Local Input Research Articles

Bioaccumulation and tissue distribution of pharmaceuticals and their transformation products in fish along the pollution gradients of a wastewater-impacted river

A field study on the occurrence and distribution of forty-three pharmaceutically active compounds (PhACs) in water and fish samples from anthropogenically impacted section of the Sava River (Croatia) was performed to estimate the importance of bioaccumulation for the environmental risk assessment of PhACs. The study was performed using a highly specific LC-MS/MS method, tailored to include the most prominent PhACs from different therapeutic categories as well as their major metabolites and/or transformation products (TPs). The results revealed a widespread occurrence of PhAC residues both in water and fish samples with a large spatial variability reflecting the distance from the dominant wastewater discharges. The most prominent PhAC categories in less polluted upstream part of the river were common psychostimulants caffeine and cotinine, therapeutic opioids and cardiovascular drugs, while in the river section affected by the local municipal and industrial wastewater inputs, antibiotic drugs became clearly predominant, especially in fish tissue samples. The apparent bioconcentration factors (BCFs) of investigated PhACs varied over several orders of magnitude, from 0.02 ± 0.01 Lkg−1 for O-desmethyl tramadol in fish muscle to 784 ± 260 Lkg−1 for terbinafine in fish liver, indicating rather large differences in their bioconcentration potential and affinity to different tissues, with the tissue-specific BCFs increasing in the following order: muscle < gills < gonads < heart < liver < kidneys. The bioconcentration potential of most of the PhACs included in this study was only low to moderate however moderately high BCFs of certain PhACs (e.g. sertraline, terbinafine, loratadine, diazepam and azithromycin) in some tissues should be taken into consideration when assessing their potential environmental risks. Moreover, it was shown that BCFs could be strongly affected by biotransformation in fish. Risk prioritization based on risk quotient (RQ) and ToxPi index, revealed antibiotics, in particular azithromycin, and therapeutic psychoactive substances as the most hazardous pharmaceutical contaminants in the Sava River.

Research on Socio-Profile of Farmers of S.A.S. Nagar District, Punjab

The socio-economic traits of farm households in Punjab's rural districts were the focus of the current study. For this objective, information was gathered from 150 farm homes from the villages of S.A.S. Nagar district, Punjab. The findings showed that the Sikh religion and General Castes comprised the majority of farm households. Most people were found to be in the 30 to 70 age range, which is the most economically active. In all farm-size categories, the sex ratio was in favour of the females. The proportion of farm households living in the joint type of family was positively associated with the farm size. Most of the persons of the marginal and small farm-size categories were illiterate and with relatively low level of education as compared to the semi-medium, medium, and large farm-size categories. The proportionate share of earners was the highest for the small farm-size category and the lowest for the semi-medium farm-size category. About 92 percent of sampled farmers owned pucca houses. The proportion of sampled farmers who owned good condition houses, bathrooms and toilets were related to the farm size. About 93% of farmers contact the local input dealers for any information needed for crop cultivation. 90 % of farmers had mobile connection. It was found that most of the farmers had an internet connection (66%) for a majority of sampled farmers in the rural areas of Punjab.

The increasing influence of atmospheric moisture transport on hydrometeorological extremes in the Euromediterranean region with global warming

The Euromediterranean area is a key region in which the link between atmospheric moisture transport and hydrometeorological extremes concerns. Atmospheric rivers, the main moisture transport mechanism affecting the region, play a notable role in extreme precipitation there. Moreover, moisture transport deficits from two of the major oceanic moisture sources of the planet, the North Atlantic Ocean and the Mediterranean Sea, are strongly related to drought occurrence in that region. Here, we examine the projected changes in these relationships with global warming, under a high-emission scenario (shared socioeconomic pathway 5–8.5). Here we show that, for the mid-21st century, a moderate increase in the influence of moisture transport on winter precipitation maxima is projected, in line with its increasing concurrence with atmospheric rivers. A stronger increase is estimated for the relationship between moisture transport deficits and drought occurrence, for which probabilities between two and three times greater than those observed in the present climate are obtained for the mid- and end-21st century. This highlights the increasing importance of moisture transport from the ocean in future droughts in the region, especially in the context of reduced local moisture inputs from terrestrial evaporation as a consequence of drier soil.

