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Influence of industrialization on economic growth in the asian tigers and lessons for India
Economic growth over the past two centuries has been driven mainly by the process of industrialization. Mechanized manufacturing, factories, and technological advancements have contributed largely to economic development. A prime example of such countries is the Asian Tigers- Hong Kong, Singapore, Taiwan, and South Korea, and the Tiger Cubs- Indonesia, Thailand, Malaysia, Philippines, and Vietnam that have achieved rapid industrialization through export-led strategies, technological innovation, and strong policies fostering economic development. India gained its independence around the same time as the Tiger, though the pursuit of industrialization hasnt been as pronounced in India as it has been in the Tigers. This study examines the impact of industrialization, proxied with industrial efficiency, on the GDP per capita of the tiger economies and India. Along with other control variables like FDI inflows, inflation, market capitalization, manufacturing exports, ICT imports, and CO2 emissions. Using data from 1991 to 2022, Using data from 1991 to 2022, a 2SLS model is applied to the Tiger economies using the instrument, control of corruption. A time series Autoregressive Distributed Lag model is used for India. The findings of this paper confirm that industrialization was the primary driver of the economic success of the Asian Tigers, while showing weaker progress in India. Building efficient infrastructure facilities, strengthening human capital formation and export-led manufacturing could allow India to emulate the strategy of the Asian Tigers. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Resilient strategies for sustainable tourism development: a land use analysis of the Kannur-Iritty corridor in Kerala, India
The study explores the resilience of the KannurIritty corridor in northern Kerala, where rapid infrastructural growth following the opening of Kannur International Airport in 2018 has reshaped mobility, land use, and tourism potential. The primary objective is to identify specific areas where challenges exist and provide policy recommendations to promote resilient and sustainable tourism development along the corridor. Integrating spatial, environmental, and perceptual data, the analysis develops a composite framework to assess environmental, infrastructural, socio-economic, and governance resilience. Results reveal strong infrastructural connectivity but moderate ecological and community adaptability. Water quality deterioration and unplanned land conversion reduce ecological resilience, while limited local awareness constrains adaptive tourism diversification. Conversely, peri-urban zones with mixed land use demonstrate higher potential for low-impact tourism such as farm and eco-tourism. Strengthening corridor governance through integrated land-use control, water-quality restoration, and community participation is essential to sustain tourism resilience. The study recommends targeted policy interventions, prioritizing sustainable infrastructure, decentralized waste management, and participatory tourism planning, to align regional development with Keralas responsible tourism agenda and provide a replicable model for other emerging tourism corridors in India. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Facile fabrication of mesh-free, GO-reinforced ZrO2-based separators for advanced alkaline water electrolysis
Alkaline Water Electrolysis (AWE) is a promising method for sustainable hydrogen production due to its maturity and use of non-noble metal catalysts. A key challenge lies in developing cost-effective, durable, and scalable separators that ensure ionic conduction and separation between the electrodes. This study presents a mesh-free composite separator composed of zirconia nanoparticles (ZrO2 NPs), polysulfone (PSU), and graphene oxide (GO), eliminating the need for expensive polyphenylene sulphide (PPS) mesh and its hazardous hydrophilic surface treatments. GO was incorporated as a multifunctional additive to enhance mechanical strength, hydrophilicity, and dispersion of ZrO2 NPs. Separators were fabricated with varying compositions of ZrO2 NPs, PSU, and GO, and tested in a zero-gap titanium-based electrolyser using nickel foam electrodes and 30?wt% potassium hydroxide (KOH) electrolyte. Amongst them, the Sep72/25/3 separator (72?wt% ZrO2, 25?wt% PSU, 3?wt% GO) showed a low area-specific resistance (ASR) of 298?m? cm2 at room temperature (RT). It also exhibited excellent wettability with a reduced contact angle of 23 after 24?h conditioning in 30?wt% KOH, along with a notable improvement in tensile strength, from 1.75?MPa (without GO) to 3.26?MPa, validating the reinforcing role of GO. The results demonstrate a simple and scalable route for fabricating mesh-free separators that strike an optimal balance between ionic resistance, mechanical strength, and wettability, thereby offering a cost-effective alternative for next-generation advanced alkaline water electrolysis (AAWE) systems. The Korean Ceramic Society 2025. -
Data-Driven Sustainability: Revolutionizing Hospital Supply Chains through Big Data Analytics
