Search results for: optimize profit

Authors: Amna Tariq Shah

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Amid the pandemic, the contemporary landscape has experienced accelerated growth in small business activities and an expanding digital marketplace, further exacerbating the issue of non-compliance among self-employed individuals through aggressive tax planning and evasion. This research seeks to address these challenges by developing strategic tax policies that promote voluntary compliance and improve taxpayer facilitation. The study employs the innovative MINDSPACE framework to examine three psychosocial factors—tax communication, tax literacy, and shaming—to optimize policy responses, address administrative shortcomings, and ensure adequate revenue collection for public goods and services. Preliminary findings suggest that incomprehensible communication from tax authorities drives individuals to seek alternative, potentially biased sources of tax information, thereby exacerbating non-compliance. Furthermore, the study reveals low tax literacy among Australian and Pakistani respondents, with many struggling to navigate complex tax processes and comprehend tax laws. Consequently, policy recommendations include simplifying tax return filing and enhancing pre-populated tax returns. In terms of shaming, the research indicates that Australians, being an individualistic society, may not respond well to shaming techniques due to privacy concerns. In contrast, Pakistanis, as a collectivistic society, may be more receptive to naming and shaming approaches. The study employs a mixed-method approach, utilizing interviews and surveys to analyze the issue in both jurisdictions. The use of mixed methods allows for a more comprehensive understanding of tax compliance behavior, combining the depth of qualitative insights with the generalizability of quantitative data, ultimately leading to more robust and well-informed policy recommendations. By examining evidence from opposite jurisdictions, namely a developed country (Australia) and a developing country (Pakistan), the study's applicability is enhanced, providing perspectives from two disparate contexts that offer insights from opposite ends of the economic, cultural, and social spectra. The non-comparative case study methodology offers valuable insights into human behavior, which can be applied to other jurisdictions as well. The application of the MINDSPACE framework in this research is particularly significant, as it introduces a novel approach to tax compliance behavior analysis. By integrating insights from behavioral economics, the framework enables a comprehensive understanding of the psychological and social factors influencing taxpayer decision-making, facilitating the development of targeted and effective policy interventions. This research carries substantial importance as it addresses critical challenges in tax compliance and administration, with far-reaching implications for revenue collection and the provision of public goods and services. By investigating the psychosocial factors that influence taxpayer behavior and utilizing the MINDSPACE framework, the study contributes invaluable insights to the field of tax policy. These insights can inform policymakers and tax administrators in developing more effective tax policies that enhance taxpayer facilitation, address administrative obstacles, promote a more equitable and efficient tax system, and foster voluntary compliance, ultimately strengthening the financial foundation of governments and communities.

Keywords: individual tax compliance behavior, psychosocial factors, tax non-compliance, tax policy

Procedia PDF Downloads 74

Authors: Mohammadreza Kashizadeh, Mohammadamin Hashemi

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This paper explores a kinetic building facade designed for optimal energy capture and architectural expression. The system integrates photovoltaic panels with soft robotic actuators for precise solar tracking, resulting in enhanced electricity generation compared to static facades. Driven by the growing interest in dynamic building envelopes, the exploration of facade systems are necessitated. Increased energy generation and regulation of energy flow within buildings are potential benefits offered by integrating photovoltaic (PV) panels as kinetic elements. However, incorporating these technologies into mainstream architecture presents challenges due to the complexity of coordinating multiple systems. To address this, the design leverages soft robotic actuators, known for their compliance, resilience, and ease of integration. Additionally, the project investigates the potential for employing Large Language Models (LLMs) to streamline the design process. The research methodology involved design development, material selection, component fabrication, and system assembly. Grasshopper (GH) was employed within the digital design environment for parametric modeling and scripting logic, and an LLM was experimented with to generate Python code for the creation of a random surface with user-defined parameters. Various techniques, including casting, Three-dimensional 3D printing, and laser cutting, were utilized to fabricate physical components. A modular assembly approach was adopted to facilitate installation and maintenance. A case study focusing on the application of this facade system to an existing library building at Polytechnic University of Milan is presented. The system is divided into sub-frames to optimize solar exposure while maintaining a visually appealing aesthetic. Preliminary structural analyses were conducted using Karamba3D to assess deflection behavior and axial loads within the cable net structure. Additionally, Finite Element (FE) simulations were performed in Abaqus to evaluate the mechanical response of the soft robotic actuators under pneumatic pressure. To validate the design, a physical prototype was created using a mold adapted for a 3D printer's limitations. Casting Silicone Rubber Sil 15 was used for its flexibility and durability. The 3D-printed mold components were assembled, filled with the silicone mixture, and cured. After demolding, nodes and cables were 3D-printed and connected to form the structure, demonstrating the feasibility of the design. This work demonstrates the potential of soft robotics and Artificial Intelligence (AI) for advancements in sustainable building design and construction. The project successfully integrates these technologies to create a dynamic facade system that optimizes energy generation and architectural expression. While limitations exist, this approach paves the way for future advancements in energy-efficient facade design. Continued research efforts will focus on cost reduction, improved system performance, and broader applicability.

