The Chagos Islands cases: the empire strikes back

Good governance requires the accommodation of multiple interests in the cause of decision making. However, undue regard for particular sectional interests can take their toll upon public faith in government administration. Historically, broad conceptions of the good of the commonwealth were employed to outweigh the interests of groups that resisted colonisation. In the decision making of the British Empire, the standard approach for justifying the marginalisation of the interests of colonised groups was that they were uncivilised and that particular hardships were the price to be paid for bringing to them the imperial dividend of industrial society. It is widely assumed that with the dismantling of the British Empire, such impulses and their accompanying jurisprudence became a thing of the past. Even as decolonisation proceeded apace after the Second World War, however, the United Kingdom maintained control of strategically important islands with a view towards sustaining its global role. In an infamous example from this twilight period of empire, in the 1960s imperial interests were used to justify the expulsion of the Chagos islanders from the British Indian Ocean Territory (BIOT). Into the twenty-first century, this forced elision of the UK’s interests with the imperial “common good” continues to take centre stage in courtroom battles over the islanders’ rights, being cited before domestic and international tribunals in order to maintain the Chagossians’ exclusion from their homeland. This article considers the new jurisprudence of imperialism which has emerged in a string of decisions which have continued to marginalise the Chagossians’ interests

The role of combining national official statistics with global monitoring to close the data gaps in the environmental SDGs

The Sustainable Development Goals (SDGs) have elevated the profile of the environmental dimension of development – and how we monitor this dimension. However, they have also challenged national statistical systems and the global statistical community to put in place both the methodologies and mechanisms for data collection and reporting on environmental indicators. According to a recent analysis, there is too little data to formally assess the status of 68% of the environment-related SDGs [1]. Many environment-related indicators were not part of the purview of national statistical systems and did not have a methodology or data collection system in place prior to the adoption of the SDG indicator framework [2]. Moderate improvements have been made, as evidenced by the reduced proportion of environment-related SDG indicators classified as Tier III between the original classification in 2016 and May 2019 – dropping from 50% to 28% [3]. As of March 2020, there are currently no Tier III indicators; however, as many of the SDG indicators have been recently reclassified the data availability and experience in compiling these indicators is severely limited. Socioeconomic indicators have far outpaced environmental indicators in this shift, with only 7% of non-environmental indicators classified as Tier III in May 2019 [1,4,5]. As the custodian agency for 26 of the environment-related SDG indicators, UN Environment is establishing methodologies and mechanisms to collect country-level data. However, many countries currently do not have national systems in place for monitoring these environmental indicators and thus there is a risk that much of the environmental dimension of development cannot be captured by using reporting mechanisms which only include traditionally collected national official statistics. For many of these indicators, UN Environment is exploring new data sources, such as data from citizen science. Citizen science has the potential to contribute to global and local level SDG monitoring. Realizing its full potential however, would require building key partnerships around citizen science data and creating an enabling environment. Global modelling is another approach to fill data gaps. These new types of data could not only improve global estimations but could be incorporated in national official statistics in order to improve nationally relevant data and analysis [6]. The Global Material Flow database, which estimates Domestic Material Consumption (covering SDG indicators 8.4.2 and 12.2.2), and the Global Surface Water Explorer application (covering SDG indicator 6.6.1) are a couple of examples of where UN Environment is complementing national data with global data products in the official SDG reporting process. In these cases the use of globally-derived data has been agreed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) [7]. Expanding globally-estimated or -modelled data to cover environment-related SDG indicators could build the foundation for a digital ecosystem for the planet, which would provide a basis for developing integrated analysis and insights. A Sustainability Gap Index could be one mechanism to bring together the environmental dimension of development into a single metric, which could inform the achievement of the SDGs, environmental assessments and national policy. This paper presents a summary of how the world is faring in terms of measuring the environmental dimension of the SDGs

Biomarkers of Rehabilitation Therapy Vary According To Stroke Severity

Biomarkers that capture treatment effects could improve the precision of clinical decision making for restorative therapies. We examined the performance of candidate structural, functional,and angiogenesis-related MRI biomarkers before and after a 3-week course of standardized robotic therapy in 18 patients with chronic stroke and hypothesized that results vary significantly according to stroke severity. Patients were 4.1 ± 1 months poststroke, with baseline arm Fugl-Meyer scores of 20–60. When all patients were examined together, no imaging measure changed over time in a manner that correlated with treatment-induced motor gains. However, when also considering the interaction with baseline motor status, treatment-induced motor gains were significantly related to change in three functional connectivity measures: ipsilesional motor cortex connectivity with (1) contralesional motor cortex (p = 0 003), (2) contralesional dorsal premotor cortex (p = 0 005), and (3) ipsilesional dorsal premotor cortex (p = 0 004). In more impaired patients, larger treatment gains were associated with greater increases in functional connectivity, whereas in less impaired patients larger treatment gains were associated with greater decreases in functional connectivity. Functional connectivity measures performed best as biomarkers of treatment effects after stroke. The relationship between changes in functional connectivity and treatment gains varied according to baseline stroke severity. Biomarkers of restorative therapy effects are not one-size-fits-all after stroke

Role of Corpus Callosum Integrity in Arm Function Differs Based on Motor Severity After Stroke