Purpose: Despite the growing interest in Big Data Analytics Capabilities (BDAC), its significant impact on hospital operations and supply chains in shaping hospital performance remains elusive. The study investigates the pivotal role of BDAC within the framework of hospital supply chains across India. Drawing upon the Resource-Based View, Dynamic Capability View, and Organisation Information Processing Theory, this research explores the intricate relationships among the organization's capability factors, BDAC, and hospital performance indicators. Design/Methodology/Approach: A conceptual model was developed and empirically tested using survey data collected from 446 hospital managers. The analysis was carried out by using partial least square-structural equation modeling (PLS-SEM). Findings: The results of this study support the significant mediating impact of BDAC on Operational Flexibility, Supply Chain Sustainability, and Organisation Revenue leading to the enhancement of organizational performance. The findings highlight the strategic importance of cultivating BDAC to improve operational efficiency and overall effectiveness in the context of Indian multispeciality hospitals. Originality/Value: This research contributes to the existing knowledge by highlighting the relationship between organization capability factors, BDAC, and performance indicators in the different settings of Indian multispeciality hospitals. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Early Disaster Detection and Monitoring Using Text Analysis and Levy Flight-based Particle Swarm Optimization Algorithm
Disasters can strike unexpectedly and leave a trail of destruction, causing immense suffering and loss of life while disrupting entire communities. These events can be natural, such as floods, earthquakes, hurricanes, wildfires, or man-made, including industrial accidents and technological failures. This study investigates a hybrid approach that uses text analysis, natural language processing, and optimization techniques to identify and monitor disaster-related events. The methodology of this paper involves collecting and analyzing text, focusing on sentiment and keywords associated with disaster-related text. Various aspects of text patterns are examined to enhance the models performance. The proposed model uses a Levy flight-based Particle Swarm Optimization algorithm to select optimal features from a vector set. It uses Text Blob for sentiment analysis, cosine similarity to classify each tweet as a disaster, Count Vectorizer for feature extraction, and XGBoost machine learning algorithm for classification. The significance of this model is that it provides early warning and insight for any disaster based on text analysis and classification. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
k-Domination Vertex Connectivity in Internet of Things Networks
The Internet of Things refers to a collection of closely connected devices that form a network through wireless or wired communication technology that work together to achieve common goals for their users. The IoT devices that are distributed in nature may cause the system to suffer from server crashes, server omissions, incorrect responses, and arbitrary errors. In this paper, we present a method of fault tolerance in IoT networks using graph theory approach to ensure the robustness of the network in case of attacks or disconnections through the concept of domination vertex connectivity in graphs. We further study this parameter in case of the Tensor product and Lexicographic product of specific graph classes, which have major implications in IoT networks. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Automated Classification of Medicinal Plants Using Lightweight Deep Learning and Transfer Learning
The identification of medicinal plants plays a pivotal role in traditional medicine, biodiversity conservation, and rural healthcare. Conventional manual identification methods are often time-consuming and error-prone, particularly when differentiating between morphologically similar species or plants at varying growth stages. Recent developments in deep learning, especially convolutional neural networks (CNNs) with transfer learning, have emerged as robust solutions for image-based classification tasks, offering efficiency and high accuracy with limited computational resources. The proposed framework employs a carefully structured deep learning pipeline integrating advanced preprocessing, lightweight architecture design, and domain-adaptive transfer learning. A large real-world dataset of 20,109 medicinal leaf images across 99 classes was standardized through resizing, normalization, and categorical encoding, followed by targeted data augmentation and class-weight balancing to address inter-class similarity and dataset imbalance. A key methodological novelty lies in the use of MobileNetV3 with an optimized transfer-learning strategy, leveraging its inverted residual blocks, Squeeze-and-Excite modules, and hard-swish activation to enhance texture-, venation-, and contour-based feature extraction in plant leaves. Unlike existing plant-recognition studies that rely on heavier CNNs, our approach introduces a computationally efficient, low-latency model specifically tailored for mobile and embedded deployment. Experimental results demonstrate that the proposed MobileNetV3-based model achieved a classification accuracy of 92.88%, with macro- and weighted-average F1-scores of 0.85 and 0.86, respectively. Precision and recall values across most classes ranged between 0.80 and 0.95, confirming the models reliability in differentiating species. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Efficient Scene Text Recognition in Noisy Environments Using Fusion-Based Adaptation and Triple-Level Confidence Modeling