Keywords: artificial intelligence, energy efficiency, kinetic photovoltaics, pneumatic control, soft robotics, sustainable building

Procedia PDF Downloads 28

Authors: Victor Mantilla, Romeu Vicente, António Figueiredo, Victor Ferreira, Sandra Sorte

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Integrating dynamic facades into contemporary building design is shaping a new era of energy efficiency and user comfort. These innovative facades, often constructed using lightweight construction systems and materials, offer an opportunity to have a responsive and adaptive nature to the dynamic behavior of the outdoor climate. Therefore, in regions characterized by high fluctuations in daily temperatures, the ability to adapt to environmental changes is of paramount importance and a challenge. This paper presents a thorough review of the state of the art on double-skin facades (DSF), focusing on lightweight solutions for the external envelope. Dynamic facades featuring elements like movable shading devices, phase change materials, and advanced control systems have revolutionized the built environment. They offer a promising path for reducing energy consumption while enhancing occupant well-being. Lightweight construction systems are increasingly becoming the choice for the constitution of these facade solutions, offering benefits such as reduced structural loads and reduced construction waste, improving overall sustainability. However, the performance of dynamic facades based on low thermal inertia solutions in climatic contexts with high thermal amplitude is still in need of research since their ability to adapt is traduced in variability/manipulation of the thermal transmittance coefficient (U-value). Emerging technologies can enable such a dynamic thermal behavior through innovative materials, changes in geometry and control to optimize the facade performance. These innovations will allow a facade system to respond to shifting outdoor temperature, relative humidity, wind, and solar radiation conditions, ensuring that energy efficiency and occupant comfort are both met/coupled. This review addresses the potential configuration of double-skin facades, particularly concerning their responsiveness to seasonal variations in temperature, with a specific focus on addressing the challenges posed by winter and summer conditions. Notably, the design of a dynamic facade is significantly shaped by several pivotal factors, including the choice of materials, geometric considerations, and the implementation of effective monitoring systems. Within the realm of double skin facades, various configurations are explored, encompassing exhaust air, supply air, and thermal buffering mechanisms. According to the review places a specific emphasis on the thermal dynamics at play, closely examining the impact of factors such as the color of the facade, the slat angle's dimensions, and the positioning and type of shading devices employed in these innovative architectural structures.This paper will synthesize the current research trends in this field, with the presentation of case studies and technological innovations with a comprehensive understanding of the cutting-edge solutions propelling the evolution of building envelopes in the face of climate change, namely focusing on double-skin lightweight solutions to create sustainable, adaptable, and responsive building envelopes. As indicated in the review, flexible and lightweight systems have broad applicability across all building sectors, and there is a growing recognition that retrofitting existing buildings may emerge as the predominant approach.

Keywords: adaptive, control systems, dynamic facades, energy efficiency, responsive, thermal comfort, thermal transmittance

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Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

Procedia PDF Downloads 226

Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 95

Authors: Luisa Barreira, Viana Castaneda, Maria J. Rodrigues, Florinda Gama, Tamara Santos, Marta Oliveira, Catarina Pereira, Maribela Pestana, Pedro Correia, Miguel Salazar, Carla Nunes, Luisa Custodio, Joao Varela

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In the last century, the world witnessed an exponential demographic increase that has put an enormous pressure on agriculture and food production. Associated also with climate changes, there has been a decrease in the amount of available freshwater and an increased salinization of soils which can affect the production of most food crops. Halophytes, however, are plants able to withstand high salinities while maintaining a good growth productivity. To cope with the excess salt, they produce secondary metabolites (e.g. vitamins and phenolic compounds) which, along with the natural presence of some minerals, makes them not only nutritionally rich but also functional foods. Some halophytes, as quinoa or salicornia, are already used in some countries, mostly as gourmet food. Hydroponic cultivation of halophytes using seawater or diluted seawater for watering can decrease the pressure on freshwater resources while producing a nutritional and functional food. The XtremeGourmet project funded by the EU aims to develop and optimize the production of different halophytes by hydroponics. One of the more specific objectives of this project is the study of halophytes’ productivity and chemical composition under different abiotic conditions, e.g. salt and nutrient concentration and light intensity. Three species of halophytes commonly occurring in saltmarshes of the South of Portugal (Inula chrithmoides, Salicornia ramosissima and Mesembryanthemum nodiflorum) were cultivated using hydroponics under different salinities, ranging from 5 to 45 dS/m. For each condition, several parameters were assessed namely: total and commercial productivity, electrical conductivity, total soluble solids, proximal composition, mineral profile, total phenolics, flavonoids and condensed tannins content and antioxidant activity. Results show that productivity was significantly reduced for all plants with increasing salinity up to salinity 29 dS/m and remained low onwards. Oppositely, the electrical conductivity and the total soluble solids content of the produced plants increased with salinity, reaching a plateau at 29 dS/m. It seems that plants reflect the salt concentration of the water up to some point, being able to regulate their salt content for higher salinities. The same tendency was observed for the ash content of these plants, which is related to the mineral uptake from the cultivating media and the plants’ capacity to both accumulate and regulate ions’ concentration in their tissues. Nonetheless, this comes with a metabolic cost which is observed by a decrease in productivity. The mineral profile of these plants shows high concentrations of sodium but also high amounts of potassium. In what concerns the microelements, these plants appear to be a good source of manganese and iron and the low amounts of toxic metals account for their safe consumption in moderate amounts. Concerning the phenolics composition, plants presented moderate concentrations of phenolics but high amounts of condensed tannins, particularly I. crithmoides which accounts for its characteristic sour and spicy taste. Contrary to some studies in which higher amounts of phenolics were found in plants cultivated under higher salinities, in this study, the highest amount of phenolic compounds were found in plants grown at the lowest or intermediate salinities. Nonetheless, there was a positive correlation between the concentration of these compounds and the antioxidant capacity of the plants’ extracts.

