Search results for: climate data validation

Authors: Lucy Patricia Onundo, Wilfred Njoroge Mwema

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The rapidly diminishing fossil fuel reserves, their exorbitant cost and the increasingly apparent negative effect of fossil fuels to climate changes is a wake-up call to explore renewable energy. Wind, bio-fuel and solar power have already become staples of Kenyan electricity mix. The potential of electric power generation from marine tidal currents is enormous, with oceans covering more than 70% of the earth. However, attempts to harness marine tidal energy in Kenya, has yet to be studied thoroughly due to its promising, cyclic, reliable and predictable nature and the vast energy contained within it. The high load factors resulting from the fluid properties and the predictable resource characteristics make marine currents particularly attractive for power generation and advantageous when compared to others. Global-level resource assessments and oceanographic literature and data have been compiled in an analysis of the technology-specific requirements for tidal energy technologies and the physical resources. Temporal variations in resource intensity as well as the differences between small-scale applications are considered.

Keywords: tidal power, renewable energy, energy assessment, Kenya

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Authors: Leveque Benjamin, Rabaud Suzanne, Anest Hugo, Catalan Caroline, Neveux Guillaume

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Human products consumption is one of the main drivers of biodiversity loss. However, few pertinent ecological indicators regarding product life cycle impact on species and ecosystems have been built. Life cycle assessment (LCA) methodologies are well under way to conceive standardized methods to assess this impact, by taking already partially into account three of the Millennium Ecosystem Assessment pressures (land use, pollutions, climate change). Coupling LCA and ecological data and methods is an emerging challenge to develop a product biodiversity footprint. This approach was tested on three case studies from food processing, textile, and cosmetic industries. It allowed first to improve the environmental relevance of the Potential Disappeared Fraction of species, end-point indicator typically used in life cycle analysis methods, and second to introduce new indicators on overexploitation and invasive species. This type of footprint is a major step in helping companies to identify their impacts on biodiversity and to propose potential improvements.

Keywords: biodiversity, companies, footprint, life cycle assessment, products

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Authors: Saul Ngarava, Leocadia Zhou, Nomakhaya Monde

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Water crises have the fourth most significant societal impact after weapons of mass destruction, climate change, and extreme weather conditions, ahead of natural disasters. Intricacies between women and water are central to achieving the 2030 Sustainable Development Goals (SDGs). The majority of the 1.2 billion poor people worldwide, with two-thirds being women, and mostly located in Sub Sahara Africa (SSA) and South Asia, do not have access to safe and reliable sources of water. There exist gendered differences in water security based on the division of labour associating women with water. Globally, women and girls are responsible for water collection in 80% of the households which have no water on their premises. Women spend 16 million hours a day collecting water, while men and children spend 6 million and 4 million per day, respectively, which is time foregone in the pursuit of other livelihood activities. Due to their proximity and activities concerning water, women are vulnerable to water insecurity through exposures to water-borne diseases, fatigue from physically carrying water, and exposure to sexual and physical harassment, amongst others. Proximity to treated water and their wellbeing also has an effect on their sensitivity and adaptive capacity to water insecurity. The great distances, difficult terrain and heavy lifting expose women to vulnerabilities of water insecurity. However, few studies have quantified the vulnerabilities and burdens on women, with a few taking a phenomenological qualitative approach. Vulnerability studies have also been scanty in the water security realm, with most studies taking linear forms of either quantifying exposures, sensitivities or adaptive capacities in climate change studies. The current study argues for the need for a water insecurity vulnerability assessment, especially for women into research agendas as well as policy interventions, monitoring, and evaluation. The study sought to identify and provide pathways through which female-headed households were water insecure in South Africa, the 30th driest country in the world. This was through linking the drinking water decision as well as the vulnerability frameworks. Secondary data collected during the 2016 General Household Survey (GHS) was utilised, with a sample of 5928 female-headed households. Principal Component Analysis and Structural Equation Modelling were used to analyse the data. The results show dynamic relationships between water characteristics and water treatment. There were also associations between water access and wealth status of the female-headed households. Association was also found between water access and water treatment as well as between wealth status and water treatment. The study concludes that there are dynamic relationships in water insecurity (exposure, sensitivity, and adaptive capacity) for female-headed households in South Africa. The study recommends that a multi-prong approach is required in tackling exposures, sensitivities, and adaptive capacities to water insecurity. This should include capacitating and empowering women for wealth generation, improve access to water treatment equipment as well as prioritising the improvement of infrastructure that brings piped and safe water to female-headed households.

