Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of low- and high-dose levothyroxine on the expression, protein level, and function of P-glycoprotein in mice

The study was investigated the effect of different doses of levothyroxine on the mRNA expression, protein level and function of Pgp. Mice were divided into 6 groups as control, low dose levothyroxine, high dose levothyroxine, fexofenadine, low dose levothyroxine+fexofenadine and high dose levothyroxine+fexofenadine. Mice received levothyroxine at doses of 8 and 80 µg/kg daily for 21 days. Fexofenadine was administered at dose of 40 mg/kg at the 24 h following the last administration of levothyroxine. The mRNA levels and protein level of Pgp in liver and small intestine were determined by RT-PCR and western blot analysis, respectively. Plasma concentrations of fexofenadine were determined using HPLC. Levothyroxine at low and high doses caused an insignificant increase intestinal mRNA expression of mdr1a, while high dose levothyroxine+fexofenadine caused a significant increase. Levothyroxine caused a dose-dependent decrease in intestinal mRNA expression of mdr1b. In liver, levothyroxine caused a dose-dependent increase in the mRNA expression of mdr1a. Fexofenadine significantly reduced the effect of levothyroxine on mRNA expression of mdr1a in liver. Levothyroxine increased the protein level of Pgp in liver and decrease in intestines. Low dose levothyroxine significantly increased the plasma concentration of fexofenadine. The effects of levothyroxine on the mRNA expression of mdr1a and b in small intestine and liver and protein level of Pgp varied depending on the dose, tissue type, and fexofenadine administration.
INT-Balance: In-Band Network-Wide Telemetry with Balanced Monitoring Path Planning

In-band Network Telemetry (INT) empowers high-resolution network monitoring by collecting hop-by-hop device-internal states through the data plane without frequently disturbing the control plane. To achieve network-wide monitoring, a high-level orchestration is made to provision multiple monitoring paths to cover the entire network. The path number and path overlapping are kept minimum to maximally reduce the telemetry overhead. However, in production deployment, except for the telemetry overhead, the telemetry timeliness is equally important for fine-grained monitoring, which creates new requirements of balanced monitoring path planning. Given the INT probes from multiple paths are collected to the central controller for analysis, the late arrival of even one probe will delay the analysis process and affect the monitoring timeliness. To address the problem, we propose INT-balance, a novel path planning algorithm for balanced INT path generation. In INT-balance, we first break the original network graph into multiple path segments at the odd vertices. Then, we iteratively splice the two shortest path segments with the joint endpoints to form a longer path segment until the path segment number reaches half the number of the odd vertices. INT-balance generates the minimum number of INT paths with well-balanced path lengths, covering every edge of the network graph without any path overlapping. Evaluation on a network of 100 switches shows that the path length variance of INT-balance is 67% less than that of INT-path, while the algorithm execution time is increased only by 0.012s.
RNN-Based Robust Smartphone Indoor Localization on Ultra-Wideband DL-TDOA

Indoor localization in the presence of permanent obstructions or temporary/partial obstacles is a unique challenge, due to the lack of practical channel models applicable in realistic scenarios. Ultra-wideband (UWB) signal-based localization, with its cm level accuracy, has gained a lot of research interest in recent years. Among the various UWB ranging methods, the downlink time difference of arrival (DL-TDOA) based localization, which does not require uplink transmissions or central computation, is the most practically applicable in large-scale industrial scenarios. However, the performance of DL-TDOA localization, based on the traditional least squares estimation method, can be severely affected by non-line-of-sight (NLOS) and multipath effects. Prior work investigates using Kalman filter-based methods on UWB and IMU sensor data for localization inaccuracy mitigation. This approach suffers from high localization error during UWB signal outage events, owing to its reliance on IMU data and the gaussian assumption of noise. In this paper, we propose a novel recurrent neural network (RNN)-based localization algorithm. The proposed algorithm utilizes the past UWB DL-TDOA values and the IMU sensor data to predict localization coordinates into the future and uses the model-generated augmented TDOA values during UWB signal outage. We also demonstrate the real-time performance of the RNN-based localizer, using NXP(SR100T) anchors and Samsung Galaxy S20 Ultra user devices. As shown in the experimental results, the proposed algorithm achieves 31% lower localization error compared to the Kalman filter-based baseline methods, while also being robust to signal outage.
Remaining Useful Life Prediction of Aircraft Engines based on FA-SDAE-BiGRU

