Cost-effective Approach Research Articles

A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

The Sex-Dependent Ameliorative Effect of Intermittent Fasting on Urinary System Functions in Genetic Absence Epileptic Rats

Epilepsy, a brain condition causing recurrent seizures, can disrupt various body functions, including urination. This study explored how alternate-day fasting (ADF), a type of intermittent fasting, a diet involving one day of eating and fasting, subsequently affects urinary system health in epileptic rats. Using a genetic rat model of epilepsy, we examined bladder and kidney function through tissue analysis, biochemical tests, and physiological assessments, focusing on differences between males and females. The results showed that epilepsy causes significant urinary system damage linked to inflammation and oxidative stress. However, ADF reduces this damage by lowering inflammation and restoring antioxidant balance. Male rats exhibited more oxidative damage, while female rats responded better to ADF, likely due to hormonal differences. These findings suggest ADF as a potential, cost-effective dietary approach to protect against urinary complications in epilepsy and similar conditions. Understanding these effects may lead to improved treatments for both genders and a better quality of life for people with chronic diseases.

CRISPR in clinical diagnostics: bridging the gap between research and practice.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) has transformed molecular biology through its precise gene-editing capabilities. Beyond its initial applications in genetic modification, CRISPR has emerged as a powerful tool in diagnostics and biosensing. This review explores its transition from genome editing to innovative detection methods, including nucleic acid identification, single nucleotide polymorphism (SNP) analysis, and protein sensing. Advanced technologies such as SHERLOCK and DETECTR demonstrate CRISPR's potential for point-of-care diagnostics, enabling rapid and highly sensitive detection. The integration of chemical modifications, CRISPR-Chip technology, and enzymatic systems like Cas12a and Cas13a enhances signal amplification and detection efficiency. These advancements promise decentralized, real-time diagnostic solutions with significant implications for global healthcare. Furthermore, the fusion of CRISPR with artificial intelligence and digital health platforms is paving the way for more accessible, cost-effective, and scalable diagnostic approaches, ultimately revolutionizing precision medicine.

Users' experiences of park accessibility and attractiveness based on online review analytics.

Understanding public experiences of urban parks is crucial for their sustainable and effective management. While traditional surveys demand substantial resources, social media data present a cost-effective approach to capturing people's perceptions. This study utilizes Google reviews to examine users' sentiments regarding Cairo's urban parks. Using topic modeling, we identified four key themes: quality and maintenance, family and activities, costs and affordability, and natural environment. Sentiment analysis revealed that 79.8% of reviews expressed positive sentiments, while 8.1% were negative. Statistical analysis indicated that: (1) safety and commercialization-related factors like food and ticket prices significantly influence park accessibility; and (2) park attractiveness is strongly associated with the park's cultural (e.g., aesthetic experiences, leisure and entertainment opportunities) and regulatory features (e.g., cleanliness, quietness). An in-depth content analysis validated these findings and added nuanced interpretation. These findings demonstrated that online reviews can serve as a valuable indicator of park accessibility and attractiveness, thereby fostering more informed park management decisions. However, we also acknowledge their limitations, such as potential bias towards more tech-savvy users and the lack of detailed demographic information. Therefore, future research should combine social media data with traditional qualitative methods to capture the broader socio-cultural dynamics influencing park use.

Beyond the classroom: The transformative experience of short rural immersion programs for health professional students: A narrative review.

Rural areas house nearly half the global population yet face a significant shortage of skilled health professionals, exacerbating health inequities. Short-term rural immersion programs offer a cost-effective approach to exposing health professional students to the unique challenges of rural healthcare. Transformative learning theory is well suited to examining how these programs foster critical reflection and perspective shifts in students. To explore the potentially transformative impact of short-term (<6 weeks) rural immersion programs on health professional students through the lens of transformative learning theory. This review synthesised findings from various studies on the impact of rural immersion programs. Comprehensive searches were conducted across multiple databases, identifying 17 studies published between 2001 and 2024. Data extraction and thematic synthesis were guided by Mezirow's transformative learning framework, identifying key patterns and insights through an iterative process. Rural immersion programs deepen students' understanding of the unique health needs of rural and remote communities. These experiences promote personal and professional growth, enhance critical thinking, problem-solving abilities, cultural competence and social accountability and foster a commitment to serving underserved populations. The collaborative role of the community, student cohort and academic staff in facilitating transformative learning is emphasised. Short-term rural immersion programs offer invaluable and transformative educational opportunities that extend beyond traditional learning environments.

