KTU Open Journal Systems (Kaunas University of technology)
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Architectural Transformation Strategies from a Vertical Slum into a Nearly Zero-Energy Hybrid Building. A Study Case in Malaga (Spain)
No full textThe transformation of a vertical slum into a hybrid building in socially excluded neighbourhoods, represents a scarcely analysed approach for a building model traditionally developed outside of these areas. Furthermore, incorporating these buildings into comprehensive urban regeneration processes in large peripheral neighbourhoods can address deficiencies in infrastructure and amenities. This paper presents and analyses strategies for the physical, energy, and social transformation that aid in the regeneration of a socially excluded neighbourhood, applied to a real case study in the city of Málaga (Spain). The methodology proposes an integrated approach from the urban, architectural, and social dimensions, organised into three phases: analysis and needs, hybrid transformation, and transformation strategies. Twelve transformation strategies were found to be structured into three categories: spatial, energy improvement and new uses strategies. Spatial strategies include incorporating new semi-public spaces and reclaiming public and community spaces, distinguished either architecturally or functionally derived from new public uses. Energy transformation focuses on enhancing and incorporating passive systems, active energy input systems, and transforming the existing facade. Finally, use strategies suggest new public uses for the neighbourhood, the distribution of uses based on height, and the relationship between new uses and plazas at height
The Intangible Phenomenon of War: Methodological Foundations of Revitalization and Justification of the Architectural and Spatial Future of Ukraine
No full textThe article considers the Russian-Ukrainian war as an intangible phenomenon that formulates a broader view in terms of Ukraine’s recovery and development. The material system of the state is formed by a morphologically different but stable environment, while intangible processes are multidirectional and differ in duration and complexity. The concept of the spatial future of Ukraine is based on the integral principles of system development and covers macro characteristics (usefulness, environmental friendliness, safety for people and the environment, aesthetic component, and duration of implementation). The authors substantiate local suggestions for the revitalization of the territories destroyed by the war and the spatial development of Ukraine based on the requirements of intangible
The Development of Composite Materials for Architectural Work Using Oil Palm Frond and Plastic Fishing Net Waste
No full textIn southern Thailand, the accumulation of oil palm fronds and plastic waste, particularly discarded fishing nets from local fishery and farming communities, poses significant environmental challenges due to limited large-scale recycling options. This study aims to develop innovative composite materials for architectural applications by utilizing oil palm fronds and plastic fishing net waste, thereby reducing environmental pollution and promoting sustainable waste management. The research employs an experimental approach to fabricate composite panels through a systematic formation process, followed by comprehensive testing of their physical and mechanical properties. Physical properties evaluated include density, thickness swelling, and water absorption, while mechanical properties encompass modulus of rupture (MOR), modulus of elasticity (MOE), and internal bonding strength (IB). The results demonstrate that incorporating plastic fishing net waste significantly enhances the composite’s density, flexural strength (MOR), modulus of elasticity (MOE), and tensile strength perpendicular to the surface. Specifically, these properties exhibit a positive correlation with the proportion of plastic fishing net waste in the composite mix, with optimal performance observed at higher plastic ratios. Conversely, thickness swelling decreases as the plastic content increases, indicating improved dimensional stability. All tested composite panel specimens meet or exceed the requirements of the Thai Industrial Standard (TIS) 876-2565 (2022) for particleboards, confirming their suitability for interior architectural applications, such as wall panels and ceiling materials. This research not only provides a sustainable solution to manage agricultural and plastic waste in southern Thailand but also contributes to the development of eco-friendly, high-performance materials for the construction industry, supporting circular economy principles and environmental conservation
Revolutionizing Palm Oil Waste Management: Cost and Benefit Analysis of Plasma Nanobubble Innovative Technology
