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Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the recent literature on UAVsSS, to analyze the current status of the technology. Primary UAVsSS applications were found to be in areas of post-wildfire reforestation, mangrove restoration, forest restoration after degradation, weed eradication, and desert greening. Nonetheless, low survival rates of the seeds, future forest diversity, weather limitations, financial constraints, and seed-firing accuracy concerns were determined as major challenges to operationalization. Based on our literature survey and qualitative analysis, twelve recommendations—ranging from the need for publishing germination results to linking UAVsSS operations with carbon offset markets—are provided for the advancement of UAVsSS applications.

In the rapidly evolving landscape of digital marketing and electronic commerce, short-form content—particularly on platforms like Twitter (now X)—has become pivotal for real-time branding, community engagement, and product promotion. The rise of Non-Fungible Tokens (NFTs) and Web3 ecosystems further underscores the need for domain-specific, engagement-oriented social media content. However, automating the generation of such content while balancing linguistic quality, semantic relevance, and audience engagement remains a substantial challenge. To address this, we propose RL-TweetGen, a socio-technical framework that integrates instruction-tuned large language models (LLMs) with reinforcement learning (RL) to generate concise, impactful, and engagement-optimized tweets. The framework incorporates a structured pipeline comprising domain-specific data curation, semantic classification, and intent-aware prompt engineering, and leverages Parameter-Efficient Fine-Tuning (PEFT) with LoRA for scalable model adaptation. We fine-tuned and evaluated three LLMs—LLaMA-3.1-8B, Mistral-7B Instruct, and DeepSeek 7B Chat—guided by a hybrid reward function that blends XGBoost-predicted engagement scores with expert-in-the-loop feedback. To enhance lexical diversity and contextual alignment, we implemented advanced decoding strategies, including Tailored Beam Search, Enhanced Top-p Sampling, and Contextual Temperature Scaling. A case study focused on NFT-related tweet generation demonstrated the practical effectiveness of RL-TweetGen. Experimental results showed that Mistral-7B achieved the highest lexical fluency (BLEU: 0.2285), LLaMA-3.1 exhibited superior semantic precision (BERT-F1: 0.8155), while DeepSeek 7B provided balanced performance. Overall, RL-TweetGen presents a scalable and adaptive solution for marketers, content strategists, and Web3 platforms seeking to automate and optimize social media engagement. The framework advances the role of generative AI in digital commerce by aligning content generation with platform dynamics, user preferences, and marketing goals.

Social media platforms such as X (formerly Twitter) increasingly shape attention formation, market visibility, and value signaling in electronic commerce, particularly in emerging digital asset markets such as Non-Fungible Tokens (NFTs). Prior work shows that social engagement correlates with NFT prices, suggesting its potential for valuation support. However, open social platforms exhibit heterogeneous user credibility, automated activity, and coordinated promotion, which can distort engagement-based inference. To address these challenges, we propose NFT-TRUST, a trust-aware social signal modeling framework that transforms raw engagement into credibility- and integrity-aware indicators for robust valuation support under manipulation-prone conditions. The framework integrates three components: (i) Credibility-Weighted Social Signal Aggregation (CW-SSA), (ii) Engagement Disproportionality Detection (EDD), and (iii) Integrity-Aware Signal Attenuation (IASA), which jointly reduce the influence of unreliable or manipulated signals while preserving informative engagement. Rather than estimating intrinsic NFT value from social signals alone, NFT-TRUST evaluates the reliability of social attention and converts it into trust-aware features. An XGBoost-based model is used to capture non-linear interactions among these features. Robustness is assessed through stress testing with RL-TweetGen-ST, a reinforcement learning–based synthetic tweet generator that simulates controlled engagement inflation. Experimental results show that NFT-TRUST achieves competitive predictive performance while demonstrating improved stability under simulated manipulation. Ablation analysis indicates that credibility and integrity components are complementary and jointly enhance the reliability of social-signal-based inference. Overall, this work advances trust-aware analytics in electronic commerce and supports more reliable social-driven valuation in emerging digital markets.

High peak-to-average power ratio (PAPR), interference, and inefficient power allocation can severely limit the capacity of a filtered Non-Orthogonal Multiple Access (NOMA) system. This paper presents a novel structurally modified filtered NOMA system integrated with a Decomposed-based Selective Mapping (D-SLM) technique, to reduce high PAPR, and thereby increase spectral efficiency and enable effective spectrum sharing. The proposed f-NOMA-D-SLM (filtered Non-Orthogonal Multiple Access Decomposed-based Selective Mapping) system follows a process similar to that of the transmitter side of filtered NOMA, up to the encoding stage. After that, it incorporates three key components: (i) Walsh–Hadamard Transform (WHT), a mathematical technique that orthogonally transforms the superimposed signal into a non-sinusoidal form, (ii) D-SLM, and (iii) physical layer for dynamic spectrum access (PHYDYAS) filter, a prototype filter designed to minimize unnecessary signal distortion and dynamically mitigate undesired effects through improved frequency domain localization. At the receiver side, Successive Interference Cancelation (SIC) is applied to decode the signals for each user sequentially. The evaluation of the f-NOMA-D-SLM system is done at the receiver side under two scenarios: (i) without relaying and (ii) with cooperative diversity relaying. The performance demonstrates that f-NOMA-D-SLM handles interference better, thereby increasing system capacity and throughput, thus suiting advanced 5G and beyond networks.

