Search results for: autonomous agricultural machines

Authors: Hanene Sahli, Aymen Mouelhi, Marwa Hajji, Amine Ben Slama, Mounir Sayadi, Farhat Fnaiech, Radhwane Rachdi

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Fetal abnormality is still a public health problem of interest to both mother and baby. Head defect is one of the most high-risk fetal deformities. Fetal head categorization is a sensitive task that needs a massive attention from neurological experts. In this sense, biometrical measurements can be extracted by gynecologist doctors and compared with ground truth charts to identify normal or abnormal growth. The fetal head biometric measurements such as Biparietal Diameter (BPD), Occipito-Frontal Diameter (OFD) and Head Circumference (HC) needs to be monitored, and expert should carry out its manual delineations. This work proposes a new approach to automatically compute BPD, OFD and HC based on morphological characteristics extracted from head shape. Hence, the studied data selected at the same Gestational Age (GA) from the fetal Ultrasound images (US) are classified into two categories: Normal and abnormal. The abnormal subjects include hydrocephalus, microcephaly and dolichocephaly anomalies. By the use of a support vector machines (SVM) method, this study achieved high classification for automated detection of anomalies. The proposed method is promising although it doesn't need expert interventions.

Keywords: biometric measurements, fetal head malformations, machine learning methods, US images

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Authors: Touil Issam, Bouraghda Skander

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This paper elaborates on the comprehensive design, development, and evaluation of an Arduino-powered robot car operated through an Android-based application. The system is built upon an Arduino UNO microcontroller, leveraging Bluetooth technology to facilitate seamless communication between the robot and the Android control interface. The primary objective of the project is to provide users with an intuitive and interactive means to control autonomous systems while ensuring simplicity, cost-efficiency, and reliability. The architecture of the system encompasses hardware and software integration, where the microcontroller acts as the central processing unit, handling signals received via Bluetooth and translating them into precise motor commands. The Android application serves as a user-friendly interface, enabling real-time control of the robot's movement and functionality. This paper delves into the technical aspects of system architecture, including the hardware components, wiring schematics, and Bluetooth module integration. Additionally, it highlights the software development process, emphasizing the programming logic, algorithm design, and debugging techniques employed. Testing and validation phases are thoroughly documented, showcasing the system's performance under various conditions, including speed, maneuverability, and Bluetooth signal range. The results confirm the project's success in achieving its goals, offering a robust and accessible solution for educational and practical applications in robotics.

Keywords: Arduino Robot, car, microcontroller, Bluetooth communication

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Authors: P. M. Naushad Ali

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India’s strength in basic research is recognized internationally. Science and Technology research in India has been performed by six distinct bodies or organizations such as Cooperative Research Associations, Autonomous Research Council, Institute under Ministries, Industrial R&D Establishment, Universities, Private Institutions. All most all these institutions are having a well-established library/information center to cater the information needs of their users like scientists and technologists. Information Management (IM) comprises disciplines concerned with the study and the effective and efficient management of information and resources, products and services as well as the understanding of the involved technologies and the people engaged in this activity. It is also observed that the libraries and information centers in India are also using modern technologies for the management of various activities and services to serve their users in a better way. Science and Technology libraries in the country are usually better equipped because the investment in Science and Technology in the country are much larger than those in other fields. Thus, most of the Science and Technology libraries are equipped with modern IT-based tools for handling and management of library services. In spite of these facts Science and Technology libraries are having all the characteristics of a model organization where computer application is found most successful, however, the adoption of this IT based management tool is not uniform in these libraries. The present study will help to know about the level use of IT-based management tools for the information management of Science and Technology libraries in India. The questionnaire, interview, observation and document review techniques have been used in data collection. Finally, the author discusses findings of the study and put forward some suggestions to improve the quality of Science and Technology institute library services in India.

