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Abstract Fire is a dangerous disaster that causes human, ecological, and financial ramifications. Forest fires have increased significantly in recent years due to natural and artificial climatic factors. Therefore, accurate and early prediction of fires is essential. While significant advancements have been made in traditional and Deep Learning (DL) methods for fire detection, challenges remain in accurately pinpointing and recognizing fire regions, especially in diverse and large environments, to prevent damage effectively. To address these challenges, this paper introduces a novel Federated Learning (FL)-based method called Indoor-Outdoor FireNet (IOFireNet) for detecting and localizing fire regions. The proposed method incorporates a Bilateral Filter (BF) to effectively preprocess fire images to reduce noise artifacts and enhance detection clarity. It employs Super Pixel-based Adaptive Clustering (SPAC) to precisely segment fire and non-fire regions. A global IOFireNet model is developed to aggregate parameters from local models, improving detection accuracy across varied environments, while MobileNet is used for efficient data processing, enabling predictions on fire spread, severity, and affected areas to support early warnings. The proposed FL-based IOFireNet attains an accuracy rate of 98.65% for fire detection and 97.14% of mean IoU for segmentation. The proposed SPAC model reaches a mean IoU of 4.06%, which is 2.45% better than the graph cut algorithm and CRF model. The proposed model achieves an accuracy of 0.23%, 4.20%, 3.29%, and 10.02%, better than VGG-19, ResNet-50, Inception, and Dense Net, respectively.

Silver and its alloys undergo tarnishing with time, which is a black stain on the surface due to the formation of Ag2S. Developing a tarnish resistant Ag alloy was attempted by alloying Ag with elements that form a passive oxide layer on the surface. Germanium is proven to provide better tarnish resistance to sterling Silver alloy (92.5 wt% pure) which is available under the trade name of Argentium©. The present work investigates the tarnish resistance behavior of sterling silver alloy (92.5 wt% pure) containing various additions of Copper, Zinc, and Germanium. The alloys were prepared by melting and casting route, followed by Passivation Heat Treatment (PHT) to create a stable and continuous oxide layer. The temperature for PHT was optimized using thermogravimetry analysis (TGA) of the alloys prepared. Accelerated tarnish test was carried out to investigate the tarnishing behavior of alloy samples obtained before and after PHT. The samples were characterized using XRD, SEM-EDX, and micro-Raman Spectroscopy. The change in reflectance of the samples after tarnish test is determined using UV–Visible reflectance spectroscopy. The mechanism behind the tarnish resistance was derived using Density Functional Theory (DFT) by comparing sulphur (S2) and Oxygen (O2) adsorption energies (BE) of the alloying elements. The lower value of S2 (BE)/O2 (BE) indicates better oxidation and tarnish resistance. The ratio ranges between 222 % (Pure Ag) and 132 % (for Ag-4.2Cu-2.8Zn-1.4Ge) and the p-p between Ge and O has contributed to the reduction in the ratio.

Background: Diabetes mellitus is a major health concern, and the development of foot ulcers is a serious complication. Pseudomonas aeruginosa is a common causative pathogen in diabetic foot infections, which can lead to delayed wound healing and increased morbidity. This study aims to compare the diagnostic performance of conventional microbiological methods and molecular techniques (polymerase chain reaction [PCR]) for the identification of P. aeruginosa in diabetic foot ulcers and to evaluate its antimicrobial susceptibility pattern. Methods: This cross-sectional study was conducted in a tertiary care hospital, involving 134 patients with diabetic foot ulcers. Samples were collected and conventional culture methods were used to identify the pathogens and perform antimicrobial susceptibility testing. In addition, a single-step DNA extraction method (AmpReady) and conventional PCR were utilized for the molecular identification of P. aeruginosa. Results: The study found that the PCR technique was more efficient than conventional culture methods in identifying P. aeruginosa, with 44% detection by PCR compared to 14% by conventional culture. According to the antimicrobial susceptibility testing, co-trimoxazole was the most resistant drug against P. aeruginosa, while amikacin was the most sensitive. Conclusion: The study highlights the importance of using molecular techniques like PCR for accurate and faster identification of pathogens like P. aeruginosa in diabetic foot ulcers, which is crucial for appropriate antibiotic treatment and management of these infections. The findings contribute to a better understanding of the diagnostic validity and antimicrobial susceptibility patterns of P. aeruginosa in diabetic foot ulcers, which can aid in improving clinical management and outcomes for these patients.

