Hasil untuk "South Asia. Southeast Asia. East Asia"

Hoang Quoc Anh, T. Le, Nhu Da Le et al.

This review provides focused insights into the contamination status, sources, and ecological risks associated with multiple classes of antibiotics in surface water from the East and Southeast Asia based on publications over the period 2007 to 2020. Antibiotics are ubiquitous in surface water of these countries with concentrations ranging from <1 ng/L to hundreds μg/L and median values from 10 to 100 ng/L. Wider ranges and higher maximum concentrations of certain antibiotics were found in surface water of the East Asian countries like China and South Korea than in the Southeast Asian nations. Environmental behavior and fate of antibiotics in surface water is discussed. The reviewed occurrence of antibiotics in their sources suggests that effluent from wastewater treatment plants, wastewater from aquaculture and livestock production activities, and untreated urban sewage are principal sources of antibiotics in surface water. Ecological risks associated with antibiotic residues were estimated for aquatic organisms and the prevalence of antibiotic resistance genes and antibiotic-resistant bacteria were reviewed. Such findings underline the need for synergistic efforts from scientists, engineers, policy makers, government managers, entrepreneurs, and communities to manage and reduce the burden of antibiotics and antibiotic resistance in water bodies of East and Southeast Asian countries.

Amir Asiaee, Chao Yan, Zachary B. Abrams et al.

Differential privacy (DP) provides a mathematical guarantee limiting what an adversary can learn about any individual from released data. However, achieving this protection typically requires adding noise, and noise can accumulate when many statistics are measured. Existing DP synthetic data methods treat all features symmetrically, spreading noise uniformly even when the data will serve a specific prediction task. We develop a prediction-centric approach operating in three regimes depending on available structural knowledge. In the causal regime, when the causal parents of $Y$ are known and distribution shift is expected, we target the parents for robustness. In the graphical regime, when a Bayesian network structure is available and the distribution is stable, the Markov blanket of $Y$ provides a sufficient feature set for optimal prediction. In the predictive regime, when no structural knowledge exists, we select features via differentially private methods without claiming to recover causal or graphical structure. We formalize this as PRISM, a mechanism that (i) identifies a predictive feature subset according to the appropriate regime, (ii) constructs targeted summary statistics, (iii) allocates budget to minimize an upper bound on prediction error, and (iv) synthesizes data via graphical-model inference. We prove end-to-end privacy guarantees and risk bounds. Empirically, task-aware allocation improves prediction accuracy compared to generic synthesizers. Under distribution shift, targeting causal parents achieves AUC $\approx 0.73$ while correlation-based selection collapses to chance ($\approx 0.49$).

Anna Fino, Gueo Grantcharov, Asia Mainenti

We investigate the existence of $p$-Kähler structures on two classes of complex manifolds: on quasi-regular fibrations, with particular emphasis on complex homogeneous spaces, and on reductive Lie groups endowed with invariant complex structures. In the latter setting, we construct non-regular complex structures on the Lie algebras $\mathfrak{sl}(2m-1,\mathbb{R})$ for $m \ge 2$ and show that these structures admit compatible balanced metrics, providing new explicit examples of balanced manifolds.

Samhita Pal, Jared D. Huling, Amir Asiaee

We develop estimators that improve precision of heterogeneous treatment effect estimates that allow borrowing information from observational studies when the available covariates in each data source do not perfectly match. Standard data-borrowing methods often assume perfectly matched covariates. We propose MR-OSCAR, an RCT-calibrated, two-stage estimation approach that first predicts the trial-missing variables using the observational data via imputation and then calibrates observational outcome predictions to the randomized trial, preserving the causal contrast, unlike the results for generalization, where imputation does not improve performance. Our theory gives finite-sample guarantees with a transparent error decomposition including an imputation error that shrinks as the observational mapping becomes more predictable. Simulations show that imputation almost always outperforms naively using only the shared covariates and clarifies when borrowing helps (strong predictability of the missing block, moderate trial size) and when it does not (poor predictability or dominant trial-only moderators). We motivate the approach with the Greenlight Plus trial on early childhood obesity and outline a forthcoming EHR analysis at Vanderbilt, highlighting the use of our method in common scenarios where data do not perfectly align.

Eva Iniguez, Daniel Masiga, Caryn Bern et al.

