Miracle-dz/UniFAR-FTUs huggingface dataset:
AVANCES Y LIMITANTES DEL PROGRAMA DE PAGO POR SERVICIOS AMBIENTALES HIDROLÓGICOS EN MÉXICO, 2003 - 2009

En el presente estudio se analiza el proceso de implementación del programa de Pago por Servicios Ambientales Hidrológicos (PSAH) en México durante el periodo 2003 - 2009, que surge como instrumento de política pública nacional dirigida a la creación de nuevos esquemas de compensación económica, para los propietarios de terrenos que cuentan con cobertura forestal, por la conservación de los bosques y de los servicios ambientales que estos proveen, incluso el hidrológico. Con este fin se recurrió al marco teórico-conceptual de Servicios Ambientales y su aceptación histórica a nivel internacional. La revisión consecuente de diversos trabajos académicos publicados sobre el tema permitió fundamentar el propósito y la utilidad del estudio, que consiste en el primer esfuerzo sistemático de análisis de implementación del programa en el país, por medio de una comparación entre los esquemas federales de PSAH en México y Costa Rica (el caso más reconocido en América Latina). El apoyo documental de la investigación se realizó mediante el trabajo de campo con aplicación de entrevistas a los tomadores de decisión, los funcionarios y algunos beneficiarios en ambos países. Finalmente, con base en cuatro criterios predeterminados (la operatividad, la regulación ambiental, el marco político-institucional y el financiero) fue posible formular los principales avances y limitantes del programa en México y reflexionar sobre sus retos a futuro, como parte del ejercicio de evaluación de las políticas públicas, el cual está dedicado al mejoramiento de sus instrumentos.
Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images

The process of road extraction from high-resolution satellite images is complex, and most researchers have shown results on a few selected set of images. Based on the satellite data acquisition sensor and geolocation of the region, the type of processing varies and users tune several heuristic parameters to achieve a reasonable degree of accuracy. We exploit two salient features of roads, namely, distinct spectral contrast and locally linear trajectory, to design a multistage framework to extract roads from high-resolution multispectral satellite images. We trained four Probabilistic Support Vector Machines separately using four different categories of training samples extracted from urban/suburban areas. Dominant Singular Measure is used to detect locally linear edge segments as potential trajectories for roads. This complimentary information is integrated using an optimization framework to obtain potential targets for roads. This provides decent results in situations only when the roads have few obstacles (trees, large vehicles, and tall buildings). Linking of disjoint segments uses the local gradient functions at the adjacent pair of road endings. Region part segmentation uses curvature information to remove stray nonroad structures. Medial-Axis-Transform-based hypothesis verification eliminates connected nonroad structures to improve the accuracy in road detection. Results are evaluated with a large set of multispectral remotely sensed images and are compared against a few state-of-the-art methods to validate the superior performance of our proposed method.
Study Shows How Degraded Surroundings Can Degrade Behavior

In a series of cleverly designed experiments reported in a paper published online by Science this week, researchers found that if people see one norm or rule being violated (such as graffiti or a vehicle parked illegally), they9re more likely to violate others--such as littering, or even stealing.
Meeting Report: Methylmercury in Marine Ecosystems—From Sources to Seafood Consumers

Mercury and other contaminants in coastal and open-ocean ecosystems are an issue of great concern globally and in the United States, where consumption of marine fish and shellfish is a major route of human exposure to methylmercury (MeHg). A recent National Institute of Environmental Health Sciences–Superfund Basic Research Program workshop titled “Fate and Bioavailability of Mercury in Aquatic Ecosystems and Effects on Human Exposure,” convened by the Dartmouth Toxic Metals Research Program on 15–16 November 2006 in Durham, New Hampshire, brought together human health experts, marine scientists, and ecotoxicologists to encourage cross-disciplinary discussion between ecosystem and human health scientists and to articulate research and monitoring priorities to better understand how marine food webs have become contaminated with MeHg. Although human health effects of Hg contamination were a major theme, the workshop also explored effects on marine biota. The workgroup focused on three major topics: a) the biogeochemical cycling of Hg in marine ecosystems, b) the trophic transfer and bioaccumulation of MeHg in marine food webs, and c) human exposure to Hg from marine fish and shellfish consumption. The group concluded that current understanding of Hg in marine ecosystems across a range of habitats, chemical conditions, and ocean basins is severely data limited. An integrated research and monitoring program is needed to link the processes and mechanisms of MeHg production, bioaccumulation, and transfer with MeHg exposure in humans.
Block Combination Selection Scheme for Neighbor Discovery Protocol