Keywords: information management, science and technology libraries, India, IT-based tools

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Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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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: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Aswathaman Vijayan, Manish Jha, Ullas Theertha

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Climate change and Global warming have become an issue for both developed and developing countries and perhaps the biggest threat to the environment. We at ITC Limited believe that a company’s performance must be measured by its Triple Bottom Line contribution to building economic, social and environmental capital. This Triple Bottom Line strategy focuses on - Embedding sustainability in business practices, Investing in social development and Adopting a low carbon growth path with a cleaner environment approach. The Agri Business Division - ILTD operates in the tobacco crop growing regions of Andhra Pradesh and Karnataka province of India. The Agri value chain of the company comprises of two distinct phases: First phase is Agricultural operations undertaken by ITC trained farmers and the second phase is Industrial operations which include marketing and processing of the agricultural produce. This research work covers the Greenhouse Gas (GHG) management strategy of ITC in the Agricultural operations undertaken by the farmers. The agriculture sector adds considerably to global GHG emissions through the use of carbon-based energies, use of fertilizers and other farming operations such as ploughing. In order to minimize the impact of farming operations on the environment, ITC has a taken a big leap in implementing system and process in reducing the GHG impact in farm value chain by partnering with the farming community. The company has undertaken a unique three-pronged approach for GHG management at the farm value chain: 1) GHG inventory at farm value chain: Different sources of GHG emission in the farm value chain were identified and quantified for the baseline year, as per the IPCC guidelines for greenhouse gas inventories. The major sources of emission identified are - emission due to nitrogenous fertilizer application during seedling production and main-field; emission due to diesel usage for farm machinery; emission due to fuel consumption and due to burning of crop residues. 2) Identification and implementation of technologies to reduce GHG emission: Various methodologies and technologies were identified for each GHG emission source and implemented at farm level. The identified methodologies are – reducing the consumption of chemical fertilizer usage at the farm through site-specific nutrient recommendation; Usage of sharp shovel for land preparation to reduce diesel consumption; implementation of energy conservation technologies to reduce fuel requirement and avoiding burning of crop residue by incorporation in the main field. These identified methodologies were implemented at farm level, and the GHG emission was quantified to understand the reduction in GHG emission. 3) Social and farm forestry for CO2 sequestration: In addition, the company encouraged social and farm forestry in the waste lands to convert it into green cover. The plantations are carried out with fast growing trees viz., Eucalyptus, Casuarina, and Subabul at the rate of 10,000 Ha of land per year. The above approach minimized considerable amount of GHG emission at the farm value chain benefiting farmers, community, and environment at a whole. In addition, the CO₂ stock created by social and farm forestry program has made the farm value chain to become environment-friendly.

Keywords: CO₂ sequestration, farm value chain, greenhouse gas, ITC limited

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Authors: Moses E. Emetere, M. L. Akinyemi

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Agricultural activities in the South–West Nigeria are mitigated by the global increase in temperature. The unpredictive surface temperature of the area had increased health challenges amongst other social influence. The satellite data of surface temperatures were compared with the ground station Davis weather station. The differential heating of the lower atmosphere were represented mathematically. A numerical predictive model was propounded to forecast future surface temperature.

Keywords: numerical predictive model, surface temperature, satellite date, ground data

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Authors: P. Tsarouhas, Ch. Achillas, D. Aidonis, D. Folinas, V. Maslis, N. Moussiopoulos

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Agricultural production is a sector with high socioeconomic significance and key implications on employment and nutritional security. However, the impacts of agrifood production and consumption patterns on the environment are considerable, mainly due to the demand of large inputs of resources. This paper presents a case study of olive oil production in Greece, an important agri-product especially for countries in the Mediterranean basin. Life Cycle Analysis has been used to quantify the environmental performance of olive oil production. All key parameters that are associated with the life cycle of olive oil production are studied and environmental “hotspots” are diagnosed.

Keywords: LCA, olive oil production, environmental impact, case study, Greece

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Authors: Nalika S. Rajapaksha, James Airton, Amina Aboobakar, Nick Chappell, Andy Dyer