Keywords: gender, principal component analysis, structural equation modelling, vulnerability, water insecurity

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Authors: Krishna Raj Regmi

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A “Climate change adaptation and disaster risk management for sustainable agriculture” project was implemented in Nepal, a Himalayan country during 2008 to 2013 sponsored jointly by Food and Agriculture Organization (FAO) and United Nations Development Programme (UNDP), Nepal. The paper is based on the results and findings of this joint pilot project. The climate change events such as increased intensity of erratic rains in short spells, trend of prolonged drought, gradual rise in temperature in the higher elevations and occurrence of cold and hot waves in Terai (lower plains) has led to flash floods, massive erosion in the hills particularly in Churia range and drying of water sources. These recurring natural and climate-induced disasters are causing heavy damages through sedimentation and inundation of agricultural lands, crops, livestock, infrastructures and rural settlements in the downstream plains and thus reducing agriculture productivity and food security in the country. About 65% of the cultivated land in Nepal is rainfed with drought-prone characteristics and stabilization of agricultural production and productivity in these tracts will be possible through adoption of rainfed and drought-tolerant technologies as well as efficient soil-water management by the local communities. The adaptation and mitigation technologies and options identified by the project for soil erosion, flash floods and landslide control are on-farm watershed management, sloping land agriculture technologies (SALT), agro-forestry practices, agri-silvi-pastoral management, hedge-row contour planting, bio-engineering along slopes and river banks, plantation of multi-purpose trees and management of degraded waste land including sandy river-bed flood plains. The stress tolerant technologies with respect to drought, floods and temperature stress for efficient utilization of nutrient, soil, water and other resources for increased productivity are adoption of stress tolerant crop varieties and breeds of animals, indigenous proven technologies, mixed and inter-cropping systems, system of rice/wheat intensification (SRI), direct rice seeding, double transplanting of rice, off-season vegetable production and regular management of nurseries, orchards and animal sheds. The alternate energy use options and resource conservation practices for use by local communities are installation of bio-gas plants and clean stoves (Chulla range) for mitigation of green house gas (GHG) emissions, use of organic manures and bio-pesticides, jatropha cultivation, green manuring in rice fields and minimum/zero tillage practices for marshy lands. The efficient water management practices for increasing productivity of crops and livestock are use of micro-irrigation practices, construction of water conservation and water harvesting ponds, use of overhead water tanks and Thai jars for rain water harvesting and rehabilitation of on-farm irrigation systems. Initiation of some works on community-based early warning system, strengthening of met stations and disaster database management has made genuine efforts in providing disaster-tailored early warning, meteorological and insurance services to the local communities. Contingent planning is recommended to develop coping strategies and capacities of local communities to adopt necessary changes in the cropping patterns and practices in relation to adverse climatic and disaster risk conditions. At the end, adoption of awareness raising and capacity development activities (technical and institutional) and networking on climate-induced disaster and risks through training, visits and knowledge sharing workshops, dissemination of technical know-how and technologies, conduct of farmers' field schools, development of extension materials and their displays are being promoted. However, there is still need of strong coordination and linkage between agriculture, environment, forestry, meteorology, irrigation, climate-induced pro-active disaster preparedness and research at the ministry, department and district level for up-scaling, implementation and institutionalization of climate change and disaster risk management activities and adaptation mitigation options in agriculture for sustainable livelihoods of the communities.

Keywords: climate change adaptation, disaster risk management, soil-water management practices, sustainable agriculture

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Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Repaul Kanji, Rajat Agrawal