A neighbor discovery protocol (NDP) is one of the critical research subjects in wireless sensor networks (WSNs) for efficient energy management of sensor nodes. A block design concept can be applied to find a neighbor discovery schedule that guarantees at least one common active slot between any pair of sensor nodes. However, the block design -- based solutions in the literature are not flexible enough because, due to the lack of a general block design scheme, only a limited set of block designs is available for some duty cycles. In this paper, a new approach for the block construction that can easily generate a set of neighbor discovery schedules with a diverse set of duty cycles is introduced. Moreover, we propose a block combination selection scheme (BCS) to choose a near-optimal block combination among the set of candidate schedules. The BCS algorithm first generates set of the candidate block combinations whose duty cycle is similar to the desired duty cycle. Then, from the set of candidates, BCS picks a block combination which has the minimum duty cycle and latency product. In this simulation study, we evaluate the performance of the BCS algorithm and compare it to the performance of other NDPs, such as Random, U-Connect, Disco, and Quorum with a target duty cycle. According to our simulation results, the BCS algorithm always picks the best block combination from the set. If the desired duty cycle is 1%, the block combination selected by BCS performs up to 56.29% better than other NDPs in terms of worst-case latency. In terms of energy consumption, the BCS algorithm performs is 58.8% better than other NDPs.
Performance Analysis of Opportunistic Relaying Scheme with Outdated Channel Information

In this paper, we investigate the effects of outdated channel state information (CSI) on an opportunistic relaying scheme (ORS) consisting of relay selection and data transmission phases. The outage probability and average symbol error rate (ASER) of the ORS are derived in a closed-form expression for an arbitrary link signal-to-noise ratio (SNR) when the CSIs in the source-relay and relay-destination links are outdated. We also analyze the diversity order and coding gain in terms of the outage probability and ASER. Analytical and numerical results show that when the CSIs in either the source-relay or relay-destination links are outdated, the diversity order is reduced to one. The overall performance of the ORS is mainly degraded by inaccurate CSI in the relay-destination link. Conversely, CSI accuracy in the source-relay link has a negligible effect on the performance of the ORS unless the CSI is perfect.
Adaptive hybrid impedance control for a 3DOF upper limb rehabilitation robot using hybrid automata

There is a growing need for effective and adaptive robot-assisted rehabilitation platforms for post-stroke patients which can facilitate considerably their sensorimotor control performance, and also ensure safety for the patients. A 3-DOF adaptive robot-assisted rehabilitation platform is proposed in this work which uses at its core a hybrid impedance control framework to track simultaneously both desired force and position trajectory, while regulating the apparent impedance of the robot as seen by the patient to ensure robot-patient compliant motion. To make the system adaptive to the patient recovery process, the impedance characteristic of the patient's impaired limb is modeled as a parameter of recovery, and is estimated online using a recursive polynomial model estimator. A hybrid automata is then implemented to specify different apparent robot impedances to track the recovery process. Preliminary simulation results showed good response of the proposed framework to the changing patient's arm impedance profile as well as good trajectory tracking of force and position in task space.
The influence of vegetation and soil characteristics on active‐layer thickness of permafrost soils in boreal forest