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Nutrient pollution and their impact on water quality is a key concern in England. Many water quality issues originate from multiple sources of pollution spread across the catchment. The river water quality in England has improved since 1990s and wastewater effluent discharges into rivers now contain less phosphorus than in the past. However, excess phosphorus is still recognised as the prevailing issue for rivers failing Water Framework Directive (WFD) good ecological status. To achieve WFD Phosphorus objectives, Wastewater Treatment Works (WwTW) permit limits are becoming increasingly stringent. Nevertheless, in some rural catchments, the apportionment of Phosphorus pollution can be greater from agricultural runoff and other sources such as septic tanks. Therefore, the challenge of meeting the requirements of watercourses to deliver WFD objectives often goes beyond water company activities, providing significant opportunities to co-deliver activities in wider catchments to reduce nutrient load at source. The aim of this study was to apply the United Utilities' Catchment Systems Thinking (CaST) strategy and pilot an innovative permitting approach - Catchment Nutrient Balancing (CNB) in a rural catchment in Cumbria (the River Petteril) in collaboration with the regulator and others to achieve WFD objectives and multiple benefits. The study area is mainly agricultural land, predominantly livestock farms. The local ecology is impacted by significant nutrient inputs which require intervention to meet WFD obligations. There are a range of Phosphorus inputs into the river, including discharges from wastewater assets but also significantly from agricultural contributions. Solely focusing on the WwTW discharges would not have resolved the problem hence in order to address this issue effectively, a CNB trial was initiated at a small WwTW, targeting the removal of a total of 150kg of Phosphorus load, of which 13kg were to be reduced through the use of catchment interventions. Various catchment interventions were implemented across selected farms in the upstream of the catchment and also an innovative polonite reactive filter media was implemented at the WwTW as an alternative to traditional Phosphorus treatment methods. During the 3 years of this trial, the impact of the interventions in the catchment and the treatment works were monitored. In 2020 and 2022, it respectively achieved a 69% and 63% reduction in the phosphorus level in the catchment against the initial reduction target of 9%. Phosphorus treatment at the WwTW had a significant impact on overall load reduction. The wider catchment impact, however, was seven times greater than the initial target when wider catchment interventions were also established. While it is unlikely that all the Phosphorus load reduction was delivered exclusively from the interventions implemented though this project, this trial evidenced the enhanced benefits that can be achieved with an integrated approach, that engages all sources of pollution within the catchment - rather than focusing on a one-size-fits-all solution. Primarily, the CNB approach and the act of collaboratively engaging others, particularly the agriculture sector is likely to yield improved farm and land management performance and better compliance, which can lead to improved river quality as well as wider benefits.

Keywords: agriculture, catchment nutrient balancing, phosphorus pollution, water quality, wastewater

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Authors: Thiago Viana

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Eventually train accidents occur on railways and for some specific cases it is necessary to use a train rescue with a crane positioned under a platform wagon. These tumbled machines are collected and sent to the machine shop or scrap yard. In one of these cranes that were being used to rescue a wagon, occurred a fall of hoist due to fracture of two large pins. The two pins were collected and sent for failure analysis. This work investigates the main cause and the secondary causes for the initiation of the fatigue crack. All standard failure analysis procedures were applied, with careful evaluation of the characteristics of the material, fractured surfaces and, mainly, metallographic tests using an optical microscope to compare the geometry of the peaks and valleys of the thread of the pins and their respective seats. By metallographic analysis, it was concluded that the fatigue cracks were started from a notch (stress concentration) in the valley of the threads of the pin applied to the right side of the crane (pin 1). In this, it was verified that the peaks of the threads of the pin seat did not have proper geometry, with sharp edges being present that caused such notches. The visual analysis showed that fracture of the pin on the left side of the crane (pin 2) was brittle type, being a consequence of the fracture of the first one. Recommendations for this and other railway cranes have been made, such as nondestructive testing, stress calculation, design review, quality control and suitability of the mechanical forming process of the seat threads and pin threads.

Keywords: crane, fracture, pin, railway

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Authors: Tarek Ninouh, Ahmed Rouili

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The Roman paths or roads offer a very interesting archaeological material, because they allow us to understand the history of human settlement and are also factors that increase territorial identity. Roman roads are one of the hallmarks of the Roman empire, which extends to North Africa. The objective of this investigation is to attract the attention of researchers to the importance of Roman roads and paths, which are found in Algeria, according to the quality of the materials and techniques used in this period of our history, and to encourage other decision makers to protect and enhance these routes because the current urbanization, intensive agricultural practices, or simply forgotten, decreases the sustainability of this important historical heritage.

