Effective Monitoring Research Articles

Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

News on the imaging of large vessel vasculitis

Large vessel vasculitis, including giant cell arteritis (GCA) and Takayasu arteritis (TAK), are autoimmune diseases primarily affecting the aorta and its branches. GCA is the most common primary vasculitis. Inflammatory changes in the vessel walls can cause serious complications such as amaurosis, stroke, and aortic dissection and rupture. Imaging techniques have become an integral part for the diagnosis and monitoring of large vessel vasculitis, allowing for effective disease monitoring. GCA and TAK exhibit similar patterns of vascular distribution. However, the temporal arteries are never involved in TAK, and axillary arteritis occurs more frequently in GCA. In most centers, ultrasound of the temporal and axillary arteries has replaced temporal artery biopsy as the primary diagnostic tool for GCA. In addition to ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and [18F]-FDG (fluorodeoxyglucose) positron emission tomography-computed tomography (PET) are important, particularly for visualizing the aorta. Moreover, PET-CT is now also capable of assessing the temporal arteries, although it is not yet widely available. In polymyalgia rheumatica (PMR), ultrasound of the shoulder and hip regions is part of the ACR/EULAR classification criteria. MRI allows detailed visualization of additional inflammatory extraarticular manifestations, showing characteristic inflammatory lesions in entheses, tendons, and ligaments. [18F]-FDG-PET-CT also enables the visualization of musculoskeletal inflammation, especially in the shoulder and hip regions, as well as paravertebral areas. Ultrasound can detect subclinical GCA in up to 23% of patients with PMR, which should be treated like GCA. Technological innovations such as new radiotracers and improved MRI imaging could further enhance the diagnosis and monitoring of large vessel vasculitis and PMR, thus playing acrucial role in improving the prognosis through faster initiation of therapy.

Practice review: Pharmacological management of severe chronic breathlessness in adults with advanced life-limiting diseases.

Severe and refractory chronic breathlessness is a common and burdensome symptom in patients with advanced life-limiting disease. Its clinical management is challenging because of the lack of effective interventions. To provide practice recommendations on the safe use of pharmacological therapies for severe chronic breathlessness. Scoping review of (inter)national guidelines and systematic reviews. We additionally searched for primary studies where no systematic review could be identified. Consensus on the recommendations was reached by 75% approval within an international expert panel. Searches in MEDLINE, Cochrane Library and Guideline International Network until March 2023. Inclusion of publications on the use of antidepressants, benzodiazepines, opioids or corticosteroids for chronic breathlessness in adults with cancer, chronic obstructive pulmonary disease, interstitial lung disease or chronic heart failure. Overall, the evidence from eight guidelines, 14 systematic reviews and 3 randomised controlled trials (RCTs) on antidepressants is limited. There is low quality evidence favouring opioids in patients with chronic obstructive pulmonary disease, cancer and interstitial lung disease. For chronic heart failure, evidence is inconclusive. Benzodiazepines should only be considered for anxiety associated with severe breathlessness. Antidepressants and corticosteroids should not be used. Management of breathlessness remains challenging with only few pharmacological options with limited and partially conflicting evidence. Therefore, pharmacological treatment should be reserved for patients with advanced disease under monitoring of side effects, after optimisation of the underlying condition and use of evidence-based non-pharmacological interventions as first-line treatment.

Assessing the Impact of Agricultural Practices and Urban Expansion on Drought Dynamics Using a Multi-Drought Index Application Implemented in Google Earth Engine: A Case Study of the Oum Er-Rbia Watershed, Morocco

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates.

