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Background In the process of designing drugs and proteins, it is crucial to recognize hot regions in protein–protein interactions. Each hot region of protein–protein interaction is composed of at least three hot spots, which play an important role in binding. However, it takes time and labor force to identify hot spots through biological experiments. If predictive models based on machine learning methods can be trained, the drug design process can be effectively accelerated. Results The results show that different machine learning algorithms perform similarly, as evaluating using the F-measure. The main differences between these methods are recall and precision. Since the key attribute of hot regions is that they are packed tightly, we used the cluster algorithm to predict hot regions. By combining Gaussian Naïve Bayes and DBSCAN, the F-measure of hot region prediction can reach 0.809. Conclusions In this paper, different machine learning models such as Gaussian Naïve Bayes, SVM, Xgboost, Random Forest, and Artificial Neural Network are used to predict hot spots. The experiment results show that the combination of hot spot classification algorithm with higher recall rate and clustering algorithm with higher precision can effectively improve the accuracy of hot region prediction.

Power transformers are the most common equipment in an electric power system, which has been manufactured in the last decade. However, overheating can damage them, considerably reducing their operation lives, which may cause economic losses to the power utilities. The motivation of this paper is to investigate the time and power overload limits that a power transformer can be subjected to and how it will impact its temperature. Investments in the grid can be delayed if a transformer can support some overload during some momentary load demand increase. In this context, this paper presents a study of a 30/40MVA power transformer by 3D finite element method (FEM) for coupled thermal-electromagnetic simulations to investigate its thermal behaviour when it is submitted to its nominal load at a steady-state operation and a variable load during a period of one day. The simulations were performed with the commercial software packages Flux 3D and AcuSolve, for electromagnetic and thermal modelling, respectively. The modelled equipment was based on a power transformer installed in the Light Serviços de Eletricidade S.A, the utility that supplies electrical energy to the city of Rio de Janeiro, in Brazil. Since the literature doesn’t present many works simulating coupled thermal-electromagnetic power transformers in 3D-FEM, this paper has the goal to bring new contributions to this field. Three study cases were modelled, and some simplifications in transformer’s geometry were made in some of them to reduce the computation time usually required for such a simulation. The obtained results are presented and compared with the measured values for the temperature in the hot spot of the transformer and in the top of the oil, to investigate the impact of these simplifications in the calculated results.

Coherent control is an ingenious tactic to steer a system to a desired optimal state by tailoring the phase of an incident ultrashort laser pulse. A relevant process is the two-photon–induced photoluminescence (TPPL) of nanoantennas, as it constitutes a convenient route to map plasmonic fields, and has important applications in biological imaging and sensing. Unfortunately, coherent control of metallic nanoantennas is impeded by their ultrafast femtosecond dephasing times so far limiting control to polarization and spectral optimization. Here, we report that phase control of the TPPL in resonant gold nanoantennas is possible. We show that, by compressing pulses shorter than the localized surface plasmon dephasing time (

Hot spot is a serious challenge limiting long pulse operation with LHCD (lower hybrid current drive) in tokamak. In order to mitigate the hot spot in guard limiter and improve LHCD capability, effect of edge plasma density on inducing hot spot and current drive has been studied in EAST. The temperature in the guard limiter of the LH antenna, inducing hot spot directly, increases with edge density and LH power. Studies show that the hot spot is mainly ascribed to the heat flux in front of LH antenna. Further simulation indicates that such spot corresponds to the peak position of edge density due to local LH electric field. In addition, due to the stronger parametric instability (PI) behavior in the case of higher edge density, the current drive capability decreases with edge density. Strike-point splitting behaviors appear with density increase, in agreement with current profiles in the edge region and the reduction of total driven current, suggesting that more power is deposited in edge region, which does more contribution to hot spot. Above studies offer one possible idea to optimize the edge density so as to satisfy the coupling, mitigate the heat flux in the guard limiter, and improve current drive capability for fusion device.

Fire behaviour of R/C structures can be strongly affected by spalling phenomenon, which may offset the positive inherent features of concrete exposed to high temperature. This makes the screening of concretes by testing a key point in the design phase of spalling-sensitive structures. To this aim, three different testing procedures have been adopted within a multi-scale approach for investigating two concrete mixes containing different amounts of fibre. The three typologies of test are discussed by comparing experimental and numerical results, so to cast some light on their main differences. Although the experimental results may appear different, a common key of interpretation is recognized.

The Tirimbina Biological Reserve is located in the lowlands on the Atlantic versant of Costa Rica. This study provides an updated comprehensive herpetofauna species list and summarizes the results of all the herpetofauna research conducted at Tirimbina over the last ten years (2009–2019) across various microhabitats, both daytime and nighttime. We also added historical records from occasional sightings and reports from researchers, staff, visitors, interns, fellows, and volunteers since the 1990’s. We found 52 amphibian and 78 reptile species on the reserve, including a few species considered at–risk according to the IUCN Red List. We conclude that Tirimbina is a herpetofauna biodiversity hot spot in Costa Rica because it provides unique habitat characteristics for a variety of species, including habitat for both forest and forest–edge specialists. Tirimbina Biological Reserve for the Tirimbina–UCR research grant. UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biodiversidad y Ecología Tropical (CIBET) UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Biología