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In this era where issues like climate change and disasters are the topics of discussion at national and international forums, it is very often that humanity questions the causative role of corporates in such events. It is beyond any doubt that rapid industrialisation and development has taken a toll in the form of climate change and even disasters, in some case. Thus, demanding to fulfill a corporate's responsibilities in the form of rescue and relief in times of disaster, rehabilitation and even mitigation and preparedness to adapt to the oncoming changes is obvious. But how can the responsibilities of the corporates be channelised to ensure all this, i.e., develop a resilient society? More than that, which factors, when emphasised upon, can lead to the holistic development of the society. To answer this query, an extensive literature review was done to identify several enablers like legislations of a nation, the role of brand and reputation, ease of doing Corporate Social Responsibility, mission and vision of an organisation, religion and culture, etc. as a tool for building disaster resilience. A questionnaire survey, interviews with experts and academicians followed by interpretive structural modelling (ISM) were used to construct a multi-hierarchy model depicting the contextual relationship among the identified enablers. The study revealed that culture and religion are the most powerful driver, which affects other enablers either directly or indirectly. Taking cognisance of the fact that an idea of separation between religion and workplace (business) resides subconsciously within the society, the study tries to interpret the outcome of the ISM through the lenses of past researches (The Integrating Box) and explores how it can be leveraged to build a resilient society.

Keywords: corporate social responsibility, interpretive structural modelling, disaster resilience and risk reduction, the integration box (TIB)

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Authors: Cheryl M. Bowen

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Understanding that children thrive in a multi-systems approach to mental health development, a growing number of schools often promote school-based positive youth development however, there is scant empirical evidence investigating effective school-based “wraparound” mental health services for low income, Latinx children and their families. This 10-month case study utilizes a sample of 281 low-income, Latinx parents and their children, and 23 K-5th grade teachers living in northern California to test the hypothesis that a school-based mental health program can strengthen students’ developmental asset attainment and positively impact the school environment. The study utilized triangulated data to ascertain the effects of two program levels - (a) mental health and (b) positive child development services. All services were site-based and meant to target a wide variety of families. Findings from the study report that the universal mental health program increased the developmental asset attainment in 5 out of 8 thriving indicators thus transforming the child within his/her environment. Data collected from the administrative referral report demonstrate that the project also positively impacted the school climate. Parents and teachers felt more connected to the school, and referrals were down for discipline (35%), academics (66%), and suspensions (51%). The study concludes that a transformational ecology model of positive child development is the most effective means to nurture connections to all socializing agencies in a child’s ecosystem.

Keywords: case study, child development, positive youth development, developmental assets, ecological systems theory

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Authors: F. Esfandyari Darabad, Z. Samadi

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The volume of runoff caused by rainfall in the river basin has enticed the researchers in the fields of the water management resources. In this study, first of the hydrological data such as the rainfall and discharge of the Khiyav river basin of Meshkin city in the northwest of Iran collected and then the process of analyzing and reconstructing has been completed. The soil conservation service (scs) has developed a method for calculating the runoff, in which is based on the curve number specification (CN). This research implemented the following model in the Khiyav river basin of Meshkin city by the GIS techniques and concluded the following fact in which represents the usage of weight model in calculating the curve numbers that provides the possibility for the all efficient factors which is contributing to the runoff creation such as; the geometric characteristics of the basin, the basin soil characteristics, vegetation, geology, climate and human factors to be considered, so an accurate estimation of runoff from precipitation to be achieved as the result. The findings also exposed the accident-prone areas in the output of the Khiyav river basin so it was revealed that the Khiyav river basin embodies a high potential for the flood creation.

Keywords: curve number, khiyav river basin, runoff estimation, SCS

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Cemile Kütmeç Yilmaz, Güler Duru Asiret, Gulcan Bagcivan

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The aim of this study was to evaluate the reliability and validity of the Turkish version of the Carer’s Assessment of Satisfaction Index (CASI-TR). The study was conducted between the dates of June 2016 and September 2017 at the Training and Research Hospital of Aksaray University with the caregiving family members of the inpatients with chronic diseases. For this study, the sample size was calculated as at least 10 individuals for each item (item number (30)X10=300). The study sample included 300 caregiving family members, who provided primer care for at least three months for a patient (who had at least one chronic disease and received inpatient treatment in general internal medicine and palliative care units). Data were collected by using a demographic questionnaire and CASI-TR. Descriptive statistics, and psychometric tests were used for the data analysis. Of those caregivers, 76.7% were female, 86.3% were 65 years old and below, 43.7% were primary school graduates, 87% were married, 86% were not working, 66.3% were housewives, and 60.3% defined their income status as having an income covering one’s expenses. Care recipients often had problems in terms of walking, sleep, balance, feeding and urinary incontinence. The Cronbach Alpha value calculated for the CASI-TR (30 items) was 0,949. Internal consistency coefficients calculated for subscales were: 0.922 for the subscale of ‘caregiver satisfaction related to care recipient’, 0.875 for the subscale of ‘caregiver satisfaction related to themselves’, and 0.723 for the subscale of ‘dynamics of interpersonal relations’. Factor analysis revealed that three factors accounted for 57.67% of the total variance, with an eigenvalue of >1. assessed in terms of significance, we saw that the items came together in a significant manner. The factor load of the items were between 0.311 and 0.874. These results show that the CASI-TR is a valid and reliable scale. The adoption of the translated CASI in Turkey is found reliable and valid to assessing the satisfaction of caregivers. CASI-TR can be used easily in clinics or house visits by nurses and other health professionals for assessing caregiver satisfaction from caregiving.

