Corrosion remains a persistent global challenge, causing severe economic losses and material degradation across multiple industries, while the accumulation of human hair waste poses an emerging environmental burden due to its slow biodegradation and widespread disposal. Addressing these two pressing issues simultaneously, this study presents a sustainable strategy that transforms waste human hair into a value-added anti-corrosive coating material. Crude melanin was extracted from human hair via a base hydrolysis method and subsequently incorporated into a biodegradable poly(vinyl alcohol) (PVA) matrix with different melanin: PVA ratios to develop an eco-friendly coating. Successful extraction and characteristic functional groups of melanin were confirmed using Fourier Transform Infrared (FTIR) Spectroscopy. The corrosion protection performance of the developed coating was evaluated using Tafel polarization and Electrochemical Impedance Spectroscopy (EIS). The Tafel polarization revealed a substantial enhancement in corrosion resistance, achieving an inhibition efficiency of 89.4% for a 4000 ppm crude melanin coating compared to bare stainless steel. Furthermore, the study demonstrates the practical feasibility of the approach, where approximately 5.000 (±0.001) g of hair waste can produce sufficient crude melanin (12,000 ppm) to coat an 8 × 8 inch stainless steel surface with a thickness of 0.10 ± 0.01 mm. Unlike previous studies employing synthetic or microbial melanin, this work demonstrates the use of minimally processed crude melanin extracted directly from waste human hair as an environmentally sustainable anticorrosive coating material, providing a practical waste-to-value strategy for corrosion protection.
This work is motivated by transducers such as Hall-effect sensors and Integrated Electronics Piezoelectric (IEPE) devices, which share a common signal characteristic when measuring time-varying signals: a low-amplitude, time-varying component of interest superimposed upon a dominant, quasi-static direct current (DC) offset. While low-cost microcontrollers such as the ESP32 offer integrated analog-to-digital converter (ADC) capabilities, their utility for precision acquisition of such signals is limited by inherent nonlinearity, reference instability, and inadequate resolution. This paper presents an integrated hardware-firmware framework that strategically couples three complementary stages through a unifying dynamic range management strategy to systematically address these limitations. The analog front-end implements a phase-linear Sallen-Key Bessel filter to preserve signal morphology during anti-aliasing, followed by an instrumentation amplifier featuring programmable gain and an automated DC-offset nulling algorithm. The algorithm drives a 12-bit digital-to-analog converter (DAC) to adaptively center the signal within the ADC’s operating range via a binary search heuristic, without prior knowledge of the offset magnitude. To address the ESP32’s well-documented ADC nonlinearity, a piecewise linear calibration strategy was developed using a TL431 precision shunt reference and an internal DAC sweep across the full input range, generating a per-device, per-channel correction lookup table (LUT). The complete signal chain was characterized metrologically against a National Instruments data acquisition (NI DAQ) system used as an independent ground truth. Across three independent device units, the uncalibrated ADC exhibited errors of approximately 158.1±19.3 mV mean absolute error (MAE) (4.8 of full-scale). The self-contained piecewise linear calibration framework, requiring no external signal generator or factory calibration infrastructure beyond a single TL431 precision shunt reference, achieved an 88.8% improvement in measurement accuracy, reducing errors to 18.6±11.4 mV MAE (0.56% of full-scale), confirming that the calibration procedure is reproducible across hardware variants with per-device improvements ranging from 82.5% to 92.4%. These results demonstrate that the proposed framework effectively bridges the metrological gap between consumer-grade embedded silicon and laboratory-grade data acquisition (DAQ).