Infectious diseases, such as COVID-19, SARS, MERS, etc., have seriously endangered human safety, economy, and education. During the spread of epidemics, restricting the range of activities of personnel is one of the options for the prevention and treatment of infectious diseases. A global navigation satellite system (GNSS), it can provide accurate coordinates of latitude and longitude to targets with GNSS receivers. However, it is not common to use GNSS coordinates to represent positions in social life. For epidemic management, it is important to know the locations (and addresses) of targets, especially in social life. When there are many targets, it is not easy to efficiently map these coordinates to locations. Therefore, we propose a GNSS-based crowd-sensing strategy for specific geographical areas that can be used to calculate how many targets are in specific geographical areas or whether a target is in a specific area. This strategy is based on the coordinates of latitude and longitude provided by GNSS to find the locations of these coordinates. As simulated data, the data records containing latitude and longitude in a well-known social networking service platform are used. The strategy is also available for mining hot spots or hot areas.

Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique that automatically identifies the module generating the hot spots in the solar module. In order to extract feature points, we used the maximally stable extremal regions (MSER) method, which derives the area of interest by using the inrange function, using the blue color of the PV module. We propose an effective matching method for feature points and a homography translation technique. The temperature data derivation method and the normal/ abnormal decision method are described in order to enhance the performance. The effectiveness of the proposed system was evaluated through experiments. Finally, a thermal image analysis of approximately 240 modules was confirmed to be 97% consistent with the visual evaluation in the experimental results.

20th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2020, online, 9 Jun 2020 - 12 Jun 2020; Energies : open-access journal of related scientific research, technology development and studies in policy and management 13(23), 6357 (2020). doi:10.3390/en13236357 special issue: "Special Issue "Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020)" / Special Issue Editor: Prof. Dr. Rodolfo Araneo, Guest Editor", Published by MDPI, Basel

Plasmonic nanostructures enhance nonlinear response, such as surface enhanced Raman scattering (SERS), by localizing the incident field into hot spots. The localized hot spot field can be enhanced even further when linear Fano resonances take place in a double resonance scheme. However, hot spot enhancement is limited with the modification of the vibrational modes, the breakdown of the molecule, and the tunneling regime. Here, we present a method which can circumvent these limitations. Our analytical model and solutions of 3D Maxwell equations show that: enhancement due to the localized field can be multiplied by a factor of 102–103. Moreover, this can be performed without increasing the hot spot intensity which also avoids the modification of the Raman modes. Unlike linear Fano resonances, here, we create a path interference in the nonlinear response. We demonstrate on a single equation that enhancement takes place due to cancellation of the contributing terms in the denominator of the SERS response. Our method can be implemented on an atomic force microscope tip, decorated (or “contaminated”) with appropriate quantum emitters.

International audience; This paper reports on the investigation of an original impinging jet configuration through a wall-resolved large-eddy simulation. The heated jet issues from a fully developed pipe flow at temperature of 130 °C and a Reynolds number based on the bulk velocity of 60000. The impinged plate is located three diameters downstream of the pipe exit. The CFD results have been validated against a specifically-created experimental database (Grenson et al., 2016). The overall statistical fields are well retrieved by the simulation both in the free jet and the wall jet region. In particular, the secondary maximum at the radial location = r D / 2 in the Nusselt number distribution is well predicted by the simulation. The underlying mechanisms from which the secondary maximum originates has been investigated. This analysis revealed that small-scale hot spots of strong convective heat transfer coefficient are responsible for the emergence of this secondary maximum. It is shown that the hot spots can be associated either to local unsteady " separation " of the flow or streaks-like structures above the impinging plate.

Background The American Cancer Society estimates that about 25% of all US cancer deaths will be due to lung cancer – more than from cancers of the colon, breast, and prostate combined. Methods We ascertained county-level age-adjusted and age-specific death rates and 95% confidence intervals from the Centers for Disease Control and Prevention Compressed Mortality File. Multiple regression analyses were used to estimate the strength and direction of relationships between county poverty, smoking, fine particulate matter (PM2.5) air pollution, and US Census divisions and race- and sex-specific lung cancer deaths. Results Poverty, smoking, and particulate matter air pollution were positively and significantly related to lung cancer deaths among white men, but of these, only poverty and smoking were significantly associated with lung cancer deaths among white women. Residence in the South Atlantic, East South Central, and West South Central US Census divisions at the time of death was significantly associated with lung cancer deaths for both white men and white women. As with white men, poverty and smoking were associated with lung cancer deaths among black men, but of these, only adult smoking had a statistically significant association among black women. Conclusions The results support the need for further research, particularly in high-risk areas, to better differentiate factors specific to race and sex and to understand the impact of local risk factors.

Air-conditioning system is an integrated part of the ship design and plays an important role in providing comfortable and healthy cabin environment for crews, passengers and also ensuring efficient thermal management in electrical compartments encompassing IT equipment and computer systems. Understanding the temperature and velocity distribution and the redistribution of cold air to electrical compartments could greatly improve energy efficiency, necessary for thermal management and ensuring reliable operation. To this end, optimized design of a cabin air distribution system is a necessary aspect of the cabin air conditioning system. Computational fluid dynamics (CFD) is an excellent modeling tool that can be used in the design process to simulate thermal performance of the mechanical systems. In this paper, CFD simulation is utilized to analyze the thermal behavior of a warship electrical compartment to pinpoint the cooling issues. Steady state simulations are conducted by employing a Cartesian grid and the standard turbulence k-ε model and grid dependency tests are performed with respect to cabin exhaust temperature. Temperature field and flow pattern of a compartment will be studied to cast a light on the compartment cooling issues.