While the corpus callosum (CC) is important to normal sensorimotor function, its role in motor function after stroke is less well understood. This study examined the relationship between structural integrity of the motor and sensory sections of the CC, as reflected by fractional anisotropy (FA), and motor function in individuals with a range of motor impairment level due to stroke. Fifty-five individuals with chronic stroke (Fugl-Meyer motor score range 14 to 61) and 18 healthy controls underwent diffusion tensor imaging and a set of motor behavior tests. Mean FA from the motor and sensory regions of the CC and from corticospinal tract (CST) were extracted and relationships with behavioral measures evaluated. Across all participants, FA in both CC regions was significantly decreased after stroke (p \u3c 0.001) and showed a significant, positive correlation with level of motor function. However, these relationships varied based on degree of motor impairment: in individuals with relatively less motor impairment (Fugl-Meyer motor score \u3e 39), motor status correlated with FA in the CC but not the CST, while in individuals with relatively greater motor impairment (Fugl-Meyer motor score ≤ 39), motor status correlated with FA in the CST but not the CC. The role interhemispheric motor connections play in motor function after stroke may differ based on level of motor impairment. These findings emphasize the heterogeneity of stroke, and suggest that biomarkers and treatment approaches targeting separate subgroups may be warranted

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method.A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application

The contributions of citizen science to the United Nations sustainable development goals and other international agreements and frameworks

The United Nations (UN) Sustainable Development Goals (SDGs) are a series of global development targets that were adopted by all UN member states in 2015 to address the world’s most pressing societal, environmental, and economic challenges by 2030 (UN 2015). They include 17 goals and 169 targets covering a wide range of topics from inclusive and equitable education to climate change. The achievement of the SDGs depends on our ability to accurately measure progress towards these topics using timely, relevant, and reliable data (Dang and Serajuddin 2020). To help in the development and implementation of such data and of monitoring mechanisms, a Global Indicator Framework was adopted by the UN General Assembly in 2017, which currently includes 231 unique indicators as a set of metrics to deliver information on the status and trends in each SDG target (UN 2017). However, the lack of resources and institutional capacity makes the monitoring of these indicators very challenging for the producers and users of official statistics, including National Statistical Offices (NSOs), line ministries, UN agencies and others that are responsible for compiling and disseminating official statistics (Fraisl et al. 2022)

Real-time electron nanoscopy of photovoltaic absorber formation from kesterite nanoparticles

Cu2ZnSnS4 nanocrystals are annealed in a Se-rich atmosphere inside a transmission electron microscope. During the heating phase, a complete S-Se exchange reaction occurs while the cation sublattice and morphology of the nanocrystals are preserved. At the annealing temperature, growth of large Cu2ZnSnSe4 grains with increased cation ordering is observed in real-time. Thisyields an annealing protocol which is transferred to an industrially-similar solar cell fabrication process resulting in a 33% increase in the device open circuit voltage. The approach can be applied to improve the performance of any photovoltaic technology that requires annealing because of the criticality of the process step

Supersymmetry discovery potential of the LHC at s=\sqrt{s}=10 and 14 TeV without and with missing ETE_T

We examine the supersymmetry (SUSY) reach of the CERN LHC operating at $\sqrt{s}=10$ and 14 TeV within the framework of the minimal supergravity model. We improve upon previous reach projections by incorporating updated background calculations including a variety of $2\to n$ Standard Model (SM) processes. We show that SUSY discovery is possible even before the detectors are understood well enough to utilize either $E_T^{\rm miss}$ or electrons in the signal. We evaluate the early SUSY reach of the LHC at $\sqrt{s}=10$ TeV by examining multi-muon plus $\ge4$ jets and also dijet events with {\it no} missing $E_T$ cuts and show that the greatest reach in terms of $m_{1/2}$ occurs in the dijet channel. The reach in multi-muons is slightly smaller in $m_{1/2}$, but extends to higher values of $m_0$. We find that an observable multi-muon signal will first appear in the opposite-sign dimuon channel, but as the integrated luminosity increases the relatively background-free but rate-limited same-sign dimuon, and ultimately the trimuon channel yield the highest reach. We show characteristic distributions in these channels that serve to distinguish the signal from the SM background, and also help to corroborate its SUSY origin. We then evaluate the LHC reach in various no-lepton and multi-lepton plus jets channels {\it including} missing $E_T$ cuts for $\sqrt{s}=10$ and 14 TeV, and plot the reach for integrated luminosities ranging up to 3000 fb$^{-1}$ at the SLHC. For $\sqrt{s}=10$ TeV, the LHC reach extends to $m_{gluino}=1.9, 2.3, 2.8$ and 2.9 TeV for $m_{squark}\sim m_{gluino}$ and integrated luminosities of 10, 100, 1000 and 3000 fb$^{-1}$, respectively. For $\sqrt{s}=14$ TeV, the LHC reach for the same integrated luminosities is to m_{gluino}=2.4,\3.1, 3.7 and 4.0 TeV.Comment: 34 pages, 25 figures. Revised projections for the SUSY reach for ab^-1 integrated luminosities, with minor corrections of references and text. 2 figures added. To appear in JHE