Scene Text Recognition (STR) involves deciphering textual content embedded within complex, natural scene images, often following detection stages or integrated into end-to-end pipelines. Addressing the challenge of STR in noisy target domains, characterized by inter-domain and intra-domain noise, cluttered backgrounds, and irregular text shapes, this study proposes a robust and understandable framework titled Fusion-Based Adaptation for Scene Text Recognition (FASTR). The framework integrates a primary classifier with an epistemically aware auxiliary classifier to model uncertainty, supported by a novel Adaptive Scale Feature Module (ASFM) that enhances localisation through pixel-level mask prediction and multi-scale fusion. A Triple-Level Confidence (TLC) strategycategorized into high, medium, and low consistency thresholdsis introduced to enforce consistency loss and improve generalisation across domains. Additionally, a pseudo-labelling scheme refines the adaptation process through self-training under structured domain noise. FASTR is trained and evaluated on both synthetic (SynthText, MJSynth) and real-world (ICDAR 2013, SVT, and IIIT5K) datasets. It achieves a word recognition accuracy of 92.4% on IIIT5K, 89.7% on SVT, and 93.1% on ICDAR 2013, outperforming state-of-the-art baselines by an average margin of 2.8%. On cross-domain benchmarks with added noise, FASTR maintains high performance, achieving an average F1-score of 90.5%, with precision and recall values of 91.2% and 89.9%, respectively. Hyperparameters, training configurations, and evaluation metrics are transparently documented to ensure reproducibility. The findings demonstrate superior scale robustness, effective domain adaptation, and resilience to cluttered backgrounds, with explainability preserved through interpretable confidence maps and visual cues. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning and Deep Learning Approaches for Guava Disease Detection
A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Dysgraphia Disorder Detection and Classification Using Deep Learning Technique
Dysgraphia, a neurological condition, impedes childrens acquisition of standard writing abilities, leading to subpar written expression. Inadequate or underdeveloped writing proficiency can adversely affect a childs educational progress and self-esteem. To address this issue, our study involved compiling a novel dataset of handwritten operations and extracting an array of features to encapsulate the various dimensions of handwriting characteristics. This research presents the Rotational Region Convolutional Neural Network (R2CNN) as a novel approach to tackle this issue. The R2CNN framework integrates a multitask refinement network for accurate tilted box detection and a text region proposal network (Text RPN) to identify potential text areas. To address the imbalance in the training categories and mitigate the overpopulation problem through feature elimination, a balance parameter is incorporated into the loss function. This research focused on identifying dysgraphia by analyzing these extracted features, which included both handwriting and geometric elements. The feature-learning stage of deep transfer learning effectively extracts and applies characteristics to identify dysgraphia. Research findings indicate that this study can use handwritten images to detect dysgraphia in children. The results of the data-gathering process show that this investigation can leverage samples of handwritten text to recognize dysgraphia among young individuals. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
3TFL-XLnet-CP: A Novel Transformer-Based Crop Yield Prediction Framework with Weighted Loss Based 3-Tier Feature Learning Model
The advancement of crop yield prediction through artificial intelligence (AI) has gained significant attention. However, the existing AI-based approaches for maximizing agricultural productivity, specifically in crop yield prediction, have not consistently delivered satisfactory results. In response to this challenge, we propose a novel framework named as Three Tier Feature Learning with XLnet based Crop Prediction (3TFL-XLnet-CP) that enhances agricultural productivity by accurately predicting crop yield. The 3TFL-XLnet-CP framework employs a three-tier feature learning approach in combination with the powerful XLnet transformer-based crop prediction model. The three-tier feature learning involves the integration of Spiking Neural Network (SNN), Graphical Neural Network (GNN), and Convolutional Neural Network (CNN) to extract distinct feature vectors from the preprocessed data. These feature vectors are then concatenated using Jaccard Similarity to measure their similarity score. Additionally, a weighted Loss function is introduced to optimize feature learning, further enhanced by a novel self-adaptive Spider Monkey Optimization algorithm (SASMO). The concatenated features are subsequently fed into the classification layer for making precise crop yield predictions. The proposed model is implemented using the Python platform and evaluated against existing models such as ANN, RNN, DNN, and BiLSTM. The comparison demonstrates the superiority of our proposed 3TFL-XLnet-CP framework in accurately predicting crop yield. The Author(s) 2025. -
Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications
The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Melatonin seed priming enhances early seedling tolerance to individual and combined abiotic stresses in rice (Oryza sativa L.)