Microbial communities play a crucial role in the survival and fitness of mosquitoes, influencing multiple stages of their life cycle. This study aimed to characterize and compare the fungal communities associated with Aedes albopictus and Culex quinquefasciatus larvae and to evaluate the entomopathogenic potential of common fungal species isolated from both genera against Aedes albopictus and Culex quinquefasciatus. Larvae (n = 240) were collected from natural and artificial breeding sites in Kandy and Galle districts. Fungi from external surfaces and total larva extract were cultured on Potato Dextrose Agar (PDA) and identified morphologically and by DNA sequencing. Four fungal species common to both mosquito genera, Aspergillus fumigatus, A. niger, Cladosporium cladosporioides, and C. langeronii, were selected for entomopathogenicity assays. Bioassays were conducted using conidial suspensions (1 × 103–1 × 107 conidia/mL), and mortalities were recorded at 24 and 48 hours. Nine fungal species were isolated from Aedes larvae, with Curvularia sp. and Trichoderma sp. being the most abundant. Twenty fungal species were isolated from Culex larvae, with Cladosporium sp. predominating. Only A. niger exhibited strong larval pathogenicity against both mosquito species, with low LC₅₀ values (Ae. albopictus: 2.60 × 10⁶ and 2.17 × 10⁵ conidia/mL respectively at 24 h and 48 h; Cx. quinquefasciatus: 5.20 × 10⁵ and 4.05 × 10⁵ conidia/mL, respectively, at 24 h and at 48 h. Cladosporium cladosporioides showed the highest larvicidal activity against Cx. quinquefasciatus larvae (LC₅₀: 2.71 × 10⁵ conidia/mL; LC₉₀: 1.95 × 10⁶ conidia/mL). In contrast, Aspergillus fumigatus and C. langeronii showed relatively low larvicidal activity and were effective only against Cx. quenquifaciatus larvae. In adult bioassays, A. niger also showed notable pathogenicity against Ae. albopictus, with LC₅₀ of 7.98 × 10⁶ and 8.72 × 10⁵ conidia/mL at 24 and 48 hours, respectively. The results suggest that A. niger can be considered as an entomopathogenic fungus and could be recommended as a potential biological control agent against both larvae and adult mosquitoes.
The purpose of this study was to explore the use of AI-assisted tools in the higher education learning process by finding out the specific tools utilized by postgraduate students and exploring the associated benefits and challenges of their integration. A case study approach was adopted, focusing on postgraduate students studying Information Technology in Education at a state university. A sample of 100 students and 10 lecturers was selected using the purposive sampling method. Data were gathered through questionnaires, document analysis of 20 assignments, and semi-structured interviews with 20 students and 10 lecturers. Quantitative data were analyzed using descriptive methods, while qualitative data were analyzed using thematic analysis. The findings revealed that postgraduate students frequently used AI-assisted tools for learning, especially in the preparation of various classroom activities, including written assignments, coding tasks, and multimedia creation. Questionnaire data revealed that 100% of the students used AI-assisted tools in their classroom activities. The interview data suggested that benefits include language fluency, quick access to information, time-saving capabilities, coding assistance in various programming languages, and the ability to create multimedia content. However, lecturers identified several challenges, especially concerning academic integrity when evaluating classroom activities, assignments, and theses. They expressed concerns over maintaining academic standards, students’ critical thinking abilities, and ethical considerations when planning lessons and assignments. While AI-assisted tools can significantly enhance teaching and learning efficiency, their improper use may hinder students’ intellectual development. Therefore, when designing classroom activities and assessments, lecturers should focus on evaluating higher-order thinking skills. It is recommended that the university raise awareness among both students and lecturers regarding the ethical and responsible use of AI-assisted tools in teaching, and implement clear policies to regulate the use of AI-assisted tools in learning.
Rivers receiving untreated or poorly treated wastewater act as reservoirs for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), posing serious environmental and public health risks. This study investigated bacterial loads, antibiotic-resistant Escherichia coli, and ARGs in the Mahaweli River in Kandy district, Sri Lanka, at two locations: a) at Gatambe, where the river received discharges from the canal Meda Ela (ME) and Kandy city wastewater treatment plant (WWTP), and b) at Haragama, where the river received the discharge of the Ntional Livestock Development Board (NLDB) farm. Water samples were collected from the river upstream, at discharge points, downstream, and from a buffalo pond inside the farm during two visits in 2024. Bacterial isolation, total coliform counts, and fecal coliform counts were conducted using standard microbiological methods. Susceptibility tests were conducted with E. coli isolates against six antibiotics, i.e., amoxicillin (AMX), tetracycline (TET), co-trimoxazole (COT), ciprofloxacin (CIP), ceftazidime (CAZ), and streptomycin (S), using the Kirby-Bauer disk diffusion method. Polymerase Chain Reaction (PCR) was used to detect ARGs blaCTX-M, tetA, aadA1, dfrA5/14, and qnrB. The highest microbial contamination and prevalence of antibiotic-resistant E. coli were recorded at the ME discharge point, with resistance highest to AMX at 87%, followed by CAZ at 43% and COT at 35%. At WWTP effluent, resistance to AMX and TET was 76% and 67%, respectively. Amoxicillin (50%) and TET (44%) resistance persisted downstream, while CIP resistance declined to 0%. Escherichia coli from the National Livestock Development Board (NLDB) farm discharge exhibited lower resistance, while water from the buffalo pond of the farm showed 50% resistance to AMX and TET, with the tetA gene. Multidrug resistance was 35% at ME and 48% at WWTP discharge points. All tested genes were detected in ME and WWTP discharges, except qnrB at WWTP. Strengthening wastewater treatment and effluent monitoring is essential to curb environmental dissemination and horizontal transfer of ARGs through Mahaweli water, which is an important source of drinking water.