Applicability of a Modified Gash Model for Artificial Forests in the Transitional Zone between the Loess Hilly Region and the Mu Us Sandy Land, China

Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the closure loss of plantation species in drought and semi-arid areas. To quantify and model the canopy interception of these plantations, we conducted rainfall redistribution measurement experiments. Based on this, we used the modified Gash model to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. Herein, Caragana korshinskii, Salix psammophila, and Pinus sylvestris plantations in the Kuye River mountain tract were chosen to measure the precipitation distribution from May to October (growing season). The applicability of a modified Gash model for different stands was then evaluated using the assessed data. The results showed that the canopy interception characteristics of each typical plantation were throughfall, interception, and stemflow. The relative error of canopy interception of C. korshinskii simulated by the modified Gash model was 8.79%. The relative error of simulated canopy interception of S. psammophila was 4.19%. The relative error of canopy interception simulation of P. sylvestris was 13.28%, and the modified Gash model had good applicability in the Kuye River Basin. The modified Gash model has the greatest sensitivity to rainfall intensity among the parameters of the C. korshinskii and S. psammophila forest. The sensitivity of P. sylvestris in the modified Gash model is that the canopy cover has the greatest influence, followed by the mean rainfall intensity. Our results provide a scientific basis for the rational use of water resources and vegetation restoration in the transitional zone between the loess hilly region and the Mu Us Sandy Land. This study is of import for the restoration and sustainability of fragile ecosystems in the region.

A PNP ion channel deep learning solver with local neural network and finite element input data

Abstract This paper presents a deep learning method for solving an improved one-dimensional Poisson–Nernst–Planck ion channel (PNPic) model, called the PNPic deep learning solver. The solver combines a novel local neural network, adapted from the neural network with local converging inputs, with an efficient PNPic finite element solver, developed in this work. In particular, the local neural network is extended to handle the complexities of the PNPic model—a system of nonlinear convection–diffusion and elliptic equations with multiple subdomains connected by interface conditions. The PNPic finite element solver efficiently generates input and reference datasets for fast training the local neural network, as well as input datasets for quickly predicting PNPic solutions with high accuracy for a family of PNPic models. Initial numerical tests, involving perturbations of model parameters and interface locations, demonstrate that the PNPic deep learning solver can generate highly accurate numerical solutions.

Cost-effectiveness of nonavalent vs bivalent HPV vaccine in Polish setting.

Human papillomavirus (HPV) is a prevalent sexually transmitted infection with significant implications for public health. In Poland, a nationwide vaccination program offers a choice between the 9-valent (9v) and 2-valent (2v) HPV vaccines. We aimed to assess the cost-effectiveness of the 9v vs 2v vaccine from the public payer perspective in Poland. A cost-effectiveness analysis was conducted to compare the public health and economic benefits of using 9v vs 2v vaccine in Poland over 100-year horizon using a previously published deterministic dynamic transmission model. A target population of girls and boys aged 12-13 years was considered. The model was populated with local epidemiological inputs, utilities, and costs, including vaccine and administration costs, as well as costs related to medical procedures for HPV-related diseases. The 9v vaccine reduced the prevalence of HPV infections and HPV-related diseases substantially more than 2v vaccine when both are compared to no vaccination strategy. The total discounted cost savings of using the 9v vaccine instead of 2v, excluding the vaccine costs, amounted to EUR 66 million. The incremental cost-effectiveness ratio amounted to 8094 EUR per quality-adjusted life year, much below the official cost-effectiveness threshold in Poland set up at the three times the annual gross domestic product per capita. 9v cost-effectiveness ratio remained unchanged when shorter time-horizons of 20, 40, 60, or 80 years were considered. Using 9v HPV vaccine in Poland is highly cost-effective compared to the 2v vaccine.

