The Philippine agriculture sector faces a serious threat from the invasive fall armyworm (Spodoptera frugiperda), which is affecting agricultural municipalities throughout the country. Corn and sugar cane production are at high risk, requiring eco-friendly control methods to prevent potential crises. This study demonstrates the green synthesis of silver nanoparticles (AgNPs) using phytochemicals from water hyacinth (Eichhornia crassipes) as a sustainable nanopesticide against S. frugiperda. Water hyacinth leaves were washed, air-dried, and extracted using methanol (70–90%) under varying temperatures (30–50°C) and times (30–90 min). Total phenolic content (TPC) was quantified via the Folin–Ciocalteu method. AgNPs were synthesized by reacting extracts with 0.1 M AgNO₃ under dark conditions. SEM-EDX confirmed nanoscale particles (≤100 nm) with high elemental purity. Acute toxicity tests compared four AgNP concentrations (500–2000 ppm) with commercial Aztron WDG pesticide using bioassays on fall armyworm larvae fed treated corn leaves. Larval mortality was assessed after 72 h. Results showed Ec-AgNPs effectively controlled larvae, although higher doses were needed compared to Aztron WDG, which was less effective against older larval stages. Optimal extraction was achieved at 47°C, 90% methanol, and 76 min, maximizing the TPC yield. Smaller AgNPs enhanced bioavailability, indicating potential for improved pest control with minimal environmental harm. This study highlights the role of methanol concentration in phenolic extraction and notes the risks of agglomeration associated with increasing extract volumes. Future work should refine synthesis to reduce impurities and investigate FTIR-based functional group analysis to optimize nanoparticle stability. Overall, water hyacinth-derived AgNPs show promise as an eco-friendly alternative to chemical pesticides for managing S. frugiperda in the Philippines.

1. Introduction

Green synthesis of NPs has gained momentum as a promising and alternative approach. The use of aqueous extracts of plants is an attractive approach as the protocol is simple, cost-effective, and does not produce any toxic wastes. Our aim was to leverage the high content of phytochemicals in fresh leaf extract of Yerba mate (Ym) to sustainably produce AgNPs and AgNc beads, and to evaluate their catalytic degradation of hazardous compounds and antibacterial efficacy.

1. Method

Sodium alginate-silver nanocomposite (SA-AgNc) beads were prepared by reduction and ionotropic crosslinking (gelation). The beads were characterized by UV-Vis absorption spectroscopy, electron microscopy, and energy dispersive X-ray spectroscopy (EDX). The catalytic potential was studied by following the degradation of organic compounds such as 2-nitrophenol, Congo red, and methylene blue. The antibacterial efficiency was tested against Escherichia coli.

1. Results

The SA-AgNc beads were close-to spherical shape with cauliflower-like morphology. The presence of pores and AgNPs on the beads were confirmed by electron microscopy and EDX mapping. Sigmoidal degradation profile reflects initial adsorption on the surface, then rapid breakdown. The beads were effective in disinfection of the bacterial solution.

1. Conclusions

SA-AgNc beads containing Ym reduced AgNPs was successfully prepared by reduction and ionotropic crosslinking with calcium ions. The catalytic and antibacterial activities of SA-AgNc were promising. In future, these types of materials hold promise in the treatment of hospital wastewater.

A Schiff base derived from benzaldehyde and o-aminophenol was synthesized, along with its Mn(II) and Co(II) complexes. The synthesized compounds were characterized using standard analytical and spectroscopic methods, including melting/decomposition temperature determination, solubility testing, magnetic susceptibility, infrared (IR) spectroscopy, atomic absorption spectroscopy (AAS), and elemental analysis. Formation of the azomethine bond was confirmed by an IR absorption band at 1614 cm⁻¹ for the free ligand, which shifted to 1603 cm⁻¹ and 1602 cm⁻¹ in the metal complexes, indicating coordination. The melting point of the Schiff base was 189 °C, while the metal complexes decomposed at 273 °C and 220 °C, respectively. Magnetic moment values of 5.78 and 4.81 B.M confirmed paramagnetic behavior. Low molar conductance values (18.5 and 14.1 Ω⁻¹ cm² mol⁻¹) indicated non-electrolytic nature of the complexes. AAS and elemental data suggested a 1:2 metal-to-ligand ratio, with the Schiff base functioning as a bidentate ligand. Biological evaluations revealed that the Schiff base and its metal complexes exhibited significant antibacterial activity against Staphylococcus aureus, Escherichia coli, and Salmonella typhi, and potent antifungal activity against Aspergillus niger, Aspergillus flavus, and Candida albicans. Notably, the antifungal effects were more pronounced at higher concentrations. Cytotoxicity studies showed moderate toxicity, with LC₅₀ values of 132.705 and 147.932 µg/mL for the free ligand and complexes, respectively. These findings suggest that Schiff base metal complexes of Mn(II) and Co(II) hold promise as effective bioactive agents, particularly in antifungal applications.

Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial, as it helps determine the levels of ecological degradation and informs remediation options. Satellite-based remote sensing techniques provide cost-effective methods for such analyses. This study aimed to identify the challenges encountered while using Landsat images to detect changes in vegetation health and land cover in Bodo, a hydrocarbon-impacted community in the Niger Delta region of Nigeria, from 2003 to 2023. Landsat 7 ETM+ and Landsat 8 OLI imagery were used to derive Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) over 20 years. Two challenges were encountered. First, Landsat 7 ETM+ was employed from 2003 to 2008 due to the unavailability of Landsat 5. However, the 2008 images were unsuitable due to a malfunction in the Scan Line Corrector (SLC). Secondly, high cloud coverage caused data inconsistencies in 2013 Landsat 8 scenes for the specified Path/Row. Hence, 2008 and 2013 were excluded from the analysis. Results from previous years revealed that NDBI values gradually increased, suggesting minor urban expansion. Stable but low NDWI levels suggest water stress, while changing NDVI values indicate vegetative health. These indices show environmental trends from 2003 to 2023, although the interrupted data sequence makes multi-year comparisons difficult. This study emphasises the importance of correcting sensor malfunctions, mitigating cloud influence, and closing temporal data gaps when utilising satellite imagery for environmental assessment. It recommends the use of cloud-masking methodologies and radar-based datasets, such as Sentinel-1, to enhance data quality and continuity. These adaptive solutions are crucial for more effective GIS-based environmental monitoring.

The strawberry plant (Fragaria x ananassa Duchesne) is an important crop in Mexico, representing 7.8% of its production. Therefore, constant efforts are being made to increase its production and improve production techniques. One way to identify whether a plant's growing conditions are optimal is through measuring the electrical signal that it produces. Furthermore, a plant's electrical signal can be measured and quantitatively related to the intensity of the stimulating source, such as solar radiation or soil water content. Therefore, in this work, we propose to characterize the electrical signal of the strawberry plant. To perform the measurements, an Arduino data acquisition board was implemented as the measurement system. The experimental methodology consisted of placing an electrode on a leaf near the stem, at a distance of approximately 1 cm. The second electrode was inserted into the stem. The distance between the electrode tips was 2 cm. The electrodes were inserted parallel to the leaf, at a slight angle of 30 degrees, and at a depth of approximately 0.2 cm. Several studies have recommended placing the electrodes closer to points of high physiological activity, suggesting a better response to the electrical signal. This was measured in millivolts. The Holt method and an ARIMA (1,1,0) model were then used to analyze the plant's electrical signal. Among the most notable results is the characterization of the electrical signal in strawberry plants under different physical conditions.

Introduction

Sodium alginate has been manufactured using biomaterial nanotechnology in the nanometer size range. Nanocomposites exhibit features of high solubility and effective degradation of toxic substances, including heavy metals, pharmaceuticals, and healthcare wastes. This project observes the swelling capacity of sustainably developed sodium alginate-poly sodium acrylate silver nanocomposites and their potential enhanced degradation of toxic organic materials, especially in wastewater.

Methodology

Sodium alginate-poly sodium acrylate polymer beads embedded with silver nanoparticles were prepared through ionotropic crosslinking in calcium chloride solution, followed by free radical polymerization initiated by ammonium persulfate. The synthesized beads were characterized using UV-Vis spectroscopy, FTIR spectroscopy, and electron microscopy.

Swelling behavior was assessed gravimetrically, while antibacterial activity was evaluated against clinically relevant pathogens, including Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa, via the incubation method. Additionally, the catalytic efficiency of the beads in degrading Congo red and 2-nitrophenol was investigated in the presence of sodium borohydride.

Preliminary results

‏The spherical and porous nanocomposite beads demonstrated a significant swelling ability, attributed to their composition of poly sodium acrylate. A distinct surface plasmon resonance (SPR) peak near 400nm confirmed the presence of silver nanoparticles. These beads further effectively suppressed the growth of E. coli and P. aeruginosa and achieved almost a complete breakdown of Congo red and 2-nitrophenol within 30 minutes.

Conclusion and work-in-progress:

The nanocomposite beads exhibited antimicrobial and catalytic capabilities, suggesting their suitability for future hospital wastewater treatment. Current efforts are directed toward assessing their reusability and performance with actual wastewater samples.