Keywords: Roman paths, quality of materials, property, valuation

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Authors: Prathiksha Kamath, Pratibha Jamkhandi, Prateek Ghanti, Priyanshu Gupta, M. Lakshmi Neelima

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Combining Image Captioning and Visual Question Answering (VQA) tasks have emerged as a new and exciting research area. The image captioning task involves generating a textual description that summarizes the content of the image. VQA aims to answer a natural language question about the image. Both these tasks include computer vision and natural language processing (NLP) and require a deep understanding of the content of the image and semantic relationship within the image and the ability to generate a response in natural language. There has been remarkable growth in both these tasks with rapid advancement in deep learning. In this paper, we present a comprehensive review of recent progress in combining image captioning and visual question-answering (VQA) tasks. We first discuss both image captioning and VQA tasks individually and then the various ways in which both these tasks can be integrated. We also analyze the challenges associated with these tasks and ways to overcome them. We finally discuss the various datasets and evaluation metrics used in these tasks. This paper concludes with the need for generating captions based on the context and captions that are able to answer the most likely asked questions about the image so as to aid the VQA task. Overall, this review highlights the significant progress made in combining image captioning and VQA, as well as the ongoing challenges and opportunities for further research in this exciting and rapidly evolving field, which has the potential to improve the performance of real-world applications such as autonomous vehicles, robotics, and image search.

Keywords: image captioning, visual question answering, deep learning, natural language processing

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Authors: Sesethu G. Njokweni, Marelize Botes, Emile W. H. Van Zyl

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An alternative strategy to the production of bioethanol is by examining the degradability of biomass in a natural system such as the rumen of mammals. This anaerobic microbial community has higher cellulolytic activities than microbial communities from other habitats and degrades cellulose to produce volatile fatty acids (VFA), methane and CO₂. VFAs have the potential to serve as intermediate products for electrochemical conversion to hydrocarbon fuels. In vitro mimicking of this process would be more cost-effective than bioethanol production as it does not require chemical pre-treatment of biomass, a sterile environment or added enzymes. The strategies of the carboxylate platform and the co-cultures of a bovine ruminal microbiota from cannulated cows were combined in order to investigate and optimize the bioconversion of agricultural biomass (apple and grape pomace, citrus pulp, sugarcane bagasse and triticale straw) to high value VFAs as intermediates for biofuel production in a consolidated bioprocess. Optimisation of reactor conditions was investigated using five different ruminal inoculum concentrations; 5,10,15,20 and 25% with fixed pH at 6.8 and temperature at 39 ˚C. The ANKOM 200/220 fiber analyser was used to analyse in vitro neutral detergent fiber (NDF) disappearance of the feedstuffs. Fresh and cryo-frozen (5% DMSO and 50% glycerol for 3 months) rumen cultures were tested for the retainment of fermentation capacity and durability in 72 h fermentations in 125 ml serum vials using a FURO medical solutions 6-valve gas manifold to induce anaerobic conditions. Fermentation of apple pomace, triticale straw, and grape pomace showed no significant difference (P > 0.05) in the effect of 15 and 20 % inoculum concentrations for the total VFA yield. However, high performance liquid chromatographic separation within the two inoculum concentrations showed a significant difference (P < 0.05) in acetic acid yield, with 20% inoculum concentration being the optimum at 4.67 g/l. NDF disappearance of 85% in 96 h and total VFA yield of 11.5 g/l in 72 h (A/P ratio = 2.04) for apple pomace entailed that it was the optimal feedstuff for this process. The NDF disappearance and VFA yield of DMSO (82% NDF disappearance and 10.6 g/l VFA) and glycerol (90% NDF disappearance and 11.6 g/l VFA) stored rumen also showed significantly similar degradability of apple pomace with lack of treatment effect differences compared to a fresh rumen control (P > 0.05). The lack of treatment effects was a positive sign in indicating that there was no difference between the stored samples and the fresh rumen control. Retaining of the fermentation capacity within the preserved cultures suggests that its metabolic characteristics were preserved due to resilience and redundancy of the rumen culture. The amount of degradability and VFA yield within a short span was similar to other carboxylate platforms that have longer run times. This study shows that by virtue of faster rates and high extent of degradability, small scale alternatives to bioethanol such as rumen microbiomes and other natural fermenting microbiomes can be employed to enhance the feasibility of biofuels large-scale implementation.