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

THE HISTORICAL BEDROCK OF HOLY QUR’AN’S MANUSCRIPT IN MALAYSIA: A STUDY ON ITS STYLE OF CALLIGRAPHY AND ILLUMINATION

The focus of this article is to examine the historical foundation of the Holy Qur’an’s manuscript in Malaysia, with specific attention to its calligraphic style and illumination. Islamic calligraphy and manuscript traditions hold significant importance within the Malaysian Muslim heritage. These traditions have been inherited and evolved over generations, with the earliest examples of Arabic calligraphy being found in the Holy Qur'an, commonly inscribed in a script known as Kufic. The historical roots of Islam are deeply intertwined with the Holy Qur'an's manuscript, making it a primary source for understanding the advent of Islam. In this research, an extensive array of resources from the Islamic Art Museum Malaysia (IAMM) and the National Library of Malaysia (NLM) were utilised, alongside personal collections and secondary sources. Data analysis involved employing philological, comparative, deductive, and inductive methodologies. Through this approach, it was revealed that the production of Malay Holy Qur'an's manuscripts has been significantly influenced by local creative input and intellectual contributions from regions such as Turkey, China, and Persia. The study particularly accentuates the diverse calligraphic and illumination techniques across different nations. Notably, manuscripts from the west coast of Peninsular Malaysia, specifically from Malacca and Johor, exhibit distinct artistic qualities that set them apart from those originating from the east coast, including Kelantan and Terengganu. This underscores the prevalence of varied artistic expressions resulting from a broad spectrum of cultural interactions. The development of calligraphy and illumination methods in Malaysian Holy Qur'an's manuscripts mirrors a wide range of artistic expressions, which are shaped by both local innovation and broader Islamic intellectual traditions.

Identifying the bottleneck and its shifts for the nexus across water supply, power generation and environment systems: Hehuang, China

Identifying the bottleneck is the key to alerting and preventing unexpected failures of the nexus across water supply, power generation, and environment (WPE) systems. However, the bottleneck identification is challenged by the unclear concept of bottleneck as well as the difficulty of simulating complex emergent behaviors. Here, we develop a generic framework for identifying the bottleneck for the WPE nexus, which is defined as the subsystem that has the highest risk of leading to the failure of WPE nexus. First, the evolution of uncertainties of the state variables of WPE nexus is simulated by a generic stochastic dynamical system. Next, the propagation matrix, accumulation ratio (AR), and risk contribution ratio are defined as effective metrics and tools to identify the system bottleneck. Finally, the effects of human decisions and input stochasticity on the shifts of bottleneck are analyzed using sensitivity and scenario analysis. The WPE system in Hehuang Region, China, is selected as a case study. Results indicate that: (a) The effects of human decisions on ARs can be superimposed, suggesting a “superimposition method” for policy-making of Hehuang Region; (b) The subsystems can be classified as “endogenous” or “exogenous”, depending on the sources of accumulated uncertainties that are from local inputs, or inputs from the other subsystems; (c) There exist multiple bottlenecks at the same time that could shift from one subsystem to another, which complicates the prevention of the failures of WPE nexus. For the Hehuang Region, water and energy subsystems are more likely to be bottlenecks at the beginning stage, while society and environment subsystems are more likely to be bottlenecks at the steady stage. This study provides the first attempt to identify the bottleneck within the WPE nexus, which helps to provide realistic insights for preventing unexpected failures.

Local and long-range input balance: A framework for investigating frontal cognitive circuit maturation in health and disease.

Frontal cortical circuits undergo prolonged maturation across childhood and adolescence; however, it remains unknown what specific changes are occurring at the circuit level to establish adult cognitive function. With the recent advent of circuit dissection techniques, it is now feasible to examine circuit-specific changes in connectivity, activity, and function in animal models. Here, we propose that the balance of local and long-range inputs onto frontal cognitive circuits is an understudied metric of circuit maturation. This review highlights research on a frontal-sensory attention circuit that undergoes refinement of local/long-range connectivity, regulated by circuit activity and neuromodulatory signaling, and evaluates how this process may occur generally in the frontal cortex to support adult cognitive behavior. Notably, this balance can be bidirectionally disrupted through various mechanisms relevant to psychiatric disorders. Pharmacological or environmental interventions to normalize or reset the local and long-range balance could hold great therapeutic promise to prevent or rescue cognitive deficits.

Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments

Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity computer experiments have emerged as a promising approach that leverages both low-fidelity and high-fidelity simulations, enhancing both the accuracy and efficiency of the analysis. In this article, we introduce a new and flexible statistical model, the Recursive Non-Additive (RNA) emulator, that integrates the data from multi-fidelity computer experiments. Unlike conventional multi-fidelity emulation approaches that rely on an additive auto-regressive structure, the proposed RNA emulator recursively captures the relationships between multi-fidelity data using Gaussian process priors without making the additive assumption, allowing the model to accommodate more complex data patterns. Importantly, we derive the posterior predictive mean and variance of the emulator, which can be efficiently computed in a closed-form manner, leading to significant improvements in computational efficiency. Additionally, based on this emulator, we introduce four active learning strategies that optimize the balance between accuracy and simulation costs to guide the selection of the fidelity level and input locations for the next simulation run. We demonstrate the effectiveness of the proposed approach in a suite of synthetic examples and a real-world problem. An R package RNAmf for the proposed methodology is provided on CRAN.

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons.

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state 1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are minimally affected by neuromodulatory inputs and are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

Collaborative graph neural networks for augmented graphs: A local-to-global perspective

In the field of graph neural networks (GNNs) for representation learning, a noteworthy highlight is the potential of embedding fusion architectures for augmented graphs. However, prevalent GNN embedding fusion architectures mainly focus on handling graph combinations from a global perspective, often ignoring their collaboration with the information of local graph combinations. This inherent limitation constrains the ability of the constructed models to handle multiple input graphs, particularly when dealing with noisy input graphs collected from error-prone sources or those resulting from deficiencies in graph augmentation methods. In this paper, we propose an effective and robust embedding fusion architecture from a local-to-global perspective termed collaborative graph neural networks for augmented graphs (LoGo-GNN). Essentially, LoGo-GNN leverages a pairwise graph combination scheme to generate local perspective inputs. Together with the global graph combination, this serves as the basis to generate a local-to-global perspective. Specifically, LoGo-GNN employs a perturbation augmentation strategy to generate multiple augmentation graphs, thereby facilitating collaboration and embedding fusion from a local-to-global perspective through the use of graph combinations. In addition, LoGo-GNN incorporates a novel loss function for learning complementary information between different perspectives. We also conduct theoretical analysis to assess its expressive power under ideal conditions, demonstrating the effectiveness of LoGo-GNN. Our experiments, focusing on node classification and clustering tasks, highlight the superior performance of LoGo-GNN compared to state-of-the-art methods. Additionally, robustness analysis further confirms its effectiveness in addressing uncertainty challenges.

SAPERI: An Emergency Modeling Chain for Simulating Accidental Releases of Pollutants into the Atmosphere

Timely forecast of atmospheric pollutants fallout due to accidental fires can provide decision-makers with useful information for effective emergency response, for planning environmental monitoring and for conveying essential alerts to the population to minimize health risks. The SAPERI project (Accelerated simulation of accidental releases in the atmosphere on heterogeneous platforms—from its Italian initials) implements a modeling chain to quickly supply evidence about the dispersion of pollutants accidentally released in the atmosphere, even in the early stages of the emergency when full knowledge of the incident details is missing. The SAPERI modeling chain relies on SPRAY-WEB, a Lagrangian particle dispersion model openly shared for research purposes, parallelized on a GPU to take advantage of local or cloud computing resources and interfaced with open meteorological forecasts made available by the Meteo Italian SupercompuTing PoRtAL (MISTRAL) consortium over Italy. The operational model provides a quantitative and qualitative estimate of the impact of the emergency event by means of a maximum ground level concentration and a footprint map. In this work, the SAPERI modeling chain is tested in a real case event that occurred in Beinasco (Torino, Italy) in December 2021, mimicking its use with limited or missing local input data as occurs when an alert message is first issued. An evaluation of the meteorology forecast is carried out by comparing the wind and temperature fields obtained from MISTRAL with observations from weather stations. The concentrations obtained from the dispersion model are then compared with the observations at three air quality monitoring stations impacted by the event.

