To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The first reader on Asian law and society scholarship, this book features reading selections from a wide range of Asian countries – East, South, Southeast and Central Asia – along with original commentaries by the three editors on the theoretical debates and research methods pertinent to the discipline. Organized by themes and topical areas, the reader enables scholars and students to break out of country-specific silos to make theoretical connections across national borders. It meets a growing demand for law and society materials in institutions and universities in Asia and around the world. It is written at a level accessible to advanced undergraduate students and graduate students as well as experienced researchers, and serves as a valuable teaching tool for courses focused on Asian law and society in law schools, area studies, history, religion, and social science fields such as sociology, anthropology, politics, government, and criminal justice.
Unmanned aerial vehicle (UAV) swarm coverage is one of the key technologies for multi-UAV cooperation, which plays an important role in collaborative investigation, detection, rescue and other applications. Aiming at the coverage optimisation problem of UAV in the target area, a collaborative visual coverage control method under positioning uncertainty is presented. First, the visual perception area with imprecise localisation, UAV model and sensor model are created based on the given task environment. Second, a regional division algorithm for the target task area is designed based on the principle of Guaranteed Voronoi (GV) diagram. Then a visual area coverage planning algorithm is designed, in which the task area is allocated to the UAV according to the corresponding weight coefficient of each area, and the input control law is adjusted by the expected state information of the UAV, so that the optimal coverage quality target value and the maximum coverage of the target area can be achieved. Finally, three task scenarios for regional division and coverage planning are simulated respectively, the results show that the proposed area coverage planning algorithm can realise the optimal regional distribution and can obtain more than 90% coverage in different scenarios.
This study aimed to articulate the roles of social support and coping strategies in the relation between childhood maltreatment (CM) and subsequent major depressive disorder (MDD) with a comprehensive exploration of potential factors in a longitudinal community-based cohort. Parallel and serial mediation analyses were applied to estimate the direct effect (DE) (from CM to MDD) and indirect effects (from CM to MDD through social support and coping strategies, simultaneously and sequentially). Sociodemographic characteristics and genetic predispositions of MDD were considered in the modeling process. A total of 902 participants were included in the analyses. CM was significantly associated with MDD (DE coefficient (β) = 0.015, 95% confidence interval (CI) = 0.002∼0.028). This relation was partially mediated by social support (indirect β = 0.004, 95% CI = 0.0001∼0.008) and negative coping (indirect β = 0.013, 95% CI = 0.008∼0.020), respectively. Social support, positive coping, and negative coping also influenced each other and collectively mediated the association between CM and MDD. This study provides robust evidence that although CM has a detrimental effect on later-on MDD, social support and coping strategies could be viable solutions to minimize the risk of MDD. Intervention and prevention programs should primarily focus on weakening negative coping strategies, then strengthening social support and positive coping strategies.
Baseline data on local status of threatened species are often limited, and alternative information sources such as local ecological knowledge (LEK) have potential to provide conservation insights but require critical evaluation. We assess the usefulness of LEK to generate conservation evidence for the Hainan Peacock-pheasant Polyplectron katsumatae, a poorly known threatened island galliform. Interview surveys in rural communities across eight forested landscapes on Hainan provided a new dataset of sightings of Peacock-pheasants and other galliforms. Fewer respondents had seen Peacock-pheasants compared to other species across most landscapes, although Peacock-pheasant sightings showed significant across-landscape variation, with substantially more total and recent sightings from Yinggeling National Nature Reserve. However, validation of interview data with camera trapping data from Houmiling Provincial Nature Reserve, a landscape with few reported sightings, suggests a more optimistic possible status for Peacock-pheasants, which were detected as frequently as Red Junglefowl Gallus gallus and Silver Pheasant Lophura nycthemera during systematic camera trap placement. Hainan Peacock-pheasant sighting rates might be influenced by various factors (e.g. restricted local access to forests), with absolute abundance possibly greater than expected from limited sightings. Conversely, relative across-landscape abundance patterns from LEK are likely to be valid, as similar detection biases exist across surveyed landscapes.
The great demographic pressure brings tremendous volume of beef demand. The key to solve this problem is the growth and development of Chinese cattle. In order to find molecular markers conducive to the growth and development of Chinese cattle, sequencing was used to determine the position of copy number variations (CNVs), bioinformatics analysis was used to predict the function of ZNF146 gene, real-time fluorescent quantitative polymerase chain reaction (qPCR) was used for CNV genotyping and one-way analysis of variance was used for association analysis. The results showed that there exists CNV in Chr 18: 47225201-47229600 (5.0.1 version) of ZNF146 gene through the early sequencing results in the laboratory and predicted ZNF146 gene was expressed in liver, skeletal muscle and breast cells, and was amplified or overexpressed in pancreatic cancer, which promoted the development of tumour through bioinformatics. Therefore, it is predicted that ZNF146 gene affects the proliferation of muscle cells, and then affects the growth and development of cattle. Furthermore, CNV genotyping of ZNF146 gene was three types (deletion type, normal type and duplication type) by Real-time fluorescent quantitative PCR (qPCR). The association analysis results showed that ZNF146-CNV was significantly correlated with rump length of Qinchuan cattle, hucklebone width of Jiaxian red cattle and heart girth of Yunling cattle. From the above results, ZNF146-CNV had a significant effect on growth traits, which provided an important candidate molecular marker for growth and development of Chinese cattle.