Development of stress concentration in the corner zone of rectangular plate opening is a well-known fact. It is usually literally taken that the largest stress concentration factor (SCF) occurs exactly in the corner (at angle coordinate of 45 degrees in case of square opening). More rigorous analyses, however, reveal that this is not perfectly true. Although maximum stress never really "leaves the corner", for some hot-spot analyses, more scrupulous investigation of this phenomenon has significance. In this paper, results of some analytical, numerical and experimental investigations of this topic, for plate in tension, are presented and compared.

Duchenne muscular dystrophy (DMD), a fatal X-linked recessive disorder, is caused mostly by frame-disrupting, out-of-frame deletions in the dystrophin (DMD) gene. Antisense oligonucleotide-mediated exon skipping is a promising therapy for DMD. Exon skipping aims to convert out-of-frame mRNA to in-frame mRNA and induce the production of internally-deleted dystrophin as seen in the less severe Becker muscular dystrophy. Currently, multiple exon skipping has gained special interest as a new therapeutic modality for this approach. Previous retrospective database studies represented a potential therapeutic application of multiple exon skipping. Since then, public DMD databases have become more useful with an increase in patient registration and advances in molecular diagnosis. Here, we provide an update on DMD genotype-phenotype associations using a global DMD database and further provide the rationale for multiple exon skipping development, particularly for exons 45⁻55 skipping and an emerging therapeutic concept, exons 3⁻9 skipping. Importantly, this review highlights the potential of multiple exon skipping for enabling the production of functionally-corrected dystrophin and for treating symptomatic patients not only with out-of-frame deletions but also those with in-frame deletions. We will also discuss prospects and challenges in multiple exon skipping therapy, referring to recent progress in antisense chemistry and design, as well as disease models.

In this paper, the plasmon resonance effects of gold nanorods was used to achieve rapid photothermal therapy for malignant melanoma cells (A375 cells). After incubation with A375 cells for 24 h, gold nanorods were taken up by the cells and gold nanorod clusters were formed naturally in the organelles of A375 cells. After analyzing the angle and space between the nanorods in clusters, a series of numerical simulations were performed and the results show that the plasmon resonance coupling between the gold nanorods can lead to a field enhancement of up to 60 times. Such high energy localization causes the temperature around the nanorods to rise rapidly and induce cell death. In this treatment, a laser as low as 9.3 mW was used to irradiate a single cell for 20 s and the cell died two h later. The cell death time can also be controlled by changing the power of laser which is focused on the cells. The advantage of this therapy is low laser treatment power, short treatment time, and small treatment range. As a result, the damage of the normal tissue by the photothermal effect can be greatly avoided.

Background Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model. Results In this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection. Conclusion In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2009-5) contains supplementary material, which is available to authorized users.

Context The variation in spatial distribution between ecosystem services can be high. Hence, there is a need to spatially identify important sites for conservation planning. The term ‘ecosystem service hotspot’ has often been used for this purpose, but definitions of this term are ambiguous. Objectives We review and classify methods to spatially delineate hotspots. We test how spatial configuration of hotspots for a set of ecosystem services differs depending on the applied method. We compare the outcomes to a heuristic site prioritisation approach (Marxan). Methods The four tested hotspot methods are top richest cells, spatial clustering, intensity, and richness. In a conservation scenario we set a target of conserving 10 % of the quantity of five regulating and cultural services for the forest area of Telemark county, Norway. Results Spatial configuration of selected areas as retrieved by the four hotspots and Marxan differed considerably. Pairwise comparisons were at the lower end of the scale of the Kappa statistic (0.11–0.27). The outcomes also differed considerably in mean target achievement, cost-effectiveness in terms of land-area needed per unit target achievement and compactness in terms of edge-to-area ratio. Conclusions An ecosystem service hotspot can refer to either areas containing high values of one service or areas with multiple services. Differences in spatial configuration among hotspot methods can lead to uncertainties for decision-making. This also has consequences for analysing the spatial co-occurrence of hotspots of multiple services and of services and biodiversity. Electronic supplementary material The online version of this article (doi:10.1007/s10980-015-0258-5) contains supplementary material, which is available to authorized users.