Climatic-hydrologic influence on redox condition in the Cryogenian interglacial Nanhua Basin: Insights from the Datangpo Formation in the northwestern Yangtze Block, South China

Tracking climatic and oceanic redox changes throughout the Neoproterozoic Cryogenian is crucial to a better understanding of the coevolution of life and environment in geological history. However, the processes and driven mechanisms of redox evolution in the Cryogenian interglacial ocean still remain uncertain. Here, we present a combined study of geochemical proxies, such as paleoclimatic proxies (CIA and Zr/Al) and paleosalinity proxies (B/Ga and Sr/Ba), from the interglacial Datangpo Formation in the shallow-water Zuojiawan section exposed in the northwestern Yangtze Block, to constrain climatic and hydrologic changes on redox conditions. Our data indicate that two apparently climatic cycles from warming to cooling occurred during the deposition of the Datangpo Formation, supplementing the monotonous climatic warming in previous research during the Cryogenian interglaciation. The low B/Ga and Sr/Ba ratios throughout Datangpo Formation suggest a continuous freshwater condition, which is in contrast to a transition of marine condition to brackish condition in relatively deep-water sections. Compiled salinity reconstructions at different paleo-depths imply the salinity gradient in the Nanhua Basin with a density stratification like modern Baltic Sea, at least during the early Cryogenian interglacial period. We find that the climate-driven local riverine freshwater input may be a significant driver for maintaining long-term freshwater condition in the Zuojiawan section. Accompanied by synchronously climatic warming, the progressive desalinization with declining B/Ga and Sr/Ba ratios in the middle to upper deposition of the Datangpo Formation are probably attributed to the persistent freshwater input to a nearshore shallow-water paleogeographic setting. Combined the improving oceanic oxygenation backdrop with the elevating seawater sulfate concentration in the Nanhua Basin in this period, we conclude that the continuous dilution of seawater in the restrict (semi-) Nanhua Basin could attenuate the density stratification and enhance the vertical mixing of watermass, favoring the ventilation of deepwater and penetration of sulfate. This contribution provides a new insight for the study of salinity changes on redox conditions in the Yangtze Block during the Cryogenian interglacial period, and provides new data for supporting a systematic understanding of the evolutional mechanism of oceanic redox state.

INVESTIGATING THE KEY LIMITATIONS AND IMPROVEMENTS INFLUENCING THE COMPETITIVE SUCCESS OF SOUTH AFRICA’S ORANGER EXPORTERS.

The objective of identifying constraining factors is to reveal that the key obstacles affecting the competitiveness of South Africa's domestic orange industry include issues such as the "quality of unskilled labor," "trust in the honesty of politicians," "electricity suppliers," "health," "South Africa’s land reform policy," "South Africa’s BEE policy," and "crime." Additionally, challenges related to the lack of quality infrastructure, bilateral agreements with trading countries, and poor performance of ports relative to their global counterparts have been identified. The constraining factors were ranked, with the top issues aligning closely with the industry's current challenges. In contrast, enhancing factors play a crucial role in boosting industry competitiveness in both domestic and international markets. The key elements contributing positively include the availability of unskilled labor, local input suppliers, scientific research institutions, technological quality, the cost of unskilled labor, soil conditions, and the sustainability of local input suppliers. Attention to maintaining and improving these factors is crucial for the sustained competitiveness of the South African orange sector.

Influence of Mg Content on Microstructure Coarsening, Molten Pool Size, and Hardness of Laser Remelted Al(-x)-Mg-Sc Alloys.

Investigations leading to high-quality surfaces and optimized properties in laser remelted Al-Mg-Sc alloys remain scarce. Laser surface remelting (LSR) has been used for the final treatment of these alloys processed through additive manufacturing. However, the direct microstructure responses of the treated cast surfaces have not yet been investigated. In the present research, Al-3, 5, and 10 wt %-Mg-0.1 wt %-Sc alloys plates were processed using LSR to study the effects of local melting and rapid solidification. The morphology of the α-Al phase, microstructure coarsening, and hardness were mapped from the bottom to the top of the molten pools, varying with local Mg content and laser heat input (2.5 J/mm, 5 J/mm, and 10 J/mm). This study aimed to create a comprehensive map of the microstructures, hardness, and molten pool sizes under various conditions. The findings may help to optimize these alloys through understanding laser processing parameters. Methods used included CALPHAD computations, optical microscopy, SEM, EDS, image analysis, hardness tests, and heat flow models. The results obtained showed α-Al cell growth with bands in all alloys with hardness changes correlating with cell spacing and heat input. Higher Mg content resulted in more refined cells and a higher fraction of bands. Increased Mg content decreased the thermal diffusivity and enthalpy of melting, enlarging the molten pool size. Hardness increased with decreasing heat input and higher Mg content in the tested alloys, especially in the Al-10 wt %-Mg-0.1 wt %-Sc alloy as the heat input was varied.

