Technical Limitations Research Articles

Extending the application of bifunctional ionic liquid-based integrated capture and conversion of CO2 to produce cyclic carbonates

Nowadays there is an urgent need for mitigating CO2 emissions through clean energy and the development of new carbon capture and utilization (CCU) technologies. Among others, the use of bifunctional ionic liquids (ILs) addressed simultaneously CO2 capture and conversion steps, having applied successfully to the propylene carbonate production case. In this work, a systematic evaluation of all representative cyclic carbonate literature was made, covering ethylene, propylene, butylene, hexylene, cyclohexene, and styrene cyclic carbonates, in order to guide the product role within the integrated CCU (ICCU) concept. The multiscale strategy combining molecular simulation (DFT -Density Functional Theory-, COSMO -COnductor-like Screening MOdel-), process simulation (COSMO/Aspen methodology), and life cycle assessment (LCA) was used to set up, simulate and evaluate the processes. ICCU configuration is the best approach when compared with sequential configuration for energy consumption analysis (reduction of 28, 28, 22, 11 and 6 %, respectively, for ethylene, propylene, butylene, hexylene, and cyclohexene cases) and CO2 emissions associated (reduction of 38, 40, 31 and 14 %, respectively, for ethylene, propylene, butylene, and hexylene cases). The main variable of the results is the boiling point of the cyclic carbonate since heavy products impose technical limitations and even discard ICCU alternative. The ICCU concept works since all cyclic carbonates’ reaction enthalpies are higher than that of the IL-CO2 one, which reduces heating requirements. Finally, energy demand can be slightly further reduced, partially recycling the cyclic carbonate to the capture unit.

Transcriptomic analyses reveals a diverse venom composition in Agelena limbata (Araneae: Agelenaidae)

Spider venom is a natural source of diverse biomolecules, but due to technical limitations, only a small fraction has been studied. With the advancement of omics technologies, research on spider venom has broadened, greatly promoting systematic studies of spider venom. Agelena limbata is a common spider found in vegetation, known for constructing funnel-shaped webs, and feeding on insects such as Diptera and Homoptera. However, due to its small size and the difficulty in obtaining venom, the composition of Agelena limbata venom has never been studied. In this study, a transcriptomics approach was used to analyze the toxin components in the venom of Agelena limbata, resulting in the identification of 28 novel toxin-like sequences and 24 peptidases. Based on sequence similarity and differences in cysteine motifs, the 28-novel toxin-like sequences were classified into 10 superfamilies. According to the results annotated in the database, the 24 peptidases were divided into six distinct families, with the serine protease family being the most common. A phylogenetic tree was constructed using the toxin-like sequences of Agelena limbata along with Psechrus triangulus and Hippasa lycosina. An analysis of the structural domains and motifs of Agelena limbata was also conducted. The results indicated that Agelena limbata is more distantly related to the other two species of funnel-web spiders, and that the toxin superfamily IX has a unique function compared to the other superfamilies. This study reveals the components of the Agelena limbata venom, deepening our understanding of it, and through bioinformatics analysis, has identified unique functions of the toxin superfamilies, providing a scientific basis for the development of bioactive drugs in the future.

TARGET IDENTIFICATION AND TRACKING IN COMPLEX ENVIRONMENT

This study focuses on developing advanced methods for the identification and classification of objects in complex environments. Over the past two years, there has been an increase in the use of advanced technologies in various challenging scenarios. This research is centered on accurately identifying targets and tracking them. The study addresses challenges related to object detection in multi-dimensional and intricate settings, taking into account natural conditions like rain and fog, as well as technical limitations such as camera capabilities. Special emphasis is placed on data collection for training the identification model, followed by extensive data preprocessing, including cleaning, labeling, and augmentation. The research employs YOLO and Deep Sport machine learning algorithms, focusing on improving the accuracy and reliability of target recognition and increasing data processing speed to minimize misidentification risks. The integration of YOLO, known for its quick real-time object detection, with Deep Sport, which excels in detailed feature extraction and classification, forms the basis of our methodology. This fusion is a complex combination of both models' strengths, with YOLO quickly identifying relevant objects and Deep Sport performing an in-depth analysis. The experimental phase involves extensive testing of the models in varied weather conditions and settings to evaluate performance under challenging circumstances. This work aims to enhance object identification techniques in complex environments, a critical aspect for the effectiveness of various advanced operations. The findings are expected to significantly contribute to the field by enabling quicker and more accurate target identification.

