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The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.

Duct noise control is an important practical problem. This paper explores the vibro-acoustic mechanism of duct noise through the membrane in tandem. Validity and feasibility of the proposed analytical model is demonstrated by comparing existing simplified models and full direct aeroacoustic simulation solved with the CE/SE model. It is shown that the coupling effect between two membranes in tandem is not negligible to predict system response. Moreover, introduction of multimembranes is important or even the only efficient way to apply this passive control method in practice.

Kira Harris,1,2 Cassie Boland,1,3 Lisa Meade,1,4 Dawn Battise1,5 1Pharmacy Practice Faculty, Wingate University School of Pharmacy, Wingate, NC, USA; 2Clinical Pharmacy Specialist – Novant Health Family Medicine Residency Program, Cornelius, NC, USA; 3Clinical Pharmacy Specialist – Novant Health Cotswold Family Medicine – Arboretum, Charlotte, NC, USA; 4Clinical Pharmacy Specialist – Piedmont HealthCare Endocrinology, Statesville, NC, USA; 5Clinical Pharmacy Specialist – Cabarrus Family Medicine – Harrisburg, Harrisburg, NC, USA Abstract: Type 1 diabetes mellitus (T1DM) is characterized by relative or absolute insulin deficiency. Despite treatment with insulin therapy, glycemic goals are not always met, and insulin therapy is sometimes limited by adverse effects, including hypoglycemia and weight gain. Several adjunctive therapies have been evaluated in combination with insulin in patients with T1DM to improve glycemic control while minimizing adverse effects. Pramlintide, an amylin analog, can improve glycemic control, primarily through lowering postprandial blood glucose levels. Patients may experience weight loss and an increased risk of hypoglycemia and require additional mealtime injections. Metformin provides an inexpensive, oral treatment option and may reduce blood glucose, especially in overweight or obese patients with minimal risk of hypoglycemia. Metformin may be more effective in patients with impaired insulin sensitivity. Glucagon-like peptide-1 receptor agonists reduce primarily postprandial blood glucose and insulin dose and promote weight loss. They are expensive, cause transient nausea, may increase risk of hypoglycemia and require additional injections. Sodium–glucose transport-2 inhibitors improve glycemic control, promote weight loss and have low risk of hypoglycemia with appropriate insulin adjustment; however, these agents may increase the risk of diabetic ketoacidosis in patients with T1DM. Patient-specific characteristics should be considered when selecting adjunctive therapy for patients with T1DM. Close monitoring, insulin dose adjustments and patient education are all important to ensure safe and effective use of these agents. Keywords: type 1 diabetes mellitus, metformin, amylin, sodium–glucose cotransporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl-peptidase 4 inhibitors

Kristina Harris,1 Jill Dawson,2 Elizabeth Gibbons,2 Chris R Lim,1 David J Beard,1 Raymond Fitzpatrick,2 Andrew J Price1 1Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, 2Nuffield Department of Population Health (HSRU), University of Oxford, Oxford, UK Objectives: To identify patient-reported outcome measures (PROMs) that have been developed and/or used with patients undergoing hip or knee replacement surgery and to provide a shortlist of the most promising generic and condition-specific instruments. Methods: A systematic review of the literature was performed to identify measures used in patients undergoing hip and knee replacement and extract and evaluate information on their methodological quality. Results: Thirty-two shortlisted measures were reviewed for the quality of their measurement properties. On the basis of the review criteria, the measures with most complete evidence to date are the Oxford Hip Score (OHS) (for patients undergoing hip replacement surgery) and the Oxford Knee Score (OKS), with OKS-Activity and Participation Questionnaire (for patients undergoing knee replacement surgery). Conclusion: A large number of these instruments lack essential evidence of their measurement properties (eg, validity, reliability, and responsiveness) in specific populations of patients. Further research is required on almost all of the identified measures. The best-performing condition-specific PROMs were the OKS, OHS, and Western Ontario and McMaster Universities Osteoarthritis Index. The best-performing generic measure was the Short Form 12. Researchers can use the information presented in this review to inform further psychometric studies of the reviewed measures. Keywords: knee, hip, patient-reported outcome measures, systematic review, measurement properties, validity, reliability.

In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity research is extended by expanding classifier domains before employing fusion methodologies. The expansion is made possible with a unique classification score algorithm developed for this purpose. Correlation and linear regression techniques reveal that the relationship between diversity metrics and accuracy is tenuous and optimal ensemble selection should be based on ensemble accuracy. The strengths and weaknesses of popular diversity metrics are examined in the context of the information they provide with respect to changing classification thresholds and accuracies.