We present a web geo-spatial framework for analyzing and continuously monitoring the spatio-temporal evolution of disease hot spots for detecting spatial areas with high concentrations of events in a geographic information system (GIS). To detect the hot spots, we adopt Extended Fuzzy C-Means algorithm. Each event is given by the geo-positional coordinates of the place of residence of the patient. The analyst can insert event data directly on the map or digitizing the address of the residence of the patient and using geo-coding services for locating the event. In our experiments, the data consist of geo-referenced patterns corresponding to the residence of patients in the district of Naples (Italy) submitted to a surgical intervention concerning the oto-laryngo-pharyngeal apparatus between the years 2008 and 2012. The results show the presence of two greatest hot spots: the first covers a geographical area that affects the city of Naples, the second covers parts of various towns around the famous vulcan Vesuvius, respectively. We present a web geo-spatial framework for analyzing and continuously monitoring the spatio-temporal evolution of disease hot spots for detecting spatial areas with high concentrations of events in a geographic information system (GIS). To detect the hot spots, we adopt Extended Fuzzy C-Means algorithm. Each event is given by the geo-positional coordinates of the place of residence of the patient. The analyst can insert event data directly on the map or digitizing the address of the residence of the patient and using geo-coding services for locating the event. In our experiments, the data consist of geo-referenced patterns corresponding to the residence of patients in the district of Naples (Italy) submitted to a surgical intervention concerning the oto-laryngo-pharyngeal apparatus between the years 2008 and 2012. The results show the presence of two greatest hot spots: the first covers a geographical area that affects the city of Naples, the second covers parts of various towns around the famous vulcan Vesuvius, respectively.

Over-temperature occurring in silicon solar panels, subject to partial shadowing, are reduced by means of a series connected voltage divider which sustains part of the reverse voltage developing across the shaded solar cells. The circuit is based on a power MOSFET which is driven by the output voltage of the solar panel. When the solar panel is uniformly illuminated the MOSFET is in the ON state and does not affect the operation of the module, while, when the solar panel is partially shaded, and the output voltage decreases, the MOSFET enters in the pinch-off operation mode, thus subtracting its voltage from the reverse voltage across the shaded cell. Experiments performed on a commercial solar panel showed one third of reduction of the reverse voltage across shaded solar cells, with a reduction of the hot spot temperature of about 25°C.

Este artículo contiene 18 páginas, 6 figuras., The movement of water, matter, organisms, and energy can be altered substantially at ecohydrological interfaces, the dynamic transition zones that often develop within ecotones or boundaries between adjacent ecosystems. Interdisciplinary research over the last two decades has indicated that ecohydrological interfaces are often “hot spots” of ecological, biogeochemical, and hydrological processes and may provide refuge for biota during extreme events. Ecohydrological interfaces can have significant impact on global hydrological and biogeochemical cycles, biodiversity, pollutant removal, and ecosystem resilience to disturbance. The organizational principles (i.e., the drivers and controls) of spatially and temporally variable processes at ecohydrological interfaces are poorly understood and require the integrated analysis of hydrological, biogeochemical, and ecological processes. Our rudimentary understanding of the interactions between different drivers and controls critically limits our ability to predict complex system responses to change. In this paper, we explore similarities and contrasts in the functioning of diverse freshwater ecohydrological interfaces across spatial and temporal scales. We use this comparison to develop an integrated, interdisciplinary framework, including a roadmap for analyzing ecohydrological processes and their interactions in ecosystems. We argue that, in order to fully account for their nonlinear process dynamics, ecohydrological interfaces need to be conceptualized as unique, spatially and temporally dynamic entities, which represents a step change from their current representation as boundary conditions at investigated ecosystems., The authors acknowledge funding from the EU-FP7-PEOPLE program for the INTERFACES Initial Training Network (FP7-PEOPLE-2013-ITN, grant 607150). We thank the fellows of this program for the stimulating discussions that critically challenged current conceptualizations to develop this framework of ecohydrological interface functioning. N.B.G. was supported in part by NSF grants 1026865 and 1457227 and S.K. by NERC NE/L003872/1.

This paper proposes a segmentation method to analyze thermographic images of large solar fields with the aim to identify the borders of photovoltaic modules. This kind of analysis allows to correctly attribute possible faults, revealed by thermography, to a well specified solar panel. Moreover, the knowledge of the specific model of solar panel allows to bring the analysis to the single solar cell. This latter feature can help to eliminate some false positive that the mere observation of hot spots might produce.

Academic analytics and learning analytics have been increasingly adopted by academic institutions of higher learning for improving student performance and retention. While several studies have reported the implementation details and the successes of specific analytics initiatives, relatively fewer studies exist in literature that describe the possible constraints that can preclude an academic or learning analytics initiative from succeeding fully, meeting the criteria of success as defined by the stakeholders affected by such initiatives. Our aim in this article is to describe the constraints that precluded a successful completion of our analytics initiative and how we re-envisioned our approach and scope to achieve our primary goals while operating within the constraints and tools associated with our academic environment.

In this paper, we analyze the effect of local shunts in photovoltaic (PV) solar cells by experimental characterization and distributed electrical simulations. To this purpose, we developed a quasi-3-D distributed electrical network that is based on two-diode circuit elementary units. It allows accounting for resistive losses associated to the transport through the emitter, the fingers and the busbars, and to local defects in the semiconductor. The electrical parameters of the equivalent circuit units are calibrated according to experiments performed on multicrystalline (mc-Si) silicon solar cells, including samples that feature local shunts due to localized defects, which lead to nonuniform distribution of electrical and optical properties. The distributed electrical simulations account for the degradation of fill factor and power conversion efficiency in case of local shunting. Moreover, by combining the proposed tool with a RC thermal network it is possible to estimate the temperature distribution in a shunted solar cell. Our analysis shows how a shunted cell that operates under hot-spot conditions is subject to significant local overheating, which possibly lead to permanent PV cell damages.