Effectiveness of speed cameras on the prevention of road traffic collisions and casualties in cameroon

Issues related to transport collision and casualties cannot be undermined as it has direct effect on human health and death, hence relevant policies are required to reduce road traffic accidents. To do this, our study has as objectives: to discuss the drivers of the used of speed cameras by drivers in Cameroon., investigate the effects of speed cameras on the prevention of road traffic collisions and casualties in Cameroon and to determine the actual problems faced by drivers in the use of speed Cameras in Cameroon. Methodologically, the study applied the probit technique to estimate the result via primary data. The sample size consisted of 200 drivers collected through a well-structured questionnaire and collect among the drivers plying the Yaoundé-Doula, Douala –Bafoussam and Bafousam-Yaoundé major highway. The key findings revealed that the factors influencing the drivers used of speed cameras are: main occupation, attainment of higher education, married, age, victim of traffic collision and casualties. The result also shows that Speed camera is highly corroborating with the prevention of road traffic collisions and casualties in Cameroon. Finally, the result shows that the lack of awareness, student speed change, inadequate signage, limited visibility, technical limitations and privacy concerns are the actual physical challenges of the use of speed camera in Cameroon. The study suggests that decision makers in the transport sector should multiply the provision of speed cameras. This is a wise step in the prevention of road traffic collisions and casualties in Cameroon and better labour market.

Review of AI-Powered Food Processing: Enhancing Safety and Sustainability

As the global population is projected to reach 9.7 billion by 2050, ensuring food security has become increasingly challenging due to factors such as population growth, climate change, resource depletion, and environmental pollution. To address these challenges, artificial intelligence (AI) is as emerging technology that plays a crucial role in the food processing industry. This review highlights AI's essential role in food processing to enhancing the quality and safety of food products sustainably while addressing industry challenges. Artificial Intelligence significantly impacts food safety, quality assurance, and waste reduction through various applications. Its integration into food processing has the potential to revolutionize industry standards, covering tasks from product categorization to personalized nutrition. However, challenges such as job displacement, technical limitations, and ethical considerations require careful attention. Investment in research, development, training, and maintenance is crucial for successful AI integration. Despite these hurdles, AI's transformative potential to advance sustainability, food safety, and consumer needs remains significant. Artificial Intelligence is poised to enhance efficiency, foster innovation, and improve sustainability in the food processing sector, effectively addressing the evolving needs of the growing global population. Artificial Intelligence promotes a hopeful outlook for the future of food processing, confidently tackling 21st-century challenges with adaptability and resourcefulness.

Optimal sequencing budget allocation for trajectory reconstruction of single cells.

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies.

The integration of augmented reality (AR) in spine surgery marks a significant advancement, enhancing surgical precision and patient outcomes. AR provides immersive, three-dimensional visualizations of anatomical structures, facilitating meticulous planning and execution of spine surgeries. This technology not only improves spatial understanding and real-time navigation during procedures but also aims to reduce surgical invasiveness and operative times. Despite its potential, challenges such as model accuracy, user interface design, and the learning curve for new technology must be addressed. AR's application extends beyond the operating room, offering valuable tools for medical education and improving patient communication and satisfaction. A literature review was conducted by searching PubMed and Scopus databases using keywords related to augmented reality in spine surgery, covering publications from January 2020 to January 2024. In total, 319 articles were identified through the initial search of the databases. After screening titles and abstracts, 11 articles in total were included in the qualitative synthesis. Augmented reality (AR) is becoming a transformative force in spine surgery, enhancing precision, education, and outcomes despite hurdles like technical limitations and integration challenges. AR's immersive visualizations and educational innovations, coupled with its potential synergy with AI and machine learning, indicate a bright future for surgical care. Despite the existing obstacles, AR's impact on improving surgical accuracy and safety marks a significant leap forward in patient treatment and care.