Keywords: functional properties, halophytes, hydroponics, nutritional composition, salinity effect

Procedia PDF Downloads 268

Authors: Dmitriy Berillo, Areej K. A. Al-Jwaid, Jonathan L. Caplin, Andrew Cundy, Irina Savina

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Chlorophenols (CPs) are used as a precursor in the production of higher CPs and dyestuffs, and as a preservative. Contamination by CPs of the ground water is located in the range from 0.15-100mg/L. The EU has set maximum concentration limits for pesticides and their degradation products of 0.1μg/L and 0.5μg/L, respectively. People working in industries which produce textiles, leather products, domestic preservatives, and petrochemicals are most heavily exposed to CPs. The International Agency for Research on Cancers categorized CPs as potential human carcinogens. Existing multistep water purification processes for CPs such as hydrogenation, ion exchange, liquid-liquid extraction, adsorption by activated carbon, forward and inverse osmosis, electrolysis, sonochemistry, UV irradiation, and chemical oxidation are not always cost effective and can cause the formation of even more toxic or mutagenic derivatives. Bioremediation of CPs derivatives utilizing microorganisms results in 60 to 100% decontamination efficiency and the process is more environmentally-friendly compared with existing physico-chemical methods. Microorganisms immobilized onto a substrate show many advantages over free bacteria systems, such as higher biomass density, higher metabolic activity, and resistance to toxic chemicals. They also enable continuous operation, avoiding the requirement for biomass-liquid separation. The immobilized bacteria can be reused several times, which opens the opportunity for developing cost-effective processes for wastewater treatment. In this study, we develop a bioremediation system for CPs based on macroporous materials, which can be efficiently used for wastewater treatment. Conditions for the preparation of the macroporous material from specific bacterial strains (Pseudomonas mendocina and Rhodococus koreensis) were optimized. The concentration of bacterial cells was kept constant; the difference was only the type of cross-linking agents used e.g. glutaraldehyde, novel polymers, which were utilized at concentrations of 0.5 to 1.5%. SEM images and rheology analysis of the material indicated a monolithic macroporous structure. Phenol was chosen as a model system to optimize the function of the cryogel material and to estimate its enzymatic activity, since it is relatively less toxic and harmful compared to CPs. Several types of macroporous systems comprising live bacteria were prepared. The viability of the cross-linked bacteria was checked using Live/Dead BacLight kit and Laser Scanning Confocal Microscopy, which revealed the presence of viable bacteria with the novel cross-linkers, whereas the control material cross-linked with glutaraldehyde(GA), contained mostly dead cells. The bioreactors based on bacteria were used for phenol degradation in batch mode at an initial concentration of 50mg/L, pH 7.5 and a temperature of 30°C. Bacterial strains cross-linked with GA showed insignificant ability to degrade phenol and for one week only, but a combination of cross-linking agents illustrated higher stability, viability and the possibility to be reused for at least five weeks. Furthermore, conditions for CPs degradation will be optimized, and the chlorophenol degradation rates will be compared to those for phenol. This is a cutting-edge bioremediation approach, which allows the purification of waste water from sustainable compounds without a separation step to remove free planktonic bacteria. Acknowledgments: Dr. Berillo D. A. is very grateful to Individual Fellowship Marie Curie Program for funding of the research.

Keywords: bioremediation, cross-linking agents, cross-linked microbial cell, chlorophenol degradation

