The Adverse Effects of Environmental Policy in Green Markets

We model green markets in which purchasers, either firms or consumers, have higher willingness-to-pay for less polluting goods. The effectiveness of pollution reduction policies is examined in a duopoly setting. We show that duopolists´ strategic behaviour may increase pollution levels. Maximum emission standards, commonly used in green markets, improve the environmental features of products. Nonetheless, overall pollution levels will rise because government regulation also affects market shares and boosts firms´ sales. Consequently, social welfare may be reduced. We also explore the effects of technological subsidies and product charges, including differentiation of charges.emission standards; subsidies; product charges; vertically differentiated duopoly

The level of recognition of physical symptoms in patients with a major depression episode in the outpatient psychiatric practice in Puerto Rico: An observational study

BACKGROUND: This study was designed to evaluate the psychiatrists' level of recognition of somatic symptoms associated to a major depressive episode (MDE) (DSM-IV-TR criteria) and the impact of those somatic symptoms on the treatment effectiveness. METHODS: This non-interventional study was conducted in 25 medical offices in Puerto Rico from February to December 2003. It had 2 visits separated by 8 weeks. The level of recognition was determined by: the correlation between the physician clinical evaluation and their patients' self-evaluations through different validated instruments using kappa statistics. Chi-square test was used to evaluate the impact of somatic symptoms on treatment antidepressants' effectiveness. RESULTS: All the 145 recruited patients reported the presence of at least one somatic symptom associated with their current MDE. In the two visits covered by the study, a fair agreement between the psychiatrists' and the patients' reports was noted for headache, abdominal pain and upper limb pains (0.4003 ≤ κ ≥ 0.6594). For other painful symptoms and painless somatic symptoms, the Kappa values obtained were non-significant. Slight but significant reductions in depression and painful symptoms severity were observed after 8 weeks of treatment. A proportional relationship between the pain and depression severity was observed (p < 0.0001). CONCLUSION: The study results show that somatic symptoms: are very common in depressed Puerto Rican patients; are significant under-reported by psychiatrists; and have a significant impact on the antidepressant effectiveness

Promoting entrepreneurial potential in adolescents: A pilot study based on intergenerational contact

Recently there has been an increasing interest in promoting entrepreneurship among undergraduates, however, there have been few studies focusing on adolescents. The two aims of this research were to demonstrate the reliability and validity of the Attitudes to Entrepreneurship (ATE) test with a sample of Spanish adolescents, and to study the effect of using an intervention based on intergenerational contact on the entrepreneurial potential of young people. Two studies were carried out with these objectives. The results from Study 1 confirmed the reliability of the ATE test; entrepreneurial potential was related to achievement motivation and affected by gender. In Study 2, we used an experimental and control groups design and pre and post-test measures. In the classroom context, older adults were interviewed by students about their life and work experiences. Entrepreneurship was increased by the intergenerational contact in the experimental group, specifically, in the Leadership, Creativity and Achievement factors, in boys. Achievement motivation in the academic context also was increased. The intergenerational contact based on emotional implications and active participation promotes latent entrepreneurship and academic interest

Correlation between Fusarium graminearum and deoxynivalenol during the 2012/13 wheat Fusarium head blight outbreak in Argentina

Fusarium graminearum (Schwabe) is reported as the main causal agent of Fusarium head blight in Argentina. The disease causes great losses in humid and semi-humid regions of the world, reducing grain yield and quality. During 2012/13 harvest season, a severe epidemic occurred in Argentina. The aims of this work were to determine the F. graminearum incidence and deoxynivalenol accumulation in wheat grain and flour samples obtained from two of the main wheat growing regions from Argentina. Levels of the pathogen and deoxynivalenol content were correlated in heads, grains and flour. Out of 69 wheat grain samples, 55 (79.7%) showed deoxynivalenol levels between 0.4 and 8.5 μg/kg. Fusarium graminearum was the main species isolated, the isolation frequency ranged from 30 to 52% of the total grains analyzed. Correlations were observed between deoxynivalenol content, % of F. graminearum infection, presence of the pathogen in heads, grain and flour

Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation

Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers. On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, non-expert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages. In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds

Profiling the Effects of Systemic Antibiotics for Acne, Including the Narrow-Spectrum Antibiotic Sarecycline, on the Human Gut Microbiota

Treatment for moderate-to-severe acne vulgaris relies on prolonged use of oral tetracycline-class antibiotics; however, these broad-spectrum antibiotics are often associated with off-target effects and negative gastrointestinal sequelae. Sarecycline is a narrow-spectrum antibiotic treatment option. Here, we investigated the effect of prolonged sarecycline exposure, compared with broad-spectrum tetracyclines (doxycycline and minocycline) upon the colonic microbiota. Three in vitro models of the human colon were instilled with either minocycline, doxycycline or sarecycline, and we measured microbiota abundance and diversity changes during and after antibiotic exposure. Significant reductions in microbial diversity were observed following minocycline and doxycycline exposure, which failed to recover post antibiotic withdrawal. Specifically, minocycline caused a ~10% decline in Lactobacillaceae and Bifidobacteriaceae abundances, while doxycycline caused a ~7% decline in Lactobacillaceae and Bacteroidaceae abundances. Both minocycline and doxycycline were associated with a large expansion (>10%) of Enterobacteriaceae. Sarecycline caused a slight decline in bacterial diversity at the start of treatment, but abundances of most families remained stable during treatment. Ruminococcaceae and Desulfovibrionaceae decreased 9% and 4%, respectively, and a transient increased in Enterobacteriaceae abundance was observed during sarecycline administration. All populations recovered to pre-antibiotic levels after sarecycline exposure. Overall, sarecycline had minimal and transient impact on the gut microbiota composition and diversity, when compared to minocycline and doxycycline

Pricing Python Parallelism: A Dynamic Language Cost Model for Heterogeneous Platforms

Execution times may be reduced by offloading parallel loop nests to a GPU. Auto-parallelizing compilers are common for static languages, often using a cost model to determine when the GPU execution speed will outweigh the offload overheads. Nowadays scientific software is increasingly written in dynamic languages and would benefit from compute accelerators. The ALPyNA framework analyses moderately complex Python loop nests and automatically JIT compiles code for heterogeneous CPU and GPU architectures. We present the first analytical cost model for auto-parallelizing loop nests in a dynamic language on heterogeneous architectures. Predicting execution time in a language like Python is extremely challenging, since aspects like the element types, size of the iteration space, and amenability to parallelization can only be determined at runtime. Hence the cost model must be both staged, to combine compile and run-time information, and lightweight to minimize runtime overhead. GPU execution time prediction must account for factors like data transfer, block-structured execution, and starvation. We show that a comparatively simple, staged analytical model can accurately determine during execution when it is profitable to offload a loop nest. We evaluate our model on three heterogeneous platforms across 360 experiments with 12 loop-intensive Python benchmark programs. The results show small misprediction intervals and a mean slowdown of just 13.6%, relative to the optimal (oracular) offload strategy

Large scale stochastic inventory routing problems with split delivery and service level constraints

A stochastic inventory routing problem (SIRP) is typically the combination of stochastic inventory control problems and NP-hard vehicle routing problems, which determines delivery volumes to the customers that the depot serves in each period, and vehicle routes to deliver the volumes. This paper aims to solve a large scale multi-period SIRP with split delivery (SIRPSD) where a customer’s delivery in each period can be split and satisfied by multiple vehicle routes if necessary. This paper considers SIRPSD under the multi-criteria of the total inventory and transportation costs, and the service levels of customers. The total inventory and transportation cost is considered as the objective of the problem to minimize, while the service levels of the warehouses and the customers are satisfied by some imposed constraints and can be adjusted according to practical requests. In order to tackle the SIRPSD with notorious computational complexity, we first propose an approximate model, which significantly reduces the number of decision variables compared to its corresponding exact model. We then develop a hybrid approach that combines the linearization of nonlinear constraints, the decomposition of the model into sub-models with Lagrangian relaxation, and a partial linearization approach for a sub model. A near optimal solution of the model found by the approach is used to construct a near optimal solution of the SIRPSD. Randomly generated instances of the problem with up to 200 customers and 5 periods and about 400 thousands decision variables where half of them are integer are examined by numerical experiments. Our approach can obtain high quality near optimal solutions within a reasonable amount of computation time on an ordinary PC