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A Novel Multiple-Input Single-Output Current-Mode Shadow Filter and Shadow Oscillator Using Current-Controlled Current Conveyors
(2024-09-01)
Kumngern, Montree
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Khateb, Fabian
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Kulej, Tomasz
This paper presents a novel multiple-input single-output current-mode shadow filter and oscillator using current-controlled current conveyors (CCCIIs). The CCCII current gains are used to set the filter’s quality factor and natural frequency. The filter is resistorless with grounded capacitors, making it more suitable for integration. The filter offers low-pass, high-pass, band-pass, band-stop and all-pass transfer functions in a single topology by appropriately applying the input signals. To validate the functionality of the new topology, the proposed circuit is simulated by SPICE using bipolar transistors arrays AT&T ALA400-CBIC-R.
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Adaptive Tap-Length Based Sub-band Mean M-Estimate Filtering for Active Noise Cancellation
(2024-09-01)
Kar, Asutosh
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Shoba, S.
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Burra, Srikanth
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Goel, Pankaj
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Kumar, Sanjeev
Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter’s structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter’s length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter’s length. In order to maximize the filter’s efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method’s variable tap length variant, and the sub-band adaptive filtering. The simulation’s findings and recommendations are supported.
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An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages
(2024-09-01)
Pinthurat, Watcharakorn
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Surinkaew, Tossaporn
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Hredzak, Branislav
The paper's state-of-the-art review focuses on an in-depth evaluation of smart home energy management systems which employ reinforcement learning-based methods to integrate energy storages. In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects of integrating energy storages into smart homes. Second, an overview of a smart home system and a theoretical background of reinforcement learning-based algorithms are given and discussed. Consequently, this study delves into the challenges and benefits of integrating energy storage, specifically looking at ways to lessen the impact of renewable sources’ intermittency, improve grid stability, and streamline efficient energy storage management. Thirdly, the paper highlights the beneficial features of smart home energy storage integration, including reduced costs, increased system resilience, and improved energy efficiency. Therefore, cutting-edge reinforcement learning-based methods utilized in smart home energy management systems that incorporate energy storage are thoroughly examined by evaluating their effectiveness and adaptability, taking into account both multi-agent and single-agent reinforcement learning-based methods. Finally, the study identifies potential research directions, including the development of hybrid reinforcement learning algorithms, integration of demand-side management strategies, and addressing privacy and security concerns in reinforcement learning-based smart home energy management systems. While some research has made use of single-agent reinforcement learning, smart home energy storage systems that use energy storages seldom use multi-agent reinforcement learning techniques. Researchers, practitioners, and policymakers will be able to use this work as a foundation to build smart, sustainable home energy systems.
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Drying effects on physicochemical and functional properties of cuttlefish (Sepia officinalis) ink powder
(2024-09-01)
Salsabila, Salma
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Kerdpiboon, Soraya
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Kerr, William L.
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Puttongsiri, Tongchai
This study investigated the physicochemical, functional, and organoleptic properties of cuttlefish (Sepia officinalis) ink powder prepared by freeze drying and hot air tray drying. Key proximate and physicochemical properties were measured to assess differences due to drying method. In addition, organoleptic tests were conducted on cuttlefish ink powder incorporated into spaghetti sauce. Freeze-dried powders had slightly higher moisture (9.86 %) than tray-dried powders (7.65 %) and more ash (15.04 versus 13.19 %). Both samples had ∼43 % protein, a light-brown to black color and a pH of 5.5–5.6 when rehydrated. There were no differences in loose or tapped density and Hausner ratios of 1.30–1.35 suggest the powders had ‘passable’ flow. Overall protein solubility varied with pH and freeze-dried materials generally had greater solubility. FT-IR showed no differences in peaks derived from organic components. FE-SEM showed both samples had spherical granules that formed 100–200 nm clusters; however, the surface texture of tray-dried powders was rougher than the freeze-dried samples. Consumer tests showed higher overall likability for spaghetti sauce with fresh ink compared to dried ink powders. Differences were attributed more to texture than to flavor or appearance. Cuttlefish ink can be utilized in the food industry, particularly as a natural flavor, food coloring or additive.
