Search Results (324)

Lily (Lilium spp.) is a perennial ornamental plant valued for its striking ornamental value and the edible and medicinal properties of its bulbs. Compared to other lilies, Oriental lilies are characterized by their large flowers and strong fragrance, making them one of the most popular types of lilies on the market. It is important to identify optimal parts of lily bulbs for rapid propagation of tissue culture seedlings. In this study, bulb scales of the Oriental lily ‘Pacific Ocean’ were used to explore the optimal parts for the tissue culture bulb induction. After 30 days of inoculation of lily scales in the induction medium, our results showed that Middle Central regions of the Inner layer scales (MCI) had the highest induction rate of 90.27 % among all regions from all layers of scales. In scales of all layers, the basal parts had the best longitudinal induction effects, followed by the middle and apical parts. Among all layers, the inner layers also had the lowest rates of contaminations. When taking both induction rate and contamination rate into account, MCI also represents the best choice among all compared parts. In summary, our results formed a practical guide for large-scale propagation of Oriental lily using tissue culture seedlings. Full article

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

The escalating accumulation of industrial solid wastes (e.g., copper slag: CS, ground-granulated blast furnace slag: GGBS) and carbon-intensive cement production has intensified environmental challenges, driving the demand for sustainable construction materials that synergize waste valorization with carbon sequestration. This study investigates the evaluation of the compressive strength, mineralogical evolution, and real-time CO₂ capture of the alkali-activated geopolymer composite materials by optimizing the mixed design of precursor materials (CS/GGBS ratio: 7/3) with MgO (0–10%) and coconut shell (CSB), peanut shell (PSB), and durian shell biochar (DSB) (0–3%). Results reveal that the 5% MgO addition achieves an 89.5% early-age compressive strength increase versus the MgO-free specimen. The compressive strength of the geopolymer composite could be further increased by a 1.5% dosage of DSB with an average pore size of 8.98 nm. In addition, the incorporation of an appropriate amount of porous biochar could not only enhance the CO₂ capture capacity of the geopolymer composite, but also further improve the CO₂ mineralization efficiency. The optimal formulation (5% MgO + 1.5% DSB) could mineralize 40.2 kg CO₂ per ton of solid waste at least. This work highlights a sustainable strategy for synchronizing industrial solid waste valorization with carbon-negative construction providing scalable CO₂ sequestration solutions. Full article

This study developed hydrogels from durian rind-derived carboxymethyl cellulose (CMC_d) blended with poly(vinyl) alcohol (PVA) for biomedical applications. The influence of NaOH concentration (10–60% w/v) on the degree of substitution (DS) of CMC_d and the crosslinking properties of the resulting hydrogels was examined. Durian rind, a biodegradable and renewable resource, was transformed into CMC_d with DS values ranging from 0.17 to 0.94. The highest yield (230.96%) was achieved using 30% NaOH (CMC_d-30). This CMC_d-30 was combined with PVA and crosslinked using citric acid to form a hydrogel with maximum crosslinking efficiency (86.16%). The resulting CMC_d-30/PVA hydrogel exhibited a high swelling ratio (125.54%), reflecting its superior water absorption and functional group availability—key traits for biomedical use. Methylene blue (MB) release from the hydrogel extended up to 1440 min, confirming its drug delivery potential. Overall, the CMC_d-30/PVA hydrogel demonstrated promising biocompatibility potential and performance, making it a promising candidate for wound dressings and controlled drug delivery systems. This work highlights the potential of agricultural waste valorization in developing sustainable and efficient biomaterials for pharmaceutical and medical applications. Full article

