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The main purpose of this paper is to explore access to full-text documents in libraries through Sci-Hub and consider whether it is a blessing in disguise to library users.

Sci-Hub has been a valuable resource for accessing full-text documents. Sci-Hub is potentially problematic from a legal standpoint but has provided a “blessing in disguise” to library users who lack access to academic resources. Sci-Hub has gained significant popularity among scholars and researchers; it has democratized access to academic research. The ability to access scholarly publications for free has provided an important avenue for research and has allowed for greater efficiency and effectiveness in research projects. The legality of the service, concerns about the quality and accountability of the available materials, and the unclear long-term sustainability of the service are some of the challenges associated with using Sci-Hub in libraries.

This paper highlights how the Sci-Hub improves access to full-text scholarly publications for library users. The blessings and challenges associated with using Sci-Hub in libraries and recommendations are discussed.

This chapter evaluates the operational efficiency of major airports in the United States. The airport is defined as a major point of contact in the aviation industry, and on-time operations is regarded as a core service factor. We develop a bounded data envelopment analysis (DEA) model that evaluates the punctuality of airports and proposes a three-stage approach that analyzes not only current operations performance but also efficiency changes over time. We classify airports into several classes according to Federal Aviation Authority (FAA) definitions and compare their class efficiencies through decomposed efficiency scores. We find significant differences in efficiency scores between classifications.

This study develops a method for categorizing airports based on the amount of cargo each airport handles in relation to its passenger traffic. Such a categorization allows for systematic comparisons of airports largely specializing in cargo with those that have more balanced freight/passenger roles. The results show that cargo-focused hub airports (i.e., major cargo-handling airports that handle little or no passenger traffic) in the continental United States shoulder a growing burden in freight movement. One of the most successful of these, Chicago Rockford International Airport, has risen to become the 15th largest airport with respect to domestic cargo shipments on the US mainland, while Texas's Fort Worth Alliance Airport and Seattle's Boeing Field also rank among the top 50. A considerable amount of volatility, however, has accompanied the evolution of this specialized grouping of airports over the past 20 years. Presently, among the 100 airports on the U.S. mainland handling the most domestic freight, cargo-focused hub airports serve only a secondary role, handling less than 5% of domestic air freight shipments as measured by weight. Most major hubs operated by air-freight integrators, such as those developed by FedEx and UPS at Memphis, TN, and Louisville, KY, respectively, are at mixed-purpose airports that have a greater balance of passenger and freight activity. The findings point to some of the unique challenges facing airports that specialize in the movement of freight.

The connectivity provided by full-service network carriers under the umbrella of airline alliances is increasingly challenged by the services of Middle Eastern airlines via their own hubs, and the rise of new passenger strategies like self-connectivity. While these two developments can potentially benefit consumers with more services and lower fares, the rise of Middle East carriers has been met with opposition by EU and US airlines that call for increased protectionism. In addition, only a few airports in the world actively support self-connections. In this context, this study aims to investigate (1) the markets in which Middle East carriers exert a stronger dominance in terms of the number of passenger connections, (2) whether EU, US, or Asian hubs provide a competitive quality of connectivity in terms of travel time, and (3) whether a significant potential for self-connections is hidden at major airports worldwide. To that end, several datasets of passenger bookings (MIDT), airline schedules, and minimum connecting times between 2012 and 2015 are combined in a connections-building methodology that delivers six market-specific airport connectivity indicators for our benchmarking exercise. Our findings show that although European and some Asian hubs have lost traffic in global markets, they remain competitive from a quality perspective. US hubs have maintained their market share and competitive position. Finally, we identify the airports and airlines with the highest potential to provide self-connecting travel options, which can become an attractive new source of revenue for the parties involved.

In their respective market outlooks, both Boeing and Airbus forecast strong growth in intercontinental passenger traffic until 2029. However, they differ substantially with respect to their assessment of the future development of airline (and alliance) networks. These deviating projections have, in turn, massively influenced their product range. Boeing, having long predicted a major growth in intercontinental point-to-point operations – based on the so-called fragmentation (dehubbing) hypothesis – has consistently opted for the development of the B787 (Dreamliner) family of midsized, and extremely efficient, wide-body aircraft. Airbus, on the contrary, is forecasting a substantial increasing demand for hub-to-hub traffic, which according to the company, will require airlines to purchase a large number of very large aircraft (VLA), especially its Airbus 380. Though both manufacturers did not put all their money where their mouths are – Boeing has reacted to the Airbus 380 challenge with an updated derivative of its Boeing 747 flagship, the Boeing 747-8 Intercontinental, while Airbus is targeting its proposed Airbus 350 family against both the Boeing 787 and Boeing 777 – the fragmentation hypothesis remains one of the most controversial issues in the civil aviation community today. Regardless of which scenario will eventually turn out to be more realistic, either will impact tremendously on aircraft manufacturers, on the airlines' route and fleet planning decisions as well as airport operators.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

Half of adulthood mental health challenges begin by the age of 14-years-old, making the need for early-intervention clear. This study aims to evaluate a new service model that promotes early-intervention through a community based low-intensity Hub.

Clinical data from 2,384 young people were analysed through within-group, pre- and post-comparisons and qualitative survey, and interview data was analysed through content analysis.

Overall, participants reported that they were highly satisfied with the Hub and the low-intensity brief interventions met their needs. Participants reported that learning new skills, having a place to talk and positive therapeutic relationships were beneficial. The Hub appeared to be less successful for young people with complex mental health difficulties. As a service, the adoption of the Hub model reduced waiting list times by more than half.

The quantitative data demonstrated that engaging with the Hub reduced symptoms of psychological distress. Qualitative analyses suggest that access to local, community, welcoming and “less clinical” support was beneficial, and the type of brief interventions offered was less important than therapeutic relationships.

This is the first study of a novel “Hub” model for low-intensity brief interventions in a socio-economically deprived area of England. Local knowledge, community integrated support, therapeutic relationships and a welcoming environment were viewed as more beneficial than the type of brief interventions offered. Consequently, community spaces can be created to be therapeutic and beneficial for mental health outside of a traditional conceptualisation of clinical support.