Combined drought and salinity pose a critical constraint to rice (Oryza sativa L.) seedling establishment, often exerting a greater impact than individual stresses such as drought, salinity, or heavy metals. In this study, we evaluated the efficacy of melatonin seed priming (50M) in mitigating the combined effects of drought-salinity stress along with individual drought, salinity, and chromium (Cr) stress during the seedling stage in the indica variety IR64, and validated the findings across three major Indian cultivars (MTU1001, KMP-225 and Jyothi). Melatonin priming significantly mitigated stress-induced root and shoot inhibition. Under combined drought and salinity stress, melatonin-primed shoots (4.17cm) were significantly higher and maintained lower oxidative stress markers, with reduced malondialdehyde and hydrogen peroxide, compared to hydro-primed controls, indicating improved membrane stability and antioxidant protection. Under individual drought, salinity, and chromium stress, melatonin-primed seedlings showed significantly enhanced root and shoot lengths compared to hydro-primed controls, supporting better early growth and resource allocation. Spectral profiling revealed higher chlorophyll content and normalized difference vegetation index values in melatonin-primed seedlings under salinity and chromium stress, indicating improved pigment stability and seedling vigor. Across all genotypes, melatonin conferred consistent benefits, indicating its potential as a low-cost and potentially scalable strategy. These findings highlight melatonin seed priming as a promising approach for improving rice resilience, with significant potential for enhancing crop establishment in stress-prone, resource-limited agroecosystems. Akadiai KiadZrt. 2026. -
Enhancing the Strength of Geopolymer Composites Synthesized from Iron Ore Tailings and Fly Ash for Use as Subgrade Material in Pavement Construction
As the idea of sustainable pavement becomes more important, an increasing number of industrial waste products and recycled materials are being used in the pavement industry to conserve natural resources. This study evaluates the potential use of iron ore tailings (IOT) blended with fly ash (FA) and activated with NaOHNa?SiO? solutions as a liquid alkaline activator (L) to synthesise IOT-FA geopolymers, which can be used as a sustainable material for the pavement subgrade layer. The influence of FA replacement levels and alkaline activator ratios on the geotechnical and microstructural behaviour of IOT was examined through compaction, unconfined compressive strength (UCS), and California bearing ratio (CBR) tests, supported by Scanning Electron Microscopy (SEM) and X-Ray Diffraction (XRD) analyses. The results show that 20% FA replacement gives the optimal mix, resulting in a 28-day UCS improvement of over four times that of untreated IOT due to enhanced geopolymerization. The liquid alkaline-activated mixes achieved CBR values of ? 8, meeting IRC requirements for subgrade applications. SEM analysis revealed dense gel formation and improved particle bonding, while XRD results indicate the development of geopolymeric reaction products. TCLP results indicate that metal leaching remained within permissible limits, establishing the environmental safety of the developed composite. The thickness of the pavement layer was designed using IITPAVE software based on CBR values and assessed against IRC:372018 criteria. The analysis indicated a reduction in layer thickness for various daily commercial vehicle counts ie, CVPD (450,1000), across all evaluated combinations. Overall, the study demonstrates that alkaline-activated IOTFA mixtures offer a technically viable and sustainable alternative for pavement subgrade construction. Chinese Society of Pavement Engineering 2026. -
Experimental Study on Warm Mix Asphalt Binders with a Focus on Rheological Performance
Warm mix asphalt (WMA) mixtures have been increasingly used in road construction due to their energy saving and environmental protect benefits. However, unsuitable additives were often adopted due to their variability and it often led to poorer quality of asphalt pavements. In this study, nine asphalt samples from two categories of warm mix additives, including six organic and three chemical additives, were prepared. The rotational viscosity test, temperature sweep test, linear amplitude sweep test (LAS), and bending beam rheometer (BBR) test were employed to comprehensively evaluate the effect of warm mix additives on the viscosity-reduction effect, high-temperature performance, fatigue resistance, and low-temperature performance of WMA, respectively. The results showed that the viscosity- reduction effect of organic additives was more significant compared to chemical additives. Besides, organic additives were generally favorable to the high-temperature and fatigue resistance of asphalt binders, but their effects on the low-temperature performance of asphalt binders were highly variable. Chemical additives had a limited effect on the high-temperature and fatigue resistance of asphalt binders. Meanwhile, the chemical additives have a marginally positive and stable impact on the low-temperature performance of asphalt binders. The findings provided a comprehensive basis for the selection and application of warm mix additives. The Author(s), under exclusive licence to Chinese Society of Pavement Engineering 2025. -