Keywords: agricultural wastes, carboxylate platform, rumen microbiome, volatile fatty acids

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Authors: Zhengkun Yi

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Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: hybrid meta-heuristic methods, substation construction, resource allocation, time-cost efficiency

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Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

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This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: flexible job shop scheduling, decision tree, priority rules, case study

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Authors: Suraj Nair, Aravindkumar Vijayalingam, Alexander Perzylo, Alois Knoll

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In the last decade robotics research and development activities have grown rapidly, especially in the domain of service robotics. Integrating service robots into human occupied spaces such as homes, offices, hospitals, etc. has become increasingly worked upon. The primary motive is to ease daily lives of humans by taking over some of the household/office chores. However, several challenges remain in systematically integrating such systems in human shared work-spaces. In addition to sensing and indoor-navigation challenges, programmability of such systems is a major hurdle due to the fact that the potential user cannot be expected to have knowledge in robotics or similar mechatronic systems. In this paper, we propose a cognitive system for service robotics which allows non-expert users to easily model system behaviour in an underspecified manner through abstract tasks and objects associated with them. The system uses domain knowledge expressed in the form of an ontology along with logical reasoning mechanisms to infer all the missing pieces of information required for executing the tasks. Furthermore, the system is also capable of recovering from failed tasks arising due to on-line disturbances by using the knowledge base and inferring alternate methods to execute the same tasks. The system is demonstrated through a coffee fetching scenario in an office environment using a mobile robot equipped with sensors and software capabilities for autonomous navigation and human-interaction through natural language.

Keywords: cognitive robotics, reasoning, service robotics, task based systems

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Authors: Rahul Paul, Kedar Nath Das

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The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models.

Keywords: machine learning, reinforcement learning, loss function, evolutionary optimization techniques

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Authors: Farnia Nayar Parshi, Mohammad Shariful Islam

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Though earth construction is an ancient technology, researchers are working on increasing its strength by adding different types of stabilizers. Ordinary Portland cement for sandy soil and lime for clayey soil is very popular practice as well as recommended by various authorities for making stabilized blocks for satisfactory performance. The addition of these additives improves compressive strength but fails to improve ductility. The addition of both synthetic and natural fibers increases both compressive strength and ductility. Studies are conducted to make earth blocks more cost-effective, energy-efficient and sustainable. In this experiment, an agricultural waste banana fiber and biochar is used to study the compressive stress-strain behavior of earth blocks made with four types of soil low plastic clay, sandy low plastic clay, very fine sand and medium to fine sand. Biochar is a charcoal-like carbon usually produced from organic or agricultural waste in high temperatures through a controlled condition called pyrolysis. In this experimental study, biochar was collected from BBI (Bangladesh Biochar Initiative) produced from wood flakes around 400 deg. Celsius. Locally available PPC (Portland Pozzolana Cement) is used. 5 cm × 5 cm × 5 cm earth blocks were made with eight different combinations such as bare soil, soil with 6% cement, soil with 6% cement and 5% biochar, soil with 6% cement, 5% biochar and 1% fiber, soil with 1% fiber, soil with 5% biochar and 1% fiber and soil with 6% cement and 1% fiber. All samples were prepared with 10-12% water content. Uniaxial compressive strength tests were conducted on 21 days old earth blocks. Stress-strain diagram shows that the addition of banana fiber improved compressive strength drastically, but the combined effect of fiber and biochar is different based on different soil types. For clayey soil, 6% cement and 1% fiber give maximum compressive strength of 991 kPa, and for very fine sand, a combination of 5% biochar, 6% cement and 1% fiber gives maximum compressive strength of 522 kPa as well as ductility. For medium-to-find sand, 6% cement and 1% fiber give the best result, 1530 kPa, among other combinations. The addition of fiber increases not only ductility but also compressive strength as well. The effect of biochar with fiber varies with the soil type.

Keywords: banana fiber, biochar, cement, compressed stabilized earth blocks, compressive strength

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Authors: Seyedmilad Kazemisangdehi, Seyedmehdi Kazemisangdehi

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Flux switching permanent magnet machines are considered for wide range of applications because of their outstanding merits including high torque/power densities, high efficiency, simple and robust rotor structure. Therefore, several topologies have been proposed like the PM exited flux switching machine, hybrid excited flux switching type, and so on. Recently, a novel laminated segmented rotor flux switching permanent magnet machine was introduced. It features flux barriers on rotor structure to enhance the performances of machine including torque ripple reduction and also torque and efficiency improvements at the same time. This is while, the design of barriers was not optimized by the authors. Therefore, in this paper three coefficients regarding the position of the barriers are considered for optimization. The effect of each coefficient on the performance of this machine is investigated by finite element method and finally an optimized design of flux barriers based on these three coefficients is proposed from different points of view including electromagnetic torque maximization and cogging torque/torque ripple minimization. At optimum design from maximum developed torque aspect, this machine generates 0.65 Nm torque higher than that of the not-optimized design with an almost 0.4 % improvement in efficiency.