Duncan Edwards, Keara Harris, Jonathan MantPrimary Care Unit, University of Cambridge, Cambridge, UKAbstract: Atrial fibrillation (AF) is common, and is associated with an increased risk of stroke. Patients' absolute risk of stroke depends on the presence or absence of additional risk factors as well as AF, including prior thromboembolism, increased age, hypertension, diabetes, structural heart disease, and female sex. The risk to benefit ratio of stroke prevention therapy differs according to the patients' absolute risk. There is evidence that even those with an estimated annual stroke risk of 2%–4%, who were once classified as medium risk, would benefit from anticoagulation and should be included in an expanded high-risk category. Alternatives to anticoagulation include the restoration of sinus rhythm and left atrial appendage surgery, but these may not be suitable for many high-risk patients with comorbidities. Antiplatelets are substantially less effective than anticoagulation and cause similar rates of bleeding. Self-monitoring and computerized decision support increases the time in therapeutic range and effectiveness of vitamin K antagonists. Novel oral anticoagulants including dabigatran, rivoraxaban, and apixaban have been shown to be noninferior to warfarin, do not require monitoring, and increase the prescribing options for stroke prevention in AF.Keywords: stroke prevention, atrial fibrillation, anticoagulants, primary prevention

This study aims at empirically examining the extent to which Image Theory, initially developed as a theoretical basis for selecting a strategy or a decision, can be a theoretical basis for predicting a decision performance in two opposite frames: positive and negative. Image compatibility are employed to operationalize such a theory and the decision under study is progress decision represented by escalation of commitment. Thus, this study also empirically examines the connection between image compatibility and escalation of commitment as well as escalation of commitment as a mediator of the relationship between image compatibility and decision performance. The research context is Indonesia Stock Exchange (IDX) that suffered from crisis in the past year (negative frame) yet has been recovered recently (positive frame). The respondents are 229 individual investors in IDX. They are involved in day-to-day decision making (progress decision making) with regard to their investment portofolio. The results of this study show that high image compatibility tends to lead to better decision performance in both frames. However, image compatibility may only positively affect the escalation of commitment in positive frame

Napapon Sailasuta, Kent Harris, Thao Tran, Brian RossClinical MR Unit, Huntington Medical Research Institutes, Pasadena, CA, USAAbstract: We applied 13C magnetic resonance spectroscopy (MRS), a nonradioactive, noninvasive brain imaging technique, to quantify the oxidation of [1-13C] acetate in a conventional clinical magnetic resonance imaging (MRI) scanner in five consecutive elderly subjects at various clinical stages of Alzheimer's disease (AD) progression. [1-13C] acetate entered the brain and was metabolized to [5-13C] glutamate and glutamine, as well as [1-13C] glutamate and glutamine, and the final glial oxidation product, 13C bicarbonate, at a linear rate. Calculation of the initial slope was similar in a single subject, examined twice, 1 month apart (test-re-test 8%). Mean rate of cerebral bicarbonate production in this elderly group was 0.040 ± 0.01 (n = 5). Assuming that the rate of conversion of acetate to bicarbonate is a reflection of glial metabolic rate and that glial metabolic rate is a surrogate marker for 'neuroinflammation', our preliminary results suggest that [1-13C] MRS may provide biomarkers for diseases, believed to involve microglia and other cells of the astrocyte series. Among these is AD, for which novel drugs which ameliorate the damaging effects of neuroinflammation before symptoms of dementia appear, are in advanced development. The value of 13C MRS as an early, noninvasive biomarker may lie in the conduct of cost-effective clinical trials.Keywords: Alzheimer's disease, noninvasive biomarker, glial activation

Sailasuta N, Harris K, Tran T, Ross BMinimally invasive biomarker confirms glial activation present in Alzheimer’s disease: a preliminary study. Neuropsychiatr Dis Treat. 2011;7:495–499.Original article

Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA's National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA's Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA's National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.

Scholars of agrarian change have long debated the nature of capitalist transition in the countryside, including whether the deepened interlinking of local, national, and transnational economic activities make past trajectories of agrarian transformation unlikely to reoccur in the present. This essay makes the case that Giovanni Arrighi's work has much to add to our understanding of the agrarian question in global historical perspective. We focus in particular on Arrighi's research on trajectories of change in the Calabrian region of southern Italy, and his essay “Capitalist Development in Hostile Environments.” In this piece, Arrighi and co-author Fortunata Piselli develop two key insights. The first is that the pathways to capitalism are diverse, non-linear, and historically contingent such that within one country—or, in the case of Italy, a single subnational region—multiple trajectories can be found. The second is that the outcomes of capitalist transition vary based on a country's position in the international hierarchy of wealth, meaning that agrarian transformation is compatible with both economic development and underdevelopment. We describe the three methodological principles that enabled Arrighi to develop his analysis of capitalist transition and explain how the papers collected in this special issue reflect and extend the Arrighian approach to agrarian political economy.

