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The maximum shear modulus (G(0(ij))) of rooted soils is crucial for assessing the deformation and liquefaction potential of vegetated infrastructures under seismic loading conditions. However, no data or theory is available to account for the anisotropy of G(0(ij)) of rooted soils. This study presents a new model that can predict G(0(ij)) anisotropy of rooted soils by incorporating the projection of the stress tensor on two independent tensors that describe soil fabric and root network. Bender element tests were conducted on bare and vegetated specimens under isotropic and anisotropic loading conditions. The presence of roots in the soil increased G(0(VH)) at all confining pressures (p '), as well as G(0(HH)) and G(0(HV)) at low p '. However, the trend was reversed at higher p ' because the roots reduced the effects of confinement on G(0(ij)) by replacing stronger soil-soil interfaces with weaker soil-root interfaces. Roots made the soil fabric and G(0(ij)) more anisotropic. The proposed model can effectively predict the observed anisotropy of G(0(ij)) under isotropic and anisotropic loading conditions. The new model also offers a new method for determining the fabric anisotropy of sand based on the anisotropy of shear modulus.

Currently, one of the most significant and rapidly growing unmet medical challenges is the treatment of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). This challenge encompasses the imperative development of efficacious therapeutic agents and overcoming the intricacies of the blood-brain barrier for successful drug delivery. Here we focus on the delivery aspect with particular emphasis on cell-penetrating peptides (CPPs), widely used in basic and translational research as they enhance drug delivery to challenging targets such as tissue and cellular compartments and thus increase therapeutic efficacy. The combination of CPPs with nanomaterials such as nanoparticles (NPs) improves the performance, accuracy, and stability of drug delivery and enables higher drug loads. Our review presents and discusses research that utilizes CPPs, either alone or in conjugation with NPs, to mitigate the pathogenic effects of neurodegenerative diseases with particular reference to AD and PD.

The positive effect of modified salinity water (MSW) injection on oil recovery has been broadly reported in the literature through core flooding experiment tests. Yet, the challenges in bridging the gap between laboratory to field scale studies still remain substantial, largely due to the lack of understanding of the underlying mechanisms involved. Also, it requires unraveling functional relationships and processes on each level. Injecting MSW into chalk reservoirs presents even more challenges due to the distinctive characteristics of chalk formations. Therefore, it necessitates a thorough comprehension of reservoir characteristics, rock-fluid–fluid interactions, and appropriate design strategies. In this work, we i) review the mechanisms controlling the MSW injection at the chalk surface, stemming from the interactions between the calcite, brine, and crude oil; ii) present the history matching of entire experiments conducted at core scale to obtain a salinity-induced shift in the relative mobility of the aqueous and oleic phases; and iii) address the current challenges in upscaling the observed benefit of MSW measured from core flood experiments to field-scale models for chalk reservoirs. Finally, we demonstrate how a set of relative permeability curves for different brines obtained from a large set of core flood experiments can be used to predict the performance of a Danish North Sea chalk reservoir.

The relationship between trade openness and the inflation rate is a contentious issue that has received considerable attention from economic policymakers. This study aims to examine the relationship between trade openness and the inflation rate in Indonesia. To achieve this objective, the study utilizes an autoregressive distributed lag model (ARDL) co-integration approach to analyze annual data covering the period from 1985 to 2022. The results of the bound test confirm the existence of a long-run relationship among the variables. Empirical findings reveal that trade openness is positively associated with inflation in the long run. Conversely, real gross domestic product (GDP) and foreign direct investment (FDI) exert negative influences on the inflation rate in the long run. This indicates that GDP and FDI are significant variables in reducing the inflation rate. Moreover, the results of the error correction model (ECM) are statistically significant with an expected negative sign. This suggests that disequilibrium occurring in the model can be corrected in less than two years at a rate of 51% per annum. These findings underscore the critical need for Indonesia to improve initial conditions for trade openness. This can be achieved through supporting and encouraging the export sector, diversifying exports into international markets, reducing reliance on raw material exports, and developing economic infrastructure.

