Search Results (354)

Fusarium solani is an emerging pathogen responsible for Fusarium-related diseases in durian trees in Thailand. Several chemical fungicides and biocontrol agents are ineffective in controlling these diseases, which affects durian trees and reduces yields. This study aimed to identify soil-derived bacteria with biocontrol activity against F. solani that surpasses traditional biocontrol bacteria. The characteristics and biocontrol efficacy of effective isolates were analyzed. Four isolates from 107 bacterial isolates were identified as effective biocontrol agents against F. solani. Isolate S301 exhibited the highest inhibition at 74.31%, exceeding that of the traditional biocontrol bacterium Bacillus subtilis. These isolates antagonized F. solani by producing siderophores, fungal cell wall lytic enzymes, and hydrogen cyanide, and by promoting plant growth. Molecular and phylogenetic analyses identified the four isolates as members of the Bacillus genus, specifically B. safensis, B. thuringiensis, B. subtilis, and B. cereus. The application of B. safensis strain S101 and B. subtilis strain S301 showed potential to reduce fungal disease symptoms on Monthong durian leaves. These findings are the first to demonstrate the potential of B. safensis and B. subtilis as promising bacterial biocontrol agents for managing F. solani-related diseases in durian trees in Thailand. Full article

Vapor Pressure Deficit (VPD) is a critical determinant of atmospheric evaporative demand and plant water stress in tropical agricultural systems. This study applied a Gaussian Mixture Model (GMM) and K-Means clustering to 36,528 hourly meteorological observations collected from Eastern Thailand between August 2021 and September 2025, with the objective of identifying distinct atmospheric moisture regimes relevant to precision irrigation management in durian cultivation. Two input configurations were evaluated: a multivariate feature space comprising air temperature, relative humidity, wind speed, solar radiation, and VPD; and a univariate input consisting of VPD alone. Model selection for GMM was guided by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while K-Means performance was assessed using the Elbow method, Silhouette Coefficient, Calinski–Harabasz Index, and Davies–Bouldin Index. For the multivariate input, GMM identified K = 7 as the optimal number of clusters, supported by the largest single-step reduction in both AIC and BIC at this transition point. For the univariate VPD input, K = 5 was selected as the most parsimonious and agriculturally interpretable solution. The seven clusters derived from the multivariate GMM were organized into four atmospheric moisture regimes, such as very low, moderate, high, and very high evaporative demand, capturing the full spectrum of diurnal and seasonal VPD variability characteristic of Eastern Thailand. The results demonstrate that GMM-based probabilistic clustering applied to multivariate meteorological inputs provides a more comprehensive characterization of atmospheric moisture dynamics than univariate or geometric clustering approaches, offering a practical framework for tiered irrigation scheduling and drought stress early warning systems in tropical fruit cultivation. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

The fallen Camellia vietnamensis fruits caused by typhoons are usually collected by the farmers to be processed into oil in order to decrease the loss of the disaster. Then, this report investigates the difference in the yield, quality, and antioxidant activity of the seed oil between the fallen fruits caused by the typhoons and the normally harvested fruits. The yield of seed oil from fallen fruits caused by typhoons (HCA) was significantly lower than that of normally harvested fruits (HCB). The physicochemical properties of HCA showed signs of quality deterioration. HCA seemed to optimize the fatty acid composition. HCA exhibited stronger DPPH^· radical scavenging, ABTS^·+ inhibitory, and ferric ion-reducing activities. Thirty-four volatile compounds were identified in both samples. HCA showed higher levels of antioxidant-rich volatiles. Overall, this investigation demonstrates that the fallen fruits caused by typhoons lead to significant seed oil yield losses and measurable quality deterioration, thereby offering clear, evidence-based insights to support more effective typhoon disaster mitigation strategies. Full article

The durian seed borer, Mudaria stahlgretschae, is a major economic pest that has significantly impacted durian cultivation in Southeast Asia; however, comprehensive biological and ecological data for this species remain limited. This study employs an integrative taxonomic approach, combining morphological examination with molecular validation of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Phylogenetic analysis (Neighbor-Joining) confirmed that all collected specimens (n = 11) formed a distinct monophyletic clade within the genus Mudaria, showing a genetic identity of 95.75–96.85% with existing GenBank accessions, thereby confirming their identity as M. stahlgretschae. Systematic monitoring using light traps in Uttaradit Province revealed a clear seasonal phenology, with adult flight activity restricted to a five-month period from April to July 2025. Population density peaked in May (55.56%), synchronized with the mid-stages of durian fruit development. Furthermore, chemical profiling of female gland volatiles via GC-MS identified 40 compounds; among these, four putative sex pheromone candidates—1-Hexacosene, (Z)-7-Hexadecenal, 11-Octadecenal, and 2-Hexadecanol—were identified as key constituents based on their consistent detection across all replicates (n = 3), high relative abundance, and absence in male extracts or blank controls. These findings establish a critical foundation for developing synthetic pheromone lures and synchronized monitoring programs, offering a robust framework for the sustainable management of M. stahlgretschae in durian agroecosystems. Full article