The present study concentrated on estimating the thermal conductivity, stability, efficiency, and resistance of a heat pipe for heat exchangers, which were essential for many industrial applications. To achieve this, copolymer of amphiphilic poly (styrene-co-2-Acrylamido-2-methylpropane sulfonic acid) poly (STY-co-AMPS) was synthesized by free radical polymerisation technique. The dispersant were used for homogeneous solution and stabilization of ZnO nanofluids. The effect of dispersant on the thermal conductivity of nanofluids was analysed using a KD2 pro thermal property analyser. There is a significant increase in fluid conductivity had a nonlinear relationship with the volume fraction. The maximum enhancement was observed at an optimized concentration of dispersant at 1.5 vol%. Same time, the influence of dispersant agent on the thermal conductivity of nanofluids were compared with linear polyelectrolytes. Further, the experimental values were compared to the existing classical models based on the reasonable aggrement, the prepared nanofluids were employed as a working medium. The conventional screen mesh heat pipe and the temperature distribution to the thermal resistance of the heat pipe was investigated experimentally. The result shows, optimum concentration of dispersants on nanoparticles exhibits an enhanced heat efficiency as compared with the base fluids. Further, the thermal resistance and temperature distribution show decreased behaviour by increasing the particle volume fraction and dispersant concentration.

We investigate the market return timing ability of fund managers of actively managed Indian mutual funds of various categories (small-cap, mid-cap, large-cap, and multi-cap funds) for the period of 2014 to 2019. To assess the exposure of different investment factors like market, value, momentum, and size, we use the 4-factor model. Further, we use the Treynor Mazuy and Henriksson Merton models, to study the timing ability of the magnitude and direction of the style, respectively. We observe that value investing has the least style timing, followed by momentum. The extent of size timing is highest, but the direction timing is least in small-cap. Mid-cap funds place second in size factor timing. The market timing factor shows best results for multi and large-cap funds while showing moderately better results for the other two compared to the value factor. Our findings add another dimension to mutual fund performance evaluation and provide a better understanding of the investment style suitable for different funds. These findings could help fund managers in returns maximisation returns by using the right style.

The upsurge of stemphylium blight disease noticed during recent cropping years is the prime global threat for lentil (Lens culinaris Medik.) production. Identification of factors that influence lentil yield with the help of an advanced regression model will speed up the progress of lentil crop improvement for biotic stress tolerance. In this context, an experiment was undertaken to identify the key control factors of lentil yield under stemphylium blight stress. The field experiment was laid out under alpha lattice design using fifty lentil genotypes with two replications. An advanced dimension reduction cum regression approach Partial Least Square Regression (PLSR) was employed to mitigate the effect of multi-collinearity among 23 yield-influencing traits along with traditional Stepwise Multiple Linear Regression (SMLR). The results of SMLR analysis indicated that pods per plant, number of seeds per pod, hundred seed weight, superoxide dismutase and pod yield per plant had considerable effects on seed yield per plant with the R-squared value of 0.940. The first four PLSR components were considered to be optimum which were cumulatively explained 93.10% of the total variance towards lentil seed yield. The trait pods per plant was recorded with the highest PLSR regression coefficient devoid of multi-collinearity effects among the independent yield attributing variables under stemphylium blight environment and hence concluded to be the most influencing trait towards lentil seed yield followed by seeds per pod, hundred seed weight, pod yield per plant and superoxide dismutase.

Phase pure lithium trivanadate (LiV3O8) was synthesized by an ultrasound-assisted rheological phase reaction method for aqueous supercapacitor applications. The precursor was exposed to ultrasound for different time periods of 2, 6 and 10 h and then calcined under similar conditions. The effects of ultrasound on the structural features of the synthesized samples was characterized by XRD, and FTIR spectroscopy and the morphological features by SEM analysis. The sample synthesized by exposing the precursor to ultrasound for 10 h showed a high value of d100spacing, which facilitated the lithium (Li+) ion movement. Also, the formation of rod-like structures by the exposure of ultrasound facilitated the fast Li+ ion movement and enhanced the electrochemical performance. The electrochemical performance was measured using a three-electrode cell setup in the potential range of −0.8 to 0.2 V using 1 M LiNO3aqueous electrolyte. The specific capacitance of the material was found to be 120 F/g at a current density of 1A/g for the sample sonicated for 10 h (10S-LVO). The symmetric supercapacitor constructed using 10S-LVO showed the highest specific capacitance of 317F/g at 0.5 A/g, with 66.28% capacitance retention at 1A/g after 150 cycles.Effect of ultrasound on the morphology of LiV3O8