Bioenergy In India: Challenges, Potential and Policy Perspectives

The increasing global energy demand underscores the need for sustainable alternatives, with bioenergy emerging as a pivotal solution. Bioenergy plays a crucial role in climate mitigation, renewable energy transition, energy security, and rural socio-economic development. In India, bioenergy offers significant potential to meet growing energy needs sustainably through its cleaner, cost-effective approach while aiding carbon sequestration and reducing greenhouse gas emissions. However, challenges such as land and water competition with food crops, unstable markets, limited economic incentives, and socio-economic impacts hinder its adoption. India's vast agricultural biomass resources present immense opportunities, particularly in rural areas, to address these challenges. Advancing second and third-generation biofuels, clear policies, financial incentives, public awareness, and enhanced R&amp;D are critical. A “Solar Energy Alliance”-like initiative for bioenergy, coupled with robust infrastructure and structured guidelines, can attract investments and foster innovation. With targeted reforms, India can unlock bioenergy's potential, achieve climate goals, and ensure a sustainable energy future.

Targeting the Antibody Fab Region Using Light-Induced Indole-3-Butyric Acid Functionalized Magnetic Microspheres.

A novel light-controlled adsorption system for direct targeting of antibody Fab fragments was developed by utilizing indole-3-butyric acid functionalized magnetic microspheres. Indole-3-butyric acid, serving as a specific small molecule ligand, was successfully conjugated to amine-functionalized magnetic microspheres via a 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/N-hydroxysuccinimide activation strategy. Under illumination at a particular wavelength, the indole-3-butyric acid ligand generated reactive radicals that interacted with the nucleotide-binding sites of antibody Fab fragments, enabling effective affinity adsorption. Static adsorption experiments demonstrated that the system's adsorption behavior obeys the Langmuir model (KF=0.122, R2=0.996), indicating a homogeneous adsorption process. Kinetic studies further revealed that the adsorption process follows a second-order kinetic model (k2=0.0257, R2=0.989). When compared with conventional antibody adsorption systems, this new system exhibited specific targeting of Fab fragments, enhanced selectivity, and adjustable properties. In particular, at pH 7.0, effective elution was achieved by increasing the salt concentration, with the eluted product retaining antigen-binding activity. The purification recovery rate exceeded 98%, and the system maintained effective adsorption and elution of Fab fragments across various pH conditions. Besides, even after 10 reuse cycles, the system retained more than 96% of its efficiency, thus presenting excellent regenerability and reusability. In summary, the developed light-controlled antibody Fab region adsorption system offers a highly efficient, stable, and cost-effective approach. It is also expected to become one of the most effective methods for antibody Fab purification in the future.

Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method

The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.

Apple tree architectural trait phenotyping with organ-level instance segmentation from point cloud

Three-dimensional (3D) plant phenotyping techniques measure organ-level traits effectively and provide detailed plant growth information to breeders. In apple tree breeding, architectural traits can determine photosynthesis efficiency and characterize the developmental stages of trees. The overall goal of this study was to develop a deep learning-based organ-level instance segmentation method to quantify the 3D architectural traits of apple trees. This study utilized PointNeXt for the semantic segmentation of apple tree point clouds, classifying them into trunks and branches, and benchmarked its performance against several competitive models, including PointNet, PointNet++, and Point Transformer V2 (PTv2). A cylinder-based constraint method was introduced to refine the semantic segmentation results. Next, the branches were identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The type of 3D skeleton vertices determined whether a cluster represented a single branch or multiple branches. If multiple, a graph-based technique further separated them. This study also directly applied the instance segmentation model SoftGroup++ to the apple tree point clouds and analyzed the segmentation results on the apple tree dataset. Finally, seven architectural traits of apple trees were extracted, including height, volume, and crown width of the tree, as well as height and diameter for the trunk, and length and count for the branches. The experimental results showed that the post-processed mIoU values for PointNet, PointNet++, PTv2, and PointNeXt were 0.8495, 0.8535, 0.9500, and 0.9481, respectively. The final instance segmentation results based on SoftGroup++ and PointNeXt achieved mAP_50 of 0.815 and 0.842, respectively. For traits such as tree height, trunk length and diameter, branch length, and branch count, the method based on PointNeXt achieved R2 values of 0.987, 0.788, 0.877, 0.796, and 0.934, with mean absolute percentage errors of 0.86 %, 2.17 %, 5.93 %, 10.24 %, and 13.55 %, respectively. The segmentation results of PTv2 and SoftGroup++ were also used to extract the phenotypic traits of apple trees, achieving results comparable to those of PointNeXt. The proposed method demonstrates a cost-effective and accurate approach for extracting the architectural traits of apple trees, which will benefit apple breeding programs as well as the precision management of apple orchards.