Keywords: carer’s assessment of satisfaction index, caregiver, validity, reliability

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Authors: Gulnara N. Nabiyeva, Stephen M. Wheeler

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Geographic Information Systems (GIS) is a multipurpose computer-based tool that provides a sophisticated ability to map and analyze data on different spatial layers. However, GIS is far more easily applied in some policy areas than others. This paper seeks to determine the areas of sustainable development, including environmental, economic, and social dimensions, where GIS has been used to date to support efforts to implement the United Nations Sustainable Development Goals (SDGs), and to discuss potential areas where it might be used more. Based on an extensive analysis of published literature, we ranked the SDGs according to how frequently GIS has been used to study related policy. We found that SDG#15 “Life on Land” is most often addressed with GIS, following by SDG#11 “Sustainable Cities and Communities”, and SDG#13 “Climate Action”. On the other hand, we determined that SDG#2 “Zero Hunger”, SDG#8 “Decent Work and Economic Growth”, and SDG#16 “Peace, Justice, and Strong Institutions” are least addressed with GIS. The paper outlines some specific ways that GIS might be applied to the SDGs least linked to this tool currently.

Keywords: GIS, GIS application, sustainable community development, sustainable development goals

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Authors: Kelli G. Thorup, Kristopher A. Blee

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Climate change enhances the occurrence of extreme weather: wildfires, drought, rising summer temperatures, all of which dramatically decline forest growth and increase tree mortality in the mixed-conifer forests of Sierra Nevada, California. However, microbiota living in mutualistic relations with plant rhizospheres have been found to mitigate the effects of suboptimal environmental conditions. The goal of this research is to isolate native beneficial bacteria, plant-growth promoting rhizobacteria (PGPR), that can alleviate heat stress in Pinus ponderosa seedlings. Bacteria were isolated from the rhizosphere of Pinus ponderosa juveniles located in mixed-conifer stand and further characterized for PGP potential based on their ability to produce key growth regulatory phytohormones including auxin, cytokinin, and gibberellic acid. Out of ten soil samples taken, sixteen colonies were isolated and qualitatively confirmed to produce indole-3-acetic acid (auxin) using Salkowski’s reagent. Future testing will be conducted to quantitatively assess phytohormone production in bacterial isolates. Furthermore, bioassays will be performed to determine isolates abilities to increase tolerance in heat-stressed Pinus ponderosa seedlings. Upon completion of this research, a PGPR could be utilized to support the growth and transplantation of conifer seedlings as summer temperatures continue to rise due to the effects of climate change.

Keywords: conifer, heat-stressed, phytohormones, Pinus ponderosa, plant-growth promoting rhizobacteria

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Authors: M. Bonello, D. Micallef, S. P. Borg