Background and Aims: The role of L-histidine (L-His) in nickel (Ni) hyperaccumulation is not well known. The present study aimed to understand the impact of L-His on Ni translocation and Ni tolerance in Odontarrhena inflata and Aurinia saxatilis. Methods: To determine the impact of L-His and L-alanine (L-Ala) on the shoot Ni accumulation, we quantified the Ni concentration in plants pretreated with amino acids for 4 h and then exposed to Ni for 8 h. Hydrogen peroxide (H2O2) and antioxidant enzymes activities were determined after 4 h of pretreatments and 48 h of Ni treatments. Results: L-histidine increased Ni translocation to shoots in O. inflata and A. saxatilis. Ni increased the activity of POD, APX, and CAT in both species, but the activities of APX and CAT were higher in O. inflata. Ni exposure increased the H2O2 concentration in A. saxatilis. L-His pretreatment did not decrease the H2O2 concentration in Ni-treated plants, but decreased the activity of POD, APX, and CAT only at 300 µM Ni in O. inflata. In A. saxatilis, L-His pretreatment, decreased the activity of CAT, but increased POD activity at 150 and 300 µM Ni. Pretreatment with L-Ala decreased POD and APX activity but had no significant impact on H2O2 concentration and CAT activity. Conclusion: L-Histidine promoted root-to-shoot Ni translocation and alleviated Ni toxicity, possibly through inducing the activities of antioxidant enzymes, in hyperaccumulator and non-accumulator plants. Histidine might not only facilitate the Ni translocation, but also contribute to Ni tolerance, at least in A. saxatlilis.

OBJECTIVE: The current study aimed to identify the association between COVID-19 vaccination and prolonged post-COVID symptoms (long-COVID) in adults who reported suffering from this condition. METHODS: This was a retrospective follow-up study of adults with long-COVID syndrome. The data were collected during a phone call to the participants in January-February 2022. We inquired about their current health status and also their vaccination status if they agreed to participate. RESULTS: In total, 1236 people were studied; 543 individuals reported suffering from long long- COVID (43.9%). Chi square test showed that 15 out of 51 people (29.4%) with no vaccination and 528 out of 1185 participants (44.6%) who received at least one dose of any vaccine had long long- COVID symptoms (p = 0.032). CONCLUSIONS: In people who have already contracted COVID-19 and now suffer from long-COVID, receiving a COVID vaccination has a significant association with prolonged symptoms of long-COVID for more than one year after the initial infection. However, vaccines reduce the risk of severe COVID-19 (including reinfections) and its catastrophic consequences (e.g., death). Therefore, it is strongly recommended that all people, even those with a history of COVID-19, receive vaccines to protect themselves against this fatal viral infection.

A staircase localization method is proposed for robots to explore urban environments autonomously. The proposed method employs a modular design in the form of a cascade pipeline consisting of three modules of stair detection, line segment detection, and stair localization modules. The stair detection module utilizes an object detection algorithm based on deep learning to generate a region of interest (ROI). From the ROI, line segment features are extracted using a deep line segment detection algorithm. The extracted line segments are used to localize a staircase in terms of position, orientation, and stair direction. The stair detection and localization are performed only with a single RGB-D camera. Each component of the proposed pipeline does not need to be designed particularly for staircases, which makes it easy to maintain the whole pipeline and replace each component with state-of-the-art deep learning detection techniques. The results of real-world experiments show that the proposed method can perform accurate stair detection and localization during autonomous exploration for various structured and unstructured upstairs and downstairs with shadows, dirt, and occlusions by artificial and natural objects., Comment: 9 pages, 10 figures

