Search results for: vision perception

Authors: Nada Al-Qallaf, Aisha Al-Barood, Djamel Lekmine, Srinivasan Vedhapuri

Abstract:

Kuwait Oil Company (KOC) under the supervision of Kuwait National Focal Point (KNFP) is planning to remediate 26 million (M) m3 of oil-contaminated soil in oil fields of Kuwait as a direct and indirect fallout of the Gulf War during 1990-1991. This project is funded by the United Nations Compensation Commission (UNCC) under the Kuwait Environmental Remediation Program (KERP). Oil-contamination of the soil occurred due to the destruction of the oil wells and spilled crude oil across the land surface and created ‘oil lakes’ in low lying land. Aerial fall-out from oil spray and combustion products from oil fires combined with the sand and gravel on the ground surface to form a layer of hardened ‘Tarcrete’. The unique fresh groundwater lenses present in the Raudhatain and Sabriya subsurface areas had been impacted by the discharge and/or spills of dissolved petroleum constituents. These fresh groundwater aquifers were used for drinking water purposes until 1990, prior to invasion. This has significantly damages altered the landscape, ecology and habitat of the flora and fauna and in Kuwait Desert. Under KERP, KOC is fully responsible for the planning and execution of the remediation and restoration projects in KOC oil fields. After the initial recommendation of UNCC to construct engineered landfills for containment and disposal of heavily contaminated soils, two landfills were constructed, one in North Kuwait and another in South East Kuwait of capacity 1.7 million m3 and 0.5 million m3 respectively. KOC further developed the Total Remediation Strategy in conjunction with KNFP and has obtained UNCC approval. The TRS comprises of elements such as Risk Based Approach (RBA), Bioremediation of low Contaminated Soil levels, Remediation Treatment Technologies, Sludge Disposal via Beneficial Recycling or Re-use and Engineered landfills for Containment of untreatable materials. Risk Based Assessment as a key component to avoid any unnecessary remedial works, where it can be demonstrated that human health and the environment are sufficiently protected in the absence of active remediation. This study demonstrates on the risks of tarcrete materials spread over areas 20 Km2 on the fresh Ground water lenses/catchment located beneath the Sabriyah and Raudhatain oil fields in North Kuwait. KOC’s primary objective is to provide justification of using RBA, to support a case with the Kuwait regulators to leave the tarcrete material in place, rather than seek to undertake large-scale removal and remediation. The large-scale coverage of the tarcrete in the oil fields and perception that the residual contamination associated with this source is present in an environmentally sensitive area essentially in ground water resource. As part of this assessment, conceptual site model (CSM) and complete risk-based and fate and transport modelling was carried out which includes derivation of site-specific assessment criteria (SSAC) and quantification of risk to identified waters resource receptors posed by tarcrete impacted areas. The outcome of this assessment was determined that the residual tarcrete deposits across the site area shall not create risks to fresh groundwater resources and the remedial action to remove and remediate the surficial tarcrete deposits is not warranted.

Keywords: conceptual site model, fresh groundwater, oil-contaminated soil, tarcrete, risk based assessment

Procedia PDF Downloads 168

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

Abstract:

Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Gospel Iyioku, Jana Mazancova, Jiri Hejkrlik

Abstract:

The “Agricultural Promotion Policy (2016–2020)” represents a strategic initiative by the Nigerian government to address domestic food shortages and the challenges in exporting products at the required quality standards. Hindered by an inefficient system for setting and enforcing food quality standards, coupled with a lack of market knowledge, the Federal Ministry of Agriculture and Rural Development (FMARD) aims to enhance support for the production and activities of key crops like cashew. By collaborating with farmers, processors, investors, and stakeholders in the cashew sector, the policy seeks to define and uphold high-quality standards across the cashew value chain. Given the challenges and opportunities faced by Nigerian cashew farmers, active participation in cashew marketing groups becomes imperative. These groups serve as essential platforms for farmers to collectively navigate market intricacies, access resources, share knowledge, improve output quality, and bolster their overall bargaining power. Through engagement in these cooperative initiatives, farmers not only boost their economic prospects but can also contribute significantly to the sustainable growth of the cashew industry, fostering resilience and community development. This study explores the perceptions and intentions of farmers regarding their involvement in cashew marketing cooperatives, utilizing an expanded version of the Theory of Planned Behaviour. Drawing insights from a diverse sample of 321 cashew farmers in Southwest Nigeria, the research sheds light on the factors influencing decision-making in cooperative participation. The demographic analysis reveals a diverse landscape, with a substantial presence of middle-aged individuals contributing significantly to the agricultural sector and cashew-related activities emerging as a primary income source for a substantial proportion (23.99%). Employing Structural Equation Modelling (SEM) with Maximum Likelihood Robust (MLR) estimation in R, the research elucidates the associations among latent variables. Despite the model’s complexity, the goodness-of-fit indices attest to the validity of the structural model, explaining approximately 40% of the variance in the intention to join cooperatives. Moral norms emerge as a pivotal construct, highlighting the profound influence of ethical considerations in decision-making processes, while perceived behavioural control presents potential challenges in active participation. Attitudes toward joining cooperatives reveal nuanced perspectives, with strong beliefs in enhanced connections with other farmers but varying perceptions on improved access to essential information. The SEM analysis establishes positive and significant effects of moral norms, perceived behavioural control, subjective norms, and attitudes on farmers’ intention to join cooperatives. The knowledge construct positively affects key factors influencing intention, emphasizing the importance of informed decision-making. A supplementary analysis using partial least squares (PLS) SEM corroborates the robustness of our findings, aligning with covariance-based SEM results. This research unveils the determinants of cooperative participation and provides valuable insights for policymakers and practitioners aiming to empower and support this vital demographic in the cashew industry.