Carbon release from thawing permafrost soils could significantly exacerbate global warming as the active‐layer deepens, exposing more carbon to decay. Plant community and soil properties provide a major control on this by influencing the maximum depth of thaw each summer (active‐layer thickness; ALT), but a quantitative understanding of the relative importance of plant and soil characteristics, and their interactions in determine ALTs, is currently lacking. To address this, we undertook an extensive survey of multiple vegetation and edaphic characteristics and ALTs across multiple plots in four field sites within boreal forest in the discontinuous permafrost zone (NWT, Canada). Our sites included mature black spruce, burned black spruce and paper birch, allowing us to determine vegetation and edaphic drivers that emerge as the most important and broadly applicable across these key vegetation and disturbance gradients, as well as providing insight into site‐specific differences. Across sites, the most important vegetation characteristics limiting thaw (shallower ALTs) were tree leaf area index (LAI), moss layer thickness and understory LAI in that order. Thicker soil organic layers also reduced ALTs, though were less influential than moss thickness. Surface moisture (0–6 cm) promoted increased ALTs, whereas deeper soil moisture (11–16 cm) acted to modify the impact of the vegetation, in particular increasing the importance of understory or tree canopy shading in reducing thaw. These direct and indirect effects of moisture indicate that future changes in precipitation and evapotranspiration may have large influences on ALTs. Our work also suggests that forest fires cause greater ALTs by simultaneously decreasing multiple ecosystem characteristics which otherwise protect permafrost. Given that vegetation and edaphic characteristics have such clear and large influences on ALTs, our data provide a key benchmark against which to evaluate process models used to predict future impacts of climate warming on permafrost degradation and subsequent feedback to climate.
TAM/wrapper Co-optimization And Test Scheduling For SOCs Based On Hybrid Genetic Algorithm

In this paper, a new method is presented for TAM/wrapper co-optimization based on hybrid genetic algorithm and two- dimensional packing problem. In this method, core test is represented by rectangles, and a hybrid genetic algorithm that provides highly optimal solution for two-dimensional packing problem is introduced for TAM allocation and test scheduling. During the scheduling, the TAM width assigned to cores could be adjusted to an appropriate size to minimize the idle time. This HGA based method was implemented in C and applied to ITC’02 SOC Test Benchmark. Experimental results show that lower testing time was obtained by this new method compared to other methods [1,4,5].
Predictability of individuals' mobility with high-resolution positioning data

The ability to foresee the next moves of a user is crucial to ubiquitous computing. Disregarding major differences in individuals' routines, recent ground-breaking analysis on mobile phone data suggests high predictability in mobility. By nature, however, mobile phone data offer very low spatial and temporal resolutions. It remains largely unknown how the predictability changes with respect to different spatial/temporal scales. Using high-resolution GPS data, this paper investigates the scaling effects on predictability. Given specified spatial-temporal scales, recorded trajectories are encoded into long strings of distinct locations, and several information-theoretic measures of predictability are derived. Somewhat surprisingly, high predictability is still present at very high spatial/temporal resolutions. Moreover, the predictability is independent of the overall mobility area covered. This suggests highly regular mobility behaviors. Moreover, by varying the scales over a wide range, an invariance is observed which suggests that certain trade-offs between the predicting accuracy and spatial-temporal resolution are unavoidable. As many applications in ubiquitous computing concern mobility, these findings should have direct implications.
Performance of the program ASHFALL for forecasting ashfall during the 1995 and 1996 eruptions of Ruapehu volcano

Abstract The program ASHFALL was developed to predict the ashfall that would result from a volcanic eruption, primarily for civil defence purposes, such as giving public warnings and planning mitigation measures. It can also be used for volcanic hazard assessments and for studies of past eruptions. During the 1995 and 1996 eruptions of Ruapehu, a volcano in the North Island of New Zealand, the program was used to provide forecasts of where ash would land if a significant eruption occurred, based on forecast wind patterns. This paper compares the program predictions with the actual ash distribution of three major ash‐producing events from Ruapehu in 1995 and 1996, and looks at the main sources of error. The accuracy of the forecast wind direction is the main factor affecting the ash distribution. If the forecast wind direction is correct, ASHFALL gives a good indication of where ash will fall. The wind strength has less effect on the ash distribution. The quantity of ash falling downwind depends on the vol.
Virus hazards from food, water and other contaminated environments