Petroleum contamination is a prevalent environmental issue, with detrimental effects extending across diverse ecosystems. Among the numerous hydrocarbons present in petroleum, diesel is a major contributor to soil pollution. Previous studies have identified bacteria as highly effective agents for diesel degradation (DD), highlighting their potential for bioremediation of oil spills. However, successful bioremediation of diesel-contaminated environments requires the identification of effective diesel-degrading microbial strains, along with key traits that support DD, such as biosurfactant production. Therefore, this study aimed to identify efficient diesel-degrading bacteria (DDB) from petroleum-contaminated sites in Kandy District, while concurrently exploring their biosurfactant production capabilities. DDB were isolated using Bushnell-Haas broth supplemented with diesel. Pure bacterial isolates were obtained by streak and spread plating techniques. Isolated bacteria were identified through morphological and biochemical methods. The DD efficiencies of the isolates were examined by turbidity assay and 2,6-dicholophenolindophenol (DCPIP) assays. Moreover, efficient DDB were used to design artificial consortia. The degradation efficiencies of artificial consortia and natural consortia were also evaluated. Additionally, the biosurfactant production ability of efficient DDB was investigated by the emulsification test, oil displacement test, and CTAB agar plates. Among the diesel-degrading bacterial strains isolated, the four most effective isolates, in the order of decreasing efficiency, belonged to the genera Pseudomonas (83.04%), Staphylococcus (71.84%), Corynebacterium (62.61%), and Streptococcus (60.50%). Further, the DD efficiencies of most of the designed consortia and natural consortia were found to be significantly higher than those of the individual bacterial isolates, with one natural consortium containing Pseudomonas, Staphylococcus, and Corynebacterium that outperformed the designed consortia. Moreover, the isolated Pseudomonas and Staphylococcus strains were identified as biosurfactant producers, suggesting that their ability to produce biosurfactants contributes to their highest DD efficiencies. The findings of this study can be used in developing locally adaptable bioremediation strategies for diesel-contaminated sites in Sri Lanka.
Carrageenan is a commercially important polysaccharide extracted from seaweeds and widely used in the food, pharmaceutical, and cosmetic industries due to its gelling, thickening, and viscosity-enhancing properties. This study aimed to compare the quantity and quality of extracted refined carrageenan using four common extraction methods: conventional Ca(OH)2 autoclaving, 6% (w/v) Ca(OH)2, 6% (w/v) KOH, and 6% (w/v) NaOH. Forty-five days old brown color morphotype of Kappaphycus alvarezii grown in Jaffna was used as a seaweed source. Carrageenan yield, transmittance under Fourier Transform Infrared (FTIR), gel strength, water holding capacity, and L*a*b* color values were measured across extraction methods. Kappaphycus alvarezii cultivated in Jaffna yielded a carrageenan content exceeding 25%, with the highest recovery (37.20%) obtained using the KOH extraction method, which was significantly greater (P<0.05) than yields obtained with the other three extraction methods. However, the FTIR spectra for four extracted carrageenan samples displayed similar patterns with the commercial carrageenan sample, with slight deviations at 845 cm-1, 925-930 cm-1, and 1220-1260 cm-1 wave ranges indicating different types of kappa-, iota- and lambda-carrageenan fractions of different extraction methods. The highest (P<0.05) gel strength (1745.86 g/cm2) was recorded in the KOH method, whereas the conventional method recorded the significantly lowest gel strength (706.26 g/cm2). Moreover, conventional and KOH-treated carrageenan samples showed the highest (P<0.05) water holding capacity (98.59% and 97.42%) along with the highest a* and b* values. By contrast, lightness (L*) did not show a significant difference among carrageenan extracted from four methods. Therefore, it can be concluded that treatment with 6% KOH produces refined carrageenan with the highest yield and desirable physicochemical properties, outperforming the other methods when K. alvarezii is used as the seaweed source. Furthermore, this study highlights the potential for significant advancement in the use of 6% KOH method for domestic superior carrageenan production in Sri Lanka.