Durian is a major tropical crop in Southeast Asia, but its yield and quality are severely impacted by a range of pests and diseases. Manual inspection remains the dominant detection method but suffers from high labor intensity, low accuracy, and difficulty in scaling. To address these challenges, this paper proposes EN-YOLO, a novel enhanced YOLO-based deep learning model that integrates the EfficientNet backbone and multimodal attention mechanisms for precise detection of durian pests and diseases. The model removes redundant feature layers and introduces a large-span residual edge to preserve key spatial information. Furthermore, a multimodal input strategy—incorporating RGB, near-infrared and thermal imaging—is used to enhance robustness under variable lighting and occlusion. Experimental results on real orchard datasets demonstrate that EN-YOLO outperforms YOLOv8 (You Only Look Once version 8), YOLOv5-EB (You Only Look Once version 5—Efficient Backbone), and Fieldsentinel-YOLO in detection accuracy, generalization, and small-object recognition. It achieves a 95.3% counting accuracy and shows superior performance in ablation and cross-scene tests. The proposed system also supports real-time drone deployment and integrates an expert knowledge base for intelligent decision support. This work provides an efficient, interpretable, and scalable solution for automated pest and disease management in smart agriculture. Full article

Wood–plastic composites (WPCs) offer a sustainable alternative to solid wood, yet their heterogeneous structure presents challenges in laser machining due to thermal sensitivity and inconsistent material behaviour. This study investigates the optimization of CO₂ laser-cutting parameters for WPCs, focusing on feed rate and assist-gas pressure. Using a 1500 W CO₂ laser, a full factorial experimental design was employed to cut 18 mm thick WPC panels at varying feed rates (1000–3000 mm/min) and gas pressures (1–3 bar). Statistical analyses including MANOVA and linear regression were conducted to evaluate their effects on key machining responses: cutting depth, heat-affected zone (HAZ) width, cut-edge quality, and surface finish. Results indicated that feed rate significantly influences both cutting depth and thermal damage, while gas pressure plays a major role in improving surface quality and reducing HAZ. Optimal combinations were identified for various performance goals, and validation trials at the selected parameters confirmed alignment with predicted outcomes. The optimized settings yielded high-quality cuts with reduced HAZ and enhanced surface characteristics. This study demonstrates the effectiveness of a statistical optimization approach in refining CO₂ laser-cutting conditions for WPCs, offering insights for improved process control and sustainable manufacturing applications. This study also introduces a multi-objective optimization approach that verifies the interaction effects of feed rate and assist-gas pressure, enabling precise and efficient CO₂ laser cutting of 18 mm thick WPCs. Full article

Artificial intelligence (AI)-powered mental health chatbots have evolved quickly as scalable means for psychological support, bringing novel solutions through natural language processing (NLP), mobile accessibility, and generative AI. This systematic literature review (SLR), following PRISMA 2020 guidelines, collates evidence from 25 published, peer-reviewed studies between 2020 and 2025 and reviews therapeutic techniques, cultural adaptation, technical design, system assessment, and ethics. Studies were extracted from seven academic databases, screened against specific inclusion criteria, and thematically analyzed. Cognitive behavioral therapy (CBT) was the most common therapeutic model, featured in 15 systems, frequently being used jointly with journaling, mindfulness, and behavioral activation, followed by emotion-based approaches, which were featured in seven systems. Innovative techniques like GPT-based emotional processing, multimodal interaction (e.g., AR/VR), and LSTM-SVM classification models (greater than 94% accuracy) showed increased conversation flexibility but missed long-term clinical validation. Cultural adaptability was varied, and effective localization was seen in systems like XiaoE, okBot, and Luda Lee, while Western-oriented systems had restricted contextual adaptability. Accessibility and inclusivity are still major challenges, especially within low-resource settings, since digital literacy, support for multiple languages, and infrastructure deficits are still challenges. Ethical aspects—data privacy, explainability, and crisis plans—were under-evidenced for most deployments. This review is different from previous ones since it focuses on cultural adaptability, ethics, and hybrid public health incorporation and proposes a comprehensive approach for deploying AI mental health chatbots safely, effectively, and inclusively. Central to this review, symmetry is emphasized as a fundamental idea incorporated into frameworks for cultural adaptation, decision-making processes, and therapeutic structures. In particular, symmetry ensures equal cultural responsiveness, balanced user–chatbot interactions, and ethically aligned AI systems, all of which enhance the efficacy and dependability of mental health services. Recognizing these benefits, the review further underscores the necessity for more rigorous academic research into the development, deployment, and evaluation of mental health chatbots and apps, particularly to address cultural sensitivity, ethical accountability, and long-term clinical outcomes. Full article