The performance of hypersonic vehicles in the take-off stage considerably influences their capability of accomplishing the flight tasks. This study is aimed at enhancing the take-off performance of a cruise aircraft using the improved chimp optimisation algorithm. The proposed algorithm, which uses the Sobol sequence for initial population generation and a function of the weight factors, can effectively overcome the problems of premature convergence and low accuracy of the original algorithm. In particular, the Sobol sequence aims to obtain a better fitness value in the first iteration, and the weight factor aims to accelerate the convergence speed and avoid the local optimal solution. The take-off mass model of the hypersonic vehicle is constructed considering the flight data obtained using the pseudo-spectral method in the climb phase. Simulations are performed to evaluate the algorithm performance, and the results show that the algorithm can rapidly and stably optimise the benchmark function. Compared to the original algorithm, the proposed algorithm requires 28.89% less optimisation time and yields an optimised take-off mass that is 1.72kg smaller.
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
In times of repeated disaster events, including natural disasters and pandemics, public health workers must recover rapidly to respond to subsequent events. Understanding predictors of time to recovery and developing predictive models of time to recovery can aid planning and management.
We examined 681 public health workers (21-72 y, M(standard deviation [SD]) = 48.25(10.15); 79% female) 1 mo before (T1) and 9 mo after (T2) the 2005 hurricane season. Demographics, trauma history, social support, time to recover from previous hurricane season, and predisaster work productivity were assessed at T1. T2 assessed previous disaster work, initial emotional response, and personal hurricane injury/damage. The primary outcome was time to recover from the most recent hurricane event.
Multivariate analyses found that less support (T1; odds ratio [OR] = .74[95% confidence interval [CI] = .60-.92]), longer previous recovery time (T1; OR = 5.22[95%CI = 3.01-9.08]), lower predisaster work productivity (T1; OR = 1.98[95%CI = 1.08-3.61]), disaster-related personal injury/damage (T2; OR = 3.08[95%CI = 1.70-5.58]), and initial emotional response (T2; OR = 1.71[95%CI = 1.34-2.19]) were associated with longer recovery time (T2).
Recovery time was adversely affected in disaster responders with a history of longer recovery time, personal injury/damage, lower work productivity following prior hurricanes, and initial emotional response, whereas responders with social support had shorter recovery time. Predictors of recovery time should be a focus for disaster preparedness planners.
Background: Despite a higher prevalence of traumatic spinal cord injury (TSCI) amongst Canadian Indigenous peoples, there is a paucity of studies focused on Indigenous TSCI. We present the first Canada-wide study comparing TSCI amongst Canadian Indigenous and non-Indigenous peoples. Methods: This study is a retrospective analysis of prospectively-collected TSCI data from the Rick Hansen Spinal Cord Injury Registry (RHSCIR) from 2004-2019. We divided participants into Indigenous and non-Indigenous cohorts and compared them with respect to demographics, injury mechanism, level, severity, and outcomes. Results: Compared with non-Indigenous patients, Indigenous patients were younger, more female, less likely to have higher education, and less likely to be employed. The mechanism of injury was more likely due to assault or transportation-related trauma in the Indigenous group. The length of stay for Indigenous patients was longer. Indigenous patients were more likely to be discharged to a rural setting, less likely to be discharged home, and more likely to be unemployed following injury. Conclusions: Our results suggest that more resources need to be dedicated for transitioning Indigenous patients sustaining a TSCI to community living and for supporting these patients in their home communities. A focus on resources and infrastructure for Indigenous patients by engagement with Indigenous communities is needed.
