Psychosis in Azheimer\u27s Disease

Much of the basic science literature on Alzheimer\u27s Disease (AD) reflects ongoing research into pathophysiology and neuropathology. Yet, despite reports of the association between psychotic symptoms and Alzheimer\u27s disease, relatively little is known about why such symptoms develop in certain patients and not in others. Neuroimaging and genetic studies may provide greater understanding of this association and allow clinicians and researchers to prevent, predict and treat the onset of psychotic symptoms in the future. This paper will review the current literature on the topic of psychosis in Alzheimer\u27s disease and focus on current recommendations for interventions by clinicians and caregivers

Incremental learning of concept drift from imbalanced data

Learning data sampled from a nonstationary distribution has been shown to be a very challenging problem in machine learning, because the joint probability distribution between the data and classes evolve over time. Thus learners must adapt their knowledge base, including their structure or parameters, to remain as strong predictors. This phenomenon of learning from an evolving data source is akin to learning how to play a game while the rules of the game are changed, and it is traditionally referred to as learning concept drift. Climate data, financial data, epidemiological data, spam detection are examples of applications that give rise to concept drift problems. An additional challenge arises when the classes to be learned are not represented (approximately) equally in the training data, as most machine learning algorithms work well only when the class distributions are balanced. However, rare categories are commonly faced in real-world applications, which leads to skewed or imbalanced datasets. Fraud detection, rare disease diagnosis, anomaly detection are examples of applications that feature imbalanced datasets, where data from category are severely underrepresented. Concept drift and class imbalance are traditionally addressed separately in machine learning, yet data streams can experience both phenomena. This work introduces Learn++.NIE (nonstationary & imbalanced environments) and Learn++.CDS (concept drift with SMOTE) as two new members of the Learn++ family of incremental learning algorithms that explicitly and simultaneously address the aforementioned phenomena. The former addresses concept drift and class imbalance through modified bagging-based sampling and replacing a class independent error weighting mechanism - which normally favors majority class - with a set of measures that emphasize good predictive accuracy on all classes. The latter integrates Learn++.NSE, an algorithm for concept drift, with the synthetic sampling method known as SMOTE, to cope with class imbalance. This research also includes a thorough evaluation of Learn++.CDS and Learn++.NIE on several real and synthetic datasets and on several figures of merit, showing that both algorithms are able to learn in some of the most difficult learning environments

Managing manure for sustainable organic Basmati rice production : farm-level trade-offs in Uttarakhand, India

Employing an agroecological framework, the research addressed the interconnected ecological, social and economic aspects of manure management on small-scale organic farms, investigating manure management as central to achieving the potential sustainability and livelihood benefits of organic farming. The primary objective of the work was to contribute to the advancement of knowledge around the performance dynamics, potential, and constraints of three manure management strategies (farmyard manure, vermicomposted manure, and biogas slurry produced from manure) at the farm level on smallholder, mixed, organic farms, and thereby identify opportunities for action to support farmers in the design and management of farm systems that better meet locally relevant sustainability and livelihood objectives. In order to address this objective, the case of smallholders producing organic Basmati rice in Uttarakhand, India was examined. On-farm surveys were coupled with literature review and simple systems modelling to generate integrated assessments of the sustainability of three manure management strategies at the farm level. Both vermicompost and biogas slurry were found to be improved technologies compared to farmyard manure. Vermicompost performed best on most sustainability indicator scales with the exception of yield and gross margin, where biogas slurry performed best. Improving the crop-nutritive value of manure-based fertilizers was identified as a crucial point for system improvement in the research context, implying a necessary shift in focus away from raising bulk manure inputs and towards system improvements that do not hinge on increased manure availability. Minimizing losses during handling, storage, and application were identified as important pathways to improving the crop nutritive quality of the small amount of manure fertilizers that farmers already have available. Key recommendations for reducing losses include using animal bedding, collecting urine, covering manure stockpiles with plastic sheeting, and making vermicompost when possible. Advisory support should be directed towards disseminating information on these improved manure management techniques. Future research efforts should focus on solutions for improving biogas slurry storage, since making biogas has such notable social benefits and biogas slurry will likely be the primary source of manure fertilizer for farmers making biogas.M-A

A SURVEY OF ORGANIZATIONAL INSTANT MESSAGING

Instant Messaging software has increasingly been used as an alternative communications platform in many organizations. Although Instant Messaging (IM) began as a personal tool for online communication, the software has now been implemented in many organizations and workplaces. The usefulness of IM software has been shown in literature to be positive, increasing efficiency and productivity in the workplace. This paper explores the perceptions of IM software users in the workplace. We solicited opinions to verify the claim of IM’s effect on efficiency and productivity. We also discuss the limitations and negative effects of IM. A pilot survey and data analysis techniques provide the measurement of IM software’s worth or liability to an organization. The results show what components of IM software are most commonly used and what limitations software places on the users. We also provide recommendation of possible enhancements to IM software in this paper

Craving Continuity from Cosmochemistry to Cosmochemists

No abstract availabl