Green synthesized silicon dioxide nanoparticles (SiO2NPs) ameliorated the cadmium toxicity in melon by regulating antioxidant enzymes activity and stress-related genes expression

Green synthesized nanoparticles (NPs) are an eco-friendly and cost-effective approach to reduce heavy metal stress in plants. Among heavy metals, cadmium (Cd) possesses higher toxicity to the crops and ultimately reduces their growth and yield. The current study aims to evaluate the effectiveness of green synthesized SiO2NPs to reduce toxic effects of Cd in melon (Cucumis melo) by regulating physiological parameters, enhancing antioxidant enzyme activity, and modulating stress-related gene expression. The SiO2NPs were synthesized using Artemisia annua plant extract having spherical shape and size within the range of 40-70 nm and characterized using advanced spectroscopic and analytical techniques. The application of SiO2NPs (75mg/L) significantly improved physiological parameters such as shoot length (SL), root length (RL), leaf fresh weight (LFW), root fresh weight (RFW), leaf dry weight (LDW) and root dry weight (RDW) by 14%, 20%, 15%, 16%, 14%, and 28%, respectively, compared to Cd-stressed plants. Photosynthetic pigments (chlorophyll and carotenoids) showed a notable increase of 15% and 40%, respectively. Furthermore, the activities of antioxidant enzymes such as SOD, POD, CAT, and APX were enhanced by 28.67%, 35.45%, 32.07%, and 42.75%, respectively. In addition, applying SiO2NPs increased the concentration of macronutrients N, P, and K by 33%, 40%, and 37%, respectively, compared to Cd-stressed plants. Moreover, SiO2NPs upregulated the expression of several stress-related genes and reduced Cd accumulation in shoots and roots. This study reveals that green synthesized SiO2NPs effectively reduced the Cd toxicity in melon by improving morphological and physiological parameters, enhancing antioxidant enzyme activity, and regulating the expression of stress-related genes. These findings suggest that green synthesized SiO2NPs could play a crucial role in sustainable agriculture by protecting crops from heavy metal stress.

Solo water-gelatinized starch enhances the barrier properties of starch/PBAT.

Glycerol-plasticized thermoplastic starch blending with biodegradable materials reduces costs and enhances toughness at the expense of deteriorating barrier properties. Herein, solo water-gelatinized corn starch (GCS) was melt-blended with polybutylene adipate terephthalate (PBAT) at 130 °C, and subsequent dehydration converted GCS into retrograded corn starch (RCS), yielding RCS-filled PBAT composites. As a barrier filler, RCS dispersed in smaller particles, combined with an interfacial compatibilizer, pentaerythritol monolaurate (PM), dramatically enhances the barrier properties. At 40 % RCS content, the oxygen permeability coefficient decreases to 0.78 × 10-15 cm3·cm/cm2·s·Pa, which is about one order of magnitude lower than that of neat PBAT. Meanwhile, the water vapor permeability coefficient, light transmittance, and haze are all comparable to neat PBAT. Although the mechanical properties of RCS-filled PBAT decreased, the tensile strength and elongation at break remained at 9.60 MPa and 462.19 % (30 % RCS), respectively. This work presents an innovative and cost-effective approach for starch-filled biodegradable polymers.