Aiming at the problems of insufficient feature extraction and inaccurate prediction in existing aircraft engines remaining useful life prediction algorithms, a fusion model based on feature attention mechanism, stacked noise reduction autoencoder and bidirectional gated cycle units (BiGRU) was proposed. Firstly, weight is assigned to each sensor data of aircraft engine by the feature attention mechanism, and new weight data is obtained. Next, the stack noise reduction autoencoder is used to extract the weighted data. Then, bidirectional gated recurrent units are employed to predict the time series data and obtain the remaining useful life of the aircraft engine. To accurately select the hyperparameters of BiGRU, the Bayesian optimization algorithm is utilized for hyperparameter optimization. Finally, ablation experiments were conducted on the CMPASS dataset to validate the effectiveness of the proposed method, resulting in a root mean square error and score of 13.51 and 252.61, respectively.
An analysis of cratering data from desert alluvium

From the large amount of data available for explosion craters in the desert alluvium at the Nevada test site it was possible to establish very reliable relationships among charge size, depth of burst, crater radii, and crater depths, A scaling law for crater dimensions of W/sup 1/3.4/ is derived, together with an efflciency factor for nuclear explosives relative to high explosives. In addition, a curve for the dependence of crater radii and depth on the depth of burst of the explosive is given. (auth)
Thickness of the Seismic Slip Zone

This article reviews geologic and other evidence constraining the thickness of the principal slip zone (PSZ) that accommodates the bulk of coseismic shear displacement during an individual rupture event. Surface deformation from rupturing may occupy swaths tens of meters or more in width, but trenches across active faults generally reveal that incremental slip is accommodated by a PSZ that is tens of centimeters or less in thickness. Geomorphic evidence, coupled with the observations from trenching, suggest a PSZ may stay well localized for distances of several kilometers through many rupture episodes. Mine exposures and exhumed fault zones demonstrate that PSZs separating different lithologies within the “fault core,” although contained within “damage zones” of variably fractured rock ranging up to hundreds of meters in thickness, often comprise just a few centimeters of gouge/ultracataclasite that have accommodated large finite displacements (>1 km). Microstructural studies demonstrate incremental slip localized still further down to 1–10 mm, as do other fault-rock assemblages (slickensides and slickenfibers, fault-veins of pseudotachylyte friction-melt, intravein septa in hydrothermal fault infills). The accumulated evidence indicates that localization of coseismic shearing to less than 10 cm on planar faults is widespread throughout the crustal seismogenic zone, with extreme localization to less than 1 cm not uncommon. However, some distributed coseismic shear may also develop, especially at rupture irregularities. Coseismic reduction of shear resistance from friction-melting (Δ T ∼ 1000°C) or from transient thermal pressurization of aqueous fluids (Δ T ∼ 100°C) requires slip during moderate-to-large earthquakes ( u > 1 m) to be restricted to narrow zones, respectively a few centimeters or tens of centimeters in thickness. Given the evidence for slip localization, the apparent scarcity of pseudotachylyte suggests either that seismic friction-melting is a rare phenomenon, or that pseudotachylyte is only rarely preserved in recognizable form within mature hydrated fault zones.
One Health: an ecological and conservation perspective.

