Miracle-dz/UniFAR-FTUs huggingface dataset:
Late Pleistocene Evolution of Tides and Tidal Dissipation

Studies of the Last Glacial Maximum (LGM; 26.5–19 ka) tides showed strong enhancements in open ocean tidal amplitudes and dissipation rates; however, changes prior to the LGM remain largely unexplored. Using two different ice sheet and sea level reconstructions, we explicitly simulate the evolution of the leading semi‐diurnal and diurnal tidal constituents (M2, S2, K1, and O1) over the last glacial cycle with a global tide model. Both sets of simulations show that global changes, dominated by the Atlantic, take place for the semi‐diurnal constituents, while changes for the diurnal constituents are mainly regional. Irrespective of the reconstruction, open ocean dissipation peaks during the sea level lowstands of MIS 2 (∼20 ka) and MIS 4 (∼60 ka), although dissipation values prior to MIS 2 are sensitive to differences in reconstructed ice sheet extent. Using the statistically significant relationship between global mean sea level and dissipation, we apply regression analysis to infer open ocean and shelf dissipation, respectively, over the last four glacial cycles back to 430 ka. Our analysis shows that open ocean tidal energy was probably increased for most of this period, peaking during glacial maxima, and returning to near‐present‐day values during interglacials. Due to tidal resonance during glacial phases, small changes in bathymetry could have caused large changes in tidal amplitudes and dissipation, emphasizing the need for accurate ice margin reconstructions. During glacial phases, once global mean sea level decreased by more than ∼100 m, the amount of open ocean tidal energy available for ocean mixing approximately doubled.
Transient electromagnetic surveys in the Okiep district

In the Okiep District early miners produced massive sulfide ore from some five deposits. Some of these deposits later contributed to the reserves of disseminated ore mined during modern times. It is unreasonable to assume that all of the massive sulfide bodies present within the area are intersected by the erosion surface and thus were discovered by the early miners. Consequently, blind massive sulfide ore bodies could be present and may have large quantities of disseminated ore associated with them. The transient electromagnetic method is ideally suited to exploring for massive sulfide bodies, and six test surveys were carried out at various sites in the district. Four of these surveys were unsuccessful whereas, at the remaining two sites, excellent anomalies were recorded. At Ezelsfontein East Extension an anomaly was recorded indicative of a massive sulfide body at shallow depth and of generally flat attitude. This anomaly has a time constant of 15 ms and the interpreted body was confirmed by a limited.
The Origin of Low-redshift Event Rate Excess as Revealed by the Low-luminosity Gamma-Ray Bursts

The relation between the event rate of long gamma-ray bursts and the star formation rate is still controversial, especially at the low-redshift end. Dong et al. confirmed that the gamma-ray burst rate always exceeds the star formation rate at a low redshift of z < 1 in spite of the sample completeness. However, the reason for low-redshift excess is still unclear. Since low-luminosity bursts are at smaller redshifts generally, we choose three Swift long burst samples and classify them into low- and high-luminosity bursts in order to check whether the low-redshift excess is existent and if the excess is biased by the sample size and completeness. To degenerate the redshift evolution from luminosity, we adopt the nonparametric method to study the event rate of the two types of long bursts in each sample. It is found that the high-luminosity burst rate is consistent with the star formation rate within the whole redshift range, while the event rate of low-luminosity bursts exceeds the star formation rate at a low redshift of z < 1. Consequently, we conclude that the low-redshift excess is contributed by the low-luminosity bursts with possibly new origins unconnected with the star formation, which is also independent of the sample size and the sample completeness.
SepBIN: Binary Feature Separation for Better Semantic Comparison and Authorship Verification

Binary semantic comparison and authorship verification are critical in many security applications. They respectively focus on the functional semantic features and developers’ programming style features of binary code, which are usually mixed without clear demarcation. Recently, researchers have proposed learning-based approaches for intelligent binary analysis. They generally addressed single tasks with hand-crafted feature sets or neural binary encoders, which suffer performance bottlenecks due to the noise in mixed features. This paper proposes SepBIN, a novel neural network framework that exploits the intrinsic correlation of binary semantic comparison and authorship verification tasks and automatically separates semantic and stylistic binary features. We first construct a strong backbone binary encoder, then utilize preliminary decomposition subnets and the flexible gating-based feature fusion mechanism to distill pure semantic-related and style-related binary representations, and further improve their quality by a feature reconstruction module. The overall SepBIN model is optimized by a multi-objective joint optimization strategy. We conduct extensive experiments on Google Code Jam (GCJ) datasets in different languages and scales. Results show that SepBIN simultaneously benefits binary semantic comparison and authorship verification tasks through the effective binary semantic-style feature separation mechanism, and provides multi-perspectives interpretability for the performance gains. For state-of-the-art approaches with different binary encoders, SepBIN can adaptively improve them with the designed separation modules. Furthermore, we adopt a pretraining-finetuning strategy to effectively transfer SepBIN’s separation capability in real-world applications, including APT malware homology detection and binary semantic comparison against code obfuscations.
A Novel Transformer-based Trajectory Options Generator for Collaborative Air Traffic Flow Management