Acinetobacter baumannii, a multidrug-resistant Gram-negative bacterium, is increasingly recognized as an oral pathogen. With rising resistance to available antibiotics, plant-derived alternatives are gaining attention. This study aimed to evaluate the antibacterial activity of extracts of Mimusops elengi seed, Aloe barbadensis gel, and root extracts of Cocos nucifera, Areca catechu, and Piper nigrum against A. baumannii. Plant extracts were tested individually as well as in different combinations to evaluate the synergistic activity. Dental plaque samples were collected and cultured in nutrient media, and pure colonies of bacterial isolates were identified based on morphology. Based on the occurrence frequency, the isolated Morphotype A, was used for Anti-Bacterial Susceptibility Testing (ABST) via the disk diffusion method. The identity of the bacterium was confirmed by 16S rRNA gene sequencing. Crude plant extracts were prepared using methanol and subjected to ABST, individually and in combinations (1:1:1 ratio). Two, 0.2 % chlorhexidine containing mouthwashes (Positive) and sterile distilled water (Negative) were used as controls. According to ABST results, A. catechu (13.573 ± 0.0200 mm) and C. nucifera (13.108 ± 0.0366 mm) extracts showed moderate inhibition, while M. elengi showed slightly higher inhibition (14.500± 0.0643 mm). Aloe barbadensis showed the least zone of inhibition (9.017 ± 0.0328 mm), whereas P. nigrum had no effect. The combination of extracts (15.853 ± 0.0288 mm) and chlorhexidine mouthwashes (15.028 ± 0.0413 mm, 15.815 ± 0.0683 mm) also produced comparable inhibition zones, with no statistically significant difference (p > 0.05). The antibacterial activity of the combined extracts did not significantly differ from the individual effects of A. catechu or C. nucifera. Overall, M. elengi seed, A. catechu, C. nucifera root, and A. barbadensis gel extracts exhibited notable antibacterial activity against A. baumannii, individually as well as in combinations, highlighting their effectiveness as plant-based alternatives for combating A. baumannii infections.
The present study investigated the potential of two cyanobacterial species, namely Nostoc ellipsosporum (NE) and Spirulina subsalsa (SS), to remediate textile wastewater (TWW) while obtaining wastewater-grown biomass for biodiesel production. This study reports their multi-faceted benefits using unsterilized and undiluted (100%) TWW for the first time. Both cyanobacterial species were cultivated in TWW under greenhouse conditions, focusing on their growth, TWW decolorization, pollutant removal, phytotoxicity, lipid content, and fatty acid profile. Results demonstrated significant growth and decolorization of TWW by both species, highlighting their potential for sustainable treatment of TWW. NE-treated TWW (NE-TWW) achieved a chemical oxygen demand (COD) removal efficiency of 96.48%, while SS-treated TWW (SS-TWW) reached 93.11%. Ammonia removal efficiencies were recorded as 76.28% for NE-TWW and 96.86% for SS-TWW. NE-TWW and SS-TWW achieved nitrate removal of 69% and 73%, respectively. Phosphate removal was 81.14% for NE-TWW and 33.33% for SS-TWW. Both species exhibited more than 75% removal of heavy metals, particularly Cr, Fe, Zn, Cu, and Ni. Seed germination studies showed enhanced germination and seedling growth of green gram (Vigna radiata) when irrigated with NE-TWW and SS-TWW, indicating phytotoxicity reduction in treated TWW. Lipid yields (w/w) of 21.5% and 25% were recorded from TWW-grown biomass of NE and SS, respectively. Both species exhibited favorable fatty acid methyl ester (biodiesel) profiles, dominated by palmitic acid (C16:0), oleic acid (C18:1), stearic acid (C18:0), lauric acid (C12:0), and myristic acid (C14:0), indicating their suitability as biodiesel feedstocks. Overall, this integrated approach not only provides an eco-friendly and cost-effective strategy for TWW treatment but also offers a sustainable feedstock for biodiesel production.