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Driven by increased comfort standards, but at the same time high energy consciousness, energy-efficient space cooling has become an essential aspect of building design. Its aims are simple, aiming at providing satisfactory thermal comfort for individuals in an interior space using low energy consumption cooling systems. In this context, evaporative cooling is both an energy-efficient and an eco-friendly cooling process. In the past two decades, several academic studies have been performed to determine the resulting thermal comfort produced by an evaporative cooling system, including studies on temperature profiles, air speed profiles, effect of clothing and personnel activity. To the best knowledge of the authors, no studies have yet considered the analysis of relative humidity (RH) profiles in a space cooled using evaporative cooling. Such a study will determine the effect of different humidity levels on a person's thermal comfort and aid in the consequent improvement designs of such future systems. Under this premise, the research objective is to characterise the resulting different RH profiles in a chamber micro-climate using the evaporative cooling system in which the inlet air speed, temperature and humidity content are varied. The chamber shall be modelled using Computational Fluid Dynamics (CFD) in ANSYS Fluent. Relative humidity shall be modelled using a species transport model while the k-ε RNG formulation is the proposed turbulence model that is to be used. The model shall be validated with measurements taken using an identical test chamber in which tests are to be conducted under the different inlet conditions mentioned above, followed by the verification of the model's mesh and time step. The verified and validated model will then be used to simulate other inlet conditions which would be impractical to conduct in the actual chamber. More details of the modelling and experimental approach will be provided in the full paper The main conclusions from this work are two-fold: the micro-climatic relative humidity spatial distribution within the room is important to consider in the context of investigating comfort at occupant level; and the investigation of a human being's thermal comfort (based on Predicted Mean Vote – Predicted Percentage Dissatisfied [PMV-PPD] values) and its variation with different locations of relative humidity values. The study provides the necessary groundwork for investigating the micro-climatic RH conditions of environments cooled using evaporative cooling. Future work may also target the analysis of ways in which evaporative cooling systems may be improved to better the thermal comfort of human beings, specifically relating to the humidity content around a sedentary person.

Keywords: chamber micro-climate, evaporative cooling, relative humidity, thermal comfort

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Authors: Adel A. Ghoneim

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In most existing buildings in hot climate, cooling loads lead to high primary energy consumption and consequently high CO2 emissions. These can be substantially decreased with integrated renewable energy systems. Kuwait is characterized by its dry hot long summer and short warm winter. Kuwait receives annual total radiation more than 5280 MJ/m2 with approximately 3347 h of sunshine. Solar energy systems consist of PV modules and parabolic trough collectors are considered to satisfy electricity consumption, domestic water heating, and cooling loads of an existing building. This paper presents the results of an extensive program of energy conservation and energy generation using integrated photovoltaic (PV) modules and parabolic trough collectors (PTC). The program conducted on an existing institutional building intending to convert it into a Net-Zero Energy Building (NZEB) or near net Zero Energy Building (nNZEB). The program consists of two phases; the first phase is concerned with energy auditing and energy conservation measures at minimum cost and the second phase considers the installation of photovoltaic modules and parabolic trough collectors. The 2-storey building under consideration is the Applied Sciences Department at the College of Technological Studies, Kuwait. Single effect lithium bromide water absorption chillers are implemented to provide air conditioning load to the building. A numerical model is developed to evaluate the performance of parabolic trough collectors in Kuwait climate. Transient simulation program (TRNSYS) is adapted to simulate the performance of different solar system components. In addition, a numerical model is developed to assess the environmental impacts of building integrated renewable energy systems. Results indicate that efficient energy conservation can play an important role in converting the existing buildings into NZEBs as it saves a significant portion of annual energy consumption of the building. The first phase results in an energy conservation of about 28% of the building consumption. In the second phase, the integrated PV completely covers the lighting and equipment loads of the building. On the other hand, parabolic trough collectors of optimum area of 765 m2 can satisfy a significant portion of the cooling load, i.e about73% of the total building cooling load. The annual avoided CO2 emission is evaluated at the optimum conditions to assess the environmental impacts of renewable energy systems. The total annual avoided CO2 emission is about 680 metric ton/year which confirms the environmental impacts of these systems in Kuwait.

Keywords: building integrated renewable systems, Net-Zero energy building, solar fraction, avoided CO2 emission

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Daniel Murrant, Andrew Quinn, Lee Chapman

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A growing population has led to increasing global water and energy demand. This demand, combined with the effects of climate change and an increasing need to maintain and protect the natural environment, represents a potentially severe threat to many national infrastructure systems. This has resulted in a considerable quantity of published material on the interdependencies that exist between the supply of water and the thermal generation of electricity, often known as the water-energy nexus. Focusing specifically on the UK, there is a growing concern that the future availability of water may at times constrain thermal electricity generation, and therefore hinder the UK in meeting its increasing demand for a secure, and affordable supply of low carbon electricity. To provide further information on the threat the water-energy nexus may pose to the UK’s energy system, this paper models the regional water demand of UK thermal electricity generation in 2030 and 2050. It uses the strategically important Energy Systems Modelling Environment model developed by the Energy Technologies Institute. Unlike previous research, this paper was able to use abstraction and consumption factors specific to UK power stations. It finds that by 2050 the South East, Yorkshire and Humber, the West Midlands and North West regions are those with the greatest freshwater demand and therefore most likely to suffer from a lack of resource. However, it finds that by 2050 it is the East, South West and East Midlands regions with the greatest total water (fresh, estuarine and seawater) demand and the most likely to be constrained by environmental standards.