Keywords: finite element analysis, FSPM, laminated segmented rotor flux switching permanent magnet machine, optimization

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Authors: Chuan-Ming Tung, Jin-Cheng Fu, Chia-En Feng

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In recent years, the rapid development of urban land in Taiwan has led to the constant increase of the areas of impervious surface, which has increased the risk of waterlogging during heavy rainfall. Therefore, in recent years, promoting runoff allocation responsibilities has often been used as a means of reducing regional flooding. In this study, the single catchment area covering both urban and rural land as the study area is discussed. Based on Storm Water Management Model, urban and rural land in a single catchment area was explored to develop the runoff allocation responsibilities according to their respective control regulation on land use. The impacts of runoff increment and reduction in sub-catchment area were studied to understand the impact of highly developed urban land on the reduction of flood risk of rural land at the back end. The results showed that the rainfall with 1 hour short delay of 2 years, 5 years, 10 years, and 25 years return period. If the study area was fully developed, the peak discharge at the outlet would increase by 24.46% -22.97% without runoff allocation responsibilities. The front-end urban land would increase runoff from back-end of rural land by 76.19% -46.51%. However, if runoff allocation responsibilities were carried out in the study area, the peak discharge could be reduced by 58.38-63.08%, which could make the front-end to reduce 54.05% -23.81% of the peak flow to the back-end. In addition, the researchers found that if it was seen from the perspective of runoff allocation responsibilities of per unit area, the residential area of urban land would benefit from the relevant laws and regulations of the urban system, which would have a better effect of reducing flood than the residential land in rural land. For rural land, the development scale of residential land was generally small, which made the effect of flood reduction better than that of industrial land. Agricultural land requires a large area of land, resulting in the lowest share of the flow per unit area. From the point of the planners, this study suggests that for the rural land around the city, its responsibility should be assigned to share the runoff. And setting up rain water storage facilities in the same way as urban land, can also take stock of agricultural land resources to increase the ridge of field for flood storage, in order to improve regional disaster reduction capacity and resilience.

Keywords: runoff allocation responsibilities, land use, flood mitigation, SWMM

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Micheal O. Alabi

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Additive manufacturing (AM) is the official industry standard term (ASTM F2792) for all applications of the technology which is also known as 3D printing technology. It is defined as the process of joining materials to make objects from 3D model data, and it is usually layer upon layer, as opposed to subtractive manufacturing methodologies. This technology has gained significant interest within the academic, research institute and industry because of its ability to create complex geometries with customizable material properties. Despite the late adoption of the technology, additive manufacturing has been active in South Africa for past 21 years and it is predicted that additive manufacturing technology will play a significant and game-changing role in the fourth industrial revolution and in particular it promises to play an ever-growing role in efforts to re-industrialize the economy of South Africa. At the end of 2006, there are approximately ninety 3D printers in South Africa and in 2015 it was estimated that there are 3500 additive manufacturing systems and 3D printers in circulation in South Africa. A reasonable number of these additive manufacturing machines are in the high end of the market, in science councils and higher education institutions and this shows that the future of additive manufacturing in South Africa is very brighter compared to other African countries. This paper reviews the past and current industrial applications of additive manufacturing in South Africa from the academic research and industry perspective and what are the benefits of this technology to manufacturing companies and industrial sectors in the country.

Keywords: additive manufacturing, 3D printing technology, industrial applications, manufacturing

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Authors: Ahmad Akrad, Rabia Sehab, Fadi Alyoussef

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Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults.

Keywords: induction machine, asymmetric nonlinear model, fault diagnosis, inter-turn short-circuit fault, root mean square, current sensor fault, fault detection and isolation

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Authors: Sawarni Hasibuan, Rudi Effendi Listyanto

