Hasil untuk "Environmental law"

Qi Song, Jiahai Shi, Xubo Li et al.

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m².

Rieke Schulte, Alexandra Loll, Fabian Essfeld et al.

Abstract Chemical contamination of aquatic ecosystems presents a major environmental challenge, with pesticides constituting a significant portion of these pollutants. This study investigates the acute and sublethal effects of the fungicide prochloraz and the insecticide endosulfan as model substances with known modes of action (MoA) on aquatic invertebrates, utilizing Daphnia magna as a model organism. Acute toxicity tests were conducted in accordance with OECD test guideline 202 and complemented by monitoring of swimming activity as well as transcriptomic analyses as sublethal endpoints to elucidate and differentiate the MoA of both compounds. The acute toxicity assays yielded EC50 values consistent with previous literature as well as the registration dossiers of the substances. In subsequent behavioral assays employing sublethal nominal test concentrations, prochloraz induced a significant reduction in swimming activity, whereas endosulfan increased swimming activity with increasing test concentrations, providing preliminary evidence of substance-specific MoA. The toxicogenomic analysis revealed significant alterations in gene expression for both pesticides. While some secondary downstream cellular processes were affected by both substances, functional transcriptome analysis underscored clear MoA distinctions: prochloraz primarily regulated genes involved in lipid, sterol, and steroid biosynthesis, whereas endosulfan predominantly influenced ion transport-related genes. In summary, our study demonstrates distinct MoA-specific behavioral and gene expression responses provoked by prochloraz and endosulfan in D. magna, offering valuable mechanistic insights for environmental risk assessment.

Sander E. van der Leeuw, Gary W Dirks

“Why do we, in the Euro-American (Western) world, often investigate the dynamics of change and assume that stability is the ‘natural’ state of systems?” And “If everything always changes, why don’t we investigate why, and how, we create stable models summarizing change?” Rather than the dominant Aristotelian approach, this paper follows Heraclitus, affirming that everything always changes and stability is a construct in our thinking. We conceive of change as a dynamic of flows of energy, matter, and information. Whereas the first two are subject to the second law of thermodynamics, information is not and can be shared. The paper thus looks at how human information processing impacts socio-environmental dynamics by shaping stable basins of attraction structuring our vision of all that surrounds us. These basins are separated by “tipping points.” Any society’s basin of attraction is different from any other, anchored in differences in cognitive structures established throughout the society’s history. These structures determine a society’s cognitive links between different aspects of its perception of its environment, its ecology and its society, as well as the structure of the community’s analytical (scientific) tools.

Victoria Koshoffa Akinkpelumi, Amarachi Paschaline Onyena, Prosper Manu Abdulai et al.

Abstract Introduction Oil exploration in the Niger Delta has resulted in severe contamination of environmental media, with polycyclic aromatic hydrocarbons (PAHs) recognized as priority pollutants by the U.S. EPA. This study assessed the levels and ecological risks of PAHs in soil, sediment, surface water, groundwater, and food crops from Ibaa, an oil-impacted community in the Niger Delta. Methods These samples were collected during the wet and dry seasons and analyzed for 16 priority PAHs using gas chromatography–mass spectrometry (GC–MS) following U.S. EPA protocols. Contamination factors, risk quotients (RQ), and diagnostic ratios were used to evaluate contamination levels and identify PAH sources. Results were compared with international standards from the WHO, USEPA, EU, and Canadian guidelines. Results Total PAH concentrations (Σ16PAHs) in soils ranged from 0.60–12.29 mg/kg, exceeding the Canadian agricultural soil guideline (0.1 mg/kg) by over 100 times. Surface water PAHs reached 0.693 mg/L, surpassing the WHO limit for drinking water (0.0002 mg/L) by more than 3000 times, while groundwater remained below but close to acceptable thresholds (RQ∑PAHs ≤ 0.157). PAHs in food crops (0.007–0.020 mg/kg) slightly exceeded the EU limit (0.01 mg/kg) but posed minimal ecological risk (RQ∑PAHs < 1). Soils and sediments in the dry season showed the highest ecological risk, with diagnostic ratios indicating a predominantly petrogenic source. Conclusion and recommendation The findings demonstrate persistent PAH contamination that threatens soil fertility, aquatic ecosystems, and food safety in Ibaa. The study indicates the potential for bioaccumulation and long-term exposure risks to local populations. Immediate remediation, strict regulatory enforcement, and continuous monitoring are recommended to mitigate ecological and health hazards in the Niger Delta.

Inci Tuney, Elizabeth G. T. Bengil, Fethi Bengil et al.