This chapter initially discusses paradigm shifts in epidemiology and ecology, as well as their converging approaches to health and disease, during the 20th century. An examination of the difficulties in defining and measuring ecosystem health, ecosystem integrity and environmental health then follows, using specific examples to illustrate the complexity of these interactions and the important role of conservation in a developing One Health paradigm. Relevant concepts of adaptive capacity, resilience and transformability in social-ecological systems are also discussed.
Effect of irrigation on growth and yield of Kabuli chickpea (Cicer arietinum L.) and narrow-leafed lupin (Lupinus angustifolius L.).

The response in growth and yield of Kabuli chickpea (Cicer arietinum L.) cv. Princepe and narrow-leafed lupin (Lupinus angustifolius L.) cv. Fest to different irrigation levels when unirrigated (water stressed), given half irrigation, full irrigation and double irrigation (waterlogged) and full irrigation with 150 kg N ha (optimum, control plots) was investigated on a Templeton silt loam soil at Lincoln University in 2007/08. Irrigation had a marked effect on growth and yield. There was a 51 % increase in the weighed mean absolute growth rate (WMAGR) with full irrigation over no irrigation. In Kabuli chickpea, WMAGR with full irrigation was 18.6 g m day and in narrowleafed lupin it was 23.0 g m day. Seed yields of fully-irrigated crops were treble the unirrigated treatment. With full irrigation, seed yield of chickpea was 326 and that of lupin 581 g m. Seed yield of the two legumes fell 45 % with double irrigation compared with full irrigation. Nitrogen (N) fertilizer did not increase seed yield in either legume. The increased seed yield resulted from increased radiation interception. With full irrigation, total intercepted photosynthetically active radiation (PAR) increased by 28 % and 33 % over that in nonirrigated plants in Kabuli chickpea and narrow-leafed lupin, respectively. The results of this study suggest that to achieve their yield potential, crops should be irrigated to replace water deficit over the whole of crop growth.
The Functional and Anatomical Impacts of Healthy Muscle Ageing

Simple Summary Ageing sometimes leads to changes in the size, shape and structure of muscles. This impacts their ability to perform their functions and so leads to an increased likelihood of injuries and negatively impacts quality of life. This work attempts to investigate how the structure and performance of individual muscles of the human leg may change due to ageing, using a combination of magnetic resonance imaging and muscle strength testing in groups of aged (65+ years old) and young (18–40 years old) individuals. The results showed a larger difference in muscle structure and strength in the muscles that extend the knee (i.e., the quadriceps) between the age groups relative to other muscles, and these muscles also showed the strongest relationships between structure and strength. There was also greater variation in fibre lengths and variables linked to fibre type and quality within individual muscles in the aged group compared to the young group. Overall, these results show that even healthy ageing may cause large changes in muscle structure and decreases in performance and that the quadriceps muscles could be a target for exercises to reduce the negative effects of ageing. Abstract Even “healthy” muscle ageing is often associated with substantial changes in muscle form and function and can lead to increased injury risks and significant negative impacts on quality of life. However, the impacts of healthy muscle ageing on the fibre architecture and microstructure of different muscles and muscle groups throughout the lower limb, and how these are related to their functional capabilities, are not fully understood. Here, a previously established framework of magnetic resonance and diffusion tensor imaging was used to measure the muscle volumes, intramuscular fat, fibre lengths and physiological cross-sectional areas of 12 lower limb muscles in a cohort of healthily aged individuals, which were compared to the same data from a young population. Maximum muscle forces were also measured from an isokinetic dynamometer. The more substantial interpopulation differences in architecture and functional performance were located within the knee extensor muscles, while the aged muscles were also more heterogeneous in muscle fibre type and atrophy. The relationships between architecture and muscle strength were also more significant in the knee extensors compared to other functional groups. These data highlight the importance of the knee extensors as a potential focus for interventions to negate the impacts of muscle ageing.
Hardware-Assisted Static and Runtime Attestation for Cloud Deployments