Background: Chordomas are rare malignant skull-base/spine cancers with devastating neurological morbidities and mortality. Unfortunately, no reliable prognostic factors exist to guide treatment decisions. This work identifies DNA methylation-based prognostic chordoma subtypes that are detectable non-invasively in plasma. Methods: Sixty-eight tissue samples underwent DNA methylation profiling and plasma methylomes were obtained for available paired samples. Immunohistochemical staining and publicly available methylation and gene expression data were utilized for validation. Results: Unsupervised clustering identified two prognostic tissue clusters (log-rank p=0.0062) predicting disease-specific survival independent of clinical factors (Multivariable Cox: HR=16.5, 95%CI: 2.8-96, p=0.0018). The poorer-performing cluster showed immune-related pathway promoter hypermethylation and higher immune cell abundance within tumours, which was validated with external RNA-seq data and immunohistochemical staining. The better-performing cluster showed higher tumour cellularity. Similar clusters were seen in external DNA methylation data. Plasma methylome-based models distinguished chordomas from differential diagnoses in independent testing sets (AUROC=0.84, 95%CI: 0.52-1.00). Plasma methylomes were highly correlated with tissue-based signals for both clusters (r=0.69 & 0.67) and leave-one-out models identified the correct cluster in all plasma cases. Conclusions: Prognostic molecular chordoma subgroups are for the first time identified, characterized, and validated. Plasma methylomes can detect and subtype chordomas which may transform chordoma treatment with personalized approaches tailored to prognosis.
Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.
The apple buprestid, Agrilus mali Matsumura, that was widespread in north-eastern China, was accidently introduced to the wild apple forest ecosystem in mountainous areas of Xinjiang, China. This invasive beetle feeds on domesticated apples and many species of Malus and presents a serious threat to ancestral apple germplasm sources and apple production worldwide. Estimating the potential area at risk of colonization by A. mali is crucial for instigating appropriate preventative management strategies, especially under global warming. We developed a CLIMEX model of A. mali to project this pest's potential distribution under current and future climatic scenarios in 2100 using CSIRO-Mk 3.0 GCM running the SRES A1B emissions scenario. Under current climate, A. mali could potentially invade neighbouring central Asia and eventually the mid-latitude temperate zone, and some subtropical areas and Pampas Steppe in the Southern Hemisphere. This potential distribution encompasses wild apples species, the ancestral germplasm for domesticated apples. With global warming, the potential distribution shifts to higher latitudes, with the potential range expanding slightly, though the overall suitability could decline in both hemispheres. In 2100, the length of the growing season of this pest in the mid-latitude temperature zone could increase by 1–2 weeks, with higher growth rates in most sites compared with current climate in mid-latitudes, at least in China. Our work highlights the need for strategies to prevent the spread of this pest, managing the threats to wild apples in Tian Shan Mountain forests in Central Asia, and commercial apple production globally. We discuss practical management tactics to reduce the spread of this pest and mitigate its impacts.
Variable camber flap technology can adjust the spanwise circulation distribution, thereby reducing the induced drag. Therefore, the concept of variable camber flap is introduced into the design of propeller aircraft wing, and the design for drag reduction of propeller aircraft is carried out. The numerical simulation of the propeller aircraft is carried out by using the actuator disc method with non-uniform distribution of radial and circumferential loads. Through the unsteady simulation of a single propeller, the aerodynamic load on a periodic propeller is extracted as a boundary condition to the steady simulation of the full aircraft. The load extracted by the actuator disc is compared with the unsteady simulation result, which verifies the reliability of the method. The design for drag reduction at cruise and climb design conditions are respectively carried out with the variable camber flap technology. The variable camber cruise configuration is evaluated at both the begin and end cruise conditions. The results show that, after the flaps deflecting at a small angle according to the circulation distribution, the camber distribution of the wing is adjusted to make the circulation distribution closer to the elliptical circulation distribution. At the design cruise condition, the drag coefficient is reduced by 1.4 counts, and the lift-drag ratio increase by 0.1. At both begin and end cruise conditions, the drag coefficient decreases by 1 count, and the lift-drag ratio increases by 0.07. At the design climb condition, the drag coefficient decreases by 1 count, and the lift-to-drag ratio increases by 0.09.
A shock-induced separation loss reduction method, using local blade suction surface shape modification (smooth ramp structure) with constant adverse pressure gradient with the consideration of radial equilibrium effect to split a single shock foot into multiple weaker shock wave configuration, is investigated on the NASA Rotor 37 for promoting aerodynamic performance of a transonic compressor rotor. Numerical investigation on baseline blade and improved one with blade modification on suction side has been conducted employing the Reynolds-averaged Navier–Stokes method to reveal flow physics of ramp structure. The results indicate that the passage shock foot of baseline is replaced with a family of compression waves and a weaker shock foot generating moderate adverse pressure gradient on ramp profile, which is beneficial for mitigating the shock foot and shrinking flow separation region as well. In addition, the radial secondary flow of low-momentum fluids within boundary layer is decreased significantly in the region of passage shock-wave/boundary-layer interaction on blade suction side, which mitigates the mass flow and mixing intensity of tip leakage flow. With the reduction of flow separation loss induced by passage shock, the adiabatic efficiency and total pressure ratio of improved rotor are superior to baseline model. This study herein implies a potential application of ramp profile in design method of transonic and supersonic compressors.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.