Ultrasensitive miRNA-let-7a detection based on AgInS2 quantum dots co-sensitization and CRISPR/Cas12a induced strand displacement reaction for signal amplification

The evolution of breast cancer is usually accompanied by abnormal expression of miRNA-let-7a. Sensitive gauging of miRNA-let-7a is crucial for breast cancer monitoring and prognosis. Herein, a novel photoelectrochemical (PEC) biosensor was served as miRNA-let-7a detection on the basis of the coupling of co-sensitization and CRISPR/Cas12a-mediated strand displacement amplification (SDA). In short, AgInS2 quantum dots (QDs), characterized by a wide visible light absorption range and a stable photocurrent response, have an obvious sensitizing effect on TiO2/Bi2MoO6. Under the condition of without miRNA-let-7a, the AgInS2 fixed DNA probe was located near the surface of the TiO2/Bi2MoO6 electrode, forming a co-sensitive structure. During the detection process, hairpin DNA2 (HP2) specifically recognized miRNA-let-7a through base complementary pairing, triggering the SDA reaction to convert massive double-stranded DNA (dsDNA). The dsDNA product then triggered CRISPR/Cas12a lateral cleavage activity, causing AgInS2-sensitized DNA to leave the electrode surface and the co-sensitization effect to disappear, further reducing the photocurrent. Using a dual effect for signal amplification, the biosensor platform had linear detection capability within the scope of 0.01–10 nM, and the detection limit was 3.35 pM. The biosensor which was fabricated effectively provided an excellent platform for the effective monitoring of various tumor biomarkers in clinical detection.

Guideline concordant screening and monitoring of extrapyramidal symptoms in patients prescribed antipsychotic medication: a protocol for a systematic literature review and narrative synthesis

IntroductionGiven the increasing rates of antipsychotic use in multiple psychiatric conditions, greater attention to the assessment, monitoring and documentation of their side effects is warranted. While a significant degree of...

Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.

Recent Advances in Assessing Environmental Effects of Marine Renewable Energy Around the World

Abstract Marine renewable energy (MRE) is increasingly of interest to coastal nations as a source of renewable energy that can support climate change mitigation goals as well as provide secure locally-produced energy for coastal and island communities. MRE extracts power from tidal streams, waves, ocean currents, run of rivers, and gradients in the ocean, with specialized devices developed and tested for each energy resource. Alongside development of MRE technologies and systems, first in Europe and then in North America, Australia, Asia, and other regions, it has been universally recognized that there is also a need to examine potential effects on marine animals, habitats, ecosystem processes, local communities and other sea users, to ensure that the MRE industry can be developed in a responsible and sustainable manner. This paper looks at the status of assessment and monitoring for potential environmental effects associated with MRE projects around the world. Over 80 projects were identified worldwide as having been tested, demonstrated, or commercially deployed with associated environmental monitoring. Five of the projects that represent tidal stream, wave, and run of river projects are examined in more detail to determine the types of data and information collected for those projects, the outputs of the monitoring campaigns, and the actions taken as a result of the data collection and analysis. Recommendations are provided for standardization of the monitoring approaches, instruments, and analysis methods at MRE project sites worldwide.

Research progress on platelets in glioma.

Gliomas are the most common primary neuroepithelial tumors of the central nervous system in adults, of which glioblastoma is the deadliest subtype. Apart from the intrinsically indestructible characteristics of glioma (stem) cells, accumulating evidence suggests that the tumor microenvironment also plays a vital role in the refractoriness of glioblastoma. The primary functions of platelets are to stop bleeding and regulate thrombosis under physiological conditions. Furthermore, platelets are also active elements that participate in a variety of processes of tumor development, including tumor growth, invasion, and chemoresistance. Glioma cells recruit and activate resting platelets to become tumor-educated platelets (TEPs), which in turn can promote the proliferation, invasion, stemness, and chemoresistance of glioma cells. TEPs can be used to obtain genetic information about gliomas, which is helpful for early diagnosis and monitoring of therapeutic effects. Platelet membranes are intriguing biomimetic materials for developing efficacious drug carriers to enhance antiglioma activity. Herein, we review the recent research referring to the contribution of platelets to the malignant characteristics of gliomas and focusing on the molecular mechanisms mediating the interaction between TEPs and glioma (stem) cells, as well as present the challenges and opportunities in targeting platelets for glioma therapy.