Analysis of the peculiarities of the method of combining an electronic cartographic navigation information system with a ship's radar station

The article analyses the importance of radar image overlay on the screen of an electronic charting navigation information system (ECNIS) for improving safety and efficiency of navigation. Based on a review of scientific publications and professional literature, the article discusses the technical aspects of the overlay function, its advantages and limitations, and the impact on the decision-making process of a navigator. The overlay of radar information on an electronic navigation chart ensures the integration of planned and actual data on the environment, which contributes to the increase of situational awareness of the navigator. The combination of the systems makes it possible to visually compare radar targets with cartographic objects, clarify the vessel's location, control drift and orientation, and detect navigation hazards. The overlay function is especially valuable when sailing in confined waters and in conditions of limited visibility. At the same time, the use of overlay technology is associated with a number of technical difficulties and limitations related to ensuring the accuracy of overlap, harmonisation of data formats, and synchronisation of information updates. Errors in the operation of positioning systems, direction guidance, and cartography errors can lead to inconsistencies in image layers and incorrect interpretation of the navigation situation. The issues of equipment reliability, display parameters settings, competence and training of navigators are of critical importance. It is emphasised that radar image overlay on ECDIS is an effective tool for improving safety of navigation, but it does not replace traditional navigation practices and does not eliminate the need for critical evaluation of data. Over-reliance on technology, ignoring its limitations and disregard for the principles of good maritime practice can lead to erroneous decisions and have a negative impact on safety. Further prospects for the development of technology can be associated with the improvement of information processing algorithms, optimisation of human-machine interaction, and the development of training methods. The article emphasises the need for an integrated approach combining the improvement of technical systems and the human factor to fully realise the potential of the overlay function in ensuring safety of navigation. Only a balanced use of technological innovations and traditional navigational practices can guarantee the reliability and efficiency of modern navigation

Hollow fiber thin-film composite membrane regulated by macrocyclic polyamine molecules for high performance organic solvent nanofiltration

Organic solvent nanofiltration (OSN) technology has the potential to separate and purify organic solvents in high efficiency. The self-supporting structure and ease scaling-up merit of hollow fiber (HF) type membrane make it an attractive option for OSN application. Nevertheless, low solvent permeance is one of technical limitation in conventional HF OSN membranes due to their excessively dense separation layer and limited free volume. Herein, we propose a straightforward methodology to fabricate HF thin-film composite (TFC) OSN membranes having exceptional solvent permeance and solute rejection. This methodology is conducted through the incorporation of macrocyclic molecules, 1,4,7,10-tetraazacyclododecane (Cyclen), into the aqueous monomer solution of m-Phenylenediamine (MPD) during the interfacial polymerization to modulate the separation layer. In this way, the average pore size of the separation layer could be precisely enlarged and narrowed on a sub-nanometer scale. Consequently, the Cyclen-modulated HF TFC OSN membrane exhibits excellent separation performance, with a pure methanol permeance of 76.7 L m−2 h−1 MPa−1 and a pure ethanol permeance of 26.5 L m−2 h−1 MPa−1, which is nearly 60 % increase compared with the baseline TFC membrane, while the Rhodamine B (RDB) rejection only shows a neglectable decrease from 99.7 % to 99.6 % at optimal fabrication conditions. Furthermore, the optimal membrane exhibits remarkable solvent resistance, withstanding a 35 d immersion in N, N-dimethylformamide (DMF) at room temperature without a significant decline in RDB rejection. Additionally, the optimal membrane shows higher than 99 % rejection for Rifampicin (823 Da), which is considerable potential for applications in the separation and recovery of pharmaceuticals. In summary, incorporating macrocyclic polyamine Cyclen into the polyamide layer is a viable method for regulating the physical structure and strengthening the performance of HF OSN membrane.