This article is devoted to the problems of static and runtime integrity for cloud deployments. Existing remote attestation solutions for cloud infrastructure do not cover static and dynamic attestation as a whole. They evaluate either the static or dynamic part, not considering the rest. We address this gap by proposing a runtime attestation process based on hardware CET technology, as an enhancement to static attestation enabled by SGX. We show how hardware-assisted protection for control-flow-related attacks can enhance virtual deployment security with minimal tradeoff. Our solution does not significantly increase the processing time. Moreover, a processing time can even be reduced when this mechanism is used as a default protection method against control-flow related attacks.
The Impact of AI Applications on Smart Decision-Making in Smart Cities as Mediated by the Internet of Things and Smart Governance

Plenteous research has been undertaken on the direct effects of artificial intelligence (AI) on smart decision-making. However, little attention has been paid to contextual factors such as the Internet of Things (IoT) and smart governance that mediate the relationship between AI and smart decision-making. This research investigates direct, mediating, and parallel-sequential multiple mediating interactions between AI, the IoT, smart governance, and smart decision-making. We used a self-structured survey to collect cross-sectional data from citizens in the Republic of Korea, and 516 responses were examined using SmartPLS structural equation modeling (PLS-SEM). A parallel-sequential multiple mediator framework is assessed using the Hayes Process Model with bootstrapping. Our results reveal a substantial and favorable multi-mediating effect from the IoT and smart governance on the relationship between AI applications and smart decision-making, as predicted. Previous scholars have investigated a few factors that influence decision-making, but our research contributes to the literature of applied and social sciences, including traditional decision theory, by examining the impact of multi-mediating factors on smart decision-making. This study presents both theoretical and practical implications for scholars and policymakers engaged in the development of smart cities. Additionally, this study provides recommendations for future research.
Frontiers of Cranial Base Surgery: Integrating Technique, Technology, and Teamwork for the Future of Neurosurgery

The landscape of cranial base surgery has undergone monumental transformations over the past several decades. This article serves as a comprehensive survey, detailing both the historical and current techniques and technologies that have propelled this field into an era of unprecedented capabilities and sophistication. In the prologue, we traverse the historical evolution from rudimentary interventions to the state-of-the-art neurosurgical methodologies that define today’s practice. Subsequent sections delve into the anatomical complexities of the anterior, middle, and posterior cranial fossa, shedding light on the intricacies that dictate surgical approaches. In a section dedicated to advanced techniques and modalities, we explore cutting-edge evolutions in minimally invasive procedures, pituitary surgery, and cranial base reconstruction. Here, we highlight the seamless integration of endocrinology, biomaterial science, and engineering into neurosurgical craftsmanship. The article emphasizes the paradigm shift towards “Functionally” Guided Surgery facilitated by intraoperative neuromonitoring. We explore its historical origins, current technologies, and its invaluable role in tailoring surgical interventions across diverse pathologies. Additionally, the digital era’s contributions to cranial base surgery are examined. This includes breakthroughs in endoscopic technology, robotics, augmented reality, and the potential of machine learning and AI-assisted diagnostic and surgical planning. The discussion extends to radiosurgery and radiotherapy, focusing on the harmonization of precision and efficacy through advanced modalities such as Gamma Knife and CyberKnife. The article also evaluates newer protocols that optimize tumor control while preserving neural structures. In acknowledging the holistic nature of cranial base surgery, we advocate for an interdisciplinary approach. The ecosystem of this surgical field is presented as an amalgamation of various medical disciplines, including neurology, radiology, oncology, and rehabilitation, and is further enriched by insights from patient narratives and quality-of-life metrics. The epilogue contemplates future challenges and opportunities, pinpointing potential breakthroughs in stem cell research, regenerative medicine, and genomic tailoring. Ultimately, the article reaffirms the ethos of continuous learning, global collaboration, and patient-first principles, projecting an optimistic trajectory for the field of cranial base surgery in the coming decade.
Graphene-based chemiresistive gas sensors