This viewpoint draws lessons from the elimination of visceral leishmaniasis (VL) as a public health problem in the Southeast Asia region (SEAR) to inform efforts in East Africa (EA), now the global epicenter of Leishmania donovani transmission. VL is fatal and there is no licensed vaccine. Success in India relied on robust surveillance, rapid diagnosis, single-dose treatment, vector control, and multi-partner coordination. EA faces additional challenges than SEAR with multiple sand fly vectors, sensitive diagnostics and longer treatment regimens, high population mobility, and gaps in ecological and epidemiological knowledge. We highlight how strategies from South Asia could be adapted while acknowledging EA's unique ecological and health system complexities. These insights aim to guide sustainable VL control towards elimination of VL as a public health concern in the region.

Kenza Tazi, Sun Woo P. Kim, Marc Girona-Mata et al.

High Mountain Asia (HMA) holds the highest concentration of frozen water outside the polar regions, serving as a crucial water source for more than 1.9 billion people. Precipitation represents the largest source of uncertainty for future hydrological modelling in this area. In this study, we propose a probabilistic machine learning framework to combine monthly precipitation from 13 regional climate models developed under the Coordinated Regional Downscaling Experiment (CORDEX) over HMA via a mixture of experts (MoE). This approach accounts for seasonal and spatial biases within the models, enabling the prediction of more faithful precipitation distributions. The MoE is trained and validated against gridded historical precipitation data, yielding 32% improvement over an equally-weighted average and 254% improvement over choosing any single ensemble member. This approach is then used to generate precipitation projections for the near future (2036-2065) and far future (2066-2095) under RCP4.5 and RCP8.5 scenarios. Compared to previous estimates, the MoE projects wetter summers but drier winters over the western Himalayas and Karakoram and wetter winters over the Tibetan Plateau, Hengduan Shan, and South East Tibet.

Filippo Fagioli, Asia Mainenti

The aim of this paper is to gain a better understanding of weak and strong positivity for exterior forms on complex vector spaces. We prove a dimensionality reduction argument for positive forms, which allows us to restrict to the case of $(2,2)$-forms in $\mathbb{C}^4$. In this setting, we find criteria for weak positivity based on the associated Hermitian matrix. As an application we prove, by duality, the strong positivity of some families of $(2,2)$-forms, already of interest in works by other authors.

Mahsa Jahangiri, Afrooz Jouzdani, Hamid Reza Khalesifard

Dust layers have already been reported to have negative impacts on the radiation budget of the atmosphere. But the questions are: How does the atmospheric surface temperature change during a dust outbreak, and what is its temporal correlation with variations of the dust outbreak strength? We investigated these at selected AERONET sites, including Bahrain, IASBS, Karachi, KAUST Campus, Kuwait University, Lahore, Mezaira, Solar Village, in Southwest Asia, and Dushanbe in Central Asia, using available data from 1998 to 2024. The aerosol optical depth at 870 nm and the temperature recorded at each site are taken as measures of dust outbreak strength and atmospheric surface temperature, respectively. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the aerosol optical depths recorded by the Moderate Resolution Imaging Spectroradiometers (MODIS) on board the Aqua and Terra satellites are used to specify the sources of the dust outbreaks. Our investigations show that in most cases, the temperature decreases during a dust outbreak, but in a considerable number of cases, the temperature rises. Temperature changes are mostly less than 5 °C. We found that a dust outbreak may affect the temperature even up to two days after its highest intensity time. This effect is more profound at sites far from large dust sources, such as IASBS in northwest Iran. For sites that are located on either a dust source or very close to it, the temperature and dust optical depth vary almost synchronously.

Yichi Zhang, Xushu Wu

Sub-daily extreme precipitation events can present significant risks such as flooding and soil erosion. Yet the space time pattern of such events on a global scale and their relationships with temperature and wind speed are not well understood. Based on over 9000 global meteorological stations and multi-source weighted-ensemble precipitation covering the warm seasons of 1980–2022, the characteristics and trends in sub-daily extreme precipitation (3 h time interval) were examined and their relationships with temperature and wind speed were explored across global major river basins. The results revealed a similar spatial pattern in the annual frequency, intensity, duration, and total amount of 3 h extreme precipitation events, though notable discrepancies were observed in specific regions such as Southeast Asia and eastern North America. Over half of the stations, particularly in South America, exhibited decreasing trends in extreme precipitation frequency, while significant increases were documented in Europe and Southeast Asia. The relationship between extreme precipitation intensity and temperature over different river basins exhibited diverse behaviors, characterized by monotonic increase (sub-daily Clausius–Clapeyron scaling, mostly in Europe), hook structure (mostly in North America), monotonic decrease (mostly in Africa), and nearly constant (mostly in Mid East, South Africa and South America). Moreover, wind speed generally increased with extreme precipitation intensity for tropical and subtropical basins, while in high-latitude basins strong winds tended to occur with lower precipitation intensity. These insights are vital for improving resilience against sub-daily extreme precipitation.