Financial Constraints and Issues of e-Governance in India

"In e-Governance, the government generally uses ICT or Information and Communication Technology for governance. e-Governance completes its function with the help of ICT. It offers government services, and the exchange of information. It also combines information portals and previously supplied services. Generally, digital governance with the help of technology completes the governance process to supply online services. India belongs to the developing countries in the world. However, it should merge the socio-economic goals with good governance efficiently. In the age of ICT, nearly every country in the world used ICT in their administration, delivering services to their citizens on time. e-Governance has an effect on every area of the Indian economy. e-Governance projects have started in India by the Indian government. It delivering as many services online as feasible. Different e-Governance projects have been undertaken. Different projects have been started by the Indian Government such as digital India, e-seva, smart govt, e-kranti and many more. These initiatives all appear to be more beneficial to the citizens overall. But, e-Governance implementation certain problems are still there. Successful e-Governance implementation, these problems may consider as an obstacle. The problems are such as cultural, technical limitations, socioeconomic, privacy and security issues etc. The government is certain that these obstacles can be removed. In this study the author has discussed about these matters."

3D Multispheroid Assembly Strategies towards Tissue Engineering and Disease Modeling.

Cell spheroids (esp. organoids) as 3D culture platforms are popular models for representing cell-cell and cell-extracellular matrix (ECM) interactions, bridging the gap between 2D cell cultures and natural tissues. 3D cell models with spatially organized multiple cell types are preferred for gaining comprehensive insights into tissue pathophysiology and constructing in vitro tissues and disease models because of the complexities of natural tissues. In recent years, an assembly strategy using cell spheroids (or organoids) as living building blocks has been developed to construct complex 3D tissue models with spatial organization. Here, a comprehensive overview of recent advances in multispheroid assembly studies is provided. The different mechanisms of the multispheroid assembly techniques, i.e., automated directed assembly, noncontact remote assembly, and programmed self-assembly, are introduced. The processing steps, advantages, and technical limitations of the existing methodologies are summarized. Applications of the multispheroid assembly strategies in disease modeling, drug screening, tissue engineering, and organogenesis are reviewed. Finally, this review concludes by emphasizing persistent issues and future perspectives, encouraging researchers to adopt multispheroid assembly techniques for generating advanced 3D cell models that better resemble real tissues.

Efferocytosis and Bone Dynamics.

This review summarizes the recently published scientific evidence regarding the role of efferocytosis in bone dynamics and skeletal health. Several types of efferocytes have been identified within the skeleton, with macrophages being the most extensively studied. Efferocytosis is not merely a 'clean-up' process vital for maintaining skeletal homeostasis; it also plays a crucial role in promoting resolution pathways and orchestrating bone dynamics, such as osteoblast-osteoclast coupling during bone remodeling. Impaired efferocytosis has been associated with aging-related bone loss and various skeletal pathologies, including osteoporosis, osteoarthritis, rheumatoid arthritis, and metastatic bone diseases. Accordingly, emerging evidence suggests that targeting efferocytic mechanisms has the potential to alleviate these conditions. While efferocytosis remains underexplored in the skeleton, recent discoveries have shed light on its pivotal role in bone dynamics, with important implications for skeletal health and pathology. However, there are several knowledge gaps and persisting technical limitations that must be addressed to fully unveil the contributions of efferocytosis in bone.

Enhancement of Texas wind turbine power predictions using fractional order neural network by incorporating machine learning models to impute missing data

In real-world datasets, missed data is often expected due to sensor errors, environmental conditions, communication errors, and other technical limitations. These factors can affect the accuracy of power predictions, particularly in wind speed and direction parameters. Enhancing the accuracy of wind energy forecasts and maintaining the electrical grid’s stability are essential in addressing related challenges. This paper proposes a bidirectional long short-term memory (Bi-LSTM) model to forecast missed wind speed and direction data to address these issues. In addition, a novel fractional-order neural network (FONN) with various developed fractional activation functions presents enhanced wind power prediction using forecasted missing wind speed and direction data. The performance of the FONN model is demonstrated in four comprehensive case studies on the Texas wind turbine dataset. The first case study uses wind speed, direction, pressure, and air temperature data. The second case study uses pressure, air temperature, wind speed, and forecasted wind direction data. Similarly, pressure, air temperature, wind direction, and forecasted wind speed data are used in the third case study. The final case study uses pressure, air temperature, and forecasted wind speed and direction to predict power. The proposed models’ performance is evaluated using mean square error and coefficient of determination metrics.

