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1. Nitro-oxidation process for sustainable production of carboxylated lignin-containing cellulose nanofibers from sugarcane bagasse

   Abdel_Aziz, Yasmeen S; Liu, Alan; Yu, Shengyu; Hsiao, Benjamin S (November 2025, Carbohydrate polymers)

   This study demonstrated a sustainable, zero-waste approach to produce carboxylated lignin-containing cellulose nanofibers (LCNFs) directly from untreated sugarcane bagasse (SCB) using nitro-oxidation process (NOP) fol lowed by high-pressure homogenization. Systematic optimization of reaction parameters was conducted, including reaction time, HNO3-to-SCB ratio, HNO3 concentration, temperature, and co-oxidant addition (KNO₂). The results revealed that HNO3 concentration played the most dominant role in tailoring LCNF properties. Notably, the resulting LCNFs exhibited high dispersibility, with zeta potential values ranging from 􀀀38 to 􀀀65 mV due to the increasing surface carboxyl content (0.43 to 1.21 mmol/g) even under relatively mild conditions (e.g., 50 ◦C, 5 h). Lowering the acid concentration significantly increased the lignin content, enhancing the thermal stability. All LCNFs exhibited nanoscale diameters (7–13 nm), high crystallinity (54 to 70 %), and shear- thinning behavior. Elemental analysis of NOP effluents confirmed their enrichment with macro- and micro- nutrients, enabling their reuse as biofertilizers. This dual valorization of solid and liquid products positions NOP as a viable nanocellulose production and nutrient recovery pathway from lignocellulosic biomass. Resulting LCNFs, with their amphiphilic, biodegradable, and tunable surface properties, represent a compelling platform to make new materials to replace some synthetic polymers and reduce microplastic and chemical pollution. 

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2. Adversarial Evasion through Semantics-Preserving Reinforcement Learning in Graph-Based Malware Detection

   Zhang, Lan; Liu, Peng (December 2025, IntechOpen)

   The deployment of deep learning-based malware detection systems has transformed cybersecurity, offering sophisticated pattern recognition capabilities that surpass traditional signature-based approaches. However, these systems introduce new vulnerabilities requiring systematic investigation. This chapter examines adversarial attacks against graph neural network-based malware detection systems, focusing on semantics-preserving methodologies that evade detection while maintaining program functionality. We introduce a reinforcement learning (RL) framework that formulates the attack as a sequential decision making problem, optimizing the insertion of no-operation (NOP) instructions to manipulate graph structure without altering program behavior. Comparative analysis includes three baseline methods: random insertion, hill-climbing, and gradient-approximation attacks. Our experimental evaluation on real world malware datasets reveals significant differences in effectiveness, with the reinforcement learning approach achieving perfect evasion rates against both Graph Convolutional Network and Deep Graph Convolutional Neural Network architectures while requiring minimal program modifications. Our findings reveal three critical research gaps: transitioning from abstract Control Flow Graph representations to executable binary manipulation, developing universal vulnerability discovery across different architectures, and systematically translating adversarial insights into defensive enhancements. This work contributes to understanding adversarial vulnerabilities in graph-based security systems while establishing frameworks for evaluating machine learning-based malware detection robustness. 

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3. Effective Thallium(I) Removal by Nanocellulose Bioadsorbent Prepared by Nitro-Oxidation of Sorghum Stalks

   https://doi.org/10.3390/nano12234156  

   Chen, Hui; Sharma, Priyanka R.; Sharma, Sunil K.; Alhamzani, Abdulrahman G.; Hsiao, Benjamin S. (December 2022, Nanomaterials)

   Thallium(I) (Tl(I)) pollution has become a pressing environmental issue due to its harmful effect on human health and aquatic life. Effective technology to remove Tl(I) ions from drinking water can offer immediate societal benefits especially in the developing countries. In this study, a bio-adsorbent system based on nitro-oxidized nanocellulose (NOCNF) extracted from sorghum stalks was shown to be a highly effective Tl(I) removal medium. The nitro-oxidation process (NOP) is an energy-efficient, zero-waste approach that can extract nanocellulose from any lignocellulosic feedstock, where the effluent can be neutralized directly into a fertilizer without the need for post-treatment. The demonstrated NOCNF adsorbent exhibited high Tl(I) removal efficiency (>90% at concentration < 500 ppm) and high maximum removal capacity (Qm = 1898 mg/g using the Langmuir model). The Tl(I) adsorption mechanism by NOCNF was investigated by thorough characterization of NOCNF-Tl floc samples using spectroscopic (FTIR), diffraction (WAXD), microscopic (SEM, TEM, and AFM) and zeta-potential techniques. The results indicate that adsorption occurs mainly due to electrostatic attraction between cationic Tl(I) ions and anionic carboxylate groups on NOCNF, where the adsorbed Tl(I) sites become nuclei for the growth of thallium oxide nanocrystals at high Tl(I) concentrations. The mineralization process enhances the Tl(I) removal efficiency, and the mechanism is consistent with the isotherm data analysis using the Freundlich model. 

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4. Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in Mainland China Using Nighttime Light and Air Quality Data

   https://doi.org/10.3390/rs12101576  

   Liu, Qian; Sha, Dexuan; Liu, Wei; Houser, Paul; Zhang, Luyao; Hou, Ruizhi; Lan, Hai; Flynn, Colin; Lu, Mingyue; Hu, Tao; et al (May 2020, Remote Sensing)

   null (Ed.)

   The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people’s daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people’s lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· cm − 2 · sr − 1 ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people’s daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world. 

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5. Queue-Sharing Multiple Access

   https://doi.org/10.1145/3416010.3423230  

   Garcia-Luna-Aceves, J.J.; Cirimelli-Low, Dylan (November 2020, MSWiM '20)

   Queue-Sharing Multiple Access (QSMA) is introduced and analyzed. The new channel-access method consists of establishing and maintaining a distributed transmission queue among nodes sharing a common channel and results in a sequence of queue cycles, with each cycle having one or multiple queue turns with collision-free transmissions from nodes that have joined the transmission queue, followed by a joining period for the current cycle. Nodes can take advantage of carrier sensing to improve the efficiency with which nodes join and use the shared transmission queue. The through- put of ALOHA with priority ACK’s, CSMA with priority ACK’s, CSMA/CD with priority ACK’s, TDMA with a fixed schedule, and QSMA with and without carrier sensing is compared analytically and by simulation in ns-3. The results show that QSMA is more efficient than TDMA with the simplicity of CSMA or ALOHA. 

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