Durian (Durio zibethinus Murray) seeds, an underutilized by-product of durian processing, were upcycled into functional flours to elucidate how varietal origin and processing govern structure–function relationships. Durian seed flours from local Bang Nara (L) and Monthong (M) varieties were prepared using three methods: native durian seed flour (NDSF; control), boiled durian seed flour (BDSF), and hydrated durian seed flour (HDSF), and benchmarked against commercial mung bean flour (MBF) and almond flour (ALF). Proximate composition, total phenolic content (TPC) and DPPH^•- scavenging activity, structural characteristics (Fourier transform infrared, FTIR; X-ray diffraction, XRD), thermal behavior, and microstructure were assessed alongside functional properties including water/oil absorption, emulsion performance, and gelation. M flours contained higher protein (8.46–10.73%), dietary fiber (6.26–9.37%), ash (3.59–4.38%), TPC (53.17–87.40 mg gallic acid equivalent/g), and DPPH^•- scavenging activity (92.39–94.54%) than L flours, whereas L flours had higher carbohydrate content (78.87–82.54%) than M flours (68.32–72.21%). Crude fat remained below 1% across all samples. FTIR and XRD profiles were comparable to MBF, confirming starch-based similarities, but distinct differences in color, bulk density, crystallinity, gelatinization behavior, and granule morphology reflected processing-driven structural modification. Functionally, NDSF exhibited the highest water absorption capacity (4.28 g/g); all durian seed flours showed low oil absorption (0.58–0.88 g/g) and gelation at 10–12%. Most samples demonstrated good emulsion activity and stability, except HDSF. Overall, NDSF and BDSF provided the best balance of yield, hydration capacity, and structural stability, demonstrating that both variety and processing determine the performance of upcycled durian seed flours. These findings support the valorization of durian seeds as sustainable, value-added functional ingredients aligned with circular economy and zero-waste food processing. Full article

Durian (Durio zibethinus Murr.), a renowned tropical fruit crop, is increasingly cultivated in the Hainan Province of China. In June 2025, symptoms of postharvest fruit rot were observed on durian fruits from a commercial orchard in Sanya City, Hainan Province, with a disease incidence of approximately 5.2%. Three fungal isolates were obtained and identified as Lasiodiplodia pseudotheobromae and L. lignicola based on morphological characterization and multi-locus phylogenetic analysis (combining ITS, TUB2, and EF1-α gene sequences). Pathogenicity assays confirmed both species as causal agents of durian postharvest rot, with rapid lesion expansion and eight tropical fruit hosts, including banana and mango, posing a threat to postharvest storage. Fungicide sensitivity tests showed imazalil and imazalil sulfate with mean EC₅₀ values of 0.07 µg/mL and 0.08 µg/mL as most effective, followed by prochloraz, iprodione, and prochloraz-Mn. L. lignicola was more sensitive to most fungicides than L. pseudotheobromae. These findings underscore the need for species-specific fungicide strategies in disease management. This is the first report of L. pseudotheobromae and L. lignicola causing durian postharvest rot in this preliminary study from Hainan. With Hainan emerging as a key production region, further research is essential to develop effective control measures against this economically significant disease. Full article

Medical waste management has been receiving increasing attention in recent years. The National Health Service (NHS) of the United Kingdom has started planning its waste strategy to comply with its Net Zero Goals. Waste management does not only involve waste disposal; the process includes segregation, collection, storage, and the transportation of waste from one point to another. Unusual characteristics of waste from the healthcare industry are that waste can be infectious and needs special storage conditions and specific transportation criteria to maintain the waste’s quality. However, entities working with the waste lack knowledge about the waste they receive and need assistance to verify the quality of the waste as well. Limited knowledge can lead to injuries, contamination, or the spread of pathogens. The global monitoring guidelines of medical waste are studied to understand the monitoring requirements and the stakeholders who are working with the waste. Application and research contributions to the digitisation of medical waste monitoring are scrutinised to look for the monitoring gaps. This paper proposes a digital system designed to connect all waste stakeholders within a blockchain environment, supported by automated data collection. A framework for stakeholder communication with data is designed. The data gathered from transporters is analysed before sending the status to the blockchain. Furthermore, the paper outlines a dashboard showcasing the digitisation of waste management, backed by a case study used for validation. A hypothetical case study in managing waste using existing manual waste monitoring in the United Kingdom is compared with monitoring using the system. By employing a proving method of all activities approach with blockchain technology, this method has achieved a 25.17% improvement in medical waste management time-taken efficiency and a 27.85% improvement while virtually eliminating the risk of fraudulent documentation. Full article