Optimizing modified natural rubber in starch-based hydrogels: A cost-effective approach for high-performance sustainable slow-release fertilizer coatings

Effective compatibility between natural rubber (NR) and cassava starch (CSt) is crucial for enhancing the water swelling and retention properties of CSt/NR-based hydrogels. In this study, NR was modified by grafting glycidyl methacrylate (NR-g-GMA, NRG) at 3, 6, and 9 parts per hundred rubber (phr) for blending with CSt. Subsequently, CSt was grafted with acrylamide (AM), crosslinked with N,N′ − methylene-bis-acrylamide (MBA), and interpenetrated with NRG to produce CSt-g-PAM/NRG hydrogels. The resulting CSt-g-PAM/NRGx (x = phr of GMA) hydrogels were characterized using FTIR, XRD, TGA, tensile testing, and water swelling measurements. Results revealed that CSt-g-PAM/NRG3 exhibited the highest equilibrium water swelling at 3300 %, surpassing CSt-g-PAM/NR (1430 %) and demonstrated the highest mechanical strength (0.92 MPa), desirable biodegradability (69 % at 30 days), and good water retention. These properties are attributed to the superior compatibility and stronger interactions between NRG and the CSt-g-PAM networks. Additionally, the WUHNRG3 (urea coated with CSt-g-PAM/NRG3 and wax layers) fertilizer demonstrated good biosafety, an acceptable slow-release rate (50.26 % at 30 days), and effectively promoted the growth of chili plants at a reasonable cost. This study demonstrates that modifying NR with optimal GMA content provides an effective CSt-g-PAM/NRG coating membrane, suitable for sustainable slow-release fertilizers and green agricultural practices.

Analytical techniques for arsenic speciation.

Due to its toxicity, contamination with arsenic, a Group 1 carcinogen, is a significant environmental and public health issue. The toxicity of arsenic varies with its chemical form. For example, inorganic species like arsenite (AsO33-) and arsenate (AsO43-) are generally more toxic than organoarsenic compounds. However, some organoarsenic species exhibit higher toxicity than inorganic species. Therefore, the precise quantification and speciation of arsenic is necessary. Chromatographic techniques, particularly liquid chromatography coupled with inductively coupled plasma mass spectrometry (LC-ICP-MS), are widely used for arsenic speciation owing to their high sensitivity and accuracy. Gas chromatography-mass spectrometry (GC-MS) is another effective technique for detecting arsenic species after derivatization. In addition to chromatographic methods, more straightforward and cost-effective techniques are available for inorganic arsenic speciation. These include adsorption techniques, colorimetric assays such as the molybdenum blue method, hydride generation reactions, and voltammetry. Emerging technologies, such as microfluidic and electrochemical devices, enable rapid and portable analysis, facilitating in situ detection of arsenite and arsenate in environmental samples. While LC-ICP-MS remains the gold standard for comprehensive arsenic speciation, other advanced technologies provide a practical, rapid, and cost-effective approach.

The viscosity and processing of molten lunar regolith

Establishing a permanent, self-sufficient habitat for humans on planetary bodies is critical for successful space exploration. In-situ resource utilisation (ISRU) of locally available resources offers the possibility of an energy-efficient and cost-effective approach. This paper considers the high-temperature processing of molten lunar regolith under conditions which represent the lunar environment, namely low gravity, low temperature, and negligible atmospheric pressure. The rheological properties of the low-titanium lunar mare regolith simulant JSC-1A are measured using concentric cylinder rheometry and these results are used to explore the influence of viscosity on processing operations involving the flow of molten regolith for fabricating construction components on the Moon surface. These include the delivery of molten regolith within an extrusion-based 3D printing technique and the ingress of molten regolith into porous structures. The energy and power required to establish and maintain sufficiently high temperatures for the regolith to remain in the liquid state are also considered and discussed in the context of lunar construction.

Transcriptomics highlights dose-dependent response of poplar to a phenanthrene contamination.