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Exploring the knowledge system in building information modeling (BIM) technology for medical construction projects (MCPS): A bibliometric analysis and prospective direction
(2024-09-01)
Chen, Jiahao
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Laokhongthavorn, Laemthong
This paper investigates the knowledge system in Building Information Modeling (BIM) technology in Medical Construction Projects (MCPs). Specifically, it proposes knowledge graph analysis utilizing scient metric techniques and employs VOSviewer and CiteSpace software to analyze the consequence of relevant statistics from the literature concerning BIM medical construction projects (BIM-MCPs). In order to analyze the collaboration network, the analysis of the co-occurrence network, and the analysis of the co-citation network, 83 journal articles were gathered. The results obtained from the research indicate that (a) the intellectual framework of BIM-MCPs can be categorized into four distinct clusters. (b) The research focus in BIM-MCPs has evolved, including model development, frameworks, implementation, system integration, application throughout the entire lifecycle, emergency medical engineering project applications, and intelligent management of medical facilities and smart buildings. (c) Prospective directions for research are delineated in three distinct areas: enhancement of use for BIM Medical Facility Management (BIM-MFM), integration of BIM technology in sustainability assessment, and application of BIM technology in emergency medical engineering and smart medical construction.
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Co-transplantation of phyllosphere and rhizosphere microbes promotes microbial colonization and enhances sugarcane growth
(2024-09-01)
Khoiri, Ahmad Nuruddin
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Wattanachaisaereekul, Songsak
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Jirakkakul, Jiraporn
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Sutheeworapong, Sawannee
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Kusonmano, Kanthida
Increasing evidence suggests that microbes colonizing both above- and below-ground parts of plants are critical for plant growth and agricultural productivity. However, the extent to which the interactions between above- and below-ground microbial communities affect microbial colonization and plant performance is not fully understood. To address this question, we performed a phyllosphere and rhizosphere microbiome transplantation experiment (MT-Exp) using sugarcane, followed by microbiome profiling with 16S rRNA amplicon sequencing. Plant phenotypic observation exhibited that transplanting field-grown sugarcane phyllosphere and/or rhizosphere onto sugarcane plantlets in a controlled growth condition significantly increased total biomass compared to un-transplanted plants. Notably, co-transplantation of phyllosphere (P) and rhizosphere (R) microbiomes (PR treatment) resulted in the most pronounced plant growth promotion. A comparison of alpha diversity revealed that microbiome transplantation enhanced the species richness of both phyllosphere and rhizosphere communities. Additionally, PERMANOVA results showed that microbiome transplantation strongly impacted the rhizosphere microbial community, while no significant differences were observed in the phyllosphere microbes. The PR treatment was found to be more effective in introducing a greater diversity of microbial taxa than single transplantation (P and R treatments), of which many of these transplanted taxa were recognized as plant growth-promoting bacteria (PGPB). Furthermore, the PR network displayed denser connectivity between above- and below-ground components compared to all other treatments. In conclusion, our findings highlight the interdependence of above-ground and below-ground microorganisms, demonstrating their indispensable role in colonizing and promoting sugarcane growth.
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An integrated AHP-TOPSIS approach for bamboo product evaluation and selection in rural communities
(2024-09-01)
Chanpuypetch, Wirachchaya
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Niemsakul, Jirawan
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Atthirawong, Walailak
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Supeekit, Tuangyot
This study introduces a decision support model integrating the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select an economic tree product champion (bamboo) to benefit rural communities. An extensive literature review and expert discussions identified sixteen sub-criteria distributed across five main criteria. The study proposes four categories of bamboo products as alternatives, emphasizing community-level production capacity. The AHP determines priority weights, while TOPSIS prioritizes alternatives conducive to becoming the product champion within a community case study. The findings affirm the efficacy of Multi-Criteria Decision-Making (MCDM) in identifying a champion, with “Value addition potential,” “Domestic market demand,” and “International market (export) demand” identified as pivotal criteria. Bamboo culm-based products for energy-related applications emerged as the chosen product champion in a community case study in Thailand. This study offers practical implications for rural communities and potential investors in economic tree ventures, allowing the customization of decision criteria and alternatives for specific contexts. Socially, the focus on bamboo highlights diverse benefits along the entire supply chain, from upstream to downstream. The research pioneers a decision support model, providing insights into market opportunity analysis and supply chain network design based on the selected product champion.
