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Despite being in existence for over a quarter century, costing multiple millions of dollars and affecting the lives of hundreds of thousands of individuals, sex offender registration and notification (SORN) laws have yet to be subject to a book-length treatment of their empirical dimensions - their premises, coverage, and impact on public safety. This volume, edited by Wayne Logan and J.J. Prescott, assembles the leading researchers in the field to provide an in-depth look at what have come to be known as 'Megan's Laws', offering a social science-based analysis of one of the most important, and controversial, criminal justice system initiatives undertaken in modern times.
Over the past decade in the Netherlands, most operators have only developed a single doublet. The learning effect from these single events is suboptimal, and operators have only been capable of developing doublets in areas with relatively low exploration risk. This ‘stand-alone’ approach can be significantly improved by a collective approach to derisk regions with similar subsurface characteristics. Such a play-based portfolio approach, which is common in the oil and gas industry, can help to accelerate the development of the geothermal industry through unlocking resource potential in areas marked by high upfront geological risk, effectively helping reduce costs for the development. The basis of the methodology is to deploy new information to the play portfolio by trading off with the risk of the first wells, resulting in a strong geological risk reduction.
The added value of the portfolio approach is demonstrated for the Netherlands in this paper through a comparison with a ‘stand-alone’ development. In the stand-alone approach, each new project will be equally risky, and therefore relatively unprofitable. In the case of a portfolio approach, all experience about the play is used optimally for derisking. In case of success, subsequent projects will have a higher chance of being successful, due to the experience gained in previous projects. Even if a project fails, this may help in increasing the probability of success for subsequent projects. For plays that are initially considered too risky for the market to start developing, the value of information (VoI) of a play-based portfolio approach will help by derisking the play to such an extent that it becomes attractive for the market to develop, even at high initial risk. It can be demonstrated for several geothermal plays in the Netherlands that by adopting the portfolio approach, the probability of a play being developed becomes higher, the number of successfully developed projects increases and the average profitability of the project will also be higher. Five more advantages are: (1) continuous improvement by integrated project development, (2) cost reduction through synergy, efficiency and standardisation, (3) optimisation of the surface heat demand and infrastructure, (4) the possibility of structural research and development (R&D) and innovation, and (5) financing advantages. The advantages reinforce each other.
A preliminary estimate of the geothermal potential of the Netherlands adopting the portfolio approach is between 90 and 275 Petajoules (PJ). For about 350 doublets being developed, producing about 70 PJ, the value of the advantage of the play-based portfolio approach is €2 billion for the three main plays: Rotliegend, Triassic and Jurassic/Cretaceous. The learning effects of synergy, efficiency and standardisation are expected to be significant.
We reviewed current state of research involving the applications of TMS and rTMS in understanding of pathophysiology as well as the treatment of ADHD.
To assess how TMS has furthered our knowledge of neurobiological models of ADHD and to consider further research. To look at possible applications of rTMS in the management of ADHD and to evaluate the current state of research.
Literature review using an online search.
The investigative studies are small in numbers, but show some promising results. TMS adds weight to the theory of a hypofunctional dopaminergic circuit involved in ADHD pathophysiology. Treatment studies (only 2) using rTMS shows some use in treatment of ADHD, such as brief improvement in attention. These studies, however, are very preliminary, small in numbers and suffer from methodological difficulties.
TMS has provided some useful information about the likely pathophysiology of ADHD, and results show that it is a safe an effective way to investigate and treat this condition. Much more research is needed to investigate the potential applications of this technology.
Drive-through clinics (DTCs) are a novel type of point of dispensing where participants drive to a designated location and receive prophylaxis while remaining inside their vehicle. The objective of this review was to identify effective practices and recommendations for implementing DTCs for mass prophylaxis dispensing during emergency events.
A systematic review was conducted for articles covering DTCs published between 1990 and 2019. Inclusion criteria were peer-reviewed, written in English, and addressed DTCs sufficiently. Effective practices and recommendations identified in the literature were presented by theme.
A total of 13 articles met inclusion criteria. The themes identified were (1) optimal DTC design and planning via decision support systems and decision support tools; (2) clinic layouts, locations, and design aspects; (3) staffing, training, and DTC communication; (4) throughput time; (5) community outreach methods; (6) DTC equipment; (7) infection prevention and personal protective equipment; and (8) adverse events prevention and traffic management.
DTCs are an essential component of emergency preparedness and must be optimally designed and implemented to successfully dispense mass prophylaxis to a community within 48 hours. The effective practices and recommendations presented can be used for the development, implementation, and improvement of DTCs for their target populations.
This paper exploits the history of Reconstruction after the American Civil War to estimate the effect of politician race on public finance. While the effect of black politicians is positive and significant, black officials may be endogenous to electoral preferences for redistribution. I therefore use the number of free blacks in the antebellum era (1860) as an instrument for black political leaders during Reconstruction. Instrumental variables (IV) estimates show that an additional black official increased per capita county tax revenue by $0.20, more than an hour’s wage at the time. The effect was not persistent, however, disappearing entirely once black politicians were removed from office at Reconstruction’s end. Consistent with the stated policy objectives of black officials, I find positive effects of black politicians on land tenancy and black literacy. These results suggest that black political leaders had large effects on public finance and individual outcomes over and above electoral preferences.
Full nutritional assessments are currently complex and invasive. There is a need for a non-invasive, timely and cost-effective method to assess nutritional status. Evidence indicates the usefulness of saliva in diagnosing oral or systemic disorders. Saliva is suggested to be a reliable and non-invasive matrix in which to measure nutritional biomarkers. The aim of this work was to systematically review the evidence for salivary biomarkers as indicators of nutritional status.
