Assessment Of Technologies Research Articles

Integration Assessment of Renewable Energy Sources (RESs) and Hydrogen Technologies in Fish Farms: A Techno-Economical Model Dispatch for an Estonian Fish Farm

A fundamental aspect of fish farms is their energy consumption, which is essential for various activities like water supply, pool aeration, thermal conditioning, lighting, filtration, and recirculation systems. Due to volatile prices and rising energy use, costs have surged, requiring energy-optimization solutions for economic viability and pollution reduction. In this context, this study aims to evaluate renewable energy integration in these installations based on real data, assessing current operations, proposing renewable energy optimization, and exploring hydrogen systems for energy needs, using HOMER PRO® to analyze different scenarios. For this purpose, it targets a rainbow trout farm in Estonia, and by simulating the various hybrid configurations proposed, it aims to optimize its energy production and storage, ensuring feasibility and technical integration. The results of the simulations primarily demonstrate the potential for using the byproduct of electrolysis to cover the oxygen demand in these types of processes, reducing the demand for raw materials. Additionally, it is observed that storage enhances performance in isolated systems; however, the economically viable integration of hydrogen technology requires three assumptions: a regulatory framework allowing surplus energy sales to the grid, an existing infrastructure for hydrogen trading, and high energy purchase prices.

Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration

Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration

Reflections on co-creating a model for the value assessment of artificial intelligence technologies.

A multidisciplinary group of experts and patients developed the Model for ASsessing the value of Artificial Intelligence (MAS-AI) to ensure an evidence-based and patient-centered approach to introducing artificial intelligence technologies in healthcare. In this article, we share our experiences with meaningfully involving a patient in co-creating a research project concerning complex and technically advanced topics. The co-creation was evaluated by means of initial reflections from the research team before the project started, in a continuous logbook, and through semi-structured interviews with patients and two researchers before and after the active co-creation phase of the project. There were initial doubts about the feasibility of including patients in this type of project. Co-creation ensured relevance to patients, a holistic research approach and the debate of ethical considerations. Due to one patient dropping out, it is important to foresee and support the experienced challenges of time and energy spent by the patient in future projects. Having a multidisciplinary team helped the collaboration. A mutual reflective evaluation provided insights into the process which we would otherwise have missed. We found it possible to create complex and data-intense research projects with patients. Including patients benefitted the project and gave researchers new perspectives on their own research. Mutual reflection throughout the project is key to maximise learning for all parties involved.

Enzymatic CO2 Capture in intensified packed-bed column bioreactors: A techno-economic assessment

To reduce the impact of industrial carbon dioxide (CO2) emissions on climate change, energy-efficient and cost-effective carbon capture technologies are needed. With this in mind, our research group has proposed an intensified enzyme-mediated CO2 capture process in packed-bed column bioreactors with carbonic anhydrase (CA) immobilized on the packing surface and on magnetic nanoparticles (MNPs). This study, in continuation of our efforts to amend these processes, aims to showcase a techno-economic assessment (TEA) of this promising technology in an industrial-scale context and to emphasize the inherent benefits of intensification. The TEA results suggest that this enzyme-mediated technology has the potential to reduce capture costs, chiefly due to the possibility of utilizing an alternative solvent that requires less energy to regenerate, while also being non-corrosive and non-toxic. The capture process consumes 39.8% less energy than the benchmark process utilizing a MEA-based solution. Immobilizing the enzyme on MNPs has tangible benefits, such as enhancing mass transfer and reducing the equipment footprint, which improves flexibility and contributes to the viability of the industrial process. The intensified enzyme-mediated CO2 capture process achieves a 28.6% reduction in capture cost compared to the benchmark technology. Sensitivity analyses were conducted to highlight the impact of key parameters on the overall cost-effectiveness of the technology, providing beneficial insights for further improvement. The results indicate that it is crucial to maximize enzyme lifespan and minimize energy consumption. The liquid flow rate and buffer concentration are additional constraints that should be thoughtfully considered to ensure the feasibility of this process.

Mechanical Assessment of Denture Polymers Processing Technologies.

Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. The compressive strength of dentures manufactured using various technologies will be investigated. Traditional, injection molding, and additive and subtractive CAD/CAM processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a compressive mechanical test, which reveals the differences in compressive strength. All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded, as well as the breaking energy. The mean force required to break the dentures for each processing technology is as follows: 4.54 kN for traditional packing-press technique, 17.92 kN for the injection molding technique, 1.51 kN for the additive CAD/CAM dentures, and 5.9 kN for the subtractive CAD/CAM dentures. The best results were obtained in the case of the thermoplastic injection system and the worst results were recorded in the case of 3D printed samples. Another important aspect depicted is the standard deviation for each group, which reveal a relatively unstable property for the thermoplastic injected dentures. Good results here in terms of absolute property and stability of the property can be conferred to CAD/CAM milled group.

Exploring fecal sludge treatment technologies in humanitarian settings at Cox’s Bazar, Bangladesh: a comprehensive assessment of treatment efficiency through characterization of fecal sludge

IntroductionEfficient treatment of fecal sludge in densely populated settings is essential as it has a direct impact on public health and the environment. This study presents a comprehensive assessment of fecal sludge treatment technologies in Rohingya camps at Cox’s Bazar, Bangladesh, focusing on removal efficiencies and compliance with regulatory standards.MethodsSeventeen treatment plants of five different technologies were evaluated based on removal efficiency and standard discharge guidelines for various physicochemical and microbiological parameters.ResultsWaste Stabilization Pond (WSP) was the top performer compared to four other different treatment technologies evaluated, achieving notable removal rates: 97.3% reduction in E. coli, 100% in helminth eggs, 98.3% for COD, 97.8% for BOD, 98.7% for TSS, 92.1% for TS, 82.8% for phosphate, and 93.3% for total nitrogen. Lime Stabilization Ponds showed lower removal rates, except for E. coli (98.9%), with reductions of 99.7% for helminth eggs, 81.6% for COD, 80.9% for BOD, 86.3% for TSS, 68.6% for TS, and 49.2% for phosphate. Upflow Filters demonstrated good removal efficiencies for E. coli (99.7%), TSS (95.9%), COD (91.7%), BOD (93.5%), and helminth eggs (93.7%). WSP consistently outperformed other technologies across all seasons. Despite these, none of the technologies fully met discharge standards.DiscussionThese findings highlight the need for a comprehensive approach, the combination of physicochemical and biological processes, to enhance efficacy. Promoting improved fecal sludge management technologies through awareness campaigns and technical support can mitigate environmental health risks in densely populated humanitarian settings.

Usability Assessment of Voice-Enabled Technologies for Users with Visual Disabilities

Voice-enabled technologies such as VoiceOver (screen reader) and the Seeing AI app (image recognition) have revolutionized daily tasks for people with visual disabilities, fostering greater independence and information access. However, a gap remains in understanding the user experience (UX) of these technologies. This study investigated how those with visual disabilities interacted with VoiceOver and the Seeing AI app. A convenience sample of eight participants with visual disabilities engaged in direct observations while using these technologies. The study utilized the System Usability Scale (SUS) to assess perceived usability and analyzed findings using descriptive statistics. Results indicated a poorer UX with VoiceOver compared to the Seeing AI app, with challenges identified in graphical user interfaces (GUIs), voice and gesture commands. Relevant recommendations were made to enhance usability. The study emphasizes the need for more intuitive GUIs and optimized voice/gesture interactions for users with visual disabilities.

A Novel Suture-Based Vascular Closure Device to Achieve Hemostasis after Venous or Arterial Access While Leaving Nothing behind: A Review of the Technological Assessment and Early Clinical Outcomes.

Vascular hemostasis after venous and arterial access in cardiovascular procedures remains a challenge. As sheath size gets larger for structural heart and vascular procedures, no dedicated closure devices exist that can overcome all the challenges of achieving vascular hemostasis, in particular on the venous side. Efficiently and reliably ensuring hemostasis at the access point is crucial for enhancing the safety of a procedure. Historically, hemostasis relied on manually compressing venous access sites. However, the shift towards larger sheaths and the more frequent use of continuous anticoagulation has strained this approach. Achieving hemostasis solely through compression in these scenarios demands heightened vigilance and prolonged application, resulting in increased patient discomfort and extended immobility. Consequently, manual compression may consume more time for healthcare providers and contribute to bed occupancy in hospitals. This review article summarizes the development of the SiteSeal® Vascular Closure Device, a novel leave-nothing-behind approach to achieve hemostasis. The introduction of this technology has provided clinicians with a safer and more effective way to achieve immediate hemostasis, facilitate early ambulation, and enable earlier discharges with fewer access site complications compared with traditional manual compression.

