We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
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 no-reply@cambridge.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 jurisprudence of international administrative tribunals holds great relevance for international organisations, as seen in the proliferation of these tribunals, the complexity of their jurisprudence, and their practical impact. This book provides a comprehensive and accessible analysis of essential topics in this field, including applicable sources, jurisdiction and admissibility, grounds for review, equality and non-discrimination, and remedies. It also covers key emerging issues, such as the rights of non-staff personnel, the growing application of international human rights law by tribunals, and the protection of acquired rights. Drawing on thousands of decisions, this book is an invaluable resource for both practitioners and scholars. For practitioners, it offers a practical guide to navigating complex cases. For scholars, it highlights common principles and key divergences across the jurisprudence of some thirty tribunals, at the same time illuminating the increasingly sophisticated interplay between international administrative law and public international law.
We prove that the mapping class group is not an h-cobordism invariant of high-dimensional manifolds by exhibiting h-cobordant manifolds whose mapping class groups have different cardinalities. In order to do so, we introduce a moduli space of ‘h-block’ bundles and understand its difference with the moduli space of ordinary block bundles.
Physical inactivity is a leading cause globally of noncommunicable diseases such as diabetes, heart attacks, and strokes. Here, we present the results from a 4-week-long experimental test of a nudge designed to promote physical activity among 206 seniors in Abu Dhabi, United Arab Emirates—a population with one of the highest rates of physical inactivity in the world. We find that the “Forever Fit” nudge—a booklet containing a simple exercise program and information about the health benefits of physical activity—has a large positive effect on 93 previously inactive seniors. The nudge increases the time previously inactive participants spend being physically active from about 5 to about 15 minutes per day.
On October 21, 2022, the Spanish Competition Agency (CNMC) sanctioned the North American pharmaceutical company Merck Sharp & Dohme for abuse of a dominant position. The practice for which it was finally sanctioned consisted of the adoption of a strategy aimed at delaying and making it difficult for another company to enter the Spanish medicines market in order to protect sales for a product marketed exclusively by that company and for which had a patent. This paper analyzes this resolution in an attempt to delimit the difference between the legitimate exercise of the right to effective judicial protection and its abuse.
People with HIV (PWH) often encounter health-harming legal needs that impede their access to care, including structural issues such as racism, discrimination, unstable housing, and stigma. Medical-Legal Partnerships (MLPs) have emerged as a promising strategy to address these challenges within HIV care settings. This study aimed to identify the characteristics and strategies of MLPs that are most effective in improving HIV care continuum outcomes. A mixed-methods analysis was conducted utilizing data from a cross-sectional survey of 60 providers in MLPs. Categorical features of MLPs, such as the personnel responsible for screening for health-harming legal needs (HHLN), the organizational structure (community-based vs. institutional), and the delivery of legal services, were examined. A multiple, variable linear regression analysis was conducted to explore the association between these variables and outcomes. Community health organizations were found to be associated with a greater number of patients achieving suppressed HIV viral load. Additionally, a higher number of on-site services were positively correlated with a greater percentage of PWH achieving decreased viral load and completing follow-up appointments. Findings underscore the significance of comprehensive care approaches within MLPs for enhancing positive patient outcomes in HIV care settings.