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The industrial sector is the largest consumer of energy consumption in Indonesia. The ceramics industry includes one of seven industries categorized as an energy-intensive industry. Energy costs on the ceramic floor production process reached 40 percent of the total production cost. The kiln is one of the machines in the ceramic industry that consumes the most gas energy reach 51 percent of gas consumption in ceramic production. The purpose of this research is to make improvement of energy consumption in kiln machine part with the innovation of burner tube to support the sustainability of Indonesian ceramics industry. The tube burner is technically designed to be able to raise the temperature and stabilize the air pressure in the burner so as to facilitate the combustion process in the kiln machine which implies the efficiency of gas consumption required. The innovation of the burner tube also has an impact on the decrease of the combustion chamber pressure in the kiln and managed to keep the pressure of the combustion chamber according to the operational standard of the kiln; consequently, the smoke fan motor power can be lowered and the kiln electric energy consumption is also more efficient. The innovation of burner tube succeeded in saving consume of gas and electricity respectively by 0.0654 GJ and 1,693 x 10-3 GJ for every ton of ceramics produced. Improvement of this energy consumption not only implies the cost savings of production but also supports the sustainability of the Indonesian ceramics industry.

Keywords: sustainable ceramic industry, burner tube, kiln, energy efficiency

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Emma O’ Keeffe, Helen Hughes, Peter McLoughlin, Shiau Pin Tan, Nick McCarthy

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Biosecurity is emerging as one of the most important issues facing the agricultural and forestry community. This is as a result of increased invasion from new pests and diseases with the main protocol for dealing with these species being the use of synthetic pesticides. However, these chemicals have been shown to exhibit negative effects on the environment. Seaweeds represent a vast untapped resource of bio-molecules with a broad range of biological activities including pesticidal. This project investigated both the antifungal and antibacterial activity of seaweed species against two problematic root rot fungi, Armillaria mellea and Heterobasidion annosum and ten quarantine bacterial plant pathogens including Xanthomonas arboricola, Xanthomonas fragariae, and Erwinia amylovora. Four seaweed species were harvested from the South-East coast of Ireland including brown, red and green varieties. The powdered seaweeds were extracted using four different solvents by liquid extraction. The poisoned food technique was employed to establish the antifungal efficacy, and the standard disc diffusion assay was used to assess the antibacterial properties of the seaweed extracts. It was found that extracts of the green seaweed exhibited antifungal activity against H. annosum, with approximately 50% inhibition compared to the negative control. The protectant activities of the active extracts were evaluated on disks of Picea sitchensis, a plant species sensitive to infection from H. annosum and compared to the standard chemical control product urea. The crude extracts exhibited very similar activity to the 10% and 20% w/v concentrations of urea, demonstrating the ability of seaweed extracts to compete with commercially available products. Antibacterial activity was exhibited by a number of seaweed extracts with the red seaweed illustrating the strongest activity, with a zone of inhibition of 15.83 ± 0.41 mm exhibited against X. arboricola whilst the positive control (10 μg/disk of chloramphenicol) had a zone of 26.5 ± 0.71 mm. These results highlight the potential application of seaweed extracts in the forestry and agricultural industries for use as biopesticides. Further work is now required to identify the bioactive molecules that are responsible for this antifungal and antibacterial activity in the seaweed extracts, including toxicity studies to ensure the extracts are non-toxic to plants and humans.

Keywords: antibacterial, antifungal, biopesticides, seaweeds

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Authors: M. Hisham, S. Cabemaiwai, S. Prasad, T. Dauvakatini, R. Ananthanarayanan

Abstract:

A solenoid valve is an important part of many fluid systems. Its purpose is to regulate fluid flow in a machine. Due to the crucial role of the solenoid valve and its design intricacy, it is quite expensive to obtain in Fiji and is not manufactured locally. A concern raised by the local health industry is that the housing of the solenoid valve gets damaged when machines are continuously being used and this part of the valve is very costly to replace due to the lack of availability in Fiji and many other South Pacific region countries. This study explores the agile manufacturing of a solenoid coil housing using the Fused Deposition Modeling (FDM) process. An emergy study was carried out to analyze the feasibility and sustainability of producing the part locally after estimating a Unit Emergy Value (or emergy transformity) of 1.27E+05 sej/j for the electricity in Fiji. The total emergy of the process was calculated to be 3.05E+12 sej, of which a majority was sourced from imported services and materials. Renewable emergy sources contributed to just 16.04% of the total emergy. Therefore, the part is suitable to be manufactured in Fiji with a reasonable quality and a cost of $FJ 2.85. However, the loading on the local environment is found to be significant and therefore, alternative raw materials for the filament like recycled PET should be explored or alternative manufacturing processes may be analyzed before committing to fabricating the part using FDM in its analyzed state.

Keywords: emergy analysis, fused deposition modeling, solenoid valve housing, sustainable production

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