The study of antagonistic bacterial strains isolated from the soil around durian tree roots demonstrated their ability to inhibit the growth of Phytophthora palmivora. The pathogens were screened from 30 samples collected around durian trees (leaves, soil around the roots, and debris under the tree) showing symptoms of root and stem rot disease. A total of 17 pathogen strains were isolated and grouped into 3 groups, TNP05, MNP13, and KNP21, originating from Chanthaburi province, Thailand. When P. palmivora isolates were tested for pathogenicity on leaves and durian trees, it was found that the strain MNP13 had the highest capacity to cause root and stem rot disease. A total of 196 beneficial bacteria isolates were collected from several samples around durian trees. The samples included leaves, soil surrounding the roots, and organic debris beneath the trees. Based on their colony characteristics on nutrient glucose agar (NGA), these isolates were divided into 8 groups. The efficacy of the beneficial bacteria against root and stem rot disease was tested using the Dual culture method and arranged in a Completely Randomized Design (CRD) with 5 replications. The experiment showed that bacterial isolates NJTU05, NJTU10, and NJTU13 effectively inhibited the growth of P. palmivora isolate MNP13, with inhibition rates of 76.66, 67.59, and 69.07%, respectively, compared to chemical control using metalaxyl 80% WP. Among the tested strains, NJTU05 was identified as the most effective bacterial strain for controlling major durian diseases. Biochemical identification and 16S rRNA sequencing revealed that bacterial strain NJTU05 was closely related to Brevibacillus formosus with a 99.70% identity. Full article

This paper presents a novel framework addressing the fundamental challenge of concurrent real-time audio acquisition and motor control in resource-constrained mobile robotics. The ESP32-based system integrates a digital MEMS microphone with rover mobility through a unified Bluetooth protocol. Key innovations include (1) a dual-thread architecture enabling non-blocking concurrent operation, (2) an adaptive eight-bit compression algorithm optimizing bandwidth while preserving audio quality, and (3) a mathematical model for real-time resource allocation. A comprehensive empirical evaluation demonstrates consistent control latency below 150 ms with 90–95% audio packet delivery rates across varied environments. The framework enables mobile acoustic sensing applications while maintaining responsive motor control, validated through comprehensive testing in 40–85 dB acoustic environments at distances up to 10 m. A performance analysis demonstrates the feasibility of high-fidelity mobile acoustic sensing on embedded platforms, opening new possibilities for environmental monitoring, surveillance, and autonomous acoustic exploration systems. Full article

This paper describes an innovative wireless mobile robotics control system based on speech recognition, where the ESP32 microcontroller is used to control motors, facilitate Bluetooth communication, and deploy an Android application for the real-time speech recognition logic. With speech processed on the Android device and motor commands handled on the ESP32, the study achieves significant performance gains through distributed architectures while maintaining low latency for feedback control. In experimental tests over a range of 1–10 m, stable 110–140 ms command latencies, with low variation (±15 ms) were observed. The system’s voice and manual button modes both yield over 92% accuracy with the aid of natural language processing, resulting in training requirements being low, and displaying strong performance in high-noise environments. The novelty of this work is evident through an adaptive keyword spotting algorithm for improved recognition performance in high-noise environments and a gradual latency management system that optimizes processing parameters in the presence of noise. By providing a user-friendly, real-time speech interface, this work serves to enhance human–robot interaction when considering future assistive devices, educational platforms, and advanced automated navigation research. Full article