NOVEL SMART CONTACT LENSES WITH EMBEDDED AI FOR PIONEERING CONTINUOUS HEALTH TRACKING AND DISEASE DETECTION

Smart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaking advancement in continuous health monitoring and disease detection. This paper explores the development and application of smart contact lenses that integrate real-time data collection with sophisticated AI algorithms to monitor various health parameters. By incorporating sensors and AI processing units directly into the lenses, these devices can continuously measure and analyze biometric data such as glucose levels, intraocular pressure, and ocular health indicators. The embedded AI system processes this data to provide real-time feedback, identify potential health issues, and predict disease onset with high accuracy. The study evaluates the efficacy of these smart contact lenses in detecting early signs of chronic conditions like diabetes and glaucoma, and their ability to enhance patient engagement through personalized health insights. Additionally, the paper discusses the technological challenges, including data accuracy, device comfort, and privacy concerns, and proposes solutions to address these issues. The integration of smart contact lenses with AI technology offers a novel approach to proactive health management, paving the way for more accessible and effective continuous monitoring and early disease detection.

Legal Transformation for the Achievement of SDGs: Integration of Multidisciplinary Approaches towards 2030

This research aims to investigate and analyze the transformation of law in an effort to achieve Sustainable Development Goals (SDGs) through the integration of a multidisciplinary approach. Along with dynamic global changes, the challenges in achieving the SDGs require innovative and adaptive legal strategies. This dissertation focuses on two main problem formulations: first, how the integration of multidisciplinary approaches in legal reform can support the achievement of SDGs; second, how the evaluation and monitoring of legal policies implemented can ensure the achievement of SDGs targets. Through literature review and case analysis, this research reveals that incorporating various disciplines-such as economics, sociology, environmental science, and politics-in the legal policy-making process is key to creating comprehensive and responsive regulations. A multidisciplinary approach not only broadens perspectives in the legal field, but also ensures that the resulting policies are able to address the complexity of sustainable development issues. The research further proposes that an effective evaluation and monitoring system is an essential element to ensure legal policies can be optimally implemented and contribute to the achievement of the SDGs. Suggested measures include the development of relevant performance indicators, stakeholder engagement, and the use of information technology to increase transparency and accountability. With this approach, it is expected that legal reform can adapt to changing conditions and make a positive contribution to the achievement of sustainable development goals, creating a more inclusive and sustainable future for all. This research makes important theoretical and practical contributions to the development of legal policies that are in line with the global agenda for sustainable development.

Advancing green education in MENA region: Challenges, opportunities and best practices

AbstractThis paper explores Green Education in the Middle East and North Africa (MENA), addressing shared environmental challenges across its 21 diverse countries. Despite differing economic structures, economies and politics, the need for green education, also known as environmental education (EE) or education for sustainable development (ESD), is evident. MENA encounters obstacles like centralised education structures, governance issues, curriculum gaps, limited funding, environmental awareness and gender disparities. Overcoming these challenges necessitates a comprehensive strategy involving curriculum integration, policy support, collaboration with non‐governmental organisations and international organisations, sufficient funding and effective monitoring. Engaging in global initiatives like the Paris Agreement and the Middle East Green Initiative provides opportunities to enhance EE. Adopting a holistic approach empowers the MENA region to address environmental concerns, promote sustainable development and align with global conservation efforts.

Exploring Diagnostic and Therapeutic Odyssey in Pulmonary Arterial Hypertension: Insights from In-Depth Semi-Structured Interviews