Measurements of the ionisation state of the intergalactic medium (IGM) can probe the sources of the extragalactic ionising background. We provide new measurements of the ionising emissivity of galaxies using measurements of the ionising background and ionising photon mean free path from high-redshift quasar spectra at $2.5 \lt z \lt 6$. Unlike most prior works, we account for radiative-transfer effects and possible neutral islands from the tail of reionisation at $z \gt 5$. We combine our results with measurements of the UV luminosity function to constrain the average escaping ionising efficiency of galaxies, $\langle f_{\textrm{esc}} \xi_{\textrm{ion}}\rangle_{L_{\textrm{UV}}}$. Assuming galaxies with $M_{\textrm{UV}} \lt -11$ emit ionising photons, we find $\log (\langle f_{\textrm{esc}} \xi_{\textrm{ion}}\rangle_{L_{\textrm{UV}}}/{\textrm {erg}^{-1}Hz}) = 24.47_{-0.17}^{+0.09}$ and $24.75_{-0.28}^{+0.15}$ at $z=5$ and 6, and $1\sigma$ upper limits of $24.48$ and $24.31$ at $z = 2.5$ and 4, respectively. We also estimate the population-averaged $f_{\textrm{esc}}$ using measurements of intrinsic ionising efficiency from JWST. We find $\langle f_{\textrm{esc}} \rangle = 0.126_{-0.041}^{+0.034}$ and $0.224_{-0.108}^{+0.098}$ at $z=5$ and 6, and $1\sigma$ upper limits of $f_{\textrm{esc}}\lt 0.138$ and $0.096$ at $z=2.5$ and 4, respectively, for $M_{\textrm{UV}} \lt -11$. Our findings are consistent with prior measurements of $f_{\textrm{esc}} \lesssim 10\%$ at $z \leq 4$, but indicate a factor of several increase between $z = 4$ and 6. The steepness of this evolution is sensitive to the highly uncertain mean free path and ionising background intensity at $z\gt5$. Lastly, we find $1.10^{+0.21}_{-0.39}$ photons per H atom are emitted into the IGM between $z=6$ and $=5.3$. This is $\approx 4\times$ more than needed to complete the last 20% of reionisation absent recombinations, suggesting that reionisation’s end was likely absorption-dominated.
How did the COVID-19 outbreak affect citizens’ democratic preferences? Were the changes persistent or temporary? We track a representative sample of Spanish citizens before, during, and after the pandemic, with eight survey waves from January 2020 to January 2024. We compare democratic attitudes before and after the pandemic with individual fixed effects models. We identify a sharp increase in preferences for technical rather than ideological policy-making at the very onset of the pandemic, as well as significant changes in voters’ preferences for competent rather than honest politicians. These changes are sudden and persistent over 4 years. Using a set of repeated survey experiments, we also document a widespread willingness to sacrifice rights and freedoms to deal with the pandemic as compared to other global threats, such as international terrorism and climate change. But this effect quickly faded over time. Overall, we identify significant changes in democratic attitudes during the pandemic and a durable shift in technocratic preferences that outlived the pandemic, setting the conditions for the long-term legacies of COVID-19 on democracy.
Distinguishing between Stomylotrema bijugum and S. vicarium is challenging due to their phenotypic plasticity. In this study, adult specimens were recovered from 9 host species in the Mexican tropical lowlands. To explore the morphological differences, 32 morphological characteristics were evaluated in 54 specimens. Linear discriminant analysis provided enough evidence to differentiate the 2 species. Additionally, a principal component analysis (PCA) was performed for each species. The PCA of S. bijugum revealed 3 groups separately corresponding to specimens from the 3 hosts, suggesting host-induced phenotypic plasticity, whereas the PCA of S. vicarium revealed that the specimens from 3 host species were clustered together, indicating morphometric homogeneity. To confirm the morphological differences between the 2 species of Stomylotrema, we sequenced 2 molecular markers: the D1–D3 domains of the large subunit (LSU) from nuclear DNA and nicotinamide adenine dinucleotide dehydrogenase subunit 1 (Nad1) from mitochondrial DNA. Sequences of the LSU were aligned and compared with the LSU sequences of other congeneric species available in GenBank. Phylogenetic analyses supported the monophyly of Stomylotrema, with 2 main subclades that corresponded to S. bijugum and S. vicarium. A haplotype network was predicted with 25 Nad1 sequences, revealing the presence of 2 clusters representing the 2 species separated from each other by 98 substitutions. The current studies on S. bijugum and S. vicarium revealed new hosts and geographical regions in the Americas, suggesting that both species addressed in the current study can complete their life cycle in the Neotropical region of Mexico.