CYBERSECURITY ON IOT SYSTEMS BASED MULTI-LAYERED SECURITY RISK ASSESSMENT

IoT systems contribute to digital transformation through the development of smart concepts. However, the IoT has also generated new security challenges that require security tools to be adapted, such as risk analysis methodologies. Many organizations use a variety of methods and frameworks to assess cybersecurity risks. However, most current risk assessment methods are for generic software systems, so there is no holistic approach to risk assessment for IoT technologies, especially due to the diversity of IoT systems The purpose of our study is based on the following question: What IoT device factors should be considered in cybersecurity risk assessment methods? In this study we first propose a cybersecurity risk assessment model based on vectors such as vulnerabilities and attacks, which evaluates the security risk from different dimensions of physical layer, network layer and application layer. Secondly, we design the mathematical assessment model for computing risk value of IoT system and then establish the mapping relationship table that the risk value is transformed into risk level.

Assessment of kharif oilseed crop varieties and technologies disseminated through cluster frontline demonstrations in Chhattisgarh

Assessment of kharif oilseed crop varieties and technologies disseminated through cluster frontline demonstrations in Chhattisgarh

A review on environmental assessment of thermochemical conversion technologies for energy generation from crop residue

A review on environmental assessment of thermochemical conversion technologies for energy generation from crop residue

The technological assessment of green buildings using artificial neural networks

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Comparative life cycle assessment (LCA) of pre-treatment technologies for desalination in South Africa

In the context of South Africa’s water scarcity, desalination has emerged as a possible solution for coastal areas. However, the quality of the intake water for desalination is often problematic, prompting the need for pre-treatment. The aim of this study was to conduct a comparative environmental life cycle assessment (LCA) on 4 seawater filtration systems intended for the pre-treatment of a reverse osmosis desalination project. These systems were implemented in a pilot trial and are based on modern water treatment technologies, namely, granular filtration (pressure driven and gravity driven), dissolved air flotation (DAF), and ultrafiltration (UF). For all 4 systems, data were collected for both the construction and operation phases, and LCAs were performed, resulting in environmental scores that allow for comparison based on the pre-treatment of 1 kL of seawater of the same quality. The SimaPro LCA tool and the ReCiPe midpoint method were used and environmental scores were calculated for 18 impact categories, including climate change, acidification, toxicity, eutrophication, resource depletion, etc. This methodology also allowed the identification of the highest environmental burdens/scores within each system. The most significant finding is that local electricity consumption is responsible for the greatest proportion of environmental impacts. Thus, the systems consuming more energy for operating equipment such as blowers, pumps, and mixers were found to have the highest environmental burdens. Hence, the DAF system has the highest environmental scores for most impacts, followed by the single-phase gravity filtration system, then the two-phase partial pressure filtration system and finally the UF system. Therefore, focus should shift towards energy optimisation of process units, especially the rotary ones, as well as energy mitigation and recovery strategies. The use of renewable energy for pre-treatment should also be considered locally.

Holistic assessment of advanced technology for ultrafine particle filtration in car cabins

Requirements on cabin air quality are constantly increasing. The objective is to protect the passengers from ultrafine particles and harmful gases, particularly in small volumes such as car cabins, where pollution is more concentrated in absence of any filtration strategy. It is necessary to extend the single filter approach and combine it with advanced filtration technologies (high separation efficiency), and thus create an effective multistage filtration system. The investigation work is built around a holistic approach. A complete 1D-simulation model has been calibrated with experimental results from a dedicated test rig and an electric vehicle. The results showed a significant improvement of particle level in the cabin with the advanced filtration system. Additionally, an appropriate ventilation strategy has been implemented to deal with the air entering the cabin by other means than the blower operation. This “infiltration”, triggered by vehicle speed, allows pollution to enter the cabin without any filtration stage.

Criteria and assessment of assistive technologies for ageing-in-place

Criteria and assessment of assistive technologies for ageing-in-place

Measuring corporate digital divide through websites: insights from Italian firms

With the increasing pervasiveness of Information and Communication Technology (ICT) in the fabric of economic activities, the corporate digital divide has become a crucial issue for the assessment of Information Technology (IT) competencies and the digital gap between firms and territories. With little granular data available to measure the phenomenon, most studies have used survey data. To address this empirical gap, we scanned the homepages of 182,705 Italian companies and extracted ten characteristics related to their digital footprint to develop a new index for the corporate digital assessment. Our results show a significant digital divide between Italian companies according to size, sector and geographical location, opening new perspectives for monitoring and data-driven analysis.