Keywords: climate change, power station cooling, UK water-energy nexus, water abstraction, water resources

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Authors: Robin Becker, Nane Roetmann, Manfred Helmus

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Construction companies lack junior staff for construction management. According to a nationwide survey of students, however, the profession lacks attractiveness. The conflict between the traditional job profile and the current desires of junior staff for contemporary and flexible working models must be resolved. Increasing flexibility is essential for the future viability of small and medium-sized enterprises. The implementation of modern work modules can help here. The following report will present the validation results of the developed work modules in construction practice.

Keywords: modern construction management, construction industry, work modules, shortage of junior staff, sustainable personnel management, making construction management more attractive, working time model

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Authors: Chanpen Meenakorn

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This research aimed to examine the relationships between customer expectations and service quality management of Banpaew Hospital Samutsakorn in Thailand. The study sample consisted of 360 customers in patient unit. Data were collected using self-administered questionnaire. Descriptive statistics used were percentage, mean, and standard deviation. The analytical statistics comprised Pearson’s product moment correlation coefficient analysis. The result showed that service quality of nurses was very good with sustainable development trend. Physical evidence was at a high level, and the process and personal were rated at a high level. Additional, the study suggested that head nurse should be encouraged to improve service quality management, management training. Nurse administrators should create an appropriate nursing department climate, and provide necessary resources in the department. In addition, the nurse administrators should continuously follow up the results of customer expectations and focus on patients/customers, process management, information and knowledge management, and evaluation of service quality also.

Keywords: Banpaew Hospital, Customer Expectations, Service Provided, Samutsakorn

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Authors: Nitin Pipralia, Amit Kmar Sinha, Gudrun de Boeck

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Atmospheric carbon dioxide (CO2) concentrations have been increasing since the beginning of the industrial revolution due to combustion of fossils fuel and many anthropogenic means. As the number of scenarios assembled by the International Panel on Climate Change (IPCC) predict a rise of pCO2 from today’s 380 μatm to approximately 900 μatm until the year 2100 and a further rise of up to 1900 μatm by the year 2300. A rise in pCO2 results in more dissolution in ocean surface water which lead to cange in water pH, This phenomena of decrease in ocean pH due to increase on pCO2 is ocean acidification is considered a potential threat to the marine ecosystems and expected to affect fish as well as calcerious organisms. The situation may get worste when the stress of salinity adds on, due to migratory movement of fishes, where fish moves to different salinity region for various specific activities likes spawning and other. Therefore, to understand the interactive impact of these whole range of two important environmental abiotic stresses (viz. pCO2 ranging from 380 μatm, 900 μatm and 1900 μatm, along with salinity gradients of 32ppt, 10 ppt and 2.5ppt) on the ecophysiologal performance of fish, we investigated various biological adaptive response in European sea bass (Dicentrarchus labrax), a model estuarine teleost. Overall, we hypothesize that effect of ocean acidification would be exacerbate with shift in ambient salinity. Oxygen consumption, ammonia metabolism, iono-osmoregulation, energy budget, ion-regulatory enzymes, hormones and pH amendments in plasma were assayed as the potential indices of compensatory responses.

Keywords: ocean acidification, sea bass, pH climate change, salinity

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Authors: Md. Abud Darda

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Different options for natural gas production in wide geographic areas may be described through diffusion of innovation models. This type of modeling approach provides an indirect estimate of an ultimately recoverable resource, URR, capture the quantitative effects of observed strategic interventions, and allow ex-ante assessments of future scenarios over time. In order to ensure a sustainable energy policy, it is important to forecast the availability of this natural resource. Considering a finite life cycle, in this paper we try to investigate the natural gas production of Myanmar and Algeria, two important natural gas provider in the world energy market. A number of homogeneous and heterogeneous diffusion models, with convenient extensions, have been used. Models validation has also been performed in terms of prediction capability.

