alternative pathways). Recent advances: this review covers the role of oxygen in metabolism and responses to hypoxia, the hemodynamic and metabolic effects of nitric oxide, and mechanisms underlying the involvement of nitric oxide in hypoxic vasodilation. Recent insights into nitric oxide metabolism will be discussed, including the role for dietary intake of nitrate, endogenous nitrite reductases, and release of nitric oxide from storage pools. The processes through which nitric oxide levels are elevated during hypoxia are presented, namely (i) increased synthesis from nitric oxide synthases, increased reduction of nitrite to nitric oxide by heme- or pterin-based enzymes and increased release from nitric oxide stores, and (ii) reduced deactivation by mitochondrial cytochrome c oxidase. Critical issues: several reviews covered modulation of energetic metabolism by nitric oxide, while here we highlight the crucial role NO plays in achieving cardiocirculatory homeostasis during acute hypoxia through both vasodilation and metabolic suppression Future directions: we identify a key position for nitric oxide in the body's adaptation to an acute energy supply-demand mismatc

Deep learning approaches to aircraft maintenance, repair and overhaul: a review

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maintenance, repair and overhaul (MRO). Challenges however are found when dealing with such large amounts of data. Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. The purpose of this paper therefore is to conduct a survey on deep learning architectures and their application in aircraft MRO. Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory, Convolutional Neural Networks and Deep Belief Networks. For each architecture, we review their main concepts, the types of problems to which these architectures are employed to, the type of data used and their outcomes. We also discuss how research in this area can be advanced by identifying current research gaps and outlining future research opportunities

Deep learning approaches to aircraft maintenance, repair and overhaul: a review

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maintenance, repair and overhaul (MRO). Challenges however are found when dealing with such large amounts of data. Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. The purpose of this paper therefore is to conduct a survey on deep learning architectures and their application in aircraft MRO. Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory, Convolutional Neural Networks and Deep Belief Networks. For each architecture, we review their main concepts, the types of problems to which these architectures are employed to, the type of data used and their outcomes. We also discuss how research in this area can be advanced by identifying current research gaps and outlining future research opportunities

Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features' importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance of estimates. Our hypothesis is that this will lead to more robust and trustworthy interpretations of the contribution of each feature to machine learning predictions. To assist test this hypothesis, we propose an extensible Framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models; (ii) predictive machine learning; (iii) feature importance quantification and (iv) feature importance decision fusion using an ensemble strategy. We also introduce a novel fusion metric and compare it to the state-of-the-art. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the feature importance ensembles studied. Results show that our feature importance ensemble Framework overall produces 15% less feature importance error compared to existing methods. Additionally, results reveal that different levels of noise in the datasets do not affect the feature importance ensembles' ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features

Peat swamp forest conservation withstands pervasive land conversion to oil palm plantation in North Selangor, Malaysia

Tropical deforestation remains one of the major global challenges of the twenty-first century driven to a large extent by the conversion of land for agricultural purposes, such as palm oil production. Malaysia is one of the world’s largest palm oil producers and has seen widespread conversion to oil palm from primary forest, including peat swamp forest (PSF). This study investigates the rate and extent of pervasive oil palm expansion in and around North Selangor Peat Swamp Forest (NSPSF) over the last three decades, exploring how land conversion has affected the region’s tropical forests, and assessing the relative success of PSF conservation measures. Time-series Landsat imagery was used to assess thematic land cover change and improvement in vegetation condition since NSPSF was given protected status in 1990. The results show a near tripling in oil palm cover throughout North Selangor, from 24,930 ha in 1989 to 70,070 ha in 2016; while at the same time tropical forest cover shrank from 145,570 ha to 88,400 ha. Despite concerns over the sustainability and environmental impact of such rapid oil palm conversion at a regional level, at the local scale, NSPSF represents a relative conservation success story. Effective land stewardship by government and non-governmental organization (NGO) management actors has limited illegal encroachment of oil palm around the reserve boundary. PSF rehabilitation measures have also markedly improved vegetation condition in NSPFS’s interior. These findings have broad significance for how oil palm agriculture is managed and especially for PSF stewardship and conservation, and the approaches described here may be usefully adopted elsewhere in Southeast Asia and around the world

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at 650∘C and 600MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications