A document by Helen Payne. Click on the document to view its contents.

Weathering is a natural geological process whereby atmospheric CO2 dissolved in rainwater attacks rocks, partly dissolving them. The CO2 is converted into alkalinity or carbonate minerals that securely store carbon on timescales of >10,000 years. Modelling studies show that if weathering rates can be increased (by selecting the most reactive rocks, increasing reactive surface area), up to an additional 2 Gt CO2 yr-1 could be removed from the atmosphere, ~40% of the amount required by 2100 to meet the Paris Agreement target.The mining industry extracts gigatonnes of rock each year, generating large amounts of freshly exposed, reactive surface area that could be used as a feedstock for enhanced weathering. Ore deposits with the highest CO2 removal capacity are those mined in high quantities that have an abundance of calcium- and magnesium-bearing silicate minerals [1]. Here, we report the results of an investigation into the reactivity of serpentinised peridotite samples  from the Sakatti Cu-Ni-PGE deposit (Finland). The deposit consists of both disseminated and massive sulphides hosted within a large olivine-cumulate  body [2]. The material consists primarily of serpentine [Mg3Si2O5(OH)4] and olivine [Mg2SiO4] minerals that have a high potential for CO2 removal via enhanced weathering. We have conducted a series of laboratory experiments whereby crushed peridotite samples were reacted with CO2-enriched solutions at 25 °C and 50 °C, and 1 bar CO2 (100% CO2).  Surface area normalised dissolution rates were determined for various grain sizes,  temperatures, mineral compositions (degree of serpentinization) and solution chemistry (with/without a chelating agent).

Affective patterns based on physiological signals reflect bodily changes linked to specific emotional states. Previous studies on the cardiac electrical signal, a key peripheral physiological signal, were limited by the measurement density of single-lead ECG signal, focusing solely on temporal pattern analysis but ignoring topographic pattern analysis that can reflect the body's emotional response. Our research advances affective peripheral pattern studies by innovatively using body surface potentials to comprehensively monitor cardiac electrical activity with increased measurement density. To tackle the challenge of extracting spatial and temporal features from multi-channel body surface potentials, we establish a dynamic correlation among these diverse channel signals through covariance matrices. Our hypothesis is that the dynamic inter-channel relationship provides a valuable source of insights into emotional clues. Experimental results demonstrate that the extracted spatial and temporal features effectively capture topographic and temporal patterns from cardiac electrical signals, and achieve excellent performance in classification tasks simultaneously. Our finding reveals affective patterns based on body surface potentials for the first time, offering novel insights into affective peripheral patterns analysis.

The multi-fold theory provides microscopic interpretations to the universe accelerated expansion, inflation mechanisms and dark energy. From the beginning, the multi-fold dark matter main mechanisms have been identified as being stronger in the presence of matter, or energy increasing spacetime curvature. One could therefore naively expect that these mechanisms will also be globally time-dependent, when it comes to the early epochs of the universe. Indeed, during the early cosmological eras, including pre-CMB, different matter densities and curvatures reigned. Each would lead to a different global average cosmological constant. Such a reasoning would predict a Hubble constant that is larger for early time than late time observations. As discussed in multiple papers and articles, proposing a time-varying dark energy density can obviously be a simple way to address the Hubble constant tension: early time estimates would correspond to such a different value, and no Hubble constant tension would exist. However, in our naïve initial analysis above, obtaining larger pre-CMB values could a priori go in the wrong direction when it comes to observations: one would expect a larger Hubble constant at early times, unless if it is rather an additional contribution to the CMB derived Hubble constant. Considering the multi-fold dark energy mechanisms, in all the early stages of the universe, i.e., pre-CMB, aka pre-recombination, which led to the CMB, plasma, dominated by electromagnetic and possibly other interactions, occupied the whole concretized spacetime. This way, multi-fold dark energy effects were partially countered by more dominant interactions, and the resulting accelerations of expansion were reduced. It can explain why current early time estimates for the Hubble constant would differ from late time results, and lead to a smaller Hubble constant value at early time. However, it may not fit that well the overall standard cosmological model à la ΛCMD. However, note that recent papers show that a short term increase of the dark energy (5% for redshift z > 5000), can resolve the tension, by pushing up the CMB inferred Hubble constant, i.e. early time estimates, and better match overall the ΛCDM. So maybe our naïve reasoning was not that bad. Combining the two considerations above, our paper shows that multi-fold dark energy mechanisms intrinsically, and microscopically, justify such a behavior that can resolve the Hubble tension. It is another hint in favor of the multi-fold mechanisms, to add to several others. There is also a range where a multi-fold universe appear fractal, which may result into scaling of the cosmological constant, and reducing its value, during this period; again a move in the right direction. The result extends to the real universe, if it is multi-fold, as hinted by past results. ____ 1 shmaes.

Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil-a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders' heightened concern for urban water supply over agricultural demand during droughts. We found that "reservoir operation" was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to "climate information" became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

10 The current global decline in biodiversity is a matter of pressing concern, necessitating the 11 conservation of diverse ecosystems across various spatial scales. Regions such as the 12 tropical Andes face the imminent threat of biotic homogenization due to intensive 13 livestock grazing, posing a significant risk to biodiversity. This study is focused on the 14 sub-humid grasslands of northwestern Bolivia, within the the National Park Apolobamba. 15 We investigate the influence of environmental factors on plant diversity within and among 16 communities across seven distinct sites. Our research reveals that local plant diversity, 17 quantified by species richness and the inverse Simpson index, is predominantly shaped by 18 soil pH. Notably, more acidic soil is associated with diminished diversity. Furthermore, 19 our findings highlight that the dissimilarity in species composition among local 20 communities may be linked to grazing intensity. This suggests that intensified grazing may 21 have the potential to homogenize plant communities across the landscape. A concerning 22 implication is the likelihood of communities becoming dominated by acquisitive species, 23 leaving them more susceptible to the impacts of climate variability. The study underlines 24 the necessity to analyze multiple facets of diversity for a comprehensive understanding of 25 the environmental factors regulating and therefore to address potential drivers of diversity 26 loss. To mitigate these threats, managers may consider adjusting livestock quantities and 27 the spatial range used by grazers, aiming to sustain multiple aspects of plant diversity and 28 prevent homogenization and degradation of grasslands in a changing world. 29

We condense Maxwell's equations into a representation of the elastodynamic equation and explain electromagnetic phenomena within the framework of elastodynamics. Here, the magnetic field arises from the interaction between positive and negative charges. We explore the characterization of magnetic waves emitted by a moving source.

A document by HUANG Yuanyuan. Click on the document to view its contents.

A document by Sven Ochs. Click on the document to view its contents.

In numerous mission-critical applications anticipated by future sixth-generation (6G), such as autonomous vehicles, achieving high accuracy in image recognition is essential. Concurrently, minimizing the data traffic, or equivalently, effective data compression due to constraints posed by transmission delay are crucial. To overcome these challenges, this paper proposes a task-oriented semantic communication system dedicated to image data, designed to extract and transmit the information required by the receiver. The goal of the system is to recognize the semantic boundaries of the images. For this For this relevant, a novel semantic encoder, based on compressed sensing (CS) is developed at the transmitter to extract semantic information. Additionally, a novel semantic decoder is proposed in the receiver, utilizing sparse reconstruction techniques to reconstruct semantic information. In contrast to prior studies that focused on conveying a broad spectrum of semantic information related to images along with all extracted features, this approach concentrates solely on isolating the semantic features relevant to the specific target edges. This method generates a sparse feature map, allowing for a reduction in compression rates by a new compressed sensing techniques implementing new sensing matrix based on polar code (SMPC)compressed sensing techniques. Furthermore, the study examines two distinct typical scenarios: noiseless measurements and noisy measurements. Our simulations show that with a lower compression rate, the classification accuracy, and exact recovery probability of 100 % can be attained in both scenarios.

Eric Mwangi| Kabarak University|PhDDecember 2023

A document by Anas Zayoun. Click on the document to view its contents.

The controlled waterway in the upper reaches of the Yangtze River has become a bottleneck for shipping due to its curved, narrow and turbulent characteristics. The vessels passing through it must obey the signal revealed by the Intelligent Vessel Traffic Signaling System (IVTSS) to pass in one direction. The accuracy of signals is directly related to traffic safety and efficiency. However, the unreliability of vessel sensing sensors in these areas and the latency of transmission and computation of large amounts of sensing data may negatively impact IVTSS. Hence, more information from the physical world is needed to ensure the stable operation of the IVTSS, and we proposed an edge computing-centric sensing and execution system based on IoT architecture to enhance the reliability of IVTSS. We conducted experiments using plug-and-play methods, reducing command and recording error rates by 89.47% and 86.27%, respectively, achieving the goal of real-time perception control.

This study highlights the need for innovative, climate-smart solutions to power the future. It advocates a comprehensive approach involving renewable energy microgrids, demand response programs, and battery storage optimization to maximize carbon footprint reduction and sustainability. Collaboration between policymakers, utilities, and consumers is essential for widespread adoption. The study identifies several key outcomes: Optimal energy production, optimal energy storage, optimal demand response, and net energy balance. During optimization, carbon emissions were reduced to 72.75 kg CO2, exceeding the original target of 83.39 kg CO2. Additionally, comparing carbon emissions under different scenarios highlights the environmental benefits of renewable energy. Compared to alternative energy sources, the integrated approach shows significant potential in reducing carbon emissions.

The Antarctic Circumpolar Current is the world’s strongest ocean current. This vast current system is linked to ocean overturning and is pivotal to the uptake of ocean heat and CO2. The strength of the Antarctic Circumpolar Current has varied across Earth’s past climates, but the exact drivers of this change remain elusive. Ocean models have not been able to adequately resolve eddies and dense shelf water formation processes that control current strength. Here, we assess a global ocean model which resolves such processes to diagnose the impact of future thermohaline and wind conditions on the Antarctic Circumpolar Current. This model suggests the strength of the Antarctic Circumpolar Current will decline by up to ∼ 20% by 2050. This decline is supported by simple scaling theory, and is driven by ice shelf melting, which weakens the density gradient historically supported by surface temperature. Such a decline in transport would have critical implications for the global ocean circulation, and hence, Earth’s climate system.

Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking which could be detrimental to business operations. Consequently, the detection of these URLs is of crucial importance however, current Machine Learning (ML) models are susceptible to backdoor attacks. These attacks involve manipulating a small percentage of training data labels, such as Label Flipping (LF), which changes benign labels to malicious ones and vice versa. This manipulation results in misclassification and leads to incorrect model behavior. Therefore, integrating defense mechanisms into the architecture of ML models becomes an imperative consideration to fortify against potential attacks. The focus of this study is on backdoor attacks in the context of URL detection using ensemble trees. By illuminating the motivations behind such attacks, highlighting the roles of attackers, and emphasizing the critical importance of effective defense strategies, this paper contributes to the ongoing efforts to fortify ML models against adversarial threats within the ML domain in network security. We propose an innovative alarm system that detects the presence of poisoned labels and a defense mechanism designed to uncover the original class labels with the aim of mitigating backdoor attacks on ensemble tree classifiers. We conducted a case study using the Alexa and Phishing Site URL datasets and showed that LF attacks can be addressed using our proposed defense mechanism. Our experimental results prove that the LF attack achieved an Attack Success Rate (ASR) between 50-65% within 2-5%, and the innovative defense method successfully detected poisoned labels with an accuracy of up to 100%.

The generation and development of ionospheric irregularities is an important topic of study in space weather, particularly due to their adverse effects on navigation positioning systems and trans-ionospheric communications. To improve our prediction capabilities, a comprehensive understanding of their variability during different geomagnetic conditions is important. The purpose of this research is to analyze the inhibition of post-sunset plasma bubbles over South America during geomagnetic storms. To conduct the analysis, we used the moving standard deviation (std), to characterize the occurrence of ionospheric irregularities in ICON IVM ion density data, std ⩾10000 indicates the presence of irregularities. We also used the peak electron density (NMAX) from GOLD night disk scan measurements to identify bubbles. Additionally, we consider ICON MIGHTI wind data (red line emission) to study the role of the neutral wind. We examine the presence of irregularities during three storms Abstract SA53A-2526

A document by Yusuke Kuwahara. Click on the document to view its contents.

The rheology of crystalline units controls the large-scale deformation geometry and dynamics of collisional orogens. Defining a time-constrained rheological evolution of such units may help unravel the details of collisional dynamics. Here, we integrate field analysis, pseudosection calculations and in-situ garnet U-Pb and mica Rb-Sr geochronology to define the structural and rheological evolution of the Rotondo granite (Gotthard nappe, Central Alps). We identify a sequence of four (D1-D4) deformation stages. Pre-collisional D1 brittle faults developed before Alpine peak metamorphism, which occurred at 34-20 Ma (U-Pb garnet ages) at 590 ± 25ºC and 0.95 ± 0.1 GPa. The reactivation of D1 structures controlled the rheological evolution, from D2 reverse mylonitic shearing at amphibolite facies (520 ± 40ºC and 0.85 ± 0.1 GPa) at 18-20 Ma (white mica Rb-Sr ages), to strike-slip, brittle-ductile shearing at greenschist-facies D3 (395 ± 25 ºC and 0.4 ± 0.1 GPa) at 14-15 Ma (white and dark mica Rb-Sr ages), and then to D4 strike-slip faulting at shallow conditions. Although highly misoriented for the Alpine collisional stress orientation, D1 brittle structures controlled the localization of D2 ductile mylonites accommodating fast (1-3 mm/yr) exhumation rates due to their weak shear strength (<10 MPa). This structural and rheological evolution is common across External Crystalline Massifs (e.g., Aar, Mont Blanc), suggesting that the entire European crust was extremely weak during Alpine collision, its strength controlled by weak ductile shear zones localized on pre-collisional deformation structures, that in turn controlled localized exhumation at the scale of the orogen.