Polycyclic aromatic hydrocarbon (PAH) contamination in industrial soils poses significant environmental challenges, necessitating cost-effective bioremediation approaches like tree-based phytoremediation. However, the defence mechanisms and adaptability of trees to PAH exposure remain poorly understood, while the identification of molecular markers could help in the detection of toxicity symptoms. This study explores the molecular response of Populus canadensis to a phenanthrene (PHE) contamination gradient (from 100 to 2000mgkg-1) using RNA-seq analysis of roots and leaves after 4weeks of exposure. Both differentially expressed genes (DEGs) and DRomics, a dose-response tool, identified transcriptomic changes, with about 50% of deregulated genes responding significantly at a benchmark dose (i.e. minimal dose that produces a significant effect) below 400mg PHE kg-1. The highest number of DEGs was found both at a low concentration (200 and 700mgkg-1) and at the highest concentrations (1500-2000mgkg-1) for both roots and leaves. Ethylene signalling genes were activated via ABA-independent pathways at low concentrations and ABA-dependent pathways at high concentrations. Across the gradient, responses to oxidative stress were triggered, including reactive oxygen species scavenging and phenylpropanoid biosynthesis, specifically at 1500-2000mgkg-1. Additionally, PHE disrupted pathways related to plant responses to biotic stress. These findings revealed unexpected dose-dependent transcriptomic shifts, demonstrating poplar's adaptive defence mechanisms against PHE toxicity.

Disseminating the plausibility of biogenic phosphorous nanoparticles as bio nano-fungicide against Aspergillus spp.

The biogenic synthesis of nanoparticles is a sustainable, environmentally friendly, and cost-effective approach that offer significant advantages over conventional methods. In this study, phosphorous nanoparticles (P-NPs) were synthesized biogenically using Serendipita indica and evaluated for their antifungal potential against Aspergillus tubingensis and Aspergillus flavus. The synthesized P-NPs were characterized using multiple techniques including UV–Vis spectroscopy, dynamic light scattering (DLS), X-ray diffraction (XRD), and Scanning and Transmission electron microscopy (SEM and TEM). UV–Vis spectra revealed a characteristic absorption peak at 359 nm, while zeta potential analysis recorded a value of −0.11 mV. XRD analysis identified distinct structural peaks and SEM and TEM confirmed rod-shaped nanoparticles with sizes ranging from 70 to 88 nm. The antifungal efficacy was assessed using a two-fold serial dilution method, revealing dose-dependent effects. Lower P-NPs concentrations caused conidial malformations, whereas higher concentrations resulted in significant loss of fungal viability. SEM analysis further demonstrated severe morphological damage to fungal hyphae, including ruptured cell membranes, and reduced spore counts. These findings suggest that biogenically synthesized P-NPs are highly effective against Aspergillus spp. and represent a promising eco-friendly alternative to conventional fungicides for sustainable fungal management.

Cost-Utility Analysis of Non-Invasive Tests to Initiate Hepatocellular Carcinoma Surveillance in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Non-invasive tests (NITs), e.g. Fibrosis-4 Index (FIB-4) and vibration-controlled elastography (VCTE), have been used to identify metabolic dysfunction-associated steatotic liver disease (MASLD) patients at high risks for hepatocellular carcinoma (HCC). This study investigates the cost-effectiveness of NITs to identify MASLD patients with advanced liver fibrosis and initiate HCC surveillance. A cost-utility analysis using a Markov model compared no use of NITs with three NIT strategies: 1) FIB-4 and VCTE (FIB-4/VCTE), 2) FIB-4 alone, and 3) VCTE alone to identify advanced liver fibrosis and initiate HCC surveillance with biannual ultrasonography with alpha-fetoprotein in 4 MASLD populations: 1) general MASLD patients, 2) MASLD patients with body mass index (BMI) >30 kg/m2, 3) MASLD patients with diabetes, and 4) MASLD patients with three metabolic traits (diabetes, hypertension and BMI >30). FIB-4/VCTE was the most cost-effective approach across all groups, showing the lowest ICER, followed by FIB-4 alone and VCTE alone. In the general MASLD population, both FIB-4/VCTE and FIB-4 alone were cost-effective in the US, while only FIB-4/VCTE was cost-effective in Thailand. For MASLD patients with BMI >30, all strategies were cost-effective in the US, while only FIB-4/VCTE was cost-effective in Thailand. In MASLD patients with diabetes or 3 metabolic traits, all strategies were cost-effective in the US, while FIB-4/VCTE and FIB-4 alone were cost-effective in Thailand. Using FIB-4/VCTE to initiate HCC surveillance is cost-effective for MASLD patients. If VCTE is unavailable, FIB-4 alone is a cost-effective alternative for MASLD patients with diabetes or 3 metabolic traits.