Introduction: Establishing a diagnosis is paramount in medical practice as it shapes patients’ experiences and guides treatment. Patients grappling with rare diseases face a triple challenge: prolonged diagnostic journeys, limited responses to existing therapies, and the absence of effective monitoring tools. Genetic diagnosis often provides crucial diagnostic and prognostic information, opening up possibilities for genotype-targeted treatments and facilitating counselling and relative testing. The NIHR BioResource – Rare Diseases (NBR) Study and the Cohort Study in Idiopathic and Hereditary Pulmonary Arterial Hypertension (PAH Cohort study) aimed to enhance diagnosis and treatment for PAH, successfully identifying the genetic cause in 25% of idiopathic cases. However, the diagnostic and therapeutic odyssey in patients with PAH remains largely unexplored. Methods: Stakeholders from the NBR and PAH Cohort studies were recruited using purposive sampling. In-depth interviews and focus groups were recorded, transcribed, anonymised, and analysed thematically using MAXQDA software. Results: The study involved 53 interviews and focus groups with 63 participants, revealing key themes across five stages of the diagnostic odyssey: initial health concerns and interactions with general practitioners, experiences of misdiagnosis, relief upon receiving the correct diagnosis, and mixed emotions regarding genetic results and the challenges of living with the disease. Following the diagnosis, participants embarked on a therapeutic journey, facing various challenges, including the disease’s impact on professional and social lives, the learning curve associated with understanding the disease, shifts in communication dynamics with healthcare providers, therapeutic hurdles, and insurance-related issues. Building on these insights, we identified areas of unmet needs, such as improved collaboration with primary care providers and local hospitals, the provision of psychological support and counselling, and the necessity for ongoing patient education in the ever-evolving realms of research and therapy. Conclusions: The study highlights the significant challenges encountered throughout the diagnostic and therapeutic journey in PAH. To enhance patient outcomes, it is crucial to raise awareness of the disease, establish clear diagnostic pathways, and seamlessly integrate genetic diagnostics into clinical practice. Streamlining the diagnostic process can be achieved by utilising existing clinical infrastructure to support research and fostering better communication within the NHS. Moreover, there is an urgent need for more effective therapies alongside less burdensome drug delivery methods.

The impact of gold mining activities: understanding the dynamics of cyanide in river ecosystems in Ecuador.

Understanding the behavior of cyanide in rivers is of utmost importance as it has a direct impact on the health of people who depend on these water sources. Cyanide contamination from gold mining activities poses a significant environmental threat to river ecosystems, particularly in southern Ecuador. This study aimed to investigate the behavior of cyanide when it enters contact with other metals in these rivers. Simulations were conducted to determine the speciation of cyanide, mercury, arsenic, lead, and manganese in a study area, taking into account the water temperature and pH at four locations. The findings revealed that CN-and HCN(aq) species were present in the research area. Additionally, mercury-cyanide (Hg(CN)2(aq), Hg(CN)3-), and manganese-cyanide (MnCN+) complexes were identified 3km downriver from the site where the mining activity is higher. These metal-cyanide complexes tend to dissociate quickly under weak acidic conditions, making them hazardous to the environment. This research is crucial, not only for the environment but also for human health, as it allows to predict toxicity risks for people supplied with this water source, emphasizing the potential harm to human health. This study highlights the importance of stringent regulations and effective monitoring practices to mitigate cyanide contamination and safeguard environmental and occupational health.

A Review of ECG Classification Techniques Based FPGA

Electrocardiogram (ECG) signals are particularly used in the diagnosis and supervision of different cardiovascular diseases. The accurate and efficient classification of ECG signals is of critical importance in clinical applications. FPGA-based systems have been identified to offer an efficient solution to the implementation of real-time ECG signal classification systems because of their parallel processing, reprogrammable nature, and low power consumption. Thus, this review paper aims to explore the existing ECG classification techniques with a focus on FPGA implementation. The review discussed several classification methods, including adaptive algorithms and neural networks, which have been optimized for arrhythmia detection. Notably, Neural network techniques like ANN, CNN, and spiking neural networks are preferable due to their ability to provide superior accuracy. For instance, Mathias et al. developed a high-precision neural network with an impressive accuracy of 99.82%. Similarly, Arona et al. deployed a DCNN on FPGA, achieving accuracies reaching up to 99.67%. Researchers widely used the MIT-BIH dataset to evaluate ECG classification techniques, This paper presents new trends and focuses on future research prospects in this field. Hence, the findings of the present review can help researchers and engineers in developing effective and efficient FPGA-based ECG monitoring and diagnostic systems for clinical and healthcare applications.