Gas sensors allow the monitoring of the chemical environment of humans, which is often crucial for their wellbeing or even survival. Miniaturization, reversibility, and selectivity are some of the key challenges for serial use of chemical sensors. This tutorial review describes critical aspects when using nanomaterials as sensing substrates for the application in chemiresistive gas sensors. Graphene has been shown to be a promising candidate, as it allows gas sensors to be operated at room temperature, possibly saving large amounts of energy. In this review, an overview is given on the general mechanisms for gas-sensitive semiconducting materials and the implications of doping and functionalization on the sensing parameters of chemiresistive devices. It shows in detail how different challenges, like sensitivity, response time, reversibility and selectivity have been approached by material development and operation modes. In addition, perspectives from the area of data analysis and intelligent algorithms are presented, which can further enhance these sensors' usability in the field.
Multi-modal Feature Fusion 3D Object Detection

: For the existing 3D small object detection is prone to false detection and missed detection and other deficiencies. A 3D object detection method based on multi-modal feature fusion is proposed. Firstly, a feature extraction module is designed. The input image data is down-sampled through the image feature extraction network, and the input point cloud data is sampled and grouped through the point cloud feature extraction network to obtain the feature information at different scales. Secondly, a multi-modal feature fusion module is constructed to realize the point correspondence between point cloud features and image features by projection operation, and then the image features and point cloud features are splicing and fused to generate the final point cloud features to compensate the deficiency of single modal feature information. The experimental results show that compared with the existing algorithms, the algorithm in this paper improves the average detection accuracy of small object by 2.03%.
In alliance with a nocturnal landscape: memory and water law in the north Baikal, Siberia

ABSTRACT This paper examines the ethnography of nocturnal fishery and relationships with water, relevant for Evenkis occupying the northern coastal area of Lake Baikal, Siberia. The material arises from Evenkis of Kumora village who live near Lake Irkana and from archival sources. Although the nocturnal fishery is declared illegal in official legislation, local residents invoke memories to mark that practice as traditional and important for the local community since it is not merely a subsistence activity but also an emotional experience and long-term relationships with the landscape. This paper argues that local social memory devoted to this practice serves as a kind of fishing tool and a tool for supporting local ideas of how fishing should be governed. The collision between memory and water law is not discussed in terms of antagonism between local groups and authorities but as ignorance between memory-gifted people and the landscape, and memory-disabled official approaches to nocturnal fishing and its histories. Finally, memory-gifted human landscape relationships termed as ‘alliance’ are approached as a powerful conglomerate that ‘consumes’ authorised visions of fishing patterns in their own way.
Attack Endgame: Proactive Security Approach for Predicting Attack Consequences in VANET

In the fast dynamic environment of Vehicle Ad Hoc Networks (VANETs), proactive security measures are necessary. Reactive security has been VAVNETs' guardian angel for some time, but now it is insufficient against current security attacks. Attack prediction is a promising solution capable of keeping up with the recent cyber security challenges. First, we need to understand where prediction fits in the attack process. To accomplish this, we introduce an attack life cycle in a VANET and exploit the proactive and retroactive phases. One of the proactive phases is the after-effect of the attack or what we call attack endgame. We use the Framework for Misbehavior Detection (F2MD) to simulate an attack effect with adverse side effects on road traffic. We implement traffic warning messages in F2MD. Then, we create attacks on these messages, namely “fake accident”, and simulate the effect of these attacks on the vehicles while capturing the results using F2MD. We simulate the impact of acting on these messages or the attack endgame, which manifested in creating hazards. We use Recurrent Neural Network (RNN) models to predict the endgame of the fake accident attack on the road. We experiment with vanilla artificial neural network solutions to create a baseline. Afterward, we use Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to build a stacked RNN model to predict the attack endgame at different time windows. They effectively predict the occurrence of a hazard up to 3.5 minutes ahead with over 80% accuracy.