Abstract Numerous viruses of human or animal origin can spread in the environment and infect people via water and food, mostly through ingestion and occasionally through skin contact. These viruses are released into the environment by various routes including water run‐offs and aerosols. Furthermore, zoonotic viruses may infect humans exposed to contaminated surface waters. Foodstuffs of animal origin can be contaminated, and their consumption may cause human infection if the viruses are not inactivated during food processing. Molecular epidemiology and surveillance of environmental samples are necessary to elucidate the public health hazards associated with exposure to environmental viruses. Whereas monitoring of viral nucleic acids by PCR methods is relatively straightforward and well documented, detection of infectious virus particles is technically more demanding and not always possible (e.g. human norovirus or hepatitis E virus). The human pathogenic viruses that are most relevant in this context are nonenveloped and belong to the families of the C aliciviridae, A denoviridae, H epeviridae, P icornaviridae and R eoviridae. Sampling methods and strategies, first‐choice detection methods and evaluation criteria are reviewed.
Breast Cancer Mortality in African-American and Non-Hispanic White Women by Molecular Subtype and Stage at Diagnosis: A Population-Based Study

Background: Higher breast cancer mortality rates for African-American than non-Hispanic White women are well documented; however, it remains uncertain if this disparity occurs in disease subgroups defined by tumor molecular markers and stage at diagnosis. We examined racial differences in outcome according to subtype and stage in a diverse, population-based series of 103,498 patients. Methods: We obtained data for all invasive breast cancers diagnosed between January 1, 2005, and December 31, 2012, and followed through December 31, 2012, among 93,760 non-Hispanic White and 9,738 African-American women in California. Molecular subtypes were categorized according to tumor expression of hormone receptor (HR, based on estrogen and progesterone receptors) and human epidermal growth factor receptor 2 (HER2). Cox proportional hazards models were used to calculate relative hazard (RH) and 95% confidence intervals (CI) for breast cancer–specific mortality. Results: After adjustment for patient, tumor, and treatment characteristics, outcomes were comparable by race for stage I or IV cancer regardless of subtype, and HR+/HER2+ or HR−/HER2+ cancer regardless of stage. We found substantially higher hazards of breast cancer death among African-American women with stage II/III HR+/HER2− (RH, 1.31; 95% CI, 1.03–1.65; and RH, 1.39; 95% CI, 1.10–1.75, respectively) and stage III triple-negative cancers relative to Whites. Conclusions: There are substantial racial/ethnic disparities among patients with stages II/III HR+/HER2− and stage III triple-negative breast cancers but not for other subtype and stage. Impact: These data provide insights to assess barriers to targeted treatment (e.g., trastuzumab or endocrine therapy) of particular subtypes of breast cancer among African-American patients. Cancer Epidemiol Biomarkers Prev; 24(7); 1039–45. ©2015 AACR.
Energy Efficient Flow Coverage Scheme for Mobile Crowd Sensing in Urban Streets

Recently, mobile crowd sensing (MCS) is an important approach for collecting data from the participants in the urban streets. The main problem in collecting data process is how to cover all segments the street sides and select a minimal number of participants in each street segment that preserves the energy of the mobile devices and prolongs the MCS network lifetime. To solve this problem, a new flow coverage scheme is proposed to cover a specific street and achieve the coverage requirements. The proposed scheme is based on using a modified localization method that uses a minimal usage of GPS and utilizes the Zig-bee technology to communicate with the neighbor nodes and estimate the distance between nodes by using the Time of Arrival method. Experimental results by using a real data show that the proposed localization and coverage scheme can achieve high localization accuracy, reduce the usage of location sensors, and prove that the proposed street coverage scheme achieves the coverage requirements.
Patient Related Outcome Measures Dovepress Nonadherence with Antipsychotic Medication in Schizophrenia: Challenges and Management Strategies

Nonadherence with medication occurs in all chronic medical disorders. It is a particular challenge in schizophrenia due to the illness's association with social isolation, stigma, and comorbid substance misuse, plus the effect of symptom domains on adherence, including positive and negative symptoms, lack of insight, depression, and cognitive impairment. Nonadherence lies on a spectrum, is often covert, and is underestimated by clinicians, but affects more than one third of patients with schizophrenia per annum. It increases the risk of relapse, rehospitalization, and self-harm, increases inpatient costs, and lowers quality of life. It results from multiple patient, clinician, illness, medication, and service factors, but a useful distinction is between intentional and unintentional nonadherence. There is no gold standard approach to the measurement of adherence as all methods have pros and cons. Interventions to improve adherence include psychoeducation and other psychosocial interventions, antipsychotic long-acting injections, electronic reminders, service-based interventions, and financial incentives. These overlap, all have some evidence of effectiveness, and the intervention adopted should be tailored to the individual. Psychosocial interventions that utilize combined approaches seem more effective than unidimensional approaches. There is increasing interest in electronic reminders and monitoring systems to enhance adherence, eg, Short Message Service text messaging and real-time medication monitoring linked to smart pill containers or an electronic ingestible event marker. Financial incentives to enhance antipsychotic adherence raise ethical issues, and their place in practice remains unclear. Simple pragmatic strategies to improve medication adherence include shared decision-making, regular assessment of adherence, simplification of the medication regimen, ensuring that treatment is effective and that side effects are managed, and promoting a positive therapeutic alliance and good communication between the clinician and patient. These elements remain essential for all patients, not least for the small minority where vulnerability and risk issue dictate that compulsory treatment is necessary to ensure adherence.
In Vivo-Induced Genes in Pseudomonas aeruginosa

ABSTRACT In vivo expression technology was used for testingPseudomonas aeruginosa in the rat lung model of chronic infection and in a mouse model of systemic infection. Three of the eight ivi proteins found showed sequence identity to known virulence factors involved in iron acquisition via an open reading frame (called pvdI) implicated in pyoverdine biosynthesis, membrane biogenesis (FtsY), and adhesion (Hag2).
Effect of flow and peristaltic mixing on bacterial growth in a gut-like channel

Significance The human colon is occupied by trillions of microbial cells. Recent sequencing studies have shown that many diseases lead to substantial changes in the composition of this gut microbiota and suggest a strong influence of composition on host physiology. However, not much is known about the underlying physiological factors shaping the gut microbiota. Here, we focus on the role of flow and mixing by colonic wall contractions. To grow in the proximal colon, microbes have to continuously overcome flow. Our in vitro study suggests that mixing helps to overcome flow, and controlled contractions by the colon strongly influence microbiota density and composition; flow and mixing are essential components toward developing a predictive understanding of the gut microbiota. The ecology of microbes in the gut has been shown to play important roles in the health of the host. To better understand microbial growth and population dynamics in the proximal colon, the primary region of bacterial growth in the gut, we built and applied a fluidic channel that we call the “minigut. ” This is a channel with an array of membrane valves along its length, which allows mimicking active contractions of the colonic wall. Repeated contraction is shown to be crucial in maintaining a steady-state bacterial population in the device despite strong flow along the channel that would otherwise cause bacterial washout. Depending on the flow rate and the frequency of contractions, the bacterial density profile exhibits varying spatial dependencies. For a synthetic cross-feeding community, the species abundance ratio is also strongly affected by mixing and flow along the length of the device. Complex mixing dynamics due to contractions is described well by an effective diffusion term. Bacterial dynamics is captured by a simple reaction–diffusion model without adjustable parameters. Our results suggest that flow and mixing play a major role in shaping the microbiota of the colon.