Procedia PDF Downloads 212

Authors: Alexandra Papagianni, George Filis, Panagiotis Papadopoulos

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The liberalization of the energy market and the increasing penetration of fluctuating renewables (e.g., wind and solar power) have heightened the importance of the spot market for ensuring efficient electricity supply. This is further emphasized by the EU’s goal of achieving net-zero emissions by 2050. The day-ahead market (DAM) plays a key role in European energy trading, accounting for 80-90% of spot transactions and providing critical insights for next-day pricing. Therefore, short-term electricity price forecasting (EPF) within the DAM is crucial for market participants to make informed decisions and improve their market positioning. Existing literature highlights out-of-sample performance as a key factor in assessing EPF accuracy, with influencing factors such as predictors, forecast horizon, model selection, and strategy. Several studies indicate that electricity demand is a primary price determinant, while renewable energy sources (RES) like wind and solar significantly impact price dynamics, often lowering prices. Additionally, incorporating data from neighboring countries, due to market coupling, further improves forecast accuracy. Most studies predict up to 24 steps ahead using hourly data, while some extend forecasts using higher-frequency data (e.g., half-hourly or quarter-hourly). Short-term EPF methods fall into two main categories: statistical and computational intelligence (CI) methods, with hybrid models combining both. While many studies use advanced statistical methods, particularly through different versions of traditional AR-type models, others apply computational techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). Recent research combines multiple methods to enhance forecasting performance. Despite extensive research on EPF accuracy, a gap remains in understanding how forecasting strategy affects prediction outcomes. While iterated strategies are commonly used, they are often chosen without justification. This paper contributes by examining whether the choice of forecasting strategy impacts the quality of day-ahead price predictions, especially for multi-step forecasts. We evaluate both iterated and direct methods, exploring alternative ways of conducting iterated forecasts on benchmark and state-of-the-art forecasting frameworks. The goal is to assess whether these factors should be considered by end-users to improve forecast quality. We focus on the Greek DAM using data from July 1, 2021, to March 31, 2022. This period is chosen due to significant price volatility in Greece, driven by its dependence on natural gas and limited interconnection capacity with larger European grids. The analysis covers two phases: pre-conflict (January 1, 2022, to February 23, 2022) and post-conflict (February 24, 2022, to March 31, 2022), following the Russian-Ukraine conflict that initiated an energy crisis. We use the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE) for evaluation, as well as the Direction of Change (DoC) measure to assess the accuracy of price movement predictions. Our findings suggest that forecasters need to apply all strategies across different horizons and models. Different strategies may be required for different horizons to optimize both accuracy and directional predictions, ensuring more reliable forecasts.

Keywords: short-term electricity price forecast, forecast strategies, forecast horizons, recursive strategy, direct strategy

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Authors: Nursuhaili Najwa Masrol, Nur Hafizah Mohammed, Nur Nadhirah Rusyda Rosnan, Vijaya Subramaniam, Sim Choon Cheak

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Replanting in oil palm cultivation is vital to enable the introduction of planting materials and provides an opportunity to improve the road, drainage, terrace design, and planting density. Oil palm replanting is fundamentally necessary every 25 years. The adoption of the digital replanting blueprint is imperative as it can assist the Malaysia Oil Palm industry in addressing challenges such as labour shortages and limited expertise related to replanting tasks. Effective replanting planning should commence at least 6 months prior to the actual replanting process. Therefore, this study will help to plan and design the replanting blueprint with high-precision translation on the ground. With the advancement of geospatial technology, it is now feasible to engage in thoroughly researched planning, which can help maximize the potential yield. A blueprint designed before replanting is to enhance management’s ability to optimize the planting program, address manpower issues, or even increase productivity. In terrace planting blueprints, geographic tools have been utilized to design the roads, drainages, terraces, and planting points based on the ARM standards. These designs are mapped with location information and undergo statistical analysis. The geospatial approach is essential in precision agriculture and ensuring an accurate translation of design to the ground by implementing high-accuracy technologies. In this study, geospatial and remote sensing technologies played a vital role. LiDAR data was employed to determine the Digital Elevation Model (DEM), enabling the precise selection of terraces, while ortho imagery was used for validation purposes. Throughout the designing process, Geographical Information System (GIS) tools were extensively utilized. To assess the design’s reliability on the ground compared with the current conventional method, high-precision GPS instruments like EOS Arrow Gold and HIPER VR GNSS were used, with both offering accuracy levels between 0.3 cm and 0.5cm. Nearest Distance Analysis was generated to compare the design with actual planting on the ground. The analysis revealed that it could not be applied to the roads due to discrepancies between actual roads and the blueprint design, which resulted in minimal variance. In contrast, the terraces closely adhered to the GPS markings, with the most variance distance being less than 0.5 meters compared to actual terraces constructed. Considering the required slope degrees for terrace planting, which must be greater than 6 degrees, the study found that approximately 65% of the terracing was constructed at a 12-degree slope, while over 50% of the terracing was constructed at slopes exceeding the minimum degrees. Utilizing blueprint replanting promising strategies for optimizing land utilization in agriculture. This approach harnesses technology and meticulous planning to yield advantages, including increased efficiency, enhanced sustainability, and cost reduction. From this study, practical implementation of this technique can lead to tangible and significant improvements in agricultural sectors. In boosting further efficiencies, future initiatives will require more sophisticated techniques and the incorporation of precision GPS devices for upcoming blueprint replanting projects besides strategic progression aims to guarantee the precision of both blueprint design stages and its subsequent implementation on the field. Looking ahead, automating digital blueprints are necessary to reduce time, workforce, and costs in commercial production.