Characterisation and visible light induced antibacterial activity of Cu-doped zinc oxide nanoparticles

ABSTRACT Zinc oxide particles doped with copper were produced using a cost-effective sol-gel approach at a temperature of 80°C. The process involved combining Zn(NO3)2, Cu(NO3)2, and citrate precursors. A variety of analytical techniques, including scanning electron microscopy, Energy-dispersive X-ray spectroscopy (EDX), diffuse reflectance spectroscopy (UV-Vis DRS), and energy dispersive X-ray diffraction spectroscopy (XRD), were employed to study the physical and chemical characteristics of the nanoparticles. The addition of copper resulted in a significant decrease in the size of wurtzite ZnO nanoparticles, reducing from 180 nm for undoped ZnO to 133 and 40 nm for 10% Cu-doped and 25% Cu-doped ZnO, respectively. The absorption spectra displayed a gradual shift towards the visible range as the concentration of copper in the ZnO particles increased. The calculated band gaps for the various samples were determined to be 3.1 eV for undoped ZnO, 2.3 eV and for 10% Cu-doped ZnO, and 1.8 eV for 25% Cu-doped ZnO. The antibacterial efficacy of Cu-doped ZnO was observed to be significantly greater than that of the undoped sample. Additionally, it demonstrated antibacterial activity under visible light conditions, which suggests the successful synthesis of zinc oxide particles that are induced by visible light.

Biocontrol efficacy of Bacillus licheniformis and Bacillus amyloliquefaciens against rice pathogens.

Biocontrol is a cost-effective and eco-friendly approach to control plant pathogens using natural enemies. Antagonistic microorganisms or their derivatives specifically target the plant pathogens while minimizing the harm to non-target organisms. Bacterial blight and brown spot are the major rice diseases caused by Xanthomonas oryzae pv. oryzae (Xoo) and Bipolaris oryzae (Bo), respectively. This study was conducted to assess the plant growth-promoting potential and biocontrol activity of root-associated bacteria against the rice pathogens, Xoo and Bo. A total of 98 bacteria were isolated from rice roots and characterized for plant growth-promoting properties including phosphate solubilization, indole-3-acetic acid production, nitrogen fixation and biofilm formation. Based on these properties, 36 bacteria were selected and tested for biocontrol potential against rice pathogens via co-culturing antagonism assay. LE7 exhibited the maximum inhibition of 79%, while FR8, PE2, LE7, LR22 and LR28 also significantly reduced the growth of Xoo. Likewise, FR2, LR22, LR35 and LE7 significantly inhibited the growth of Bo, in which LR22 exhibited the maximum inhibition of 81%. Under controlled-conditions, LE7 and LR22 significantly reduced the disease incidence of Xoo and Bo, respectively, and improved the growth of rice. Full-length 16S rRNA gene sequencing of most potential bacterial isolates, LE7 and LR22, revealed their maximum identity with Bacillus amyloliquefaciens and Bacillus licheniformis, respectively. Application of Bacillus spp. as biocontrol agent represents enormous potential in rice farming. The most promising bacterial isolates could be used as bioinoculants for rice disease management and improved production in a sustainable manner.

Cross-linked hydrophilic polymers: universal sensors for analyzing solutions containing polar organic compounds

Abstract This study presents a quantitative assessment of cross-linked polyvinyl alcohol (PVA) granules as sensing elements for detecting aliphatic carboxylic acids and their sodium salts in aqueous solutions. Swelling kinetics were measured across solute concentrations ranging from 0.2 N to 1.0 N, with the granule radius varying between 0.31- and 0.43-mm. Results indicated that both carbon chain length and the presence of additional carboxyl groups exert a pronounced effect on the equilibrium swelling degree, thereby highlighting the interplay between hydrophobic interactions and hydrogen-bond formation. To interpret these observations, a heterophase physicomathematical model was employed, yielding three main kinetic coefficients (K1, K2, and K3) that capture solvent flux, polymer network elasticity, and solute transport. The model fits exhibited root-mean-square deviations below 1 %, attesting to its reliability in describing complex swelling - deswelling processes. Additionally, three-dimensional kinetic surfaces were constructed to illustrate how swelling evolves over time and concentration, revealing that initial swelling curves can serve as a rapid indicator of solute concentration. By leveraging the reversible nature of polymer swelling, this method offers a non-invasive, cost-effective approach suitable for monitoring organic acids in diverse fields such as environmental analysis, pharmaceutical processes, and chemical engineering.