Design and Implementation of a Single Phase Resonant Converter with Natural Power Factor Correction for Onboard Electric Vehicle Charging Applications
The proposed converter introduces a dual inductor dual capacitor (LCLC) resonant configuration by integrating the series inductance as the transformers leakage inductance and adding a parallel capacitor to the magnetizing inductance, enhancing power density and efficiency. Dual inductor capacitor (LLC) resonant converters used for alternating current to direct current (AC/DC) conversion are highly suitable for electric vehicle (EV) chargers due to their superior efficiency, high power density, and soft switching capabilities. This work increases power density by minimizing the size of the series inductor typically required in LLC converters through integration with the transformers leakage inductance. To control the output DC voltage, switching frequency control is utilized. However, the power factor of AC/DC resonant converters is generally poor. To improve the power factor, the proposed converter uses a boost converter at the front end, operating in discontinuous conduction mode (DCM) to achieve a unity displacement power factor. By sharing the same switches for both the power factor correction (PFC) and resonant stages, the converter is made more compact and cost effective. Furthermore, a bridgeless rectification technique is implemented to minimize the count of switching devices. The proposed topology and control strategy have been verified through hardware results on a 1500W LCLC AC/DC resonant converter with a 48 V, 30Ah lithium-ion (Li-ion) battery pack. This topology achieves high efficiency with zero voltage switching (ZVS), improved power factor, reduced component count, and a compact, cost effective design by sharing switches between PFC and resonant stages. The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2026. -
Impact of WO3:CeO2@MXene/gC3N4 nano disk on sunlight-driven photocatalytic removal of fluoroquinolone antibiotic and high-performance supercapacitor application
This research highlights the use of a WO3:CeO2@MXene/gC3N4 (MGWC) nanodisk as a versatile material. MGWC demonstrates efficient photocatalytic degradation of moxifloxacin (MOF) in water under sunlight and also shows great promise for high-performance supercapacitor applications. MGWC was synthesized using a modified hydrothermal method and thoroughly characterized using various techniques. The MGWC showed a band gap energy of 2.79eV determined through UVVis DRS analysis and an average crystallite size of 39.6nm calculated from XRD. A promising photocatalytic activity was observed for the degradation of MOF, outperforming other photocatalysts. Additionally, preliminary studies examined variations in catalyst concentration, pH, kinetics, electrolytes, scavengers, reusability, and TOC, contributing valuable insights. Under optimal conditions, the MOF achieved almost complete degradation, reaching about 99.7% within 180min using the MGWC photocatalyst. Additionally, MGWC exhibits promising potential in supercapacitor applications. EIS and CV studies have been used to examine MGWCs exceptional charge transfer properties. CV tests confirm the pseudo-capacitive nature of MGWC electrodes. GCD studies of MGWC exhibit a high specific capacitance of 551 F/g at 1 A/g with incomparable capacitance retention of 98.1% over 10,000 cycles. This research not only aids in reducing emerging environmental pollutants but also sets the stage for sustainable energy solutions. The Author(s), under exclusive licence to Korean Carbon Society 2025. -
Microbial degradation of textile dye reactive blue 250 (RB 250) by the novel Pseudomonas aeruginosa RGB11: a sustainable approach
Untreated textile effluent discharge has increased due to rapid industrialization and human activity, posing a serious and growing threat to environmental well-being over the past few decades. This study isolates a novel bacterial strain, Pseudomonas aeruginosa RGB11, from sewage sludge capable of degrading the azo dye Reactive Blue 250 dye (RB 250) as identified via 16S rRNA gene sequencing. Decolorization of RB 250 was tested in Minimal Salt Media (MSM) throughout the studies. Under static conditions, pH 7, and 37C, the dye showed 88.91% decolorization after 24h, which increased to 96.27% at pH 9 and 93.15%at 45C. Adding 1% w/v sucrose and yeast extract as carbon and nitrogen sources increased the decolorization to 96.83% and 97.54%, respectively. A study on growth kinetics over 8h showed that as the Optical Density (OD) of bacteria increased at 600nm, the absorbance of the dye decreased at 604nm, indicating the bacterial role in the decolorization process. FT-IR analysis of the metabolite extracted after decolorization revealed the shift in the intensity of