Demand for air travel has increased significantly in recent decades, possibly exceeding capacity. Researchers have made efforts to achieve demand and capacity balance (DCB), such as Ground Delay Programs (GDPs) and Collaborative Trajectory Options Program (CTOP). However, previous trajectory design works in Collaborative Air Traffic Flow Management (C-ATFM) provide optimal trajectory based on abstract airspace structures built by human experience, and it may be difficult to accurately capture the complex flight operation environment. Therefore, this paper proposes a Transformer-based trajectory options generator (TTOG) for collaborative air traffic flow management to provide AUs with effective trajectory options, thus improve the performance of ATFM. Specifically, the generator utilizes the Transformer architecture to learn trajectory planning knowledge from historical data and generate a series of candidate trajectories that conform to the principles of trajectory planning. Then we formulate the 4D trajectory planning problem as an integer linear programming model to obtain the global 4D trajectories and minimize the total delay costs. Experiment results suggest that the generator could provide effective trajectory options and significantly reduce total flight delay costs.
MRI and CT Fusion in Stereotactic Electroencephalography (SEEG)

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method’s efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used.
Potential use of cowpea protein hydrolysate as a dietary supplement in broiler chickens: effects on growth, intestinal morphology, muscle lipid profile, and immune status

Abstract In the current study, we tested the effects of dietary cowpea protein hydrolysate (CPH) on broilers’ growth, carcase traits, intestinal histomorphology, The fatty acid profile of breast muscle, blood biochemical parameters, and immune status. Three-day age male Ross-308 broilers (n = 300, average weight 86.72 g ± 0.20) were distributed into five groups and fed on five diets supplied with five levels of CPH at 0, 2, 4, 6, and 8 g kg−1 for 35 days. The results revealed that dietary CPH did not change (P < 0.05) the broilers’ growth performance or carcase traits, except for a quadratic decrease (P < 0.05) in FCR at concentrations 2 and 4 g kg−1 during the finisher period. The villus heights (VH) and goblet cell count (GCC) were increased (P < 0.05) in the duodenum and jejunum; however, the villus width decreased (P < 0.05) in the duodenum without showing changes (P < 0.05) in the jejunum and ileum. Dietary supplementation with 6 g CPH kg−1 increased (P < 0.05) the duodenal crypt depth (CD). Dietary supplementation with 8 g CPH kg −1 increased the VH: CD in the duodenum compared to the control; nevertheless, a quadratic (P < 0.05) change was reported in the jejunum. The VH: CD did not change (P < 0.05) in the ileum; however, dietary supplementation with 6 CPH kg−1 increased (P < 0.05) the ileal VH and GCC. The inclusion of CPH led to a linear (P < 0.05) increase in the total n-3 polyunsaturated fatty acids (PUFA), n-6 PUFA, and n-3/n-6 PUFA in broilers’ breast muscle. An increase in the assessed metabolic and immune status indices was reported, as evidenced by increases (P < 0.05) in thyroid hormones, growth hormone, total protein, albumin, globulin, lysozymes, interferon-gamma, interleukin10, and complement 3 serum levels. However, the serum glucose and leptin levels did not show significant (p > 0.05) changes in response to dietary CPH. Moreover, the dietary CPH levels 4–8 g kg−1 upregulated the immunostaining of IgG in the spleen of these groups. These findings suggested that including CPH in broiler diets could be an effective candidate for modulating the chickens’ metabolic and immune status; however, it had no increased effect on broiler growth. HIGHLIGHTS Cowpea protein hydrolysate (CPH) did not positively affect the broiler growth performance or carcase traits, except for a reduction in FCR during the finisher period at levels 2 and 4 g CPH kg−1 diet. CPH improved the chickens’ metabolic and immune status in a concentration-dependent manner. Dietary supplementation with CPH would be feasible in the production of broilers.
Green synthesis trends and potential applications of bimetallic nanoparticles towards the sustainable development goals 2030