Keywords: replanting, geospatial, precision agriculture, blueprint

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Authors: L. Diogo, A. Legrand, J. Nguyen-Cao, J. Rogeau, S. Bornhofen

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Lenia is a system of cellular automata with continuous states, space and time, which surprises not only with the emergence of interesting life-like structures but also with its beauty. This paper reports ongoing research on a GPU implementation of Lenia using CUDA C++ and OpenGL Interoperability. We demonstrate how CUDA as a low-level GPU programming paradigm allows optimizing performance and memory usage of the Lenia algorithm. A comparative analysis through experimental runs with existing implementations shows that the CUDA implementation outperforms the others by one order of magnitude or more. Cellular automata hold significant interest due to their ability to model complex phenomena in systems with simple rules and structures. They allow exploring emergent behavior such as self-organization and adaptation, and find applications in various fields, including computer science, physics, biology, and sociology. Unlike classic cellular automata which rely on discrete cells and values, Lenia generalizes the concept of cellular automata to continuous space, time and states, thus providing additional fluidity and richness in emerging phenomena. In the current literature, there are many implementations of Lenia utilizing various programming languages and visualization libraries. However, each implementation also presents certain drawbacks, which serve as motivation for further research and development. In particular, speed is a critical factor when studying Lenia, for several reasons. Rapid simulation allows researchers to observe the emergence of patterns and behaviors in more configurations, on bigger grids and over longer periods without annoying waiting times. Thereby, they enable the exploration and discovery of new species within the Lenia ecosystem more efficiently. Moreover, faster simulations are beneficial when we include additional time-consuming algorithms such as computer vision or machine learning to evolve and optimize specific Lenia configurations. We developed a Lenia implementation for GPU using the C++ and CUDA programming languages, and CUDA/OpenGL Interoperability for immediate rendering. The goal of our experiment is to benchmark this implementation compared to the existing ones in terms of speed, memory usage, configurability and scalability. In our comparison we focus on the most important Lenia implementations, selected for their prominence, accessibility and widespread use in the scientific community. The implementations include MATLAB, JavaScript, ShaderToy GLSL, Jupyter, Rust and R. The list is not exhaustive but provides a broad view of the principal current approaches and their respective strengths and weaknesses. Our comparison primarily considers computational performance and memory efficiency, as these factors are critical for large-scale simulations, but we also investigate the ease of use and configurability. The experimental runs conducted so far demonstrate that the CUDA C++ implementation outperforms the other implementations by one order of magnitude or more. The benefits of using the GPU become apparent especially with larger grids and convolution kernels. However, our research is still ongoing. We are currently exploring the impact of several software design choices and optimization techniques, such as convolution with Fast Fourier Transforms (FFT), various GPU memory management scenarios, and the trade-off between speed and accuracy using single versus double precision floating point arithmetic. The results will give valuable insights into the practice of parallel programming of the Lenia algorithm, and all conclusions will be thoroughly presented in the conference paper. The final version of our CUDA C++ implementation will be published on github and made freely accessible to the Alife community for further development.

Keywords: artificial life, cellular automaton, GPU optimization, Lenia, comparative analysis.

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Kimberly Winnie Achieng Otieno

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The Nairobi Hospital plays a pivotal role in healthcare provision in East and Central Africa, yet it faces challenges in providing accessible dialysis care. This paper explores strategic interventions to enhance dialysis care, improve access and streamline health information management, with an aim of fostering an integrated and patient-centered healthcare system in our region. Challenges at The Nairobi Hospital The Nairobi Hospital currently grapples with insufficient dialysis machines which results in extended turn around times. This issue stems from both staffing bottle necks and infrastructural limitations given our growing demand for renal care services. Our Paper-based record keeping system and fragmented flow of information downstream hinders the hospital’s ability to manage health data effectively. There is also a need for investment in expanding The Nairobi Hospital dialysis facilities to far reaching communities. Setting up satellite clinics that are closer to people who live in areas far from the main hospital will ensure better access to underserved areas. Community Outreach and Education Implementing education programs on kidney health within local communities is vital for early detection and prevention. Collaborating with local leaders and organizations can establish a proactive approach to renal health hence reducing the demand for acute dialysis interventions. We can amplify this effort by expanding The Nairobi Hospital’s corporate social responsibility outreach program with weekend engagement activities such as walks, awareness classes and fund drives. Enhancing Efficiency in Dialysis Care Demand for dialysis services continues to rise due to an aging Kenyan population and the increasing prevalence of chronic kidney disease (CKD). Present at this years International Nursing Conference are a diverse group of caregivers from around the world who can share with us their process optimization strategies, patient engagement techniques and resource utilization efficiencies to catapult The Nairobi Hospital to the 21st century and beyond. Plans are underway to offer ongoing education opportunities to keep staff updated on best practices and emerging technologies in addition to utilizing a patient feedback mechanisms to identify areas for improvement and enhance satisfaction. Staff empowerment and suggestion boxes address The Nairobi Hospital’s organizational challenges. Current financial constraints may limit a leapfrog in technology integration such as the acquisition of new dialysis machines and an investment in predictive analytics to forecast patient needs and optimize resource allocation. Streamlining Health Information Management Fully embracing a shift to 100% Electronic Health Records (EHRs) is a transformative step toward efficient health information management. Shared information promotes a holistic understanding of patients’ medical history, minimizing redundancies and enhancing overall care quality. To manage the transition to community-based care and EHRs effectively, a phased implementation approach is recommended. Conclusion By strategically enhancing dialysis care access and streamlining health information management, The Nairobi Hospital can strengthen its position as a leading healthcare institution in both East and Central Africa. This comprehensive approach aligns with the hospital’s commitment to providing high-quality, accessible, and patient-centered care in an evolving landscape of healthcare delivery.