High-valued black pepper-based products are adulterated with papaya (Carica papaya) seeds, and chilli (Capsicum annuum) seeds. Those are causes of food allergic reactions and food poisonings. Therefore, a cost-effective and efficient system should be developed to identify those adulterants. According to studies, each plant material has its own unique microscopic image fingerprint. Phase contrast microscopes are able to differentiate those features based on their refraction indexes. This research was developed for identifying the best deep learning architecture to differentiate adulterated black pepper powder from genuine black pepper powder by using microscopic images. TensorFlow deep learning models (InceptionV3, Inception ResNetV2, ConvNeXtLarge, Xception, VGG-19, and ResNet-50) were used as the backbone. The Adam optimizer and categorical cross-entropy as a loss function were used in both the evaluation stages and the training stages. Categorical Accuracy, Precision, F1Score, False Negatives, False Positives, True Negatives, and True Positives were used for evaluating the trained models. 0.001 and 0.0001 Fine-tuning with learning rates were used for model training. According to the results of evaluation matrices, InceptionV3, Xception, and ResNet-50 showed superior performances in differentiating genuine black pepper powder from other powder classes. But overfitting was identified when increasing the number of training epochs at lower learning rates. Therefore image segmentation should be introduced for overcoming the challenges in whole image labelling. Selective hierarchical neural network layer fine-tuning and hyper parameter tuning can be implemented for overcoming the model training based issues. According to the findings, those microscopic image based techniques are able to identify genuine black pepper powder from other adulterated black pepper powder.
Pyrite sulfur and fossil diatom analysis, coupled with the radiocarbon dating, provides important information regarding the sedimentary environment and past sea level fluctuations. Since this knowledge is beneficial in predicting future climate change patterns and sea level variations, the present study examines the paleo sea-level changes of Fujii, Okayama Prefecture, Japan, using fossil diatom assemblage and sedimentary pyrite formation. Based on the pyrite sulfur content, a marine environment has been developed at a depth of 1.8 meters around 6,745 +/- 80 years BP. This result has been verified with the finding of high pyrite sulfur percentage and the presence of marine diatoms. Based on the paleo salinity variations defined by concentrations of pyrite sulfur, a sea level rising period has been observed from 11,035 +/- 160 years BP to 6,745 +/- 80 years BP (from 2.4 m to 1.8 m depth), and it perfectly aligns with the sea level changes in the early Holocene period. Additionally, the diatom assemblage in this depth change from freshwater (i.e. Navicula sp., Melosira sp.) to marine (i.e. Melosira architecturalis Brun, Nitzschia sp.), supporting the findings. Finally, the reducing pyrite concentration above 1.8 m reflects the reduced marine influence due to receding sea levels and the introduction of more freshwater, which is supported by the presence of more freshwater diatoms.
Recent developments have focused on utilizing natural fibers, such as hemp and coconut shell, which are biodegradable, environmentally friendly, and readily available. When combined with polymers to enhance strength while minimizing composite weight, such green composites have attracted attention for applications in manufacturing, packaging, aerospace, sports, housing, and other sectors. This study determined the fabrication of epoxy-based composites reinforced with coconut shell powder (CSP) of varying grain sizes and areca husk fly ash as a novel matrix for improved green polymer composites. The mechanical properties, including tensile and flexural strengths, were evaluated. Results showed that CSP-reinforced composites with a grain size of 150 & micro;m exhibited the highest tensile strength (19.48 MPa) and flexural strength (58.52 MPa), whereas composites with a 600 & micro;m grain size showed the lowest values (11.79 MPa and 44.97 MPa for tensile and flexural strengths, respectively). Composites with 600 & micro;m exhibited reductions of 39.48% in tensile strength and 33.91% in tensile modulus, and 23.15% in flexural strength and 21.38% in flexural modulus compared to CSP-areca husk fly ash composites with 150 & micro;m grain size. SEM analysis indicated that smaller grain sizes provide a larger surface area for resin adhesion, enhancing the mechanical performance of the composites. These lightweight, natural fiber-reinforced composites show potential as sustainable alternatives to conventional materials in structural, locomotive, and household applications.