Conquering the beyond Rule of Five Space with an Optimized High-Throughput Caco-2 Assay to Close Gaps in Absorption Prediction.

Current drug development tends towards complex chemical molecules, referred to as "beyond rule of five" (bRo5) compounds, which often exhibit challenging physicochemical properties. Measuring Caco-2 permeability of those compounds is difficult due to technical limitations, including poor recovery and detection sensitivity. We implemented a novel assay, with optimized incubation and analytics, to measure permeability close to equilibrium. In this setup an appropriate characterization of permeability for bRo5 compounds is achievable. This equilibrated Caco-2 assay was verified with respect to data validity, compound recovery, and in vitro to in vivo correlation for human absorption. Compared to a standard assay, it demonstrated comparable performance in predicting the human fraction absorbed (fa) for reference compounds. The equilibrated assay also successfully characterized the permeability of more than 90% of the compounds analyzed, the majority of which were bRo5 (68%). These compounds could not be measured using the standard assay. Permeability and efflux ratio (ER) were highly predictive for in vivo absorption for a large set of internal bRo5 compounds. Reference cut-offs enabled the correct classification of high, moderate, and low absorption. This optimized equilibrated Caco-2 assay closes the gap for a high-throughput cellular permeability method in the bRo5 chemical space.

Circulating Nucleosomes as a Novel Biomarker for Sepsis: A Scoping Review.

Circulating nucleosome levels are commonly elevated in physiological and pathological conditions. Their potential as biomarkers for diagnosing and prognosticating sepsis remains uncertain due, in part, to technical limitations in existing detection methods. This scoping review explores the possible role of nucleosome concentrations in the diagnosis, prognosis, and therapeutic management of sepsis. A comprehensive literature search of the Cochrane and Medline libraries from 1996 to 1 February 2024 identified 110 potentially eligible studies, of which 19 met the inclusion criteria, encompassing a total of 39 SIRS patients, 893 sepsis patients, 280 septic shock patients, 117 other ICU control patients, and 345 healthy volunteers. The enzyme-linked immunosorbent assay [ELISA] was the primary method of nucleosome measurement. Studies consistently reported significant correlations between nucleosome levels and other NET biomarkers. Nucleosome levels were higher in patients with sepsis than in healthy volunteers and associated with disease severity, as indicated by SOFA and APACHE II scores. Non-survivors had higher nucleosome levels than survivors. Circulating nucleosome levels, therefore, show promise as early markers of NETosis in sepsis, with moderate diagnostic accuracy and strong correlations with disease severity and prognosis. However, the available evidence is drawn mainly from single-center, observational studies with small sample sizes and varied detection methods, warranting further investigation.

THE POTENTIAL FOR THE DEVELOPMENT OF THE HYDRO- GEN ECONOMY IN UKRAINE UNTIL 2030

The proposed study is devoted to defining a set of means, methods and conditions that enable the creation of a sustainable and efficient hydrogen economy in Ukraine for the period up to 2030. The study itself is aimed at studying the features of the operation of the hydrogen square concept, which illustrates the various stages of the hydrogen value chain from production to final use, and the potential opportunities for the development of the hydrogen economy in Ukraine until 2030. Using the hydrogen square, safeguards across the entire hydrogen value chain – production, storage, transport and use – are discussed, highlighting the need for a balanced approach to ensure a sustainable and efficient hydrogen economy. It has been determined that the greatest potential opportunities for the development of the hydrogen economy in Ukraine for the period up to 2030 are the transportation of a mixture of hydrogen with natural gas (gitan) through the Ukrainian GTS and the production of methane from green hydrogen (synthetic methane) through the implementation of Power-to-Gas technology. It has been found that the readiness of gas transport networks to transport a mixture of hydrogen with natural gas (gitan) differs greatly in different EU countries, and the industry itself is currently at a very early stage of development. Blending is likely to be a temporary or transitional solution, given the existence of a technical and economic limit to the volume of hydrogen concentration that traditional gas infrastructure can handle. The possibility of using Power-to-Gas technology in Ukraine, in the city of Dnipro, is described. The production of synthetic methane through the implementation of the Power-to-Gas technology will provide an opportunity to obtain the gitan mixture without the use of fossil fuels in the future, which will enable the hydrogen economy to function completely without fossil fuels.