The present work investigates the dry sliding wear and friction behaviour of an alloy steel developed through powder metallurgy. Prealloyed Atomet 4601 powder was thoroughly mixed with elemental powders of 0.35 C, 0.25 Mn, 0.1 Si and 0.9 Cr (in wt. %) to create this alloy steel, whose composition closely matches EN24 (AISI 4340) wrought steel. The dry sliding friction and wear test experiments were performed in a pin-on-disc tribometer. For the various cases considered in friction and wear tests, the wear rate is lowest (0.32 × 10–3 mm3/m) at 15 N and 1.77 m/s and highest (1.02 × 10–3 mm3/m) under severe conditions of 32.5 N and 2.83 m/s. The coefficient of friction is highest (0.673) at 15 N and 0.71 m/s and lowest (0.1311) at 32.5 N and 2.83 m/s. Subsequently, regression models are developed based on experimental results and the interaction effect of load and speed on the response parameters are plotted using MATLAB. An R2 value of 0.98 indicates a strong correlation between the response and the input variables. X-ray diffraction (XRD) and optical and scanning electron microscope (SEM) analyses of the most worn specimens revealed carbides and oxides in the ferrite grain matrix, with adhesive, abrasive and oxidative wear being the primary mechanisms observed

Abstract Yield gap analysis is used to characterize the untapped production potential of cropping systems. With emerging large-n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. Here we characterize the contributing factors to rice yield gaps across seven Indian states, with a case study region used to assess the power of intervention targeting. Primary yield constraints in the case study region were nitrogen and irrigation, but scenario analysis suggests modest average yield gains with universal adoption of higher nitrogen rates. When nitrogen limited fields are targeted for practice change (47% of the sample), yield gains are predicted to double. When nitrogen and irrigation co-limitations are targeted (20% of the sample), yield gains more than tripled. Results suggest that analytics-led strategies for crop intensification can generate transformative advances in productivity, profitability, and environmental outcomes.

Abstract Lessons learned from past experiences push for an alternate way of crop production. In India, adopting high density planting system (HDPS) to boost cotton yield is becoming a growing trend. HDPS has recently been considered a replacement for the current Indian production system. It is also suitable for mechanical harvesting, which reducing labour costs, increasing input use efficiency, timely harvesting timely, maintaining cotton quality, and offering the potential to increase productivity and profitability. This technology has become widespread in globally cotton growing regions. Water management is critical for the success of high density cotton planting. Due to the problem of freshwater availability, more crops should be produced per drop of water. In the high-density planting system, optimum water application is essential to control excessive vegetative growth and improve the translocation of photoassimilates to reproductive organs. Deficit irrigation is a tool to save water without compromising yield. At the same time, it consumes less water than the normal evapotranspiration of crops. This review comprehensively documents the importance of growing cotton under a high-density planting system with deficit irrigation. Based on the current research and combined with cotton production reality, this review discusses the application and future development of deficit irrigation, which may provide theoretical guidance for the sustainable advancement of cotton planting systems.