Hydrophobised soils found in the superficial region of earthen infrastructure can affect the hydrological processes of these structures. Hysteretic water retention curve (WRC), which governs these processes, for hydrophobised soils has rarely been reported. Existing apparatus was unable to measure the WRC of unsaturated soil as they cannot control the condition of pore water pressure (u(w)) in excess of pore air pressure (u(a)) when the contact angle is larger than 90 degrees. This study created a new apparatus, which sandwiches a soil sample by a pair of high water-entry value (WEV) membrane and a high air-entry value (AEV) ceramic disk, to control the u(w) < u(a) and u(w) > u(a) conditions. Contact angle hysteresis and menisci evolution in the test materials during wet-dry cycles were measured to interpret the WRC. Results show that the WEV of soil was increased with decreasing chemical heterogeneity and particles size. When drying the hydrophobised soils, they could retain water before reaching a certain negative u(w). The WEV identified from the second wetting cycle was lower than that from the first cycle, whereas the AEV remained largely unchanged. The WEV and AEV decreased when the particle size of the hydrophobised materials was increased, resulting in a smaller hysteresis size.

This paper addresses the problem of autonomous robotic inspection in complex and unknown environments. This capability is crucial for efficient and precise inspections in various real-world scenarios, even when faced with perceptual uncertainty and lack of prior knowledge of the environment. Existing methods for real-world autonomous inspections typically rely on predefined targets and waypoints and often fail to adapt to dynamic or unknown settings. In this work, we introduce the Semantic Belief Behavior Graph (SB2G) framework as a novel approach to semantic-aware autonomous robot inspection. SB2G generates a control policy for the robot, featuring behavior nodes that encapsulate various semantic-based policies designed for inspecting different classes of objects. We design an active semantic search behavior to guide the robot in locating objects for inspection while reducing semantic information uncertainty. The edges in the SB2G encode transitions between these behaviors. We validate our approach through simulation and real-world urban inspections using a legged robotic platform. Our results show that SB2G enables a more efficient inspection policy, exhibiting performance comparable to human-operated inspections.

Much of the research in social computing analyzes data from social media platforms, which may inherently carry biases. An overlooked source of such bias is the over-representation of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations, which might not accurately mirror the global demographic diversity. We evaluated the dependence on WEIRD populations in research presented at the AAAI ICWSM conference; the only venue whose proceedings are fully dedicated to social computing research. We did so by analyzing 494 papers published from 2018 to 2022, which included full research papers, dataset papers and posters. After filtering out papers that analyze synthetic datasets or those lacking clear country of origin, we were left with 420 papers from which 188 participants in a crowdsourcing study with full manual validation extracted data for the WEIRD scores computation. This data was then used to adapt existing WEIRD metrics to be applicable for social media data. We found that 37% of these papers focused solely on data from Western countries. This percentage is significantly less than the percentages observed in research from CHI (76%) and FAccT (84%) conferences, suggesting a greater diversity of dataset origins within ICWSM. However, the studies at ICWSM still predominantly examine populations from countries that are more Educated, Industrialized, and Rich in comparison to those in FAccT, with a special note on the 'Democratic' variable reflecting political freedoms and rights. This points out the utility of social media data in shedding light on findings from countries with restricted political freedoms. Based on these insights, we recommend extensions of current "paper checklists" to include considerations about the WEIRD bias and call for the community to broaden research inclusivity by encouraging the use of diverse datasets from underrepresented regions., Comment: 11 pages, 2 figures, 7 tables

This paper addresses the problem of object-goal navigation in autonomous inspections in real-world environments. Object-goal navigation is crucial to enable effective inspections in various settings, often requiring the robot to identify the target object within a large search space. Current object inspection methods fall short of human efficiency because they typically cannot bootstrap prior and common sense knowledge as humans do. In this paper, we introduce a framework that enables robots to use semantic knowledge from prior spatial configurations of the environment and semantic common sense knowledge. We propose SEEK (Semantic Reasoning for Object Inspection Tasks) that combines semantic prior knowledge with the robot's observations to search for and navigate toward target objects more efficiently. SEEK maintains two representations: a Dynamic Scene Graph (DSG) and a Relational Semantic Network (RSN). The RSN is a compact and practical model that estimates the probability of finding the target object across spatial elements in the DSG. We propose a novel probabilistic planning framework to search for the object using relational semantic knowledge. Our simulation analyses demonstrate that SEEK outperforms the classical planning and Large Language Models (LLMs)-based methods that are examined in this study in terms of efficiency for object-goal inspection tasks. We validated our approach on a physical legged robot in urban environments, showcasing its practicality and effectiveness in real-world inspection scenarios.

Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be improved upon with the use of a colored noise distribution. Our choice of distribution allows for the emphasis on low frequency control signals, which can result in smoother and more exploratory samples. We use this frequency-based sampling distribution with Model Predictive Path Integral (MPPI) in both hardware and simulation experiments to show better or equal performance on systems with various speeds of input response., Comment: Published to RA-L

Mean-field games (MFG) provide a statistical physics inspired modeling framework for decision making in large-populations of strategic, non-cooperative agents. Mathematically, these systems consist of a forward-backward in time system of two coupled nonlinear partial differential equations (PDEs), namely the Fokker-Plank and the Hamilton-Jacobi-Bellman equations, governing the agent state and control distribution, respectively. In this work, we study a finite-time MFG with a rich global bifurcation structure using a reduced-order model (ROM). The ROM is a 4D two-point boundary value problem obtained by restricting the controlled dynamics to first two moments of the agent state distribution, i.e., the mean and the variance. Phase space analysis of the ROM reveals that the invariant manifolds of periodic orbits around the so-called `ergodic MFG equilibrium' play a crucial role in determining the bifurcation diagram, and impart a topological signature to various solution branches. We show a qualitative agreement of these results with numerical solutions of the full-order MFG PDE system., Comment: 32 pages, 16 figures

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments., Comment: 28 pages, 24 figures. Submitted to Field Robotics

Subterranean exploration and mapping for search and rescue robotics have become an emerging research direction since the DARPA organized Subterranean Challenge. As part of development efforts within the team CoSTAR (Collaborative SubTerranean Autonomous Robots) in the Sub-T challenge, this work establishes a novel traversable graph-based exploration strategy that utilizes frontiers for local navigation and a fast collision risk-aware graph building for global navigation. The exploration strategy extracts frontiers in an unknown area that contribute to safe navigation while maximizing information gain for the robot. The exploration problem is further bifurcated into local and global exploration for faster decision-making at junctions with the goal of rapidly exploring the area. The local exploration guarantees collision-free straight-line paths to informative frontiers for rapid forward navigation, while global re-positioning utilizes a traversable graph subject to geometrical collision checks within the occupancy map. The pathfinding in a graph is addressed using a heuristic, which combines risk margins and travel costs to assist in short yet safe paths to the global frontier in case of a dead end in local exploration. The presented exploration strategy is developed with the goal of making exploration algorithms platform agnostic in order to be able to use it with aerial, as well as ground robots. The proposed method is also evaluated against different state-of-the-art exploration planners in simulated fixed-time budget-based exploration missions on an Unmanned Aerial Vehicle (UAV) in order to benchmark the capabilities and highlight the novelty., Full text license: CC BY-NC-ND

Penelitian ini membahas tentang pengaruh content richness, perceived value, dan social influence sebagai variabel independen terhadap variabel dependen minat berlangganan pada pengguna Vidio di Kota Padang. Penelitian ini menggunakan metode kuantitatif dengan menggunakan sumber data primer. Sampel penelitian berjumlah sebanyak 140 orang. Data diperoleh melalui kuesioner dengan menggunakan skala likert yang dibagikan kepada responden secara online. Pengolahan data dilakukan dengan menggunakan perangkat lunak IBM SPSS versi 25. Hasil penelitian menunjukkan bahwa content richness berpengaruh secara positif dan signifikan terhadap minat berlangganan, perceived value berpengaruh positif dan signifikan terhadap minat berlangganan, serta social influence berpengaruh positif dan signifikan terhadap minat berlangganan.