Bobrov V.V., Makhrov A.A.

The article describes the II Congress of the Russian Association of explorers of the Himalayas and Tibet, created in 2019, which was held on November 28–29, 2024 at the Zoological Institute of the Russian Academy of Sciences (St. Petersburg). The authors briefly present the topics of the reports of the scientific conference organized within the framework of the congress, which can be grouped into several areas: history of travel and exploration, oriental studies, botany and zoology. They also provide information on the organizations and members of the Association who delivered their reports and submitted articles to the collection of materials. Almost all of them are based on the authors’ original field research. The article analyzes trends in Russian studies of the Himalayas and Tibet and notes an increase in the number of reports on interdisciplinary topics and enhanced cooperation between scientific organizations from different cities.

Yun Mao, Xuelei Chu, Feiyu Xie et al.

ObjectiveTo quantify the global macroeconomic burden of breast cancer in 2021, reveal disparities in economic losses across regions, age groups, and gender groups, and provide a basis for optimizing the allocation of prevention and control resources.MethodsBased on breast cancer disability-adjusted life years (DALY) data from the Global Burden of Disease (GBD 2021) database, combined with World Bank economic indicators, the value of lost welfare (VLW) model was employed to assess economic losses. The model standardized losses across countries using the statistical value of life (VSL), with a core parameter income elasticity of 1.0. Regional disparities were analyzed through stratification by Socio-demographic Index (SDI), and statistical uncertainty was calculated using a Bayesian model with 95% uncertainty intervals.ResultsIn 2021, the global VLW due to breast cancer reached 2,538.849 billion US dollars, accounting for 1.65% of global GDP. Regional analysis revealed significant inequality: High-income North America bore the highest economic loss (557.9 billion US dollars), followed by Western Europe (551.4 billion US dollars), while Central Europe, Eastern Europe, and Central Asia, despite lower absolute losses, had an economic burden as a proportion of GDP as high as 3.01%, far exceeding the global average. In terms of population distribution, the female group dominated absolutely (accounting for 24% of female cancer deaths), and economic losses for patients aged 50 and above accounted for over 70%, highlighting the characteristic burden of disease aging. The forecast indicates that the total VLW caused by breast cancer worldwide in 2050 will reach US$21,008.2 billion, with the top regions incurring the highest economic losses being South Asia, East Asia, High-income North America, and Southeast Asia.ConclusionBreast cancer causes significant macroeconomic losses and exhibits marked regional inequality, with high-income countries bearing the highest absolute burden, while low- and middle-income regions face more severe relative economic impacts. It is imperative to implement targeted prevention and control strategies based on SDI stratification to promote optimal allocation of global health resources.

Swarup Sarkar, Diah Saminarsih, J.V.R. Prasada Rao et al.

Lu Liu, Jialin Wang, Jieru Zhai et al.

Rapid industrialization and urbanization in Asia have significantly increased heavy metal emissions, leading to severe challenges in soil contamination. This review critically examines the diverse sources of heavy metal pollution, regional disparities in contamination levels, and various remediation strategies across Asia. The connections between pollution sources and the resulting heavy metal contamination are explored, with a focus on individual assessments of pollution status in East Asia, South Asia, Southeast Asia, Central Asia, and West Asia. These assessments consider human, geographical, policy, and economic factors. The advantages and limitations of physical, chemical, and biological remediation techniques, as well as their combined applications, are analyzed. Additionally, the importance of regulatory measures, sustainable practices, and public awareness is emphasized for ensuring the long-term health and sustainability of Asian soils. This review aims to contribute to the sustainable development of Asian soils by providing region-specific strategies for the effective remediation of heavy metal contamination.

Xinjie Zhao, Shyaman Maduranga Sriwarnasinghe, Jiacheng Tang et al.