Abstract Background Seagrass habitats are vital for maintaining marine biodiversity and ecosystem functions. In the Eastern Mediterranean Sea, Posidonia oceanica and Cymodocea nodosa are among the dominant seagrass species; however, these ecosystems are increasingly threatened by rising seawater temperatures, invasive herbivores, and anthropogenic pressures. Marine protected areas (MPAs) and active restoration practices have emerged as key strategies for their conservation. This study was conducted in the Gökova Bay MPA, South Aegean Sea, to evaluate the effectiveness of seagrass transplantation and the impact of invasive herbivores on restoration outcomes. Transplants were established within both open plot and exclusion cage systems to compare herbivory pressure and transplantation performance. Metrics, such as shoot density and leaf length were used to assess transplantation success, while visual surveys recorded evidence of grazing. Results The results revealed species-specific responses: P. oceanica showed significant variability in shoot density under different experimental conditions, whereas C. nodosa consistently declined across all treatments. Cage systems had no significant effect on the biomass performance of P. oceanica, but negatively affected C. nodosa, leading to reduced shoot density. Conclusions These findings highlight the importance of tailored restoration strategies that account for species-specific ecological responses and offer preliminary insights for improving habitat conservation efforts in the region.

Qingfeng Miao, Dandan Yu, Haibin Shi et al.

Abstract Understanding the interactions between crop growth and abiotic stressors (water, salt, and nitrogen) is crucial for optimizing fertilizer use, improving plant stress resistance, and promoting agricultural productivity and environmental sustainability. Herein, we investigated the effects of organic fertilizer type, organic fertilizer ratio, and supplemental irrigation on soil water and salt transport, crop growth, and yield in mildly to moderately salinized soils. Using the APSIM model, we simulated crop growth and soil moisture under different organic fertilizer application ratios in mildly to moderately saline soils. Based on sunflower field experiments, four machine learning models (regression trees, random forest, support vector machines, and XGBoost) and two deep learning models (deep neural networks and neural networks) were developed to predict soil salinity. Results showed that reducing nitrogen application and using organic fertilizers decreased soil salinity by 11.1–22.8% at a 0–60 cm depth. A 50% organic to inorganic fertilizer ratio minimized salt accumulation. In mildly salinized soils, supplemental irrigation increased leaf area index (LAI) and biomass by 1.8–7.1% and 9–35%, respectively. Moreover, in mildly salinized farmlands, the combination of 75% organic fertilizer and 44 mm of supplemental irrigation resulted in relatively lower soil salinity. In moderately salinized farmland, lower soil salinity accumulation was observed with 25% organic fertilizer and 44 mm supplemental irrigation. In mildly saline–alkali soils, maximum yield was achieved with 50% organic nitrogen substitution + 22 mm supplemental irrigation. In moderately saline–alkali soils, the same substitution rate (50%) yielded peak production but required 44 mm irrigation to counteract osmotic stress. Compared to natural farm manure, commercial organic fertilizer with supplemental irrigation increased crop yield, agronomic efficiency (Ac), and harvest index (Hi). The maximum crop yield and yield components were achieved with 50% organic fertilizer and 22 mm supplemental irrigation. In the moderately salinized soil, the highest irrigation productivity was achieved with 75% organic fertilizer. Although the APSIM-sunflower model can be used to simulate growth and development (R 2 = 0.7–0.9; NRMSE = 0.1–0.2), its simulation of soil water dynamics is unsatisfactory (R 2 = 0.4–0.5; NRMSE = 0.3). In simulating soil salinity, deep learning models generally outperform machine learning models (EVS ≤ 0.3; R 2 ≤ 0.42), with the deep neural network (DNN (EVS ≤ 0.3; R 2 ≤ 0.82)) algorithm demonstrating the best simulation performance. The adjustment of the organic–inorganic fertilizer ratio and supplemental irrigation strategies can optimize resource utilization in saline-alkali soils. DNN provides a more accurate method for predicting soil salinity, achieving a balance between productivity improvement and environmental protection in salt-affected areas.

Dimas Febriansyah Krisna Dwiputra, Enok Maryani, Fahmi Nugraha Heryanto

Environmental conservation and disaster mitigation still emphasize science and technology, while the potential of the socio-cultural wealth within communities remains underutilized. This shows a substantial gap, as active community involvement, rooted in socio-cultural wealth, is essential for effective environmental conservation and disaster mitigation. To address the existing gap, this research aims to identify, analyze, and interpret the values of Sundanese Indigenous laws (Pikukuh) that can be utilized and relevant for these efforts. This study used a qualitative method with a case study design in Kampung Naga, Baduy, Kuta, and Cikondang. The results showed that Pikukuh embodied important values for environmental conservation efforts. These rules played a significant role in conserving forests, improving vegetation conditions, enhancing rainwater absorption, ensuring soil stability, preventing erosion, and maintaining the groundwater cycle. These principles are crucial for mitigating natural disasters like landslides, floods, droughts, and climate change. Pikukuh principles need to be incorporated more broadly and actively applied in policies and strategic initiatives to advance sustainable development, as this goes beyond romanticizing traditions and is essential for securing a sustainable future life

Sofia Borgosano, Daniele Martini, Michela Longo et al.