Keywords: Africa, urology, diaylsis, healthcare

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Authors: Richa Jaiswal, Vidisha Sharma, Amulya Rattan, Sushma Sagar, Subodh Kumar, Amit Gupta, Biplab Mishra, Maneesh Singhal

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Background: Adequate nutritional support and daily patient monitoring have an independent therapeutic role in the successful management of high output fistulae and early recovery after abdominal trauma. Case presentation: An 18-year-old girl was brought to AIIMS emergency with alleged history of fall of a heavy weight (electric motor) over abdomen. She was evaluated as per Advanced Trauma Life Support(ATLS) protocols and diagnosed to have significant abdominal trauma. After stabilization, she was referred to Trauma center. Abdomen was guarded and focused assessment with sonography for trauma(FAST) was found positive. Complete duodenojejunal(DJ) junction transection was found at laparotomy, and end-to-end repair was done. However, patient was re-explored in view of biliary peritonitis on post-operative day3, and anastomotic leak was found with sloughing of duodenal end. Resection of non-viable segments was done followed by side-to-side anastomosis. Unfortunately, the anastomosis leaked again, this time due to a post-anastomotic kink, diagnosed on dye study. Due to hostile abdomen, the patient was planned for supportive care, with plan of build-up and delayed definitive surgery. Percutaneous transheptic biliary drainage (PTBD) and STSG were required in the course as well. Nutrition: In intensive care unit (ICU), major goals of nutritional therapy were to improve wound healing, optimize nutrition, minimize enteral feed associated complications, reduce biliary fistula output, and prepare the patient for definitive surgeries. Feeding jejunostomy (FJ) was started from day 4 at the rate of 30ml/h along with total parenteral nutrition (TPN) and intra-venous (IV) micronutrients support. Due to high bile output, bile refeed started from day 13.After 23 days of ICU stay, patient was transferred to general ward with body mass index (BMI)<11kg/m2 and serum albumin –1.5gm%. Patient was received in the ward in catabolic phase with high risk of refeeding syndrome. Patient was kept on FJ bolus feed at the rate of 30–50 ml/h. After 3–4 days, while maintaining patient diet book log it was observed that patient use to refuse feed at night and started becoming less responsive with every passing day. After few minutes of conversation with the patient for a couple of days, she complained about enteral feed discharge in urine, mild pain and sign of dumping syndrome. Dye study was done, which ruled out any enterovesical fistula and conservative management were planned. At this time, decision was taken for continuous slow rate feeding through commercial feeding pump at the rate of 2–3ml/min. Drastic improvement was observed from the second day in gastro-intestinal symptoms and general condition of the patient. Nutritional composition of feed, TPN and diet ranged between 800 and 2100 kcal and 50–95 g protein. After STSG, TPN was stopped. Periodic diet counselling was given to improve oral intake. At the time of discharge, serum albumin level was 2.1g%, weight – 38.6, BMI – 15.19 kg/m2. Patient got discharge on an oral diet. Conclusion: Successful management of post-traumatic proximal high output fistulae is a challenging task, due to impaired nutrient absorption and enteral feed associated complications. Strategic- and goal-based nutrition support can salvage such critically ill patients, as demonstrated in the present case.

Keywords: nutritional monitoring, nutritional support, duodenal fistula, abdominal trauma

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: Delia Gabriela Moisa, Demos Parapanos, Tim Heap

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The tourism sector is mainly comprised of small and medium-sized tourism enterprises (SMEs), representing approximately 80% of global businesses in this field. These entities require focused attention and support to address challenges, ensuring their competitiveness and relevance in a dynamic industry characterized by continuously changing customer preferences. To address these challenges, it becomes imperative to consider not only socio-demographic factors but also delve into the intricate interplay of psychological elements influencing consumer behavior. This study investigates the impact of personality traits and travel motivations on visitor activities in Cumbria, United Kingdom, an iconic region marked by UNESCO World Heritage Sites, including The Lake District National Park and Hadrian's Wall. With a £4.1 billion tourism industry primarily driven by SMEs, Cumbria serves as an ideal setting for examining the relationship between tourist psychology and activities. Employing the Big Five personality model and the Travel Career Pattern motivation theory, this study aims to explain the relationship between psychological factors and tourist activities. The study further explores SME perspectives on personality-based market segmentation, providing strategic insights into addressing evolving tourist preferences.This pioneering mixed-methods study integrates quantitative data from 330 visitor surveys, subsequently complemented by qualitative insights from tourism SME representatives. The findings unveil that socio-demographic factors do not exhibit statistically significant variations in the activities pursued by visitors in Cumbria. However, significant correlations emerge between personality traits and motivations with preferred visitor activities. Open-minded tourists gravitate towards events and cultural activities, while Conscientious individuals favor cultural pursuits. Extraverted tourists lean towards adventurous, recreational, and wellness activities, while Agreeable personalities opt for lake cruises. Interestingly, a contrasting trend emerges as Extraversion increases, leading to a decrease in interest in cultural activities. Similarly, heightened Agreeableness corresponds to a decrease in interest in adventurous activities. Furthermore, travel motivations, including nostalgia and building relationships, drive event participation, while self-improvement and novelty-seeking lead to adventurous activities. Additionally, qualitative insights from tourism SME representatives underscore the value of targeted messaging aligned with visitor personalities for enhancing loyalty and experiences. This study contributes significantly to scholarship through its novel framework, integrating tourist psychology with activities and industry perspectives. The proposed conceptual model holds substantial practical implications for SMEs to formulate personalized offerings, optimize marketing, and strategically allocate resources tailored to tourist personalities. While the focus is on Cumbria, the methodology's universal applicability offers valuable insights for destinations globally seeking a competitive advantage. Future research addressing scale reliability and geographic specificity limitations can further advance knowledge on this critical relationship between visitor psychology, individual preferences, and industry imperatives. Moreover, by extending the investigation to other districts, future studies could draw comparisons and contrasts in the results, providing a more nuanced understanding of the factors influencing visitor psychology and preferences.