Durian (Durio zibethinus Murray) is Thailand’s most economically significant fruit export, yet foliar diseases pose a major threat to productivity and crop quality. Early-stage symptoms of several durian leaf diseases are visually similar, making reliable diagnosis difficult for farmers and even trained agronomists. This study aims to develop and evaluate an automated deep learning-based system for durian leaf disease classification under realistic field conditions. A dataset of 6119 leaf images representing six classes—Leaf_Healthy, Leaf_Colletotrichum, Leaf_Algal, Leaf_Phomopsis, Leaf_Blight, and Leaf_Rhizoctonia—was compiled from public datasets and field-collected samples. Six convolutional neural network (CNN) architectures—ConvNeXt, ResNet, DenseNet201, InceptionV3, EfficientNet-B3, and MobileNetV3—were benchmarked using a unified transfer-learning training protocol. Class imbalance was addressed using weighted cross-entropy loss, and performance was evaluated on a stratified held-out test set using accuracy, precision, recall, and F1-score metrics. The results show that ConvNeXt achieved the highest performance with 98.00% accuracy and a weighted F1-score of 0.98, followed by ResNet (96.82%) and DenseNet201 (96.09%), while efficiency-oriented models plateaued near 91%. Confusion matrix analysis revealed consistent misclassification among visually similar disease categories—Leaf_Algal, Leaf_Blight, and Leaf_Phomopsis—indicating biological similarity in lesion appearance rather than model limitations. The best-performing model was deployed as a publicly accessible web application using Gradio, enabling real-time disease diagnosis with an average inference time of approximately 0.54 s per image. Unlike prior studies, this work combines large-scale architecture benchmarking, class imbalance mitigation, and real-world deployment within a single unified framework. These findings demonstrate that modern CNN architectures can provide highly accurate and scalable disease detection tools, supporting precision agriculture by enabling early diagnosis, reducing inappropriate pesticide use, and improving decision-making for durian farmers. Full article

This study reports a mechanistic analysis of Pb²⁺ and Cu²⁺ adsorption on activated carbon (AC) obtained from durian skin. Pb²⁺ and Cu²⁺ adsorption tests were carried out at 303–323 K and pH 5.5 to interpret the heavy metal—durian skin AC systems. A monolayer model was applied to simulate experimental isotherms and calculate steric and energy parameters. The results indicated that the removal of these target pollutants was a multi-ionic process. The saturation adsorption capacities of this AC improved with increasing aqueous solution temperature, ranging from 81 to 139 mg/g for Pb²⁺ and from 95 to 180 mg/g for Cu²⁺ under the tested operating conditions. The calculated interaction energies indicated a physisorption mechanism where oxygenated functional groups played a relevant role. Durian skin AC can be used as an alternative adsorbent for purifying wastewater and industrial streams polluted by heavy metals. Full article

The expansion of fifth-generation (5G) wireless networks has increased environmental exposure to mid-band and millimeter-wave radiofrequency electromagnetic fields (RF-EMF), but their molecular effects on male reproductive tissues remain insufficiently understood. This study evaluated whether repeated exposure to 3.5 GHz and 24 GHz RF-EMF alters testicular stress-associated molecular responses by integrating electromagnetic dosimetry with an in vivo rat model. Whole-body specific absorption rate (SAR) and 10 g peak SAR were estimated using a rat voxel model and scaled to the 20 cm antenna-to-cage geometry used during exposure. Thirty-six adult male Sprague Dawley rats were allocated to sham, 3.5 GHz, or 24 GHz groups and exposed for 1 h/day or 7 h/day over 60 days. Testes were examined histologically and assessed for HSP27, HSP70, and HSP90 protein expression. SAR values were low overall, although absorption was higher at 3.5 GHz than at 24 GHz. Histological evaluation showed preserved seminiferous tubule architecture without consistent structural injury. In contrast, molecular analysis demonstrated frequency- and duration-dependent modulation of heat shock proteins, including early HSP70 downregulation at both frequencies, followed by HSP90 upregulation at 3.5 GHz and HSP27 upregulation at 24 GHz. These findings indicate that low-level 5G-relevant RF-EMF exposure can modify molecular stress responses in testicular tissue even in the absence of overt histological damage. Full article