Materials and Methods:
Studies identifying salivary biomarkers in relation to nutritional status or dietary intake outcomes were included. A search strategy combined terms “saliva” AND “biomarkers” AND “nutrition”. Four databases were searched, MEDLINE, EMBASE, Web of Science and Scopus. All study designs conducted in humans of all ages, from all countries and settings were included. Non-English and animal studies were excluded. Risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias tool where applicable. (PROSPERO Registration Number:CRD42018107667)
6585 papers were identified, 4836 papers remained after removing duplicates, 4715 were irrelevant, 134 full-texts were assessed for eligibility and 64 papers included in the final analysis. A number of potential salivary biomarkers related to nutritional status were identified including: total protein, albumin, prealbumin, transferrin, ferritin and iron. Total protein levels in saliva in malnourished individuals were significantly different to controls in 7/10 studies (70%). In one study conducted in individuals with iron deficiency anaemia (IDA), total protein was significantly different to controls. Albumin levels in malnourished individuals were significantly different to controls in 5/8 studies (62.5%). Prealbumin and transferrin levels in malnourished individuals were significantly different to controls in 3/3 studies (100%). In one study conducted in malnourished individuals, salivary ferritin levels was significantly different to controls. Ferritin levels in individuals with IDA were significantly different to controls in 3/3 studies (100%). Iron levels in individuals with IDA were significantly different in 2/2 studies (100%). However, even within the studies above where significant differences existed, the direction of salivary biomarker differences was sometimes inconsistent. For example, total protein in malnourished individuals was significantly lower than controls in three studies, higher in three studies and one showed mixed findings. In addition, overall the quality of evidence available was very poor.
Despite conflicting evidence in salivary nutritional biomarkers in individuals with malnutrition or IDA, saliva may be a useful non-invasive matrix to assess nutritional status. Further high quality research exploring the utility of these biomarkers is required.
This research was carried out to quantify the effects of a range of variables on milk fat globule (MFG) size for a herd of Holstein-Friesian cows managed through an automatic milking system with year-round calving. We hypothesised that the overall variation in average MFG size observed between individual animals of the same herd cannot sufficiently be explained by the magnitude of the effects of variables that could be manipulated on-farm. Hence, we aimed to conduct an extensive analysis of possible determinants of MFG size, including physiological characteristics (parity, days in milk, days pregnant, weight, age, rumination minutes, somatic cell count) and milk production traits (number of milkings, milk yield, fat yield, protein and fat content, fat-protein ratio) on the individual animal level; and environmental conditions (diet, weather, season) for the whole herd. Our results show that when analysed in isolation, many of the studied variables have a detectable effect on MFG size. However, analysis of their additive effects identified days in milk, parity and milk yield as the most important variables. In accordance with our hypothesis, the estimated effects of these variables, calculated using a multiple variable linear mixed model, do not sufficiently explain the overall variation between cows, ranging from 2.70 to 5.69 µm in average MFG size. We further show that environmental variables, such as sampling day (across seasons) or the proportion of pasture and silage in the diet, have limited effects on MFG size and that physiological differences outweigh the effects of milk production traits and environmental conditions. This presents further evidence that the selection of individual animals is more important than the adjustment of on-farm variables to control MFG size.
Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces.
Data quality in survey research remains a paramount concern for those studying mass political behavior. Because surveys are conducted in increasingly diverse contexts around the world, ensuring that best practices are followed becomes ever more important to the field of political science. Bringing together insights from surveys conducted in more than 80 countries worldwide, this article highlights common challenges faced in survey research and outlines steps that researchers can take to improve the quality of survey data. Importantly, the article demonstrates that with the investment of the necessary time and resources, it is possible to carry out high-quality survey research even in challenging environments in which survey research is not well established.
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.
This article overviews the ultrasonic welding process, a solid-state joining method, using the example of welding of a magnesium alloy as well as the joining of magnesium alloys in general. In situ high-speed imaging and infrared thermography were utilized to study interfacial relative motion and heat generation during ultrasonic spot welding of AZ31B magnesium (Mg) alloys. A postweld ultrasonic nondestructive evaluation was performed to study the evolution of local bond formation at the faying interface (contact surface of the joint between the top and bottom Mg sheets) at different stages of the welding process. Two distinct stages were observed as the welding process progresses. In the early stage, localized reciprocating sliding occurred at the contact faying interface between the two Mg sheets, resulting in localized rapid temperature rise from the localized frictional heating. Microscale (submillimeter) bonded regions at the Mg–Mg faying surface started to form, but the overall joint strength was low. The early-stage localized bonds were broken during the subsequent vibrations. In the later stage, no relative motion occurred at any points of the faying interface. Localized bonded regions coalesced into a macroscale joint that was strong enough to prevent the Mg–Mg interface from further breakage and sliding. With increasing welding time, the bonded area continued to increase.
Advanced lightweight materials, including high-strength steels, aluminum, magnesium, plastics, and reinforced polymer composites, are increasingly used in industry. Combinations of mixed materials are becoming commonplace in the design of structures. Adhesives can be used to join a wide range and combinations of materials. However, joining of materials depends on their specific characteristics. The choice of adherend material is one particular and important parameter that influences adhesively bonded joint performance, and its effect should be taken into consideration in the design of adhesive joints. This article overviews experimental and modeling investigations on the influence of adherend properties on the strength of adhesively bonded joints.