The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an empirically grounded methodological framework designed to transform participatory development research, situated in the challenging multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, this framework deploys a multi-agent system architecture that redefines how data collection, analysis, and community engagement are conducted in linguistically and culturally diverse research settings. This structured agent-based approach enables participatory research that is both scalable and responsive, ensuring that community perspectives remain integral to research outcomes. Field experiences reveal the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, offering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research agenda advocates for AI-driven participatory research tools that maintain ethical considerations, cultural respect, and operational efficiency, highlighting strategic pathways for deploying AI systems that reinforce community agency and equitable knowledge generation, potentially informing broader research agendas across the Global South.

Anna Fino, Asia Mainenti

In the paper we study the existence of balanced metrics of Hodge-Riemann type on non-Kähler complex manifolds. We first find some general obstructions, for instance that a Hodge-Riemann balanced manifold of complex dimension $n$ has to be $(n - 2)$-Kähler. Then, we focus on the case of compact quotients of Lie groups by lattices, endowed with an invariant complex structure. In particular, we prove non existence results on non-Kähler complex parallelizable manifolds and some classes of solvmanifolds, and we show that the only nilmanifolds admitting invariant structures of this type are tori. Finally, we construct the first non-Kähler example of a Hodge-Riemann balanced structure, on a non-compact complex manifold obtained as the product of the Iwasawa manifold by $\mathbb C$.

Kazakov O.I.

Tokyo’s negative attitude towards Moscow’s policies in connection with the start of Russia’s special military operation in Ukraine led to a sharp deterioration in relations between Russia and Japan, which the Russian Foreign Ministry characterizes as “degradation” and “dismantling of bilateral ties”. Recently, Japan has begun to play a more active role on the international arena , including within the G7, criticizing Russia and introducing political and economic sanctions against it. In response, Russia also began to impose sanctions against Japan, which caused the collapse of relations. However, the degradation of political and economic relations has not yet led to the destruction of humanitarian ties between the countries, although it has noticeably hit them. It seems that maintaining humanitarian ties in such a difficult political situation can play a positive role in achieving constructive cooperation between the countries in the future.

R. Urraca, G. Janssens-Maenhout, G. Janssens-Maenhout et al.

<p>Gridded bottom-up inventories of CO<span class="inline-formula">₂</span> emissions are needed in global CO<span class="inline-formula">₂</span> inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO<span class="inline-formula">₂</span> emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project.</p> <p>CoCO2-MOSAIC 1.0 provides gridded (0.1<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 0.1<span class="inline-formula">^∘</span>) monthly emissions fluxes of CO<span class="inline-formula">₂</span> fossil fuel (CO<span class="inline-formula">₂</span>ff, long cycle) and CO<span class="inline-formula">₂</span> biofuel (CO<span class="inline-formula">₂</span>bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO<span class="inline-formula">₂</span>ff (36.7 Gt) and CO<span class="inline-formula">₂</span>bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a<span id="page502"/> higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (<a href="https://doi.org/10.5281/zenodo.7092358">https://doi.org/10.5281/zenodo.7092358</a>; Urraca et al., 2023) and at the JRC Data Catalogue (<span class="uri">https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389</span>, last access: 15 May 2023; Urraca Valle et al., 2023).</p>

Kim En Un, Samsonova V.G., Fedyunina M.A.

The article is devoted to the 28th conference of Korean studies experts from Russia and the CIS countries, which took place on March 28-29, 2024 in the Institute of China and Contemporary Asia RAS (Moscow). The korean scholars from leading scientific institutions in Russia and the CIS countries examined a wide range of political, economic, cultural, and historical issues related to the Korean Peninsula. The article highlights the main theses presented at the conference by the speakers, the number of whom exceeded 30 people this year. It is noted that much attention was paid to the analysis of the current political situation on the Korean Peninsula; prospects for cooperation between the Russian Federation and the DPRK; economic cooperation between the Russian Federation and the Republic of Korea under sanctions; analysis of the domestic and foreign policy of the current administration of the Republic of Korea; relations between the PRC and both Korean states. A separate plenary session was devoted to the topic of Koryoin, as well as works on the study of Korean culture and art.

Takashi Okamoto, Shin Kawashima, Kaoru Iokibe

Alessandro Fabris, Nina Baranowska, Matthew J. Dennis et al.

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.