Assessment of hydrogen production technologies from agricultural residues in Vietnam

Green hydrogen is one of the alternative options to decarbonize the energy sector and limit the global temperature rise below 1.5 °C. Vietnam has various biomass resources, including agricultural and forestry by-products. However, this country has not had any research to evaluate the suitability of hydrogen production technologies using biomass sources in general and agricultural residues in particular with all relevant factors as a basis for investment and development. The paper assesses three green hydrogen production technologies from agricultural residue, including pyrolysis, gasification, and fermentation, by simultaneously applying the Strength Weakness Opportunity Threat (SWOT) analysis and Analytic Hierarchy Process (AHP). The hydrogen production technologies and agricultural residues in Vietnam are used as case studies. The obtained results on the weighting factors indicate that the economic indicator accounts for 50.7 %, followed by the technical indicator at 27.8 % and the environmental indicator at 21.5 %. Among the hydrogen production technologies, gasification is the most prioritized technology, at 43.3 %. The priorities of pyrolysis and fermentation are not considerably different, at 29.6 % and 27.1 %, respectively. Rice straw has the highest hydrogen production capacity with a total potential of about 50.43 billion m3 while the lowest total hydrogen production potential is cassava pomace with a value of 0.26 billion m3. Designing and simulating the hydrogen production process from agricultural waste using selected technologies will be studied in further work to finalize and verify the used assumptions, methods, and information in this study.

Quantitative assessment of machine learning reliability and resilience.

Advances in machine learning (ML) have led to applications in safety-critical domains, including security, defense, and healthcare. These ML models are confronted with dynamically changing and actively hostile conditions characteristic of real-world applications, requiring systems incorporating ML to be reliable and resilient. Many studies propose techniques to improve the robustness of ML algorithms. However, fewer consider quantitative techniques to assess changes in the reliability and resilience of these systems over time. To address this gap, this study demonstrates how to collect relevant data during the training and testing of ML suitable for the application of software reliability, with and without covariates, and resilience models and the subsequent interpretation of these analyses. The proposed approach promotes quantitative risk assessment of ML technologies, providing the ability to track and predict degradation and improvement in the ML model performance and assisting ML and system engineers with an objective approach to compare the relative effectiveness of alternative training and testing methods. The approach is illustrated in the context of an image recognition model, which is subjected to two generative adversarial attacks and then iteratively retrained to improve the system's performance. Our results indicate that software reliability models incorporating covariates characterized the misclassification discovery process more accurately than models without covariates. Moreover, the resilience model based on multiple linear regression incorporating interactions between covariates tracks and predicts degradation and recovery of performance best. Thus, software reliability and resilience models offer rigorous quantitative assurance methods for ML-enabled systems andprocesses.

Assessment of Production Technologies on Dairy Farms in Terms of Animal Welfare

Assessment of Production Technologies on Dairy Farms in Terms of Animal Welfare

Assessment of Metaverse wearable technologies for smart livestock farming through a neuro quantum spherical fuzzy decision-making model

Livestock wearable technologies are innovations designed to ensure livestock health management. However, the user aspect of these devices from farmers’ perspective is still questionable. Additionally, livestock wearables are still in progress compared to the other wearables. Thus, this research aims to identify key design features regarding wearable smart collars (WSCs) and rank the alternative WSC prototypes within Metaverse, allowing farmers to select the best wearable device. To this end, an integrated neuro quantum spherical fuzzy multi-criteria decision-making (MCDM) framework is introduced via facial expressions to obtain the priority weights of WSC criteria with the improved decision-making trial and evaluation laboratory (DEMATEL) approach and to rank the WSC alternatives in Metaverse through the improved multi-objective optimization based on ratio analysis (MOORA) model. The novelties of this research are: (1) to build and introduce a novel decision support tool based on facial expressions, expert recommendations, and the quantum spherical fuzzy sets, (2) to guide industrial designers about the essential features of WSCs, whereas they are designing these devices, and (3) to help smallholder farmers to decide on the best WSC to enhance animal welfare and efficiency of animal production. Concerning the findings, “sound and stress analyzer” is the most significant feature, followed by “disease detection” and “price.” Moreover, Prototype 3 is the best WSC for farmers to adopt for livestock health management. Some essential implications are further presented.