Keywords: diffusion models, energy forecast, natural gas, nonlinear production

Procedia PDF Downloads 227

Authors: Claire Williams

Abstract:

Randomised controlled Trials (RCTs) of efficacy are still perceived as the gold standard for the generation of evidence, and whilst advances in data collection methods are well developed, this progress has not been matched for the reporting of adverse events (AEs). Assessment and reporting of AEs in clinical trials are fraught with human error and inefficiency and are extremely time and resource intensive. Recent research conducted into the quality of reporting of AEs during clinical trials concluded it is substandard and reporting is inconsistent. Investigators commonly send reports to sponsors who are incorrectly categorised and lacking in critical information, which can complicate the detection of valid safety signals. In our presentation, we will describe an electronic data capture system, which has been designed to support clinical trial processes by reducing the resource burden on investigators, improving overall trial efficiencies, and making trials safer for patients. This proprietary technology was developed using expertise proven in the delivery of the world’s first prospective, phase 3b real-world trial, ‘The Salford Lung Study, ’ which enabled robust safety monitoring and reporting processes to be accomplished by the remote monitoring of patients’ EHRs. This technology enables safety alerts that are pre-defined by the protocol to be detected from the data extracted directly from the patients EHR. Based on study-specific criteria, which are created from the standard definition of a serious adverse event (SAE) and the safety profile of the medicinal product, the system alerts the investigator or study team to the safety alert. Each safety alert will require a clinical review by the investigator or delegate; examples of the types of alerts include hospital admission, death, hepatotoxicity, neutropenia, and acute renal failure. This is achieved in near real-time; safety alerts can be reviewed along with any additional information available to determine whether they meet the protocol-defined criteria for reporting or withdrawal. This active surveillance technology helps reduce the resource burden of the more traditional methods of AE detection for the investigators and study teams and can help eliminate reporting bias. Integration of multiple healthcare data sources enables much more complete and accurate safety data to be collected as part of a trial and can also provide an opportunity to evaluate a drug’s safety profile long-term, in post-trial follow-up. By utilising this robust and proven method for safety monitoring and reporting, a much higher risk of patient cohorts can be enrolled into trials, thus promoting inclusivity and diversity. Broadening eligibility criteria and adopting more inclusive recruitment practices in the later stages of drug development will increase the ability to understand the medicinal products risk-benefit profile across the patient population that is likely to use the product in clinical practice. Furthermore, this ground-breaking approach to AE detection not only provides sponsors with better-quality safety data for their products, but it reduces the resource burden on the investigator and study teams. With the data taken directly from the source, trial costs are reduced, with minimal data validation required and near real-time reporting enables safety concerns and signals to be detected more quickly than in a traditional RCT.

Keywords: more comprehensive and accurate safety data, near real-time safety alerts, reduced resource burden, safer trials

Procedia PDF Downloads 85

Authors: Linda A. O’Higgins, Astrid Wingler, Jorge Oliveira

Abstract:

The growth of Chlorella vulgaris was characterized as a function of irradiance in a laboratory turbidostat (1 L) and compared to batch growth in sunlit modules (5–25 L) of the commercial Phytobag photobioreactor. The effects of variable sunlight and culture density were deconvoluted by a mathematical model. The analysis showed that algal growth was light-limited due to shading by external construction elements and due to light attenuation within the algal bags. The model was also used to predict maximum biomass productivity. The manipulative experiments and the model predictions were confronted with data from a production season of a 10m2 pilot-scale photobioreactor, Phytobag (10,000 L). The analysis confirmed light limitation in all three photobioreactors. An additional limitation of biomass productivity was caused by the nitrogen starvation that was used to induce lipid accumulation. Reduction of shading and separation of biomass and lipid production are proposed for future optimization.