The world faces threats that the United Nations has classified into 17 categories with different objectives as solutions for each challenge that are enclosed in the Sustainable Development Goals (SDGs). These actions involved the widespread use of science and technology as pathways to ensure their implementation. In this regard, sustainability science seeks the research community's contribution to addressing sustainable development challenges. Specifically, nanotechnology has been recognized as a key tool to provide disruptive and effective strategies to reach the SDGs. This review proposes the application of bimetallic nanoparticle substances capable of providing possible solutions to achieve target SDG 3: good health and well-being, SDG 6: clean water and sanitation, and SDG 12: responsible consumption and production. Furthermore, the term green nanotechnology is introduced in each section to exemplify how green synthesized bimetallic nanoparticles have been used to resolve each target SDG. This review also outlines the current scenario regarding the utilization of metallic nanomaterials in the market, together with the upscaling challenges and the lack of understanding of the long-term effects and hazards to the environment regarding bimetallic nanoparticles.
Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach

Complexity of urban systems necessitates the consideration of interdependency among various factors for land use change modeling and prediction. The objective of this study is to explore the applicability of computational neural networks in modeling urban sprawl and land use change coupled with geographic information systems (GIS) in Hilton Head Island, South Carolina. We are particularly interested in the capabilities of neural networks to identify land use patterns, to model new development, and to predict future change. A binary logistic regression model is estimated comparison. The results indicate the neural network model is an improvement over the logistic regression model in terms of prediction accuracy.
The ponder for study the intension and locus of continental dynamics in China

Continental dynamics has become a focus and central subject of Earth science in the 21st century.It is an opportunity a challenge when it is facing resources,energy,disaster and environmental and other social and economic development needs of growing.Therefore,determining the content and direction of its research is very important.Based on years of research and exploration,this article will discuss the following five aspects:1)The connotation and definition of the continental dynamics research;2)The orientation and track of the continental dynamics research;3)The contradictions between geophysical inversion results and interpretation in study continent dynamics;4)Strengthening the development course cross to constitute interdisciplinary science;5)The ponder for academic activities in 2011.
A 65-dB SNDR Continuous-Time Delta-Sigma ADC Integrated by IZO Thin-Film Transistors

This brief presents a first-order continuous-time delta-sigma ADC in IZO TFT process. The presented ADC achieves 65 dB SNDR in 30-Hz bandwidth and 43-dB SNDR in 304-Hz bandwidth. For TFT analog building block, a 60-dB dc gain, 400-kHz unity-gain frequency operational amplifier (OPA) using hybrid Miller/feedthrough compensation is proposed. And a 3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> propagation delay latch comparator is proposed.
Seismic tomography for longwall stress analysis

Anomalous geologic or stress-induced conditions can create a hazardous working environment for underground miners. Seismic attenuation tomography provides a valuable tool for understanding static and dynamic conditions induced by mining. As a longwall shearer cuts coal, it generates seismic energy. As this energy travels through the rock mass it is attenuated in a manner proportional to the degree of fracturing of the rock mass. By calculating this attenuation at specific locations underground, the degree of fracturing of the rock mass, and hence the relative stress state or material properties can be inferred. The goal of this work has been to create a tool which can be easily used by the mining industry to obtain information about the distribution of stress concentrations or material properties across a broad area in advance of mining.
Optimizing environmental monitoring in IoT: integrating DBSCAN with genetic algorithms for enhanced clustering

In our study, we introduce an advanced clustering method designed for IoT-based environmental monitoring. We’ve combined two powerful techniques, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithms (GA), to create a specialized approach called EC-GAD (Enhanced-Clustering using Genetic Algorithms and DBSCAN). This integrated system model relies on DBSCAN, a robust clustering algorithm capable of handling irregular shapes and varying data densities, to group sensor nodes based on their physical proximity. To improve clustering performance, we’ve harnessed Genetic Algorithms to optimize the parameters of DBSCAN. Through a repetitive process involving selection, crossover, and mutation, GA refines parameter settings based on the quality of environmental clustering as assessed by fitness metrics. Our approach is tailored specifically for IoT deployments in environmental monitoring, which involve collecting data from sensor nodes and integrating DBSCAN and GA. We’ve paid special attention to choosing an appropriate distance metric and fine-tuning DBSCAN parameters such as epsilon (ε) and minPts to match the unique needs of environmental monitoring applications. Furthermore, we’ve taken energy efficiency into account by implementing energy-aware node selection and optimizing cluster formation to minimize energy consumption.
Assessment of Ant Colony Optimization on Inducing Non-Uniform Population Distribution for UAS Risk Assessment in Urban Environments