In rock bolting systems, grout acts as a medium to transfer initiated stress from the bolt to stable rock mass, and also to transfer the in-situ stress from surrounding rock to the bolt-grout interface. In this study interaction between the mechanical characteristics of the grout and rock bolt behaviour was investigated in different confining pressure conditions. First, the uniaxial compression strength of the grouts was experimentally determined, and then pull-out tests were carried out on rock bolts encapsulated using the same grouts to quantify the bonding behaviour. Numerical models using ABAQUS explicit finite element code were then applied to further analyse the effects of mechanical properties of the grouts and the confining stress on the behaviour of the rock bolting systems. The compression of the simulated results with the experimental tests showed that the proposed FE models simulate the axial behaviour of rock bolts efficiently. The results of the parametric study indicated that grout mechanical properties and the level of confining stress affect the ultimate bearing capacity of the encapsulated rock bolts and the force-displacement behaviour. The level of damage that occurred at the specimens due to pull-out load is also significantly influenced by these factors.

Although autonomy has gained widespread usage in structured and controlled environments, robotic autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, rubble, and other post-disaster sites pose unique and challenging problems for autonomous navigation. Based on our participation in the DARPA Subterranean Challenge, we propose an approach to improve autonomous traversal of robots in subterranean environments that are perceptually degraded and completely unknown through a traversability and planning framework called STEP (Stochastic Traversability Evaluation and Planning). We present 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC), 4) fast recovery behaviors to account for unexpected scenarios that may cause failure, and 5) risk-based gait adaptation for quadrupedal robots. We illustrate and validate extensive results from our experiments on wheeled and legged robotic platforms in field studies at the Valentine Cave, CA (cave environment), Kentucky Underground, KY (mine environment), and Louisville Mega Cavern, KY (final competition site for the DARPA Subterranean Challenge with tunnel, urban, and cave environments).

We show that bilayer graphene in the presence of a 2D superlattice potential provides a highly tunable setup that can realize a variety of flat band phenomena. We focus on two regimes: (i) topological flat bands with nonzero Chern numbers, C, including bands with higher Chern numbers |C|>1 and (ii) an unprecedented phase consisting of a stack of nearly perfect flat bands with C=0. For realistic values of the potential and superlattice periodicity, this stack can span nearly 100 meV, encompassing nearly all of the low-energy spectrum. We further show that in the topological regime, the topological flat band has a favorable band geometry for realizing a fractional Chern insulator (FCI) and use exact diagonalization to show that the FCI is in fact the ground state at 1/3 filling. Our results provide a realistic guide for future experiments to realize a new platform for flat band phenomena.

Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article., Validerad;2023;Nivå 2;2023-08-08 (joosat);Full text license: CC BY;This article has previously appeared as a manuscript in a thesis

Mapping and exploration of a Martian terrain with an aerial vehicle has become an emerging research direction, since the successful flight demonstration of the Mars helicopter Ingenuity. Although the autonomy and navigation capability of the state of the art Mars helicopter has proven to be efficient in an open environment, the next area of interest for exploration on Mars are caves or ancient lava tube like environments, especially towards the never-ending search of life on other planets. This article presents an autonomous exploration mission based on a modified frontier approach along with a risk aware planning and integrated collision avoidance scheme with a special focus on energy aspects of a custom designed Mars Coaxial Quadrotor (MCQ) in a Martian simulated lava tube. One of the biggest novelties of the article stems from addressing the exploration capability, while rapidly exploring in local areas and intelligently global re-positioning of the MCQ when reaching dead ends in order to efficiently use the battery based consumed energy, while increasing the volume of the exploration. The proposed novel algorithm for the Martian exploration is able to select the next way point