Selective detection of gaseous ammonia with specifically functionalized silicon photonic microring resonator: towards low cost and portable breath monitoring

We demonstrate a selective ammonia sensor based on a silicon photonic microring resonator functionalized with nanoporous silicate films. The potential of this sensor as a low cost, portable and real-time medical breath monitoring device is discussed

Fungal pretreatment of miscanthus for fermentable sugar production: experimental and techno-economic evaluation

Lignocellulosic biomass is an abundant, renewable feedstock for biorefineries, but pretreatment is usually required to overcome its high recalcitrance to biodegradation. Fungal pretreatment, an alternative process to traditional pretreatments, can enhance the enzymatic digestibility of the lignocellulosic biomass. Fungal pretreatment can be performed at low temperature, without added chemicals, and no wastewater generation. However, in comparison with traditional pretreatments, longer residence times, lower yields, and feedstock sterilization requirements make it challenging to implement. This work investigated the fungal pretreatment of the dedicated energy crop Miscanthus × giganteus with the white rot fungus Ceriporiopsis subvermispora. Fungal pretreatment of non-sterile miscanthus was performed in batch using miscanthus previously colonized with the fungus as inoculum. The process enhanced the enzymatic digestibility of miscanthus by 2-fold over that of untreated miscanthus, and was comparable to the pretreatment of sterilized miscanthus inoculated with a pure culture of the white rot fungus. The finished material from the unsterilized pretreatment was used as inoculum for two more generations in a sequential fungal pretreatment process, where no increase in enzymatic digestibility was observed. A propagation of indigenous fungi that out-colonized C. subvermispora was observed through the generations, showing that sterilization is a required step for the stability and reproducibility of fungal pretreatment. A techno-economic analysis of the production of fermentable sugars from miscanthus using fungal pretreatment showed that the process was not feasible at full biorefinery scale due to the high capital cost caused by long residence time, low feedstock bulk density, and low sugar yields

Palaeomagnetism of Precambrian igneous rocks in Australia and East Antarctica: implications for the pre-Pangea supercontinents and supercontinent cycle

This thesis presents a series of new, high-quality palaeomagnetic data from Precambrian igneous rocks in Western Australia, South Australia and East Antarctica. The ages of the corresponding new palaeomagnetic poles range from late Archaean to late Mesoproterozoic. The new data improve significantly the Australian and East Antarctic palaeomagnetic database and shed lights on the tectonic evolution of Australia and global palaeogeographic reconstructions

Improving Sample Collection Of Trace Particles Of Mock Explosive On Nano Coated Sensor

In protection against explosive-based terrorism, development and mass deployment of miniature sensors can play a tremendous role. In trace explosive detection, one of the challenges is bringing explosive vapor samples from the environment to the sensor element. Such collection of a selective and sufficient amount of air sample will enable the device detect the explosive at lower concentration. This can be done by adsorption of the explosive vapor on a substrate. This research implements the idea by developing a nano coated sensor on a lead zirconate titanate (PZT) substrate. The effects of varying the amount of polyethyleneimine in the nano coating solution of the sensor to adsorb trace particles of a mock explosive are studied. A nano coating mixture of ferrofluid, polyethyleneimine and epoxy are coated on the surface of PZT substrate, and exposed to a magnetic field to create a pattern of cones. Then it is exposed to ultraviolet rays for curing during a 24 hours period. Finally, adsorption tests are conducted on the newly created sensor. In the adsorption test, nitrogen gas is used as carrier and 2-nitrotoluene is used as the mock explosive. The carrier gas is routed to the 2-nitrotoluene in a bubbler. Then the vapor mixture of 2-nitrotoluene and nitrogen is routed to the sensor box. Next the sensor is scanned with a Raman spectrometer for spectral identification. This procedure is conducted on different sensors which are made by varying the amount of polyethyleneimine, and tested before and after plasma etching using argon gas. The results shothat increasing the amount of polyethyleneimine by mass yields an increase in the adsorption rate and also leads to the adsorption of a smaller concentration of the mock explosive. In addition, plasma etching of the sensor further improved these results. It enabled adsorption at a less concentration up to 19 ppm. This research shothat the best composition for consistent and reliable adsorption is 80% ferrofluid, 15% polyethyleneimine and 5% epoxy. The trends in this work indicate further research can lead to this sensor concept being able to capture trace explosive particles on a much lower level

Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols

Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregation-layer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and protocols for loanable funds (PLFs). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.Comment: arXiv admin note: text overlap with arXiv:2105.1389

implications for health and disease

Many aspects of human physiology and behavior display rhythmicity with a period of approximately 24 h. Rhythmic changes are controlled by an endogenous time keeper, the circadian clock, and include sleep-wake cycles, physical and mental performance capability, blood pressure, and body temperature. Consequently, many diseases, such as metabolic, sleep, autoimmune and mental disorders and cancer, are connected to the circadian rhythm. The development of therapies that take circadian biology into account is thus a promising strategy to improve treatments of diverse disorders, ranging from allergic syndromes to cancer. Circadian alteration of body functions and behavior are, at the molecular level, controlled and mediated by widespread changes in gene expression that happen in anticipation of predictably changing requirements during the day. At the core of the molecular clockwork is a well-studied transcription-translation negative feedback loop. However, evidence is emerging that additional post-transcriptional, RNA-based mechanisms are required to maintain proper clock function. Here, we will discuss recent work implicating regulated mRNA stability, translation and alternative splicing in the control of the mammalian circadian clock, and its role in health and disease

Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Logic Chain Injection

Jailbreak attacks on Language Model Models (LLMs) entail crafting prompts aimed at exploiting the models to generate malicious content. Existing jailbreak attacks can successfully deceive the LLMs, however they cannot deceive the human. This paper proposes a new type of jailbreak attacks which can deceive both the LLMs and human (i.e., security analyst). The key insight of our idea is borrowed from the social psychology - that is human are easily deceived if the lie is hidden in truth. Based on this insight, we proposed the logic-chain injection attacks to inject malicious intention into benign truth. Logic-chain injection attack firstly dissembles its malicious target into a chain of benign narrations, and then distribute narrations into a related benign article, with undoubted facts. In this way, newly generate prompt cannot only deceive the LLMs, but also deceive human

The Response of Haricot Bean (Phaseolus Vulgaris L) Varieties to Phosphorus Levels on Nitosols at Wolaita Zone, Ethiopia

A field experiment was conducted at two locations (Bolosso Sore and Damot Sore) in Wolaita Zone of Southern Nations Nationalities and People’s Regional State to evaluate the response of two varieties of haricot bean (phaseolus vulgaris L.) to phosphorus fertilizer rates on acidic soils. Combinations of four levels of P (0, 10, 20 and 30 kg ha-1) were used on two varieties. The treatments were arranged in factorial RCBD with three replications. Analysis resulted of soil samples showed that Available P, Organic carbon, Total Nitrogen & Soil pH values were very low. Application of lime with P resulted significant changes on these chemical properties of the soils in the two locations.  The maximum values of these parameters recorded at 30 kg Pha-1. Growth parameters yield and yield components were significantly increased with increasing rates of P at the two locations. Maximum grain yields (1488.40 and 1523.7 kgha-1 for Hawse Dume at Gunno and Dollar, respectively and 1242.12 and 1352.01 kgha-1 for Omo-95 at Gunn and Dollar, respectively) recorded at rates of 30 kgPha-1 in the both locations. From the result of this study it could be conclude that improve soil pH, Available P and performance of haricot bean varieties but till now there is some gap on correcting P application of grain yield of the varieties. So application of P could be   increased the production of the crops. Keywords: Hawse Dume, Omo-95, soil acidity and yiel

Fine-grained, Content-agnostic Network Traffic Analysis for Malicious Activity Detection

The rapid evolution of malicious activities in network environments necessitates the development of more effective and efficient detection and mitigation techniques. Traditional traffic analysis (TA) approaches have demonstrated limited efficacy and performance in detecting various malicious activities, resulting in a pressing need for more advanced solutions. To fill the gap, this dissertation proposes several new fine-grained network traffic analysis (FGTA) approaches. These approaches focus on (1) detecting previously hard-to-detect malicious activities by deducing fine-grained, detailed application-layer information in privacy-preserving manners, (2) enhancing usability by providing more explainable results and better adaptability to different network environments, and (3) combining network traffic data with endpoint information to provide users with more comprehensive and accurate protections. We begin by conducting a comprehensive survey of existing FGTA approaches. We then propose CJ-Sniffer, a privacy-aware cryptojacking detection system that efficiently detects cryptojacking traffic. CJ-Sniffer is the first approach to distinguishing cryptojacking traffic from user-initiated cryptocurrency mining traffic, allowing for fine-grained traffic discrimination. This level of fine-grained traffic discrimination has proven challenging to accomplish through traditional TA methodologies. Next, we introduce BotFlowMon, a learning-based, content-agnostic approach for detecting online social network (OSN) bot traffic, which has posed a significant challenge for detection using traditional TA strategies. BotFlowMon is an FGTA approach that relies only on content-agnostic flow-level data as input and utilizes novel algorithms and techniques to classify social bot traffic from real OSN user traffic. To enhance the usability of FGTA-based attack detection, we propose a learning-based DDoS detection approach that emphasizes both explainability and adaptability. This approach provides network administrators with insightful explanatory information and adaptable models for new network environments. Finally, we present a reinforcement learning-based defense approach against L7 DDoS attacks, which combines network traffic data with endpoint information to operate. The proposed approach actively monitors and analyzes the victim server and applies different strategies under different conditions to protect the server while minimizing collateral damage to legitimate requests. Our evaluation results demonstrate that the proposed approaches achieve high accuracy and efficiency in detecting and mitigating various malicious activities, while maintaining privacy-preserving features, providing explainable and adaptable results, or providing comprehensive application-layer situational awareness. This dissertation significantly advances the fields of FGTA and malicious activity detection. This dissertation includes published and unpublished co-authored materials