Does share pledging affect corporate sustainability performance? Empirical evidence from an emerging market

Using data of Chinese A-share firms between 2010 and 2021, this paper examines whether and how share pledging affects corporate environmental, social, and governance (ESG) performance. We find that share pledging by major shareholders reduces corporate ESG performance, and this finding remains robust after various robustness tests, such as addressing endogeneity issues and using alternative measures. We also find that major shareholders' share pledging inhibits ESG performance by exacerbating short-term financial behaviors such as tunneling and earnings management and suppressing sustainable investments such as green innovation, social donations, and internal control. It suggests that firms with share pledging prioritize short-term financial activities and decrease investment in sustainable development. The heterogeneity tests show that investors focusing on long-term development, effective external monitoring, and internal governance could mitigate the negative impact of share pledging on sustainability performance. Furthermore, we find that the negative influence of share pledging on ESG performance occurs mainly in the sample in which the pledged funds are invested in entities other than the focal listed companies. Based on sustainable business development, this paper contributes to the literature on the economic consequences of share pledging. These findings are valuable and motivating for regulators and investors in their decision-making.

Experimental and numerical study of cadmium fate and transport mechanisms during artificial recharge in agricultural regions

Agricultural Managed Aquifer Recharge (AgMAR) uses agricultural lands and floodwater to enhance groundwater recharge, but its effectiveness can be hindered by heavy metals like cadmium (Cd), which pose risks to groundwater quality. Cd is particularly concerning due to its high mobility and persistence in the environment. This study investigates Cd's fate and transport in agricultural regions during MAR, focusing on sandy loam soils through batch and column experiments. Equilibrium and kinetic batch studies were conducted under varying Cd concentrations and exposure times to quantify the adsorption capacity and rate. HYDRUS-2D was used to simulate Cd's transport in soil under various ponding depths and Cd concentrations. Results showed a maximum Cd adsorption capacity of 439.58 mg/kg, with the Freundlich isotherm providing a better fit (R2 = 0.98) and indicating heterogeneous adsorption sites (n = 0.389). The kinetic experiment indicated chemisorption as the predominant mechanism, with an equilibrium adsorption capacity of 236.49 mg/kg. The pseudo-second-order kinetic model (rate constant 0.0016 h⁻1, R2 = 0.99) suggested that adsorption kinetics are influenced by Cd concentration and available adsorption sites. The column experimental findings supported by HYDRUS-2D modeling successfully explained the fate and transport of Cd within the soil columns. The model fitted parameter values for Freundlich adsorption isotherm coefficient (KF), linearity factor (Nu), and kinetic rate coefficient are (α) 47.37 L/kg, 0.00389 cm³/ppm and 0.0029 min⁻1, respectively. Modeling scenarios further elucidated the transport dynamics of Cd under simulated AgMAR conditions. Modeling scenarios indicated that with constant ponding of 5 cm over a year, Cd at 20 and 40 ppb concentrations in floodwater could potentially migrate below root zone systems. This study highlights the critical role of understanding Cd fate and transport in optimizing AgMAR systems and reducing Cd pollution risks, providing valuable insights for developing effective monitoring and management strategies.

Effectiveness of Using Online Single Submission Applications

Effectiveness refers to how an organization successfully achieves its goals by measuring and assessing performance in carrying out tasks or activities. This study aims to evaluate how effective the use of the Online Single Submission Application is at the Office of the Investment and One-Stop Integrated Service of South Sulawesi Province. This study is important to identify factors that support or hinder the effectiveness of the use of the OSS application. The research method used is descriptive with a qualitative approach, which describes how the use of the OSS application takes place in the office. Data were collected through observation, interviews, and documentation. The study shows that the implementation of the Online Single Submission Application at the Office of the Investment and One-Stop Integrated Service of South Sulawesi Province was considered successful. Informants were satisfied with the clarity of the strategy for achieving goals, the quality of policy analysis, careful planning, the preparation of appropriate programs, and adequate work facilities. Good implementation, an effective monitoring system, and strict control also play a role in maintaining information security and preventing unauthorized access.