Chile has built one of the largest networks of preferential trade agreements (PTAs) in the world. While Chile’s initial objectives in PTAs focused on trade expansion, their scope has expanded to cover new issues and go deeper to include various behind-the-border measures. Therefore, an assessment of the impact of Chile’s PTAs beyond their effects on trade flows is imperative and may shed light on the impact of PTAs in general. Specifically, this chapter assesses the impact of trade in services provisions on women’s labor force participation, particularly in the services sector. We argue that the inclusion of services provisions in PTAs can promote the development of the services sector in the economy as a whole, which should have a positive impact on women’s employment. In addition, we find that the impact of PTAs on women’s employment should be more pronounced than on men’s, which in turn should help to reduce gender gaps in this sector. The chapter draws on new data and advanced methodologies to test our hypotheses. The results of the chapter show that the inclusion of services has a positive impact on women’s employment. The estimation results suggest that the inclusion of deep services provisions in Chilean PTAs had a positive impact on women’s employment, especially in the services sector. For men, the results show a negative or insignificant effect. Finally, the analysis of the impact of these provisions on gender gaps shows that these agreements have contributed to reducing gender gaps in the labor force.
Peripartum depression (PPD) is a prevalent mental health disorder in the peripartum period. However, a recent systematic review of clinical guidelines relating to PPD has revealed a significant inconsistency in recommendations.
Aims
This study aimed to collect up-to-date evidence on the effectiveness of interventions and provide recommendations for prevention, screening and treating PPD.
Method
A series of umbrella reviews on the effectiveness of PPD prevention, screening and treatment interventions was conducted. A search was performed in five databases from 2010 until 2023. The guidelines were developed according to the GRADE framework and AGREE II Checklist recommendations. Public stakeholder review was included.
Results
One hundred and forty-five systematic reviews were included in the final analysis and used to form the guidelines. Forty-four recommendations were developed, including recommendations for prevention, screening and treatment. Psychological and psychosocial interventions are strongly recommended for preventing PPD in women with no symptoms and women at risk. Screening programmes for depression are strongly recommended during pregnancy and postpartum. Cognitive–behavioural therapy is strongly recommended for PPD treatment for mild to severe depression. Antidepressant medication is strongly recommended for treating severe depression in pregnancy. Electroconvulsive therapy is strongly recommended for therapy-resistant and life-threatening severe depression during pregnancy. Other recommendations are offered to healthcare professionals, stakeholders and researchers in managing PPD in different contexts.
Conclusion
Treatment recommendations should be implemented after carefully considering clinical severity, previous history, risk–benefit for mother and foetus/infant and women’s values and preferences. Implementation of evidence-based clinical practice guidelines within country-specific contexts should be facilitated.
Research on the political consequences of terrorism often finds a rally around the flag effect: terrorist attacks, as other types of threats, tend to produce spikes in popularity and support for the incumbent, as citizens turn to those in power seeking protection. Most research, however, is based on single case studies that analyze very salient attacks from international terrorist organizations. Even if these studies are well identified, the question of generalizability remains, as the studied attacks are often very idiosyncratic. In this paper, we explore the rally around the flag effect in an arguably difficult context: a sustained terrorist campaign held by domestic terrorist groups in a parliamentary democracy (Spain). To overcome the limitations of the single-attack studies, we use a multiple unexpected event approach: we developed a systematic process of matching the occurrence of terror attacks during the fieldwork of official public opinion surveys in Spain, through which we identified 142 valid attack-survey pairs. We find that in the attacked region support for the incumbent increases, on average, around 4 percentage points right after an attack, while support for the opposition decreases in a similar amount. These effects seem to occur mostly for the conservative incumbent and are especially relevant for the attacks that target civilians. We use a survey experiment to provide additional evidence in support for our interpretation of the findings.