Anju Murayama1,2*; Kenichi Higuchi1; Yuki Senoo, MD, MUDr3

This work unveils the fundamental limits of linear and nonreciprocal plasmonic metasurfaces in terms of isolation and loss. The proposed bounds are related to surface waves and only depend on the nonreciprocal material employed within the metasurface, thus being independent of geometrical considerations and the presence of other materials. We apply these fundamental limits to explore two different platforms, namely drift-biased and magnetically-biased graphene metasurfaces. For each platform, we first analytically derive the upper bounds in terms of graphene conductivity. Then, we explore devices proposed in the literature and benchmark their response against their upper bounds. Results highlight that drift-biased hyperbolic metasurfaces exhibit outstanding performance in the mid-infrared region, whereas magnetically-biased devices are better suited for the low terahertz band. More broadly, our bounds allow to quickly assess the performance of nonreciprocal plasmonic metasurfaces with respect to their fundamental limit, thus streamlining the device design process and preventing that significant efforts are dedicated to marginal performance improvements. The proposed bounds pave the way toward the development of quasi-optimal nonreciprocal metasurfaces, with important applications in sensing, imaging, communications, and nonlinear optics, among many others.

The self-adaptive, multi-recombinative (µ/µ_I , λ)-ES (Evolution Strategy) is investigated on the highly-multimodal Rastrigin test function by theoretical and experimental means. To this end, the self-adaptation response function is derived in the limit of large populations, which are necessary to achieve high success rates. The established dynamical systems approach is introduced and steady state conditions on Rastrigin are discussed and compared to the sphere function. Then, a characteristic τ is derived to tune the sampling process of the self-adaptive ES. The obtained result is compared to default τ-values. Furthermore, expected runtime experiments are conducted varying τ and population parameters of the ES. Theoretical and experimental results regarding τ are compared in terms of efficiency and robustness showing good agreement.

Abstract— This research paper investigates the phenomenon of information leakage through social networking sites (SNS), examining its methods, consequences, prevention strategies, and future challenges. The paper commences with an introduction that underscores the increasing ubiquity of SNS and the subsequent risks associated with inadvertent data disclosure. The methodology section delineates various mechanisms through which information leakage occurs on these platforms. Subsequently, the consequences of such leaks, ranging from identity theft to reputational damage, are explored in detail. The paper then delves into prevention measures, emphasizing user awareness, privacy settings, and organizational protocols to mitigate information leakage risks. The study discusses the evolving landscape and challenges, providing insights into the future of information security in the context of social networking. The conclusion summarizes key findings and highlights the necessity for a proactive approach to navigate the complex interplay between social networking and data privacy.Keywords--- Information Leakage, Social Networking Sites, Data Privacy, Cybersecurity, Digital, Identity Theft, Reputational Damage, Prevention Strategies, User Awareness, Future Trends.

Cotton is an important cash crop. Its growth and development is influenced by several environmental factors such as change in temperature, amount and distribution of rainfall and carbon dioxide concentration which attribute to climate change. A field experiment was conducted to identify critical meteorological variables of the crop growth stages of the standard weeks over deficit irrigation scheduling on growth, yield and yield components of cotton during 2014 and 2015 kharif season. The experiment was laid out with three standard weeks/ sowing time (24, 26 and 28th) and four deficit irrigation schedules (0.8, 0.6, 0.4 IW/CPE and rain fed) arranged in split plot design. Crop growth parameters, yield, yield components and weather variables were recorded during the study season. The analysis showed those meteorological variables of crop phenophases significant, positive and negative in correlation and regression with growth, yield and yield components of cotton. Among the regressed variables, over 80% impact was noticed for rainfall during square initiation growth stage; and temperature, relative humidity, rainfall, pan evaporation, stress degree day, and intercepted solar radiation during first flower; rainfall, pan evaporation and relative humidity during boll opening growth stage. Thus, it can be concluded that rainfall, maximum temperature, stress degree day, minimum and maximum relative humidity and pan evaporation were found to be significant for cotton growth, yield, and higher quality returns.