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Exploring the influence of live streaming on consumer purchase intention: A structural equation modeling approach in the Chinese E-commerce sector
(2024-09-01)
Yang, Gu
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Chaiyasoonthorn, Wornchanok
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Chaveesuk, Singha
The e-commerce landscape is evolving rapidly, and businesses seek novel ways to enhance their competitive edge. In this context, understanding consumers' purchase intentions becomes crucial. Drawing from the Stimulus-Organism-Response (SOR) theory, we propose a conceptual framework investigating the factors influencing purchase intention in e-commerce live-streaming marketing. The research adopts Structural Equation Modeling (SEM), Confirmatory Factor Analysis (CFA), and hypothesis testing to evaluate the relationships. The findings revealed that the attributes of live streaming, including the live streamer, the product, and the field, as well as trust and impulsiveness, positively influence purchase intention. This study provides a novel perspective on live streaming e-commerce with evidence on how e-commerce live streaming drives purchase intentions, enriches the content of live streaming e-commerce literature, and explores the practical implications for marketing managers who are looking for marketing by live streaming.
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Exploring the adoption of photovoltaic cleaning robots: An institutional management approach
(2024-09-01)
Promvongsanon, Rubporn
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Sawmong, Sudaporn
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Khalid, Bilal
There is increased adoption of renewable energy, particularly photovoltaic (PV), as a major source of clean and environmentally friendly energy. With the wide application of PV technologies, commonly called solar panels, their appropriate maintenance and cleaning are vital for optimum PV energy generation. The research focuses on exploring the various factors attributed to influence the want, intention and need to use PV panel cleaning robots in Thailand from an institutional management perspective. The study adopted the extended C-TAM-TPB by adding three more variables-Trust in technology, awareness of renewable energy, and environmental concern. A quantitative approach was used, where primary data was collected from PV institutional users. A population sample of 411 respondents was used. The confirmatory factor analysis (CFA) came in handy in evaluating the performance model, with the study's hypotheses being evaluated by the structural equation modeling (SEM). Results indicated that most of the subjective behaviors, perceived behavioral controls, and trust in technology played a key role in determining the intention and need to use solar panel cleaning robots. Perceived usefulness was found to have significant influence towards PV panel adoption. The influence of attitude was mediated by several factors – environmental concern, renewable energy awareness, trust in technology. The study recommended that the institutional users of solar panels should consider investing in knowledge regarding perceived behavioral control, developing confidence in the use of renewable and sustainable energy, and developing trust of the renewable and sustainable energy technologies. This could be achieved through programs such as public awareness, sharing accurate information regarding solar panel cleaning robots, and providing support and after sales support. Educational initiatives to change users' attitudes toward renewable energy technologies were recommended.
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Enhancement of joining interface of barium and barium–zinc borosilicate glass–ceramic SOFCs sealant by CaO and SrO addition: crystallization behaviour and microstructural aspects
(2024-09-01)
LAORODPHAN, NATTAPOL
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KINGNOI, NAMTHIP
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CHAIYAPUT, SALISA
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AYAWANNA, JIRATCHAYA
Sealant properties of CaO- and SrO-containing BaO–B<inf>2</inf>O<inf>3</inf>–SiO<inf>2</inf> and BaO–ZnO–B<inf>2</inf>O<inf>3</inf>–SiO<inf>2</inf> glasses were first reported in this work. CaO and SrO were used as glass modifiers to improve thermal properties and optimize CTE value and possess a good joining interface between SOFC components. Addition of CaO and SrO enhanced the glass transition and softening temperature due to the higher ionic field strength of Ca and Sr ions promoting the boroxol ring formation and a denser glass network. IR spectra confirmed a less significant effect of CaO and SrO in the glass structures. Addition of doubled CaO and SrO retained the compatibility of CTE to that of SDC electrolyte and Crofer22APU interconnect. Crystalline phases with compatible CTE were formed in the CaO- and SrO-containing glass ceramics. This provided good adhesion without defects at the interface, which is close to the electrolyte and interconnect after prolonged operation at 800°C for 50 h.

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