In recent years, the Electric Vehicles (EVs) industry has experienced rapid growth, driven by advancements in battery technology, environmental awareness, and government incentives. However, traditional charging infrastructure&#x2019;s limited availability and long charging times pose significant challenges, especially for long-distance travel and public service vehicles like taxis, buses, and law enforcement vehicles. This work explores the innovative concept of Battery Swap Stations (BSSs), an emerging technology poised to transform the EV charging landscape. It specifically focuses on electric taxis operating in Chicago&#x2019;s urban environment, highlighting the substantial benefits this technology can offer. BSSs demonstrated to dramatically reduce charging times, improving taxi service efficiency and increasing revenue potential. Instead, conductive charging impacts the working time of taxis across all case studies (as observed in the Level 2 charger scenario) While BSS technology has its drawbacks, such as optimal location challenges and battery management complexities, it has the potential to significantly enhance service quality. Additionally, these stations hold the promise of not only increasing urban transportation system efficiency but also contributing to their sustainability.

Guanghan Yan, Xueyan Yin, Xing Wang et al.

Abstract In this study, 15 sampling sites were set up in Dongting Lake, a typical river-connected lake in China, to investigate water quality and diatioms in March, June, September and December from year 2017 to 2022. Seven diatom indices, including relative abundance of diatoms (RAD), percentage motile diatoms (PMD), generic diatom index (GDI), diatom quotient (DU), pollution tolerance index for diatoms (PTI), trophic diatom index (TDI), and Pampean diatom index (IDP), were selected to screen the adaptability of water quality assessment comparing with the Nemero index (NI), which is simple to calculate and has always been the main method for water quality assessment in Dongting Lake. The results from 2017 to 2019 showed that the diatom density in Dongting Lake ranged from 0.7 × 104 to 85.5 × 104 ind./L, with a certain decreasing trend. The spatial and temporal changes of some water quality factors were obvious, just like the temperature of water (WT), ammonia nitrogen (NH4 +–N), dissolved oxygen (DO) and the comprehensive trophic level index (∑TLI) ranged from 45.99 to 50.72, with an average value of 47.85, indicating that the overall condition of Dongting Lake was medium nutrition. Correlation analysis showed that PTI, RAD and PMD could represent the information of DU, GDI, TDI and IDP, and were significantly positively correlated with DO (p < 0.01), while significantly negatively correlated with electrical conductivity (Cond), potassium permanganate (CODMn), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr) and ∑TLI (p < 0.001). The index verification results from year 2020 to 2022 showed that PTI, RAD and PMD were all significantly positively correlated with NI (p < 0.001). Taking into account the data integrity of the index calculation and the difficulty degree, RAD was finally selected as the biological indicator for evaluating the water quality of Dongting Lake. The results of this study provide a new path or alternative method for water quality assessment of the river-connected lakes.

Patrick Gould, Guanqun Song, Ting Zhu

This paper analyzes the proportion of Input/output devices made obsolete by changes in technology generations. This obsolescence may be by new software/hardware generations rendering otherwise functional devices unusable. Concluding with brief analysis on the economic and environmental impacts of the e-waste produced.

Rômulo Meira-Góes, Ian Dardik, Eunsuk Kang et al.

A safety verification task involves verifying a system against a desired safety property under certain assumptions about the environment. However, these environmental assumptions may occasionally be violated due to modeling errors or faults. Ideally, the system guarantees its critical properties even under some of these violations, i.e., the system is \emph{robust} against environmental deviations. This paper proposes a notion of \emph{robustness} as an explicit, first-class property of a transition system that captures how robust it is against possible \emph{deviations} in the environment. We modeled deviations as a set of \emph{transitions} that may be added to the original environment. Our robustness notion then describes the safety envelope of this system, i.e., it captures all sets of extra environment transitions for which the system still guarantees a desired property. We show that being able to explicitly reason about robustness enables new types of system analysis and design tasks beyond the common verification problem stated above. We demonstrate the application of our framework on case studies involving a radiation therapy interface, an electronic voting machine, a fare collection protocol, and a medical pump device.