Keywords: personality trait, SME, tourist behaviour, tourist motivation, visitor activity

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Authors: Erika T. Fajardo-Ariza, Luis A. Castillo-Sanabria, Andrea Nieto-Veloza, Carlos M. Zuluaga-Domínguez

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The growing emphasis on sustainable food production and preservation has driven the development of innovative solutions to minimize postharvest losses and improve market access for small-scale farmers. This project focuses on designing, constructing, and selecting materials for solar dryers in certain regions of Colombia where inadequate infrastructure limits access to major commercial hubs. Postharvest losses pose a significant challenge, impacting food security and farmer income. Addressing these losses is crucial for enhancing the value of agricultural products and supporting local economies. A comprehensive survey of local farmers revealed substantial challenges, including limited market access, inefficient transportation, and significant postharvest losses. For crops such as coffee, bananas, and citrus fruits, losses range from 0% to 50%, driven by factors like labor shortages, adverse climatic conditions, and transportation difficulties. To address these issues, the project prioritized selecting effective materials for the solar dryer. Various materials, recovered acrylic, original acrylic, glass, and polystyrene, were tested for their performance. The tests showed that recovered acrylic and glass were most effective in increasing the temperature difference between the interior and the external environment. The solar dryer was designed using Fusion 360® software (Autodesk, USA) and adhered to architectural guidelines from Architectural Graphic Standards. It features up to sixteen aluminum trays, each with a maximum load capacity of 3.5 kg, arranged in two levels to optimize drying efficiency. The constructed dryer was then tested with two locally available plant materials: green plantains (Musa paradisiaca L.) and snack bananas (Musa AA Simonds). To monitor performance, Thermo hygrometers and an Arduino system recorded internal and external temperature and humidity at one-minute intervals. Despite challenges such as adverse weather conditions and delays in local government funding, the active involvement of local producers was a significant advantage, fostering ownership and understanding of the project. The solar dryer operated under conditions of 31°C dry bulb temperature (Tbs), 55% relative humidity, and 21°C wet bulb temperature (Tbh). The drying curves showed a consistent drying period with critical moisture content observed between 200 and 300 minutes, followed by a sharp decrease in moisture loss, reaching an equilibrium point after 3,400 minutes. Although the solar dryer requires more time and is highly dependent on atmospheric conditions, it can approach the efficiency of an electric dryer when properly optimized. The successful design and construction of solar dryer systems in difficult-to-access areas represent a significant advancement in agricultural sustainability and postharvest loss reduction. By choosing effective materials such as recovered acrylic and implementing a carefully planned design, the project provides a valuable tool for local farmers. The initiative not only improves the quality and marketability of agricultural products but also offers broader environmental benefits, such as reduced reliance on fossil fuels and decreased waste. Additionally, it supports economic growth by enhancing the value of crops and potentially increasing farmer income. The successful implementation and testing of the dryer, combined with the engagement of local stakeholders, highlight its potential for replication and positive impact in similar contexts.

Keywords: drying technology, postharvest loss reduction, solar dryers, sustainable agriculture

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Authors: Oluwaseun Adeleke, Samuel O. Ikani, Fidelis Edet, Anthony Nwala, Mopelola Raji, Simeon Christian Chukwu