Here, we report a graphene-based electric field enhancement structure achieved by several adjacent metal nanoribbons which form the hot spots of the electric field and thus promote the absorption of the single layered graphene below the hot spots. Based on the tunability of the graphene&rsquo, s Fermi level, the absorption rate can be modulated from near 100% to 35% under low electrostatic gating, leading to a 20 dB modulation depth of reflectance. Compared with the existing near infrared spatial light modulators such as optical cavities integrated with graphene and other structures utilizing patterned or highly doped graphene, our design has the advantages of strong optical field enhancement, low power dissipation and high modulation depth. The proposed electro-optic modulator has a promising potential for developing optical communication and exploiting big data interaction systems.

Wireless Body Area Networks (WBANs) are in the spotlight of researchers and engineering industries due to many applications. Remote health monitoring for general as well as military purposes where tiny sensors are attached or implanted inside the skin of the body to sense the required attribute is particularly prominent. To seamlessly accomplish this procedure, there are various challenges, out of which temperature control to reduce thermal effects and optimum power consumption to reduce energy wastage are placed at the highest priority. Regular and consistent operation of a sensor node for a long-time result in a rising of the temperature of respective tissues, where it is attached or implanted. This temperature rise has harmful effects on human tissues, which may lead to the tissue damage. In this paper, a Temperate Aware and Energy Optimized (TAEO) routing protocol is proposed that not only deals with the thermal aspects and hot spot problem, but also extends the stability and lifetime of a network. Analytical simulations are conducted, and the results depict better performance in terms of the network lifetime, throughput, energy preservation, and temperature control with respect to state of the art WBAN protocols.

Silicon-on-insulator (SoI) devices are particularly interesting for thermal management studies as they suffer from severe self-heating because of the very low and isotropic thermal conductivity of silicon dioxide that is being used as a buried oxide (BOX) layer. La5Ca9Cu24O 41 (LCCO) exhibits highly anisotropic magnon-mediated thermal conductivity while at the same time is electrically insulating. A series of simulation studies has been carried out that deal with the design of an effective thermal management solution for transistors built on extremely thin SoI wafers with an LCCO BOX layer. It is shown that a heat channeling effect is produced, which can result in a significant reduction of the transistor operating temperature, the magnitude of which depends on the thickness of the BOX layer. © 2013 IEEE. 60 3330 3334 3330-3334

This paper proposes an automated diagnostic procedure able to detect and classify malfunctioning leading to the overheating of cells embedded in solar panels. The analysis of the temperature gradients over defective cells allows to distinguish hot spots caused by shadowing from overheating caused by cell defects. Such nonintrusive approach provides an accurate and inexpensive diagnostic tool, thus reducing operation and maintenance costs and enhancing the energy yield of PV plants.

The Mediterranean basin is one of the most varied areas worldwide in terms of biodiversity and species richness due to its climatic and geomorphological features, and it is characterized by multi-faceted habitats where forests play a crucial role. Nowadays, the geographic distribution of forest species is well known and multiple geographic datasets are available with different spatial details. However, protection and conservation strategies need more specific information to identify areas with high conservation priority or more vulnerable to the ongoing environmental change (“hot spots”). To this purpose, tree species distribution data were investigated through hot spot analysis using Geographic Information Systems. The analysis was carried out on presence data of ten relevant forest tree species/classes across Mediterranean Europe. By combining spatial analysis and spatial statistics, we identified high and very high hot spot areas for the selected species/classes, which were validated by assessing their biological significance. Given the sub-continental extent of the study, a multiple scale approach was applied ranging from regional, sub-regional to local scale, coherently with the potential multi-level and multi-sector users of similar data and tools. Our results confirm the feasibility of the approach used to increase the quality and quantity of information achievable from available forest distribution datasets. The hot spot maps obtained are a useful support for further spatial evaluations, and may help environmental decision makers to identify priority areas for forest protection and conservation.