Keywords: microalgae, batch cultivation, Chlorella vulgaris, Mathematical model, photobioreactor, scale-up

Procedia PDF Downloads 116

Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

Procedia PDF Downloads 437

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

Procedia PDF Downloads 94

Authors: Trevor C. Brown, David J. Miron

Abstract:

Gas-solid physical adsorption methods are central to the characterization and optimization of the effective surface area, pore size and porosity for applications such as heterogeneous catalysis, and gas separation and storage. Properties such as adsorption uptake, capacity, equilibrium constants and Gibbs free energy are dependent on the composition and structure of both the gas and the adsorbent. However, challenges remain, in accurately calculating these properties from experimental data. Gas adsorption experiments involve measuring the amounts of gas adsorbed over a range of pressures under isothermal conditions. Various constant-parameter models, such as Langmuir and Brunauer-Emmett-Teller (BET) theories are used to provide information on adsorbate and adsorbent properties from the isotherm data. These models typically do not provide accurate interpretations across the full range of pressures and temperatures. The Langmuir adsorption isotherm is a simple approximation for modelling equilibrium adsorption data and has been effective in estimating surface areas and catalytic rate laws, particularly for high surface area solids. The Langmuir isotherm assumes the systematic filling of identical adsorption sites to a monolayer coverage. The BET model is based on the Langmuir isotherm and allows for the formation of multiple layers. These additional layers do not interact with the first layer and the energetics are equal to the adsorbate as a bulk liquid. This BET method is widely used to measure the specific surface area of materials. Both Langmuir and BET models assume that the affinity of the gas for all adsorption sites are identical and so the calculated adsorbent uptake at the monolayer and equilibrium constant are independent of coverage and pressure. Accurate representations of adsorption data have been achieved by extending the Langmuir and BET models to include pressure-varying uptake capacities and equilibrium constants. These parameters are determined using a novel regression technique called flexible least squares for time-varying linear regression. For isothermal adsorption the adsorption parameters are assumed to vary slowly and smoothly with increasing pressure. The flexible least squares for pressure-varying linear regression (FLS-PVLR) approach assumes two distinct types of discrepancy terms, dynamic and measurement for all parameters in the linear equation used to simulate the data. Dynamic terms account for pressure variation in successive parameter vectors, and measurement terms account for differences between observed and theoretically predicted outcomes via linear regression. The resultant pressure-varying parameters are optimized by minimizing both dynamic and measurement residual squared errors. Validation of this methodology has been achieved by simulating adsorption data for n-butane and isobutane on activated carbon at 298 K, 323 K and 348 K and for nitrogen on mesoporous alumina at 77 K with pressure-varying Langmuir and BET adsorption parameters (equilibrium constants and uptake capacities). This modeling provides information on the adsorbent (accessible surface area and micropore volume), adsorbate (molecular areas and volumes) and thermodynamic (Gibbs free energies) variations of the adsorption sites.

Keywords: Langmuir adsorption isotherm, BET adsorption isotherm, pressure-varying adsorption parameters, adsorbate and adsorbent properties and energetics

Procedia PDF Downloads 234

Authors: Patricia O. Carvalho, Marcia C. F. Messias, Salvador Sanchez Vinces, Caroline F. A. Gatinoni, Vitor P. Iordanu, Carlos A. R. Martinez

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Lipidomics methods are widely used in the identification and validation of disease-specific biomarkers and therapy response evaluation. The present study aimed to identify a panel of potential lipid biomarkers to evaluate response to neoadjuvant chemoradiotherapy in rectal adenocarcinoma (RAC). Liquid chromatography–mass spectrometry (LC-MS)-based untargeted lipidomic was used to profile human serum samples from patients with clinical stage T2 or T3 resectable RAC, after and before chemoradiotherapy treatment. A total of 28 blood plasma samples were collected from 14 patients with RAC who recruited at the São Francisco University Hospital (HUSF/USF). The study was approved by the ethics committee (CAAE 14958819.8.0000.5514). Univariate and multivariate statistical analyses were applied to explore dysregulated metabolic pathways using untargeted lipidic profiling and data mining approaches. A total of 36 statistically significant altered lipids were identified and the subsequent partial least-squares discriminant analysis model was both cross validated (R2, Q2) and permutated. Lisophosphatidyl-choline (LPC) plasmalogens containing palmitoleic and oleic acids, with high variable importance in projection score, showed a tendency to be lower after completion of chemoradiotherapy. Chemoradiotherapy seems to change plasmanyl-phospholipids levels, indicating that these lipids play an important role in the RAC pathogenesis.

Keywords: lipidomics, neoadjuvant chemoradiotherapy, plasmalogens, rectal adenocarcinoma

Procedia PDF Downloads 131

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

Abstract:

Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

Procedia PDF Downloads 160