Sugarcane is one of the major crops cultivated in tropical and subtropical regions worldwide. Assessing crop maturity is important for optimizing harvest timing and improving yield. Conventional sugarcane maturity evaluations use agronomic characteristics, past trends, and eye inspections, which are labor-intensive and not precise, particularly over large plantations. Some sugarcane varieties mature to complete ripeness earlier than their expected maturity age, rendering physical observation inefficient and unsuitable. To address this point, this experimental research study introduces a novel, cost-effective approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors to estimate sugarcane maturity through remote sensing and deep learning techniques. The primary objective is to develop an efficient deep learning-based classification system for identifying mature sugarcane fields from multispectral images gathered using UAVs. Pelwatte Lanka Sugar Company (Pvt) Ltd geo-referenced yield data were used together with multispectral imagery of 3-12-month-old plant-crop sugarcane fields from intermediate and dry regions. Fields were classed as 'matured' (Brix > 10) or 'immatured' (Brix <= 10) based on mean Brix values. Red, Red Edge, Green, Near-Infrared (NIR), and spectral bands and vegetation indices NDVI and NDRE were investigated. 17,256 images with a resolution of 200 & times;200 pixels were utilized (2,876 for each band/index). The dataset was split between training and validation sets. Modeling was done in two phases: comparison of the feature extractor and constructing a specific Convolutional Neural Network (CNN). The proposed CNN achieved a maximum accuracy of 93% on NIR images, whereas Red, Green, Red Edge, NDVI, and NDRE achieved 84%, 81%, 76%, 69%, and 59% accuracy, respectively. The results indicated that the model can classify sugarcane maturity with a high level of accuracy, thus improving precision agriculture methods.
Allium cepa has emerged as a valuable model organism for toxicity testing, offering a viable alternative to animal testing worldwide. It is regarded as an efficient bioindicator for genotoxicity testing, primarily due to its rapid root growth rate and the presence of relatively few large chromosomes. The use of A. cepa is highly sought after because of its cost-effectiveness, widespread availability, and ease of handling, demonstrating a strong correlation with mammalian test systems. In this review, we emphasize the diverse applications of the A. cepa bioassay in environmental assessments. A comprehensive literature search was conducted using Google Scholar and ResearchGate with the keywords “Allium cepa, bioassay, environmental assessments, and toxicity testing” over the period of February to April 2024. This search initially yielded 131 articles, which were subsequently narrowed down to 31 relevant studies that specifically assessed the use of A. cepa as an evaluation tool. The findings reveal the applicability of the A. cepa bioassay in a variety of environmental assessments, including toxicity testing of pesticides, heavy metals, and industrial effluents, as well as monitoring pollution effects and water quality. Additionally, the review discusses chemical testing across a wide spectrum, from pesticides to azo dyes, food preservatives, and hydrocarbons, highlighting various industrial applications. This synthesis of current literature underscores the utility of the A. cepa bioassay in contributing to environmental monitoring and pollution control.
Medical education is inherently stressful due to its rigorous curriculum, long training hours, and high expectations placed on students. Female students pursuing the MBBS program may encounter additional gender-specific challenges that further increase stress levels. This exploratory cross-sectional study was conducted in an all-women’s medical college and tertiary care hospital using a questionnaire specifically developed and validated for this study. A total of 484 female medical students across all years of the course participated. The instrument assessed stress across four domains: Academic, Social, Personal, and Gender-specific. The total mean personal stress score was 391.75, representing the largest contribution to the overall stress score. Mean stress scores per student were 2.24 (First Year MBBS), 2.63 (Second Year MBBS), 2.66 (Third Year MBBS), and 2.70 (Final Year MBBS), indicating that Final Year students experienced the highest stress levels. Most students (45.04%) reported high stress, while 30.99% reported severe stress, with stress levels increasing progressively with each year of study. All four stressor domains were significantly correlated with each other, the total stress score, and overall stress level (p = 0.000). Personal domain stressors contributed most to the overall stress. Within the academic domain, less time for self-study (p = 0.000) and attendance targets (p = 0.000) were identified as the strongest predictors of stress.