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Background The Delivering Innovations in Selfcare (DISC) project promotes universal access to quality selfcare services beginning with subcutaneous depot medroxy progesterone acetate (DMPA-SC) contraceptive self-injection (SI) option. Self-inject (SI) offers women a highly effective and convenient option that saves them frequent trips to providers. Its increased use has the potential to improve the efficiency of an overstretched healthcare system by reducing provider workloads. State Social and Behavioral Change Communications (SBCC) Officers lead project demand creation and service delivery innovations that have resulted in significant increases in SI uptake among women who opt for injectables. Strategies Service Delivery Innovations The implementation of the "Moment of Truth (MoT)" innovation helped providers overcome biases and address client fear and reluctance to self-inject. Bi-annual program audits and supportive mentoring visits helped providers retain their competence and motivation. Proper documentation, tracking, and replenishment of commodities were ensured through effective engagement with State Logistics Units. The project supported existing state monitoring and evaluation structures to effectively record and report subcutaneous depot medroxy progesterone acetate (DMPA-SC) service utilization. Demand creation Innovations SBCC Officers provide oversight, routinely evaluate performance, trains, and provides feedback for the demand creation activities implemented by community mobilizers (CMs). The scope and intensity of training given to CMs affect the outcome of their work. The project operates a demand creation model that uses a schedule to inform the conduct of interpersonal and group events. Health education sessions are specifically designed to counter misinformation, address questions and concerns, and educate target audience in an informed choice context. The project mapped facilities and their catchment areas and enlisted the support of identified influencers and gatekeepers to enlist their buy-in prior to entry. Each mobilization event began with pre-mobilization sensitization activities, particularly targeting male groups. Context-specific interventions were informed by the religious, traditional, and cultural peculiarities of target communities. Mobilizers also support clients to engage with and navigate online digital Family Planning (FP) online portals such as DiscoverYourPower website, Facebook page, digital companion (chat bot), interactive voice response (IVR), radio and television (TV) messaging. This improves compliance and provides linkages to nearby facilities. Results The project recorded 136,950 self-injection (SI) visits and a self-injection (SI) proportion rate that increased from 13 percent before the implementation of interventions in 2021 to 62 percent currently. The project cost-effectively demonstrated catalytic impact by leveraging state and partner resources, institutional platforms, and geographic scope to scale up interventions. The project also cost effectively demonstrated catalytic impact by leveraging on the state and partner resources, institutional platforms, and geographic scope to sustainably scale-up these strategies. Conclusion Using evidence-informed iterations of service delivery and demand creation models have been useful to significantly drive self-injection (SI) uptake. It will be useful to consider this implementation model during program design. Contemplation should also be given to systematic and strategic execution of strategies to optimize impact.

Keywords: family planning, contraception, DMPA-SC, self-care, self-injection, innovation, service delivery, demand creation.

Procedia PDF Downloads 74

Authors: Tsegay Kahsay Gebrekidan, Gebremariam Gebrezgabher Gebremedhin, Abraha Kahsay Weldemariam, Meaza Kidane Teferi

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Municipal solid waste management (MSWM) in Ethiopia is a complex issue with institutional, social, political, environmental, and economic dimensions, impacting sustainable development. Effective MSWM planning necessitates understanding the generation rate and composition of waste. This systematic review synthesizes qualitative and quantitative data from various sources to aggregate current knowledge, identify gaps, and provide a comprehensive understanding of municipal solid waste management in Ethiopia. The findings reveal that the generation rate of municipal solid waste in Ethiopia is 0.38 kg/ca/day, with the waste composition being predominantly food waste, followed by ash, dust, and sand, and yard waste. Over 85% of this MSW is either reusable or recyclable, with a significant portion being organic matter (73.13% biodegradable) and 11.78% recyclable materials. Physicochemical analyses reveal that Ethiopian MSW is suitable for composting and biogas production, offering opportunities to reduce environmental pollution, and GHGs, support urban agriculture, and create job opportunities. However; challenges persist, including a lack of political will, weak municipal planning, limited community awareness, and inadequate waste management infrastructure, and only 31.8% of MSW is collected legally, leading to inefficient and harmful disposal practices. To improve MSWM, Ethiopia should focus on public awareness; increased funding, infrastructure investment, private sector partnerships, and implementing the 4 R principles (reduce, reuse, and recycle). An integrated approach involving government, industry, and civil society is essential. Further research on the physicochemical properties and strategic uses of MSW is needed to enhance management practices. Implications: The comprehensive study of municipal solid waste management (MSWM) in Ethiopia reveals the intricate interplay of institutional, social, political, environmental, and economic factors that influence the nation’s sustainable development. The findings underscore the urgent need for tailored, integrated waste management strategies that are informed by a thorough understanding of MSW generation rates, composition, and current management practices. Ethiopia’s lower per capita MSW generation compared to developed countries and the predominantly organic composition of its waste present significant opportunities for sustainable waste management practices such as composting and recycling. These practices can not only minimize the environmental impact but also support urban greening, agriculture, and renewable energy production. The high organic content, suitable physicochemical properties of MSW for composting, and potential for biogas and briquette production highlight pathways for creating employment, reducing waste, and enhancing soil fertility. Despite these opportunities, Ethiopia faces substantial challenges due to inadequate political will, weak municipal planning, limited community awareness, insufficient waste management infrastructure, and poor policy implementation. The high rate of illegal waste disposal further exacerbates environmental and health issues, emphasizing the need for a more effective and integrated MSWM approach. To address these challenges and harness the potential of MSW, Ethiopia must prioritize increasing public awareness; investing in infrastructure, fostering private sector partnerships, and implementing the principles of reduce, reuse, and recycle (3 R). Developing strategies that involve all stakeholders and turning waste into valuable resources is crucial. Government, industry, and civil society must collaborate to implement integrated MSWM systems that focus on waste reduction at the source, alternative material use, and advanced recycling technologies. Further research at both federal and regional levels is essential to optimize the physicochemical analysis and strategic use of MSW. Prompt action is required to transform waste management into a pillar of sustainable urban development, ultimately improving environmental quality and human health in Ethiopia.

Keywords: biodegradable, healthy environment, integrated solid waste management, municipal

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