Giovanni Arrighi joined together questions of agrarian political economy with questions concerning the livelihoods of rural migrants and the fate of peasant communities as they dissipated. In this article, we apply Arrighi's concerns to the case of Iran to examine how processes of agrarian transformation link with trends in social stratification and upward mobility. First, we argue that land reform implemented during the reign of Mohammad Reza Shah Pahlavi contributed to a heterogenous social differentiation of the Iranian peasantry. Second, we claim that the widening of access to credentials fostered by Ayatollah Ruhollah Khomeini's Islamic Republic laid the tracks by which peasants and rural households could convert landholding assets into newly accessible forms of cultural capital. The benefits of these transformational processes, however, fell disproportionately to the rural middle strata created under the Pahlavi monarchy. Through the use of a new survey dataset, we show how pre-1979 land reform in Iran favoured segments of the peasantry, not for those who remained in rural agricultural production, but instead for those who utilized landholdings as a means to transfer status and opportunities to their children after the 1979 Iranian revolution.

Accurate timing corrections for seismic data recorded by ocean bottom seismometers (OBSs) are essential for a wide range of applications. The synchronization of internal OBS clocks with Global Positioning System (GPS) is typically only possible prior to deployment on the seafloor and upon retrieval. Thus, untracked, clock errors in seismic data may accumulate over the deployment period. The measurement of the clock's offset from GPS at retrieval, referred to as 'skew', can be used to correct the data solely under the assumption of a uniform rate of clock drift throughout the whole deployment. However, clock errors can be non-linear. We, therefore, develop a new workflow along with an associated open-source, interactive graphical user interface to estimate clock drift of large aperture OBS arrays. We use the workflow to estimate OBS clock drift curves for 40 OBSs of the large-scale UPFLOW seafloor array in the Madeira–Azores–Canaries region deployed for ∼14 months in 2021–2022. We use the relative shift of daily empirical Green's functions obtained from seismic ambient noise recorded by all available data channels to track clock error. We find that 95 per cent of our OBS clock drift observations have a substantial non-linear component: most maximum deviations to linearity are ∼0.75–1 s (and up to 2 s) occurring mainly halfway through the deployment. We test our drift curves by using them to correct teleseismic earthquake recordings, which enables larger numbers of high-quality P -wave traveltime measurements than when using linear drift corrections. Our drift curves have on average an uncertainty of ∼0.11 s indicating the suitability of the corrected data for future seismological studies such as for seismic tomography, seismicity analysis and moment tensor inversions. [ABSTRACT FROM AUTHOR]

To grasp what might exist beyond neoliberalism, we need to rethink the history of development before neoliberalism. This article makes two arguments. First, for poorer countries, processes of commodification which are highlighted as evidence of neoliberalism often predate the neoliberal era. Third World development policies tended to make social and economic life more precarious as a corollary to capital accumulation before neoliberalism as an ideology took hold. Second, the intense theoretical and discursive focus on neoliberalism has obscured a tangible shift towards de-commodification in much of the global South. The most salient examples today are state-led social assistance programs that have been implemented across the former Third World. These emerged not out of technocratic fixes from above but often out of political and social struggles from below. The rise and spread of these programs are not only in stark contrast to popular conceptions of a neoliberal reinforcement, but are also specifically targeted at social strata whose precarity commonly originated in developmental policies before the neoliberal era. Utilizing a database of 183 active flagship social assistance programs in 84 developing countries, we present macro-level quantitative evidence of the rise and spread of social protection policies over the past two decades in the global South. We then detail these programs for four middle-income countries—China, Brazil, India, and South Africa. To those who lament that the 2008 crisis has produced no Polanyian double movement, we argue that these state-driven social assistance policies are such a mechanism.

Wallerstein is, understandably, a popular thinker in middle- and lower-income countries like Iran. Most of these places spent the past several generations strenuously cycling through a variety of governments, political ideologies and economic policies. A drawback of being a celebrity scholar is that complex ideas sketched across decades in books and articles are blurred into a Rorschach blot. The first step toward realizing the Leader's bold plan, an op-ed in the newspaper Donya-ye Eqtesad proclaimed, was strengthening the private sector. Mohammad Khoshchehreh, a former Ahmadinejad supporter turned sour, delighted in telling Fars that the Leader was not referring to 1980s wartime hangovers of autarky or self-sufficiency. The coordination required for such a scheme cannot rely on revolutionary rhetorical flourishes or a mercurial private sector. It demands not only organizational links between politicians, foreign investors and domestic brokers, but also the cooperation of newly educated Iranians themselves.

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and tradeoffs and provide a firm foundation for the design of UAV-assisted IoT networks. [ABSTRACT FROM AUTHOR]