This Campbell systematic review examines the effects of focusing police crime prevention efforts on crime ‘hot spots’, and whether focused police actions at specific locations result in crime displacement (i.e. crime moving around the corner) or diffusion (i.e. crime reduction in surrounding areas) of crime control benefits. The review includes 19 studies covering 25 cases. Seventeen of the studies were conducted in the USA. Investing police agencies' limited resources on hot spot policing in a small number of high‐activity crime places will prevent crime in these and surrounding areas, reducing total crime. Problem oriented policing approach allows for developing tailored responses to specific recurring problems in high activity crime spots. Implementing situational prevention strategies that reduce police reliance on aggressive enforcement strategies may also have positive benefits for police‐community relations. The reactions of local communities to hot spot policing must be considered. Residents may welcome efforts to reduce crime. But if policing programmes are seen as heavy‐handed, or focus too much on particular population groups, they may end up driving a wedge between the police and those they are trying to help. Abstract BACKGROUND In recent years, crime scholars and practitioners have pointed to the potential benefits of focusing crime prevention efforts on crime places. A number of studies suggest that there is significant clustering of crime in small places, or “hot spots,” that generate half of all criminal events. A number of researchers have argued that many crime problems can be reduced more efficiently if police officers focused their attention to these deviant places. The appeal of focusing limited resources on a small number of high‐activity crime places is straightforward. If we can prevent crime at these hot spots, then we might be able to reduce total crime. OBJECTIVES To assess the effects of focused police crime prevention interventions at crime hot spots. The review also examined whether focused police actions at specific locations result in crime displacement (i.e., crime moving around the corner) or diffusion (i.e., crime reduction in surrounding areas) of crime control benefits. SEARCH STRATEGY A keyword search was performed on 15 online abstract databases. Bibliographies of past narrative and empirical reviews of literature that examined the effectiveness of police crime control programs were reviewed and forward searches for works that cited seminal hot spots policing studies were performed. Bibliographies of past completed Campbell systematic reviews of police crime prevention efforts and hand searches of leading journals in the field were performed. Experts in the field were consulted and relevant citations were obtained. SELECTION CRITERIA To be eligible for this review, interventions used to control crime hot spots were limited to police enforcement efforts. Suitable police enforcement efforts included traditional tactics such as directed patrol and heightened levels of traffic enforcement as well as alternative strategies such as aggressive disorder enforcement and problem‐oriented policing. Studies that used randomized controlled experimental or quasi‐experimental designs were selected. The units of analysis were limited to crime hot spots or high‐activity crime “places” rather than larger areas such as neighborhoods. The control group in each study received routine levels of traditional police enforcement tactics. DATA COLLECTION AND ANALYSIS 19 studies containing 25 tests of hot spots policing interventions were identified and full narratives of these studies were reported. Ten of the selected studies used randomized experimental designs and nine used quasi‐experimental designs. A formal meta‐analysis was conducted to determine the crime prevention effects in the eligible studies. Random effects models were used to calculate mean effect sizes. RESULTS 20 of 25 tests of hot spots policing interventions reported noteworthy crime and disorder reductions. The meta‐analysis of key reported outcome measures revealed a small statistically significant mean effect size favoring the effects of hot spots policing in reducing citizen calls for service in treatment places relative to control places. The effect was smaller for randomized designs but still statistically significant and positive. When displacement and diffusion effects were measured, unintended crime prevention benefits were associated with the hot spots AUTHORS' CONCLUSIONS The extant evaluation research provides fairly robust evidence that hot spots policing is an effective crime prevention strategy. The research also suggests that focusing police efforts on high‐activity crime places does not inevitably lead to crime displacement and crime control benefits may diffuse into the areas immediately surrounding the targeted locations.

With the increase of the computing demand, cluster is becoming one of the most important computing infrastructures. Most of cluster systems use central air conditioning to remove heat. Therefore, the hot spot is unavoidable. Motivated by alleviating the hot spot issue and optimizing workload distribution, we propose a thermal-aware workload distribution policy. It ranks the nodes according to the thermal characteristics of racks layout and dispatches the workloads based on the ranked node queue. We analyze the saving power trend and validate that this policy can reduce power consumption to some extent with no impact on quality of service (QoS).

This paper identifies some of the main challenges of data governance modeling in the context of learning analytics for higher education institutions, and discusses the critical factors for designing data governance models for learning analytics. It identifies three fundamental common challenges that cut across any learning analytics data governance model, viz., the ownership of the learning analytics data sets, its interpretation and the enacting of decision-making on the basis of this learning analytics data. It also proposes a set of high-level requirements that are necessary for modeling data governance for learning analytics.

The disconnect between data collection and analysis across sectors of academic institutions makes it challenging to incorporate data into curricular design. Understanding the factors related to student persistence and success is unlikely to occur by focusing only on one sector at a time. By promoting course design that is informed by data from all sectors we are more likely to understand important connections that could encourage more effective and holistic change. Facilitating evidence-based course design might begin with the creation of a tool that allows real-time exploration of data across sectors for integration into the traditional course/curricular design. Our paper describes how data from institutional, learning, and what we call “developmental” analytics can be incorporated into course and curricular design by using a purposefully built analysis tool that permits the exploration of student and course objects. This Browser of Student and Course Objects (BoSCO) is being built in a faculty driven-process and can be used as a bridge between the analytics space and the course/curriculum design environments. Such a tool can promote alignment of research within institutions and facilitate communication about analytics, therefore encouraging teachers to use analytics for course and curricular design.

With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and “big data” related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.

Cataloged from PDF version of article. We introduced fractal geometry to the conventional bowtie antennas. We experimentally and numerically showed that the resonance of the bowtie antennas goes to longer wavelengths, after each fractalization step, which is considered a tool to miniaturize the main bowtie structure. We also showed that the fractal geometry provides multiple hot spots on the surface, and it can be used as an efficient SERS substrate. (C)2014 Optical Society of America.

Data Elaboration Centers are characterized by high specific energy consumptions, due to the requirements for the working environment of IT machines and their large working loads. This is due to the fact that the control of the indoor air parameters is of paramount importance in order to maintain the servers in safe working conditions. Reliable diagnostic procedures are therefore needed. The work herewith presented proposes an innovative diagnostic method, which makes use of infrared thermal imaging for real-time monitoring of the air temperature field in the proximity to the server air inlet. The method is validated by means of an experimental assessment, with the aim to assess the capability of an industrial infrared camera to evaluate the air temperature field next to the inlet surface of the server racks, in real working conditions. Potential and limitations of this procedure are presented and discussed.