This study evaluated the effects of dietary inclusion of Spike-topped apple snail (Pomacea diffusa) meal on feed pellet quality, growth performance, colour intensity, stress tolerance, and economic efficiency in guppy (Poecilia reticulata). Four isonitrogenous and isoenergetic diets were formulated: a fishmeal-based control diet (CD) and three diets replacing fishmeal with snail meal (SM) at 25% (25 SM), 50% (50 SM), and 75% (75 SM). Snail meal inclusion increased feed pellet expansion (CD:-1.02%+5.15, 25 SM: 7.0%+5.34, 50 SM: 2.06%+6.53, 75 SM: 2.36%+6.89). A total of 360, 28-day-old male golden chest guppy fry (mean initial weight: 0.09 g) were randomly assigned to 12 outdoor tanks (n = 3) and fed with one of the experimental diets for 35 days under natural photoperiod. Growth performance and colour intensity of fish were assessed. At the end of the experiment, fish were exposed to 30 ppt salinity in a 500 mL pre-aerated water bath for stress response testing. Mean feed intake, specific growth rate, and feed conversion ratio were not significantly affected across diets. Guppies fed 25 SM and 50 SM showed significantly lower (p < 0.05) mean grey values, indicating enhanced caudal fin colour, as lower grey values correspond to more intense coloration. The 75 SM diet yielded the highest profit index and the lowest incidence cost. Survival during the stress test was similar across all treatments. These findings suggest that fishmeal can be replaced with snail meal up to 75% without negative impacts on feed pellet quality, and growth and stress tolerance of guppy, while 25%-50% replacement also enhances colour intensity, offering additional benefits.
Aedes aegypti and Aedes albopictus are major vectors of arboviral diseases and pose significant public health concerns. This study evaluated their breeding site preferences and prevalence across sub-regions of the Matara District, Sri Lanka. Monthly entomological surveillance was conducted in all Medical Officer of Health (MOH) areas from 2022 to 2024 using standard sampling methods. Chi-square tests were used to identify preferred breeding container types for each species. Cluster analysis using Ward's linkage grouped MOH areas into three clusters based on vector prevalence, representing a gradient of vector receptivity. Monthly Premise Index (PI) values were analyzed alongside average rainfall to assess seasonal trends. Stepwise multiple regression was used to develop predictive PI models incorporating rainfall variables with different time lags. A total of 58,745 water-holding containers were recorded, of which 824 and 8,015 were positive for Ae. aegypti and Ae. albopictus, respectively. Oviposition preferences differed markedly between species: Ae. aegypti was associated exclusively with man-made containers, whereas Ae. albopictus showed a strong preference for containers linked to the natural environment. Spatially, Ae. aegypti predominated in densely populated coastal areas, while Ae. albopictus exhibited a broader inland distribution. Regression analysis revealed significant linear relationships between rainfall and PI, with a one-month lag for Ae. albopictus and no lag for Ae. aegypti (P < 0.005). The predictive models for both species and pooled PI demonstrated low root mean square error (RMSE) and high explanatory power. Notably, the pooled PI model showed a high R & sup2; value, indicating strong predictability of district-level vector prevalence using rainfall data, supporting its use as an early warning tool for proactive vector management. These findings highlight the importance of integrating ecological, spatial, and meteorological factors into routine surveillance systems and strengthening community-based interventions to enhance dengue prevention and control in endemic settings such as Sri Lanka.
The demand for plant-based milk alternatives has increased in recent years due to concerns related to human nutrition and environmental sustainability. Among plant-based milk products, cashew nut milk has gained considerable attention because of its nutritional value, distinctive flavour, desirable mouthfeel, and recognition as a premium product. However, variations in extraction methods often result in inconsistencies in milk quality. Therefore, this study was conducted to determine the optimum extraction conditions for cashew nut milk production. A central composite design (CCD) within response surface methodology (RSM) was employed to optimize extraction time (X1), extraction temperature (X2), and cashew nut-to-water ratio (X3) for the extraction of cashew nut milk. Milk yield, physicochemical characteristics, proximate composition, and sensory properties were evaluated using standard analytical methods. The results revealed that extraction time, extraction temperature, and cashew nut-to-water ratio significantly influenced (P < 0.05) the yield, physicochemical properties, proximate composition, and sensory attributes of cashew nut milk. Among these factors, the cashew nut-to-water ratio had the greatest influence, affecting all measured responses except pH. The optimized extraction conditions were 13 min extraction time, 60 degrees C extraction temperature, and a 1:1 cashew nut-to-water ratio, resulting in an optimum milk yield of 84.43%. The findings of this study indicate that maintaining a cashew nut-to-water ratio not exceeding 1:1 is essential for producing cashew nut milk with consistent and desirable quality attributes.