D. Dhinakaran, S. Gopalakrishnan, M. D. Manigandan et al.

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

Ryan Boldi, Thomas Helmuth, Lee Spector

Down-sampling training data has long been shown to improve the generalization performance of a wide range of machine learning systems. Recently, down-sampling has proved effective in genetic programming (GP) runs that utilize the lexicase parent selection technique. Although this down-sampling procedure has been shown to significantly improve performance across a variety of problems, it does not seem to do so due to encouraging adaptability through environmental change. We hypothesize that the random sampling that is performed every generation causes discontinuities that result in the population being unable to adapt to the shifting environment. We investigate modifications to down-sampled lexicase selection in hopes of promoting incremental environmental change to scaffold evolution by reducing the amount of jarring discontinuities between the environments of successive generations. In our empirical studies, we find that forcing incremental environmental change is not significantly better for evolving solutions to program synthesis problems than simple random down-sampling. In response to this, we attempt to exacerbate the hypothesized prevalence of discontinuities by using only disjoint down-samples to see if it hinders performance. We find that this also does not significantly differ from the performance of regular random down-sampling. These negative results raise new questions about the ways in which the composition of sub-samples, which may include synonymous cases, may be expected to influence the performance of machine learning systems that use down-sampling.

František Bartoš, Maximilian Maier, Eric-Jan Wagenmakers et al.

Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from d = 0.20 to d = 0.07 in economics, from d = 0.37 to d = 0.26 in psychology, from d = 0.62 to d = 0.43 in environmental sciences, and from d = 0.24 to d = 0.13 in medicine.

Yang Xiao, Xubo Liu, James King et al.

Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation resources (e.g., model size, running memory). To address this issue, we propose a simple and efficient continual learning method. Our method selects the historical data for the training by measuring the per-sample classification uncertainty. Specifically, we measure the uncertainty by observing how the classification probability of data fluctuates against the parallel perturbations added to the classifier embedding. In this way, the computation cost can be significantly reduced compared with adding perturbation to the raw data. Experimental results on the DCASE 2019 Task 1 and ESC-50 dataset show that our proposed method outperforms baseline continual learning methods on classification accuracy and computational efficiency, indicating our method can efficiently and incrementally learn new classes without the catastrophic forgetting problem for on-device environmental sound classification.

Stacey Law

In this article we investigate the positivity of Sylow branching coefficients for symmetric groups when $p = 3$. In particular, we complete the discussion begun by Giannelli and the author in arXiv:1712.02642 (J. Algebra) and developed in arXiv:1909.09446 (J. London Math. Soc.) concerning the case of odd primes.

Hei Law, Jia Deng

We propose a new approach, Synthetic Optimized Layout with Instance Detection (SOLID), to pretrain object detectors with synthetic images. Our "SOLID" approach consists of two main components: (1) generating synthetic images using a collection of unlabelled 3D models with optimized scene arrangement; (2) pretraining an object detector on "instance detection" task - given a query image depicting an object, detecting all instances of the exact same object in a target image. Our approach does not need any semantic labels for pretraining and allows the use of arbitrary, diverse 3D models. Experiments on COCO show that with optimized data generation and a proper pretraining task, synthetic data can be highly effective data for pretraining object detectors. In particular, pretraining on rendered images achieves performance competitive with pretraining on real images while using significantly less computing resources. Code is available at https://github.com/princeton-vl/SOLID.

B. Gaudet, R. Linares, R. Furfaro

Abstract This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt in real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four challenging environments with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a RL meta-learning optimized policy to implement a guidance law using observations consisting of only Doppler radar altimeter readings in a Mars landing environment, and LIDAR altimeter readings in an asteroid landing environment thus integrating guidance and navigation.

Liliana Belecciu

The public water supply and sewerage service includes the totality of activities of public utility and general economic and social interest carried out for the purpose of the collection, treatment, transportation, storage and distribution of drinking water on the territory of the administrative-territorial unit, as well as for the purpose of the collection and purification of wastewater. This service is regulated, in particular, by the Law on public water supply and sewerage service No. 303/2013. The object of the law represents the creation of the legal framework for the establishment, organization, operation, regulation and monitoring of the public drinking water supply and sewerage service in the conditions of accessibility, availability, reliability, continuity, competitiveness, transparency, respecting quality, safety and environmental protection. Everything that exceeds these activities is not subject to the regulation of the Law No. 303/2013. And the application of the “share” is an illegal activity that is punishable in accordance with the legislation in force.

Dian Yunari, Moh Saleh

House buying and selling can be performed with cash or installments through bank or KPR/PPR. In addition, a method of buying and selling houses between housing developers and buyers by installments can be also done without mortgage process called inhouse system. The purpose of this study is to determine and analyze the legal aspects of the house sale and purchase agreement using the in-house method. A house sale and purchase agreement and land rights must pay attention to the elements of Article 1320 BW regarding the validity of an agreement. Buyers are required to check the legality of a housing development company and the legality of the housing unit to be built. Prospective buyers are expected not to be tempted by prices far below the housing market price, even though the company offers credit facilities without usury and bank mortgages. It's a good idea for buyers to make transactions if the building has been built, although partly to avoid fraud by the developer. To prevent development companies that do not comply with statutory regulations on settlements, the Regional Government should supervise and enforce the law regarding the legality of housing and infrastructure development, facilities and utilities as well as other environmental permits so that buyers' rights can be fully guaranteed.