Durian, often referred to as the king of fruits, is widely consumed in Southeast Asia. However, the classification of its varieties is complicated by the lack of distinct visual differences between them. In this study, a fine-tuned MobileNet, a lightweight deep learning model, is applied for the classification of durian varieties. Transfer learning techniques are employed to adapt the MobileNet architecture using a custom dataset of durian images, enabling accurate differentiation between multiple varieties. First, the original MobileNet model is evaluated, which is found to yield low accuracy (8.22%) and a high loss (2.0553). A durian-specific classification layer is then added, and the model is trained for 100 epochs (2 min 21 s), achieving 76.28% training accuracy (0.5985 loss) and 73.20% validation accuracy (0.7606 loss). Further fine-tuning is performed, resulting in 100% training accuracy (4.9623 × 10⁻⁴ loss) and 93.69% validation accuracy (0.2281 loss) after 100 epochs (3 min 55 s). The findings demonstrate that the fine-tuned MobileNet model is capable of high classification accuracy while maintaining computational efficiency, making it suitable for real-time durian variety identification in agricultural and commercial settings. Full article

TCP transcription factors constitute a key regulatory family in plants, playing crucial roles in plant growth and development. Although this gene family has been extensively studied across diverse plant species, research in Gypsophila paniculata remains limited. Through genome-wide identification and analysis, this study identified 17 GpTCP in G. paniculata. Our analysis revealed that all GpTCP proteins contain a conserved TCP domain, with subcellular localization predictions indicating nuclear localization. Promoter analysis identified multiple cis-regulatory elements associated with plant organ development and growth regulation. Chromosomal synteny studies showed that gene expansion within the G. paniculata TCP gene family occurred after subfamily differentiation. Over-expression of GpTCP10 in Arabidopsis thaliana caused root development inhibition, leaf curling, smaller flowers, and yellowing of flowers. Further studies showed that in two normally growing G. paniculata varieties with different flower sizes, GpTCP10 was specifically expressed in leaf and floral tissues, with significantly higher expression levels in the smaller-flowered G. paniculata. These findings reveal the evolutionary characteristics of the TCP family in G. paniculata, and highlight the role of GpTCP10 in regulating organ growth and development in transgenic Arabidopsis thaliana and floral organ size in G. paniculata. Full article

Cucumber Fusarium wilt, which is induced by the soil-borne pathogen Fusarium oxysporum f. sp. Cucumerinum (FOC), represents a highly destructive disease. Cucumber seedling grafted onto figleaf gourd (Cucurbita ficifolia Bouché) rootstock (CFC) demonstrated better resistance to FOC. However, the molecular mechanism underlying this enhanced disease resistance capability is largely unknown. To elucidate this, we performed transcriptome, small RNA, and degradome sequencing for leaves from CFC and self-grafted cucumbers (SGC) as controls, with and without FOC infections, respectively. Our results indicated that three NAC genes, all predicted as targets of csa-miR164, were significantly up-regulated in CFC after FOC infection. Co-transformation assay in Nicotiana benthamiana confirmed that csa-miR164f directly inhibits NAC2, and transient overexpression of NAC2 in cucumber enhanced resistance to FOC, supporting its positive role in defense. Therefore, our results suggest that three NACs, upregulated in CFC, as an alternative pathway, enhance the reactive oxygen species burst and hypersensitive response, which further elevates the resistance to FOC infection. These results provide new insights into the molecular basis for improved FOC resistance in CFC. Full article

Crowd mass disasters occur over a relatively short time, and rescue operations in disasters, such as earthquakes, are challenging because of people’s behavior, type, or location. Therefore, it is essential to devise means and methods to manage such problems to minimize the consequences as much as possible. During disasters, rescue operations should be conducted in a timely conducted to save people’s lives. Otherwise, losses and consequences are severe, and if there are no proper rescuing operation models, the situation worsens, and the consequences are devastating. In particular, the allocation and coordination of limited rescue resources have a critical impact on response times and the number of lives saved. This paper aims to develop an Agent-Based Simulation (ABS) model for rescuing operations in crowd-mass disasters with six main intelligent agents. The proposed model explicitly represents the interactions among victims, rescuers, command-and-control entities, transportation assets, road networks, and affected infrastructure within a GIS-based urban environment. The developed model is based on an enhanced approach to improve rescue agents’ tasks allocation operations that enable modeling and simulation to make critical decisions for people to be rescued in a crowded mass disaster. Our task-allocation mechanism incorporates dynamic accessibility of roads, time-dependent rescue capacity, and context-aware prioritization of victims. Three related task-allocation strategies from the literature are used as baselines under identical scenarios, and performance is compared in terms of average rescue time and number of rescued victims. Results show that the proposed model achieves more efficient and robust rescue operations in most simulated experiments. Full article