Keywords: marketing cooperatives, theory of planned behaviour, structural equation modelling, cashew farmers

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Authors: Adriana De Serio, Donato Forenza

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Aims. In this experimental research the Authors present the methodological plan “Musictherapy and Gardentherapy” that they created interconnected with the garden landscape ecosystems and aimed at PsychoPhysical Disability (MusGarPPhyD). In the context of the environmental education aimed at spreading the landscape culture and its values, it’s necessary to develop a solid perception of the environment sustainability to implement a multidimensional approach that pays attention to the conservation and enhancement of gardens and natural environments. The result is an improvement in the life quality also in compliance with the objectives of the European Agenda 2030. The MusGarPPhyD can help professionals such as musictherapists and environmental and landscape researchers strengthen subjects' motivation to learn to deal with the psychophysical discomfort associated with disability and to cope with the distress and the psychological fragility and the loneliness and the social seclusion and to promote productive social relationships. Materials and Methods. The MusGarPPhyD was implemented in multiple spaces. The musictherapy treatments took place first inside residential therapeutic centres and then in the garden landscape ecosystem. Patients: twenty, set in two groups. Weekly-sessions (50’) for three months. Methodological phases: - Phase P1. MusicTherapy treatments for each group in the indoor spaces. - Phase P2. MusicTherapy sessions inside the gardens. After each Phase, P1 and P2: - a Questionnaire for each patient (ten items / liking-indices) was administrated at t0 time, during the treatment and at tn time at the end of the treatment. - Monitoring of patients' behavioral responses through assessment scales, matrix, table and graph system. MusicTherapy methodology: pazient Sonorous-Musical Anamnesis, Musictherapy Assessment Document, Observation Protocols, Bodily-Environmental-Rhythmical-Sonorous-Vocal-Energy production first indoors and then outside, sonorous-musical instruments and edible instruments made by the Author/musictherapist with some foods; Administration of Patient-Environment-Music Index at time to and tn, to estimate the patient’s behavior evolution, Musictherapeutic Advancement Index. Results. The MusGarPPhyD can strengthen the individual sense of identity and improve the psychophysical skills and the resilience to face and to overcome the difficulties caused by the congenital /acquired disability. The multi-sensory perceptions deriving from contact with the plants in the gardens improve the psychological well-being and regulate the physiological parameters such as blood pressure, cardiac and respiratory rhythm, reducing the cholesterol levels. The secretions of the peptide hormones endorphins and the endogenous opioids enkephalins increase and bring a state of patient’s tranquillity and a better mood. The subjects showed a preference for musictherapy treatments within a setting made up of gardens and peculiar landscape systems. This resulted in greater health benefits. Conclusions. The MusGarPPhyD contributes to reduce psychophysical tensions, anxiety, depression and stress, facilitating the connections between the cerebral hemispheres, thus also improving intellectual performances, self-confidence, motor skills and social interactions. Therefore it is necessary to design hospitals, rehabilitation centers, nursing homes, surrounded by gardens. Ecosystems of natural and urban parks and gardens create fascinating skyline and mosaics of landscapes rich in beauty and biodiversity. The MusGarPPhyD is useful for the health management promoting patient’s psychophysical activation, better mood/affective-tone and relastionships and contributing significantly to improving the life quality.

Keywords: musictherapy, gardentherapy, disability, life quality

Procedia PDF Downloads 51