The rapid proliferation of electronic devices has heightened the demand for efficient electromagnetic interference (EMI) shielding materials, as conventional alternatives increasingly fall short in mitigating harmful electromagnetic radiation. In this study, we report the fabrication of acrylonitrile butadiene styrene (ABS) nanocomposite films reinforced with graphene nanoplatelets (GNPs), offering a promising solution to this growing challenge. A persistent issue in incorporating GNPs into the ABS matrix is their poor wettability, which impedes uniform dispersion. To overcome this, a sonication-assisted casting technique was employed, enabling effective integration of GNPs at loadings of 1, 3, and 5 wt%. The resulting nanocomposite films exhibit uniform dispersion and enhanced functional properties. Comprehensive characterization using FESEM, UV-Vis spectroscopy, TGA, DSC, FTIR, and dielectric/EMI analyses revealed significant improvements in thermal stability, UV absorption, and dielectric behavior. Notably, the films demonstrated moderate EMI shielding effectiveness, reaching 0.0064 dB at 4 MHz. These findings position the developed GNP-reinforced ABS nanocomposites as promising candidates for advanced applications in the automotive, aerospace, and electronics industries. Full article

Agricultural residues offer promising opportunities for the development of biocomposites. Durian husk, a lignocellulosic by-product abundantly available in Southeast Asia, and bamboo waste, an underutilized biomass resource, present considerable potential for sustainable particleboard production. This study focuses on developing single-layer bio-based particleboards using varying proportions of durian husk and bamboo waste bonded with urea formaldehyde resin. The fabricated boards were evaluated for thickness swelling, modulus of rupture, and internal bond strength according to relevant European standards. Results indicated that all particleboards met the Type P1 requirements for general-purpose use under dry conditions, as specified in BS EN 312:2010. The findings demonstrate the feasibility of converting agricultural waste into value-added, eco-friendly materials, supporting waste valorization, promoting circular economy practices, and contributing to the development of bio-based materials. Full article

This paper investigated the effects of the successive Typhoons Durian and Utor on the chlorophyll-a concentration in the overlapping regions of the South China Sea in 2006. Satellite observations were employed to analyze the spatial–temporal variability of chlorophyll-a concentrations. The results show that the strong vertical mixing and upwelling after the passage of the first Typhoon Durian led to a rapid increase in chlorophyll-a concentration, while the effects of the subsequent Typhoon Utor showed regional variability: the chlorophyll-a concentration in the area to the right of the path of Typhoon Utor increased significantly, but it did not continue to increase in the area of the overlap with Durian and showed a decreasing trend. Studies have shown that the impacts of successive typhoons on marine ecology are not simply additive but can be modulated by changes in the marine environment caused by the previous typhoon. This study revealed the complexity of the impacts of successive typhoons on marine productivity and provides a new perspective for understanding how typhoons affect marine productivity. Full article

In this work, 1,3-diketone synthesized via the Claisen condensation method and nano-copper particles modified by the Brust–Schiffrin method were added into a commercial marine medium-speed diesel engine cylinder piston oil to evaluate their effects on boundary lubrication performance. Friction and wear tests conducted on CKS-coated piston ring and cast-iron cylinder liner samples demonstrated significant reductions in both friction and wear with the addition of 1,3-diketone and nano-copper particles. Compared to the original oil without additives, the friction force was reduced by up to 16.7%, while the wear of the piston ring and cylinder liner was decreased by up to 21.6% and 15.1% at 150 °C, respectively. A worn surface analysis indicated that the addition of 1,3-diketone and functionalized nano-copper particles influenced the depolymerization and tribo-chemical reactions of the anti-wear additive ZDDP (zinc dialkyldithiophosphate) in the original engine oil. This modification enhanced the oil’s anti-friction and anti-wear properties, offering valuable insights into the development of eco-friendly lubricants for energy-efficient systems. Full article