A yield formulation model to estimate the amount of lithography distortion expected in a printed layout is proposed. The yield formulation relates the probability of non-failure of a lithography hotspot with the yield loss. The application of the yield model is demonstrated for three different layout configurations showing that unidimensional designs may improve manufacturing yield.

The Trafoi rockslide is very similar to the Val Pola landslide before it evolved in a catastrophic rock avalanche in 1987. The Trafoi rockslide was recognized and mapped on the basis of geomorphic evidences such as double crests and detachment niches/scarps as well as geologic proxies such as gravitational offset of distinctive bedrock levels and trenching and fracturing of rock masses. In order to assess the activity status of the landslide, not discernible from field evidences only, periodic GPS monitoring on 11 benchmarks was started in 2007 and continued until 2010 with static-rapid measurements repeated three to four times per year. Results have shown that portions of the rockslide move at some cm/year rate. This highlights the potential for a possible evolution of the mass movement and the necessity of hazard scenarios mapping and run-out modelling. In order to define volume of possible detachments scenarios, digital aerial photo-interpretation were used together with High Resolution DEM and information collected from geomechanical field survey. The Trafoi rockslide is among the test sites of the ongoing European project “Monitor II”, that is proposing the usage of hot spots in the process domain as a practical mean to describe hazard scenarios. Following such approach, simplified hazard and risk scenarios were schematized on the basis of the data collected with monitoring and analysis of ongoing and potential slope instability processes.

In nanoscopic systems, size, geometry, and arrangement are the crucial determinants of the light-matter interaction and resulting nanoparticles excitation. At optical frequencies, one of the most prominent examples is the excitation of localized surface plasmon polaritons, where the electromagnetic radiation is coupled to the confined charge density oscillations. Here, we show that beyond direct near- and far-field excitation, a long-range, indirect mode of particle excitation is available in nanoplasmonic systems. In particular, in amorphous arrays of plasmonic nanodiscs we find strong collective and coherent influence on each particle from its entire active neighborhood. This dependency of the local field response on excitation conditions at distant areas brings exciting possibilities to engineer enhanced electromagnetic fields through controlled, spatially configured illumination.

This paper presents an investigation of the aerothermal performance of a modern unshrouded high-pressure (HP) aero-engine turbine subject to nonuniform inlet temperature profile. The turbine used for this study was the MT1 turbine installed in the QinetiQ turbine test facility based in Farnborough (UK). The MT1 turbine is a full scale transonic HP turbine, and is operated in the test facility at the correct nondimensional conditions for aerodynamics and heat transfer. Datum experiments of aerothermal performance were conducted with uniform inlet conditions. Experiments with nonuniform inlet temperature were conducted with a temperature profile that had a nonuniformity in the radial direction defined by (T max-T min)/T-=0.355, and a nonuniformity in the circumferential direction defined by (T max-T min)/T-=0.14. This corresponds to an extreme point in the engine cycle, in an engine where the nonuniformity is dominated by the radial distribution. Accurate experimental area surveys of the turbine inlet and exit flows were conducted, and detailed heat transfer measurements were obtained on the blade surfaces and end-walls. These results are analyzed with the unsteady numerical data obtained using the in-house HybFlow code developed at the University of Firenze. Two particular aspects are highlighted in the discussion: prediction confidence for state of the art computational fluid dynamics (CFD) and impact of real conditions on stator-rotor thermal loading. The efficiency value obtained with the numerical analysis is compared with the experimental data and a 0.8% difference is found and discussed. A study of the flow field influence on the blade thermal load has also been detailed. It is shown that the hot streak migration mainly affects the rotor pressure side from 20% to 70% of the span, where the Nusselt number increases by a factor of 60% with respect to the uniform case. Furthermore, in this work, it has been found that a nonuniform temperature distribution is beneficial for the rotor tip, contrary to the results found in open literature. Although the hot streak is affected by the pressure gradient across the tip gap, the radial profile (which dominates the temperature profile being considered) is not fully mixed out in passing through the HP stage, and contributes significantly to cooling the turbine casing. A design approach not taking into account these effects will underestimate the rotor life near the tip and the thermal load at midspan. The temperature profile that has been used in both experiments and CFD is the first simulation of an extreme cycle point (more than twice the magnitude of distortion of all previous experimental studies): It represents an engine-take-off condition combined with the full combustor cooling. This research was part of the EU funded Turbine AeroThermal External Flows 2 program. © 2012 American Society of Mechanical Engineers.

We study the reset dynamics of niobium (Nb) superconducting nanowire single-photon detectors (SNSPDs) using experimental measurements and numerical simulations. The numerical simulations of the detection dynamics agree well with experimental measurements, using independently determined parameters in the simulations. We find that if the photon-induced hotspot cools too slowly, the device will latch into a dc resistive state. To avoid latching, the time for the hotspot to cool must be short compared to the inductive time constant that governs the resetting of the current in the device after hotspot formation. From simulations of the energy relaxation process, we find that the hotspot cooling time is determined primarily by the temperature-dependent electron-phonon inelastic time. Latching prevents reset and precludes subsequent photon detection. Fast resetting to the superconducting state is, therefore, essential, and we demonstrate experimentally how this is achieved. We compare our results to studies of reset and latching in niobium nitride SNSPDs.