Predicting long-term outcome trajectories in psychosis remains a crucial and challenging goal in clinical practice. The identification of reliable neuroimaging markers has often been hindered by the clinical and biological heterogeneity of psychotic disorders and the limitations of traditional case-control methodologies, which often mask individual variability. Recently, normative brain charts derived from extensive magnetic resonance imaging (MRI) data-sets covering the human lifespan have emerged as a promising biologically driven solution, offering a more individualised approach.
Aims
To examine how deviations from normative cortical and subcortical grey matter volume (GMV) at first-episode psychosis (FEP) onset relate to symptom and functional trajectories.
Method
We leveraged the largest available brain normative model (N > 100 000) to explore normative deviations in a sample of over 240 patients with schizophrenia spectrum disorders who underwent MRI scans at the onset of FEP and received clinical follow-up at 1, 3 and 10 years.
Results
Our findings reveal that deviations in regional normative GMV at FEP onset are significantly linked to overall long-term clinical trajectories, modulating the effect of time on both symptom and functional outcome. Specifically, negative deviations in the left superior temporal gyrus and Broca’s area at FEP onset were notably associated with a more severe progression of positive and negative symptoms, as well as with functioning trajectories over time.
Conclusions
These results underscore the potential of brain developmental normative approaches for the early prediction of disorder progression, and provide valuable insights for the development of preventive and personalised therapeutic strategies.
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
The theory of kernels offers a rich mathematical framework for the archetypical tasks of classification and regression. Its core insight consists of the representer theorem that asserts that an unknown target function underlying a dataset can be represented by a finite sum of evaluations of a singular function, the so-called kernel function. Together with the infamous kernel trick that provides a practical way of incorporating such a kernel function into a machine learning method, a plethora of algorithms can be made more versatile. This chapter first introduces the mathematical foundations required for understanding the distinguished role of the kernel function and its consequence in terms of the representer theorem. Afterwards, we show how selected popular algorithms, including Gaussian processes, can be promoted to their kernel variant. In addition, several ideas on how to construct suitable kernel functions are provided, before demonstrating the power of kernel methods in the context of quantum (chemistry) problems.
In this chapter, we change our viewpoint and focus on how physics can influence machine learning research. In the first part, we review how tools of statistical physics can help to understand key concepts in machine learning such as capacity, generalization, and the dynamics of the learning process. In the second part, we explore yet another direction and try to understand how quantum mechanics and quantum technologies could be used to solve data-driven task. We provide an overview of the field going from quantum machine learning algorithms that can be run on ideal quantum computers to kernel-based and variational approaches that can be run on current noisy intermediate-scale quantum devices.
In this chapter, we introduce the field of reinforcement learning and some of its most prominent applications in quantum physics and computing. First, we provide an intuitive description of the main concepts, which we then formalize mathematically. We introduce some of the most widely used reinforcement learning algorithms. Starting with temporal-difference algorithms and Q-learning, followed by policy gradient methods and REINFORCE, and the interplay of both approaches in actor-critic algorithms. Furthermore, we introduce the projective simulation algorithm, which deviates from the aforementioned prototypical approaches and has multiple applications in the field of physics. Then, we showcase some prominent reinforcement learning applications, featuring some examples in games; quantum feedback control; quantum computing, error correction and information; and the design of quantum experiments. Finally, we discuss some potential applications and limitations of reinforcement learning in the field of quantum physics.
This chapter discusses more specialized examples on how machine learning can be used to solve problems in quantum sciences. We start by explaining the concept of differentiable programming and its use cases in quantum sciences. Next, we describe deep generative models, which have proven to be an extremely appealing tool for sampling from unknown target distributions in domains ranging from high-energy physics to quantum chemistry. Finally, we describe selected machine learning applications for experimental setups such as ultracold systems or quantum dots. In particular, we show how machine learning can help in tedious and repetitive experimental tasks in quantum devices or in validating quantum simulators with Hamiltonian learning.