the characteristic peak of the RB 250 dye and the formation of new peaks, which can be attributed to the degradation of dye and generation of intermediates in the decolorized solution. The LCMS study further confirms degradation as the parental peak in the dye disappeared and smaller new peaks were observed, possibly due to breakage of characteristic bonds in dye like the azo bond. Haemolysis test on blood agar demonstrated gamma haemolysis confirming that the novel strain of Pseudomonas aeruginosa RGB11 doesnt produce haemolysins and is a non-pathogenic strain. Thus, emphasizing the efficiency of Pseudomonas aeruginosa RGB11 as potential candidate for dye decolourizer for textile effluents. It has immense potential to serve as a contributor to environmental studies by providing a means of sustainable bioremediation for textile effluents. The Author(s) under exclusive licence to Sociedade Brasileira de Microbiologia 2025. -
Influence of fruit and vegetable waste substrates on the nutritional profile of black soldier fly (Hermetia illucens) larvae and prepupa
The black soldier fly, Hermetia illucens, larva is widely recognized for efficiently converting organic biowaste into high-quality biomass, making it a key player in organic waste management. However, the nutritional value of the black soldier fly larvae (BSFL) is dependent on the substrates they feed on. This study investigated the nutritional profiles of different stages of BSFL- 3rd instar to 5th, and prepupa, reared on two distinct organic wastes, namely fruit (FW) and vegetable waste (VW). Analysis of crude protein, carbohydrate, crude lipid, minerals, and fatty acid composition was conducted across various growth stages, such as 3rd instar to 5th instar, and prepupa. The prepupa stage reared on FW exhibited the highest crude protein content (54.16 0.64%), while VW 5th instarhad the highest crude lipid content (12.4 0.20%). BSFL reared on FW displayed a high fatty acid composition with higher saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA), regardless of the substrate. Calcium and potassium were the most abundant minerals in BSFL, followed by magnesium, manganese and zinc, with substantial concentration variations between substrates. Amino acid profiling focused only on BSFL reared on FW, due to superior results in the chemical composition analysis, revealing that the prepupal stage contained the highest amount of essential and non-essential amino acids compared to the other stages. This study suggests that BSFL meal has the potential to serve as a novel and sustainable source of nutrient-rich animal feed ingredients in aquaculture and other animal husbandry practices. African Association of Insect Scientists 2025. -
A study on resonant vibro-acoustography for tissue heating: a pathway towards breast cancer detection
Purpose: Develop a new active thermography method for breast cancer detection. Methods: The computational tissue was excited at its resonant frequency using the vibro-acoustography technique. A nonlinear acoustic wave equation was solved using the finite element method to analyze the propagation of the acoustic wave within the computational phantom. The computational approach included separate calculations for the temperature in the focal region, considering both acoustic absorption and vibration effects. The study was validated through experimental tests using an agar phantom. Results: Depending on the tissues attenuation coefficient, the applied ultrasonic signal generates localized heating in the target area. Malignant breast tissue typically exhibits a higher attenuation coefficient than healthy tissue. Consequently, ultrasonic signals lead to an elevated temperature in such tissue. When a focused ultrasound is applied to heterogeneous tissue for 90 seconds using a dual transducer, the temperature can increase by. In contrast, using the same input power level in healthy tissue causes a temperature increase of only. Enhanced temperature levels achieved through this acoustic method can improve thermal contrast in deeper tumors when thermography is employed. With the application of this acoustic force, it becomes possible to detect a tumor of 3 mm at a depth of 10 mm, whereas, in the absence of this source, such a tumor of this size can only be detected when it is located at a depth of 4 mm. Approximately 22% of the simulated temperature rise from vibrational effects can be attributed to ultrasound forces in resonant modes. The experimental results further validated the simulation results. Conclusion: The suggested resonant vibro-acoustography technique represents an effective approach to increasing thermal contrast between malignant and normal tissue. Significance: This approach can serve as an active thermography method for detecting breast cancer. Since the resonant frequency varies across different stages of the disease and the acoustic attenuation coefficient is higher in affected tissues than in normal ones, this technique can improve thermal contrast through vibration and acoustic absorption. The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2026.