Petroleum hydrocarbons (PHs) are compounds composed mostly of carbon and hydrogen, originating from crude oil and its derivatives. PHs are primarily released into the environment through the diffusion of oils, resulting from anthropogenic activities like transportation and offshore drilling, and accidental incidents such as oil spills. Once released, these PHs can persist in different ecosystems and cause long-term detrimental ecological impacts. While the hazards associated with such PH contaminations are often assessed by the concentrations of total petroleum hydrocarbons in the environment, studies focusing on the risks associated with individual PHs are limited. In this study, different network-based frameworks were utilized to explore and understand the adverse ecological effects associated with PH exposure. First, a list of 320 PHs was systematically curated from published reports. Next, biological endpoint data from toxicological databases was systematically integrated, and a stressor-centric adverse outcome pathway (AOP) network, linking 75 PHs with 177 ecotoxicologically-relevant high confidence AOPs within AOP-Wiki, was constructed. Further, stressor-species networks, based on reported toxicity concentrations and bioconcentration factors data within ECOTOX, were constructed for 80 PHs and 28 PHs, respectively. It was found that crustaceans are documented to be affected by many of these PHs. Finally, the aquatic toxicity data within ECOTOX was used to construct species sensitivity distributions for PHs, and their corresponding hazard concentrations (HC05), that are harmful to 5% of species in the aquatic ecosystem, were derived. Further, the predicted no-effect concentrations (PNECs) and risk quotients for the US EPA priority polycyclic aromatic hydrocarbons (PAHs) were computed by using their environmental concentration data for Indian coastal and river waters. Overall, this study highlights the importance of using network-based approaches and risk assessment methods to understand the PH-induced toxicities effectively.

The current research aspires to explore the novel utilization of Annona squamosa bark extract consisting of various phytochemical components as a capping agent with zinc acetate as a precursor to form zinc oxide particles. The origination of ZnO NPs is implied by the UV-Visible absorption band at 357 nm and the distinctive XRD peaks at the appropriate positions. The occurrence of highly crystalline, hexagonal phase, and impurity-free As-ZnO NPs was made apparent by the XRD pattern. Employing XRD, the average particle size from the crystallites has been identified to be 56.23 nm. The FTIR spectral analysis verified the existence of biomolecules on the ZnO NP surface. These ZnO NPs have hexagonal structures and flake-like morphology as revealed by FESEM analysis, respectively. EDS study proved the existence of Zn (51.77 weight%) and O (20.56 weight%). The fabricated As-ZnO NPs demonstrated outstanding efficacy in various biological applications, spanning in vitro antimicrobial, antioxidant, antihemolytic, antiinflammatory, antidiabetic, and anticancer properties. On top of that, As-ZnO NPs had a high degree of photocatalytic activity against methylene blue dye, with a degradation efficiency of 93.37% within 150 min exposure to sunlight.

Unmanned aerial vehicles (UAVs) represent a cutting-edge technology that holds the promise of revolutionizing the conventional tasks carried out in the realm of agriculture. On a global scale, UAVs are gaining prominence for pesticide applications, particularly with a focus on utilizing low spray volumes. Nevertheless, there remains a notable gap in research concerning the impacts of employing low spray volumes on herbicide efficacy. Hence, multi-location field studies were conducted at Agricultural Research Station, Bhavanisagar and Wetland Station, Coimbatore, Tamil Nadu, India in 2022 to evaluate the impact of different spray volumes using UAV and Knapsack Manual Sprayer (KMS) on droplet deposition, droplet density, and weed control efficacy. The treatments included UAV sprays at 30 and 45 L ha-1, as well as KMS at 500 L ha-1, with a weedy check as a control. Bispyribac-sodium 10% SC was applied at a rate of 35 g a.i ha-1 during the 2-to 3-leaf stage of the crops. The results revealed that droplet deposition, area coverage, and volume median diameter were affected by sprayer type (UAV and KMS). However, the two spray volumes tested using the UAV method provided similar droplet parameters. The KMS system at 500 L ha-1 exhibited the highest droplet deposition, while the UAV system achieved better droplet distribution at a spray volume of 30 L ha-1. Remarkably, the UAV system at 30 L ha-1 demonstrated effective weed control, which was statistically comparable to the KMS system at 500 L ha-1. These findings emphasize that the UAV spraying system with a spray volume of 30 L ha-1, delivering effective weed control while utilizing less carrier volume compared to the manual knapsack spraying method. Thus, the UAV spray system has great potential as a viable alternative to manual knapsack spraying for herbicide application in direct-seeded rice.