The implementation of small-scale renewable energy systems in remote island environments presents multifaceted challenges that require tailored engineering and logistical solutions. This study presents a technical case analysis of the installation of a 1kW Vertical Axis Wind Turbine (VAWT) in Pulau Tioman, Malaysia, aimed at enhancing localized energy generation in off-grid coastal regions. The project encountered significant constraints, including limited access to heavy lifting equipment, reliance on basic machinery such as backhoes, and restricted transportation infrastructure. These limitations necessitated a redesign of the installation workflow, including modular component handling, manual assembly techniques, and terrain-adapted foundation engineering.

Environmental factors such as high humidity, frequent rainfall, and variable wind conditions further influenced the construction timeline and structural integrity considerations. Soil instability and uneven terrain required geotechnical adjustments to the foundation design, while electrical integration with the local microgrid demanded custom voltage regulation and cabling solutions. Community involvement played a critical role in labor coordination and logistical support, contributing to the project's successful execution.

The findings from this installation underscore the importance of context-aware planning, resilient engineering design, and stakeholder engagement in deploying renewable energy technologies in isolated regions. This case study provides actionable insights for future VAWT deployments in similar environments, contributing to the broader discourse on decentralized energy systems and sustainable infrastructure development in Southeast Asia.

The objective of this study is to integrate artificial intelligence (AI) and machine learning (ML) in the nuclear power industry to address environmental issues. The problem addressed stems from the nuclear sector's growing need to modernize aging infrastructure and meet stricter environmental and regulatory standards, all while facing data scarcity, cybersecurity concerns, and a shortage of skilled personnel.
Methods: This study employs a systematic literature review and analysis of case studies to assess the current applications of AI and ML in nuclear power. It evaluates various AI techniques, including neural networks, fuzzy logic, and deep learning, in tasks such as fault diagnostics, reactor control, and performance optimization. Additionally, this study examines challenges related to the deployment of these technologies, focusing on data requirements, model complexity, and compliance with nuclear safety regulations.
Results: AI and ML have demonstrated considerable success across multiple domains within nuclear operations. Neural networks and fuzzy logic systems have enhanced the accuracy of reactor monitoring and the stability of control processes. Deep learning models have enabled the real-time optimization of operational parameters and predictive maintenance, resulting in significant reductions in downtime and maintenance costs. However, the implementation of these systems is hindered by the lack of explainable AI frameworks and robust datasets necessary for training high-performance models.
Conclusion: Our findings underscore the transformative potential of AI and ML in the nuclear sector. To fully harness their capabilities, the industry must overcome existing barriers through targeted research focusing on explainable AI, improved data governance, and adaptive regulatory frameworks.

Industrial reformers are important parts of clean energy and making fertilizer. At their core, these systems need high-temperature heat sources to make the chemical processes happen. In the past, people had to start up the heat-generating units in reformers by hand, which was very dangerous. These concerns include the possibility of gas leaks or ignition failure, which might put people and nearby equipment in danger. Also, human control typically leads to problems like unstable fuel–air mixes and incomplete combustion, which in turn impact how much energy is used and how much work is achieved. The developed system included an Automated Thermal Management System that can control and monitor how heat sources work in the chamber by using a thermal imaging camera and different sensors. The system takes care of important duties including safe ignition sequencing, real-time flame detection, and operational regulation on its own. Some of its benefits are improved safety through automatic shutdown during failures, increased thermal efficiency through optimized fuel–air mixing, and continuous data logging to help with system diagnosis and optimization. ATMS used Raspberry Pi and IoT, where the design places a strong emphasis on safety. Also, the system uses sensors to monitor the fuel and air input and changes the ratios of these inputs on the fly to get the best combustion. The proposed system is modeled and can improve in industrial reformer situations through different controlling techniques.

The spectroscopic phenomena, such as electromagnetically induced transparency (EIT) and electromagnetically induced absorption (EIA), as well as switching between them, have been extensively studied in the steady-state regime for stationary excitations. The stationary line shape is understood through the imaginary part of the atomic polarization, which accounts for absorption, and the real part, which corresponds to atomic dispersion. In this paper, we adopt a time-dependent approach that allows for varying chirp mechanics through a weak probe field in the Y-type configuration of alkali vapors with two control fields. In this scenario, the absorption power spectrum (APSD) as well as the emission power spectral density (EPSD) of the atomic coherences and populations exhibit distinctive features that are beyond the traditional stationary spectrum. For weak chirping as sigmoid functions, the APSD shows a double-double EIT-like spectrum with a single EIA-like spike at the line center. For short interaction times and controlled active-chirping times, the APSD for coherence between upper levels shows a rich mixed EIA-like and EIT-like spectrum. The EPSD is analyzed through the variation of populations. Finally, the spectrum is analyzed for gain media where the atomic coherences are temporally negative at most times. We provided a fast estimation technique for APSD and EPSD based on the periodogram of signals with chirping, which might be an alternative approach to the quantum regression hypothesis and the physical spectrum.