No full textPalm oil is a crucial agricultural commodity that holds substantial importance in the Indonesian economy, enhancing the export revenues and developing a rural economy. Production levels have consistently and substantially increased during the previous decades. However, this increase aligns with the substantial amount of palm oil mill waste (POME) that has the potential to pollute the environment, causes damage through water pollution and greenhouse gas emissions. The utilization of Plasma Nanobubble (PNB) technology could efficiently tackle this problem. Nevertheless, more research is necessary to assess the effects of this method on the treatment of palm oil residue. The aim of this study is to provide a thorough review of the costs and benefits related to the use of PNB technology for treating palm oil waste. The results of this study demonstrate a significant reduction in costs by 23%, from Indonesia Rupiah or IDR. 9,370,939,000 to IDR 7,214,155,960. The utilization of PNB technology generates several benefits, including improved environmental effects. A notable benefit discovered is the considerable decrease in color before and after the application of the PNB technology. In addition, there was a significant decrease in total suspended solids (TSS) of –969.8%, biological oxygen demand (BOD) of –1790.2%, and chemical oxygen demand (COD) of –2534.1% after the implementation of PNB technology. Therefore, PNB technology emerges as a viable solution for the palm oil industry to address the POME waste problem, contributing to creating new knowledge on waste management that can be applied by other palm oil producing countries
The Blue Carbon Value in Mangrove Ecosystems Under Different Growth Conditions
No full textEach mangrove ecosystem had different carbon absorption capacities depending on the mangrove stand itself. The aim of this study was to compare the blue carbon value of mangrove ecosystems under different growing conditions. The research was conducted in Sangkaragung Village, Jembrana Regency, Bali. The measurement technique used to assess the blue carbon value of mangroves was conducted at the surface without harvesting (non-destructive sampling), following the method of carbon stock estimation for mangrove ecosystems. The research results showed that the mangrove species in the study location included Avicennia marina, Avicennia alba, Rhizophora apiculata, Ceriops decandra, Sonneratia alba, Bruguiera gymnorrhiza, and Xylocarpus granatum. The highest biomass carbon storage value was found in the natural mangrove ecosystem in the pond, amounting to 17.53 C/ha. The highest organic matter value at a depth of 0–15 cm was found in the planted mangrove in the pond, with a value of 17.01%, at a depth of 15–30 cm in the natural mangrove in the pond, with a value of 14.82%, and at a depth of 30–50 cm, the planted mangrove in the pond showed the highest value at 28.32%. The highest organic carbon storage was found in the planted mangrove in the pond at a depth of 0–15 cm, with 205.05 C/ha, at a sediment depth of 15–30 cm in the natural mangrove in the pond, with 240.12 C/ha, and at a sediment depth of 30–50 cm, the natural mangrove in the pond also had the highest carbon absorption, with 332.12 C/ha. The total tree biomass carbon storage was 31.95 tonnes C/ha, and the sediment carbon storage was 598.19 tonnes C/ha, resulting in a total carbon storage of 630.14 tons C/ha
Factors Affecting Tourists\u27 Intentions to Visit and Willingness to Pay a Premium for Green Destinations
No full textA green footprint destination is regarded as a core element of sustainable tourism, and all the effort to build a green tourism site is deserved. The study focuses on tourists’ perspectives and intent to identify factors that contribute to the visit intention and willingness to pay more for visiting a green footprint destination. Employing the theory of planned behavior, the study proposes green activities, including green knowledge, green publicity, and green practices, will influence tourists’ attitude, trust, and perceived consumer effectiveness, which will later explain their intention and willingness to pay more for visiting green tourism sites. A survey is conducted to collect data from Vietnamese tourists, and 464 were qualified for verification research hypotheses using SPSS and AMOS software. The results reveal that tourists’ attitude, trust, and perceived consumer effectiveness mediate the influence of green activities and their visit intention as well as willingness to pay more for visiting green footprint destinations. The findings provide valuable insight for scholars and practitioners in understanding and encouraging pro-environmental behaviors among Vietnamese tourist
Optical-Flow Based Symmetric Feature Extraction for Facial Expression Recognition