Oral Traditions and Local Authority: Viewing the Indonesian Revolution through a Cultural Lens

ABSTRACT Most accounts of the Indonesian revolution in South Sulawesi have focused on the two groups fighting for power: the Republic of Indonesia as a new nation-state and the Dutch, who wanted to regain control. What is absent from this way of viewing history are the views of local elites, who wanted to retain their traditional authority and whose ideas about the nation-state were based on their oral traditions, which have the potential to affect the reconstruction and politicisation of collective memory in the struggle for cultural rights and political authority. This article examines how the Polombangkeng community used oral traditions to justify local input into the cause of the revolution in South Sulawesi. During the Indonesian revolution, the Polombangkeng people drew on their oral tradition to define leadership positions, claims of political authority, and alliance formation. Their narratives about their identity were supported by ritual ceremonies. The article argues that the Indonesian revolution in Polombangkeng was more than just a power struggle between ‘Indonesian’ and ‘Dutch’ groups: it was also a process of negotiating and/or renegotiating identities, driven by the desire to stake traditional authority claims through the justification of oral traditions.

Natural Farming: Embracing Regenerative Agriculture for Sustainable Crop Production

Natural farming, a regenerative agriculture is advocated and promoted worldwide to produce safe and quality produce and to live in harmony with nature. It is “Chemical free farming” or “do-nothing farming” is a sustainable farming approach that aims to work with nature instead of trying to change it. It focuses on improving soil biological fertility without relying on synthetic chemicals or external inputs. It lies in a simple principle of utilizing low-cost and local inputs with zero utilization of chemicals. Major strategy is continuous application of cow dung and cow urine-based concoctions beside following effective recycling of crop residues, leguminous intercrops as soil cover, pre- monsoon dry sowing, minimizing the irrigation and balancing the soil, air and moisture by irrigating at noon. The farmer is considered only to be a facilitator - the real work is done by Nature herself. No- tillage and farming without the application of herbicides, inorganic fertilizers and pesticides is practiced. Here, actual physical work and labor has been seen to reduce by up to 80% compared to other farming systems. The essence of natural farming is minimizing the external inputs to the farm land, which degenerate the soil nature and improving the crop yield. The Natural farming a cost-effective farming practices with scope for raising employment and rural development. Natural farming also offers a solution to various problems such as food insecurity, farmers distress, health problems arising due to pesticide, fungicide and fertilizers residue in food and water, climate change and natural calamities.

Legacy and novel contaminants in surface sediments of Admiralty Bay, Antarctica Peninsula

Despite being one of the most remote areas on the planet, the Antarctic continent is subject to anthropogenic influences. The presence of various groups of contaminants, including persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs), has been documented in the region over the past decades. However, a significant knowledge gap remains regarding the detection of new pollutants, such as emerging contaminants (ECs), in Antarctic coastal environments. This study analyzed the occurrence and levels of selected POPs, PAHs, ECs in surface sediments from Admiralty Bay, Antarctica Peninsula. Non-target screening was employed to identify potential novel contaminants in the region. Samples (n = 17) were extracted using an accelerated solvent extraction (ASE) system and instrumental analyses were performed using gas chromatography coupled to a triple-quadrupole mass spectrometer (GC/MS-MS). Regarding regulated contaminants, concentrations of Σ5PCBs ranged from <LOD to 0.5 ng g−1, while Σ16PAHs ranged from 2.6 to 617.4 ng g−1. These compounds were predominant in the Martel inlet, suggesting influences of local input sources, especially for PAHs. ECs were detected in some sites of the Ezcurra and Martel inlets, especially nearby the Brazilian and Polish research stations. The UV filters homosalate (0.3 to 251.3 ng g−1) and octocrylene (0.7 to 93.8 ng g−1) were the most abundant EC, followed by TPP (0.1 to 87.4 ng g−1) and galaxolide (0.2 to 55.0 ng g−1). Applying non-target analysis, phthalates, and the fragrance OTNE (octahydro-tetramethyl-naphthalenyl-ethanone) were identified for the first time in Antarctic sediments. The data highlights the contribution of human activities in the region, possibly through wastewater effluents. This study provides data on the current levels of PAHs and POPs in the region, which are steadily decreasing over the years.