Blood pressure measurement and assessment of arterial structure and function: an expert group position paper.

Measuring blood pressure (BP) and investigating arterial hemodynamics are essential in understanding cardiovascular disease and assessing cardiovascular risk. Several methods are used to measure BP in the doctor's office, at home, or over 24 h under ambulatory conditions. Similarly, several noninvasive methods have been introduced for assessing arterial structure and function; these methods differ for the large arteries, the small ones, and the capillaries. Consequently, when studying arterial hemodynamics, the clinician is faced with a multitude of assessment methods whose technical details, advantages, and limitations are sometimes unclear. Moreover, the conditions and procedures for their optimal implementation, and/or the reference normality values for the parameters they yield are not always taken into sufficient consideration. Therefore, a practice guideline summarizing the main methods and their use in clinical practice is needed. This expert group position paper was developed by an international group of scientists after a two-day meeting during which each of the most used methods and techniques for blood pressure measurement and arterial function and structure evaluation were presented and discussed, focusing on their advantages, limitations, indications, normal values, and their pragmatic clinical application.

The implications of the Indo-Pacific Partnership for Maritime Domain Awareness (IPMDA) in the South China Sea: A Chinese perspective

The emergence of China as a major power and its adjustment of foreign policy have raised concerns in the U.S. The Quad, led by the U.S., aims to counterbalance China’s growing influence in East and Southeast Asia, and the Indo-Pacific Maritime Domain Awareness (IPMDA) acts as an important component of its strategy. However, this article notes that the endeavor faces significant challenges, including legal and geopolitical risks, as well as economic and technical limitations. Intelligence-gathering activities may infringe upon the sovereign rights and jurisdiction of coastal states, especially within disputed maritime areas. Cooperative enforcement under IPMDA can raise legal questions concerning the transfer of maritime jurisdiction under UNCLOS to non-contracting parties. China perceives the IPMDA initiative as a threat posed by the Quad. The Chinese government is concerned that it could spark renewed criticism of its fishing industry and strain diplomatic ties with neighboring countries. China’s response could entail the establishment of alternative mechanisms and increased transparency in maritime activities. Against the backdrop of intensifying competition between the U.S. and China and ongoing disputes in the South China Sea, the IPMDA could exacerbate existing tensions in the SCS area. The exclusivity of the IPMDA mechanism may not serve the best interests of maritime security and prosperity in the Asia-Pacific region. In this context, China is encouraged to play a more constructive role in defusing tensions.

A Novel Method for Estimating the Undrained Shear Strength of Marine Soil Based on CPTU Tests

The undrained shear strength is an essential parameter in the foundation design of marine structures. Due to the complex marine environment and technical limitations, it is difficult and costly to obtain offshore samples. Piezocone penetration tests (CPTU) are relatively low-cost compared to drilling and sampling methods. Therefore, based on the soil behavior type index (Ic) derived from CPTU results, a model for estimating cone factors (Nkt, Nke) is proposed to improve the accuracy of estimation of undrained shear strength. The result shows that the soil behavior type index (Ic) and cone factors take on a negatively correlated exponential relation. Incorporating a cone factor that varies with the soil behavior type index (Ic) significantly enhances the accuracy of undrained shear strength predictions compared to the conventional method of using a constant cone factor. This approach reduces the root mean square error (RMSE) for Nkt (Nke) from 0.124 (0.126) MPa to 0.056 (0.06) MPa, and the mean absolute error (MAE) from 0.0154 (0.016) MPa to 0.0032 (0.0036) MPa. The method was validated at an additional location and the predictions were in high agreement with the results of the consolidated quick direct shear test. The developed method can serve as an effective tool used in the design of foundations of marine structures.

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.