No full textFacial expression analysis is one of the most essential tools for behavior interpretation and emotion modeling in Intelligent Human-Computer Interaction (HCI). Although humans can easily interpret facial emotions, computers have great difficulty doing so. Analyzing changes and deformations in the face is one of the methods through which machines can interpret facial expressions. However, maintaining great precision while being accurate, stable, and quick is still challenging in this field. To address this issue, this research presents an innovative and novel method to fully automatically extract critical features from a face during a facial expression. Various machine learning models are used on these features to analyze emotions. We used the optical flow algorithm to extract motion vectors divided into sections on the subject’s face. Finally, each section and its symmetric section were used to calculate a new vector. The final features produce a state-of-the-art accuracy of over 98% in emotion classification in the Extended Cohen-Kanade (CK+) facial expression dataset. Furthermore, we proposed an algorithm to filter the most important features with an SVM classifier and achieved an accuracy of over 97 % by only looking at 15% of the face area
Optimization of Speed Reducer Design based on an Enhanced Grey Wolf Optimizer
No full textTraditional swarm intelligence optimization methods perform erratically in engineering design due to difficulties in handling nonlinear data, local optimal errors and premature convergence. To address these problems, we developed an enhanced Gray Wolf Optimizer (OGWO) that employs Levy flight and elite adversarial-based learning methods. We evaluated its effectiveness using 20 benchmark functions and compared it with other GWO variants and popular algorithms. The results show that OGWO is superior in terms of convergence speed, accuracy, and freedom from stagnation, as confirmed by the Wilcoxon rank sum test. Furthermore, the effectiveness of OGWO in training Multilayer Perceptron (MLP) has been evaluated using the UCL datasets. Finally, OGWO has been applied to solve the gearbox design problem, proving its ability to provide optimal solutions in addressing real-life engineering issues
Improved YOLOv8n based lotus seedpod detection algorithm
No full textThese Aiming at the influence of the shape appearance, color and growth environment of lotus seedling, lotus seedling detection exists problems such as low efficiency, low precision, leakage and misdetection, etc., an improved lotus seedling detection algorithm FSM-YOLOv8 is proposed based on the YOLOv8n model. First, the C2f-Faster module reduces the number of model parameters while ensuring the structural feature extraction capability of the YOLOv8n network. Then, the SimAM attention mechanism is applied to the model feature extraction module, which enhances the multi-scale and spatial feature extraction capability of the model. Finally, MPDIoU is used as the boundary loss function to effectively solve the problem of low detection rate caused by the spatial overlap and occlusion of the lotus seed pods and lotus leaves.The results show that the improved FSM-YOLOv8 achieves 84.8%, 84.1%, and 87.9% of detection accuracy, 84.1%, and 87.9% of recall, respectively, compared with the YOLOv8n model, and reduces 13.4% of the parameter amount. 13.4%, which is a significant improvement in detection accuracy and model lightweighting, and can realize rapid identification of lotus seedpods in complex environments, and meet the demand of real-time identification of lotus seedpod picking robots in the process of picking
Data-Fusion Based On Transfer Learning For Plant Disease Recognition
No full textIn this paper, the research focused on wild and introduced cultivated flowers with multiple diseases such as Stephanitis, Sooty Mould, Xanthosis, and Leaf Blight, utilizing transfer learning and and data fusion technology to construct a plant disease detection model employing Faster R-CNN.The self-built data set collected during the flower growth cycle was trained and identified.To solve the problem of disease category imbalance in the actual collected data samples, the data of small category samples is enhanced from the perspective of category balance and label balance, and FocalLoss is used to improve the original classification loss function. Based on this self-built data set, the constructed IFRCNN disease detection model was compared with the SSD (Single Shot multibox Detector), ResNet18 and Yolov3 models. The results showed that for several common plant diseases in the dataset, the mAP of IFRCNN disease detection model was significantly higher than that of the other three models. It can effectively locate plant leaf disease areas, realize the detection of multiple diseases, and provide reference for accurate disease prevention and control