The proliferation of Unmanned Aircraft Systems (UAS) in commercial domains accentuates the imperative need for reliable safety measures, particularly within urban landscapes, during Unmanned Aerial Vehicle (UAV) operations. Contemporary frameworks, such as the Specific Operations Risk Assessment (SORA) proposed by the Joint Authorities for Rulemaking of Unmanned Systems (JARUS), predominantly use static methodologies for estimating population density, implicitly presuming uniform population distribution. Such assumptions potentially introduce inaccuracies in the quantification of third-party risks due to the dismissal of temporal fluctuations in population distribution. This study seeks to address this gap by employing Ant Colony Optimization (ACO), a bio-inspired algorithm traditionally used to solve optimization problems such as the Traveling Salesman Problem (TSP). Singapore serves as the case study in this context, where bus stops within neighbourhood subzones are assigned as network nodes. The ACO algorithm, coupled with an appropriate heuristic data input, is employed to induce non-uniformity in population distribution. Furthermore, the study introduces a metric, termed ’population exposure’, that acts as the heuristic data input to the ACO algorithm, and is devised to encapsulate dynamic patterns of population inflow and outflow. Preliminary findings, primarily conveyed through visualizations, show promising alignment with qualitative assumptions regarding population distribution about time and locale. This initial assessment could herald a nuanced approach to UAS operational risk assessments, accommodating the dynamic and non-uniform nature of urban population distribution.
New constraints on the rupture process of the 1999 August 17 Izmit earthquake deduced from estimates of stress glut rate moments

SUMMARY This paper illustrates the use of integral estimates given by the stress glut rate moments of total degree 2 for constraining the rupture scenario of a large earthquake in the particular case of the 1999 Izmit mainshock. We determine the integral estimates of the geometry, source duration and rupture propagation given by the stress glut rate moments of total degree 2 by inverting long-period surface wave (LPSW) amplitude spectra. Kinematic and static models of the Izmit earthquake published in the literature are quite different from one another. In order to extract the characteristic features of this event, we calculate the same integral estimates directly from those models and compare them with those deduced from our inversion. While the equivalent rupture zone and the eastward directivity are consistent among all models, the LPSW solution displays a strong unilateral character of the rupture associated with a short rupture duration that is not compatible with the solutions deduced from the published models. With the aim of understand this discrepancy, we use simple equivalent kinematic models to reproduce the integral estimates of the considered rupture processes (including ours) by adjusting a few free parameters controlling the western and eastern parts of the rupture. We show that the joint analysis of the LPSW solution and source tomographies allows us to elucidate the scattering of source processes published for this earthquake and to discriminate between the models. Our results strongly suggest that (1) there was significant moment released on the eastern segment of the activated fault system during the Izmit earthquake; (2) the apparent rupture velocity decreases on this segment.
Sound-based Two-Factor Authentication: Vulnerabilities and Redesign

Reducing the level of user effort involved in traditional two-factor authentication (TFA) constitutes an important research topic. An interesting representative approach, Sound-Proof, leverages ambient sounds to detect the proximity between the second-factor device (phone) and the login terminal (browser), and eliminates the need for the user to transfer PIN codes. In this paper, we identify a weakness of the Sound-Proof system that makes it completely vulnerable to passive “environment guessing” and active “environment manipulating” remote attackers and proximity attackers. Addressing these security issues, we propose Listening-Watch, a new TFA mechanism based on a wearable device (watch/bracelet) and active browser-generated random speech sounds. As the user attempts to log in, the browser populates a short random code encoded into speech, and the login succeeds if the watch’s audio recording contains this code (decoded using speech recognition), and is similar enough to the browser’s audio recording. The remote attacker, who has guessed/manipulated the user’s environment, will be defeated since authentication success relies upon the presence of the random code in watch’s recordings. The proximity attacker will also be defeated unless it is extremely close (< 50 cm) to the watch since the wearable microphones are usually designed to capture only nearby sounds (e.g., voice commands).
Search and Rescue Using Multiple Drones in Post-Disaster Situation

We present the development and application of multiple autonomous aerial vehicles in urban search and rescue missions. The missions are designed by the 2014 International Micro Aerial Vehicle Competition, held in Delft, the Netherlands, August 2014. Different mission tasks are identified for search and rescue missions, such as aerial photography, low altitude flight in urban environment, indoor navigation and rooftop landing. These tasks are all of paramount importance for rescuers in a disaster-hit place. We have designed a team of micro aerial vehicles with specific configurations to meet the mission requirements. A range of key technologies have been developed, including robust controller design, real-time map stitching, indoor navigation and roof-top perching. The proposed solutions are successfully demonstrated in the competition.