The paper introduces the issue of the typical defects in PhotoVoltaic (PV) cells and focuses the attention on two specific defects: linear edge shunt and hole. These defects are modeled by means of Finite Element Method (FEM) and implemented in Comsol Multiphysics environment in order to analyze the temperature distribution in the whole defected PV-cell. Usually the defects reveal themselves as hot spots and can be pointed out by means of thermo-graphy. Simulation results are compared with the thermo-grams of real cases and the accuracy of the models is confirmed.

In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hotspot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches. � 2010 Elsevier Inc. All rights reserved.

We propose a distributed algorithm to construct a balanced communication tree that serves in gathering data from the network nodes to a sink. Our algorithm constructs a near-optimally balanced communication tree with minimum overhead. The balancing of the node degrees results in the minimization of packet collisions during query execution, that would otherwise require numerous retransmissions and reduce the lifetime of the network. We compare our simple distributed algorithm against previous work and a centralized solution and show that for most network layouts it outperforms competition and achieves tree balance very close to the centralized algorithm. It also has the smallest energy overhead possible to construct the tree, increasing the lifetime of the network even more. Copyright 2010 ACM. 33 40 Sponsors: ACM SIGMOD Conference code: 82003 Cited By :11

Microblogs are short texts carried with limited information, which will increase the difficulty of topic mining. This paper proposes the use of PAM (Pachinko Allocation Model) probabilistic topic model to extract the generative model of text’s implicit theme for microblog hot spot mining. First, three categories of microblog and the main contribution of this paper are illustrated. Second, for there are four topic models which are respectively explained, the PAM model is introduced in detail in terms of how to generate a document, the accuracy of document classification and the topic correlation in PAM. Finally, MapReduce is described. For the number of microblogs is huge as well as the number of contactors, the totally number of words is relatively small. With MapReduce, microblogs data are split by contactor, document-topic count matrix and contactor-topic count matrix can be locally stored while the word-topic count matrix must be globally stored. Thus, the hot spot mining can be achieved on the basis of PAM probabilistic topic model.

The diagnostic accuracy of nuclear medicine reporting can be improved by awareness of these instrument-related artifacts. Both awareness and experience are also important when it comes to detecting and identifying normal (and abnormal) variants. We present a case of hot spot on the upper right chest in the region of right subclavicular region resulting from injection of radiotracer from central subclavian line. A 52-year-old woman with a history of left breast cancer and recent bone pain was referred to our nuclear medicine department for skeletal survey. Anterior views of chest show a focus of increased radiotracer uptake corresponding to anterior arch of one of the right second rib. The nuclear physician reported it as a focal rib bony lesion and recommended radiological evaluation. As technician later explained, physicians realized that injection site was a central subclavian line on the right side and hot spot on that region is due to injection site. The appearance of both skeletal and soft-tissue uptake depends heavily on imaging technique (such as the route of radiotracer administration) and the interpreting physicians should be aware of the impact of technical factors on image quality.

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit

All programmers should understand the concept of software families and know the techniques for constructing them. This paper suggests that classic problems, such as well-known algorithms and data structures, are good sources for examples to use in a study of software family design. The paper describes two case studies that can be used to introduce students in a Java software design course to the construction of software families using software frameworks. The first is the family of programs that use the well-known divide and conquer algorithmic strategy. The second is the family of programs that carry out traversals of binary trees.

Background Amyloids are proteins capable of forming fibrils whose intramolecular contact sites assume densely packed zipper pattern. Their oligomers can underlie serious diseases, e.g. Alzheimer’s and Parkinson’s diseases. Recent studies show that short segments of aminoacids can be responsible for amyloidogenic properties of a protein. A few hundreds of such peptides have been experimentally found but experimental testing of all candidates is currently not feasible. Here we propose an original machine learning method for classification of aminoacid sequences, based on discovering a segment with a discriminative pattern of site-specific co-occurrences between sequence elements. The pattern is based on the positions of residues with correlated occurrence over a sliding window of a specified length. The algorithm first recognizes the most relevant training segment in each positive training instance. Then the classification is based on maximal distances between co-occurrence matrix of the relevant segments in positive training sequences and the matrix from negative training segments. The method was applied for studying sequences of aminoacids with regard to their amyloidogenic properties. Results Our method was first trained on available datasets of hexapeptides with the amyloidogenic classification, using 5 or 6-residue sliding windows. Depending on the choice of training and testing datasets, the area under ROC curve obtained the value up to 0.80 for experimental, and 0.95 for computationally generated (with 3D profile method) datasets. Importantly, the results on 5-residue segments were not significantly worse, although the classification required that algorithm first recognized the most relevant training segments. The dataset of long sequences, such as sup35 prion and a few other amyloid proteins, were applied to test the method and gave encouraging results. Our web tool FISH Amyloid was trained on all available experimental data 4-10 residues long, offers prediction of amyloidogenic segments in protein sequences. Conclusions We proposed a new original classification method which recognizes co-occurrence patterns in sequences. The method reveals characteristic classification pattern of the data and finds the segments where its scoring is the strongest, also in long training sequences. Applied to the problem of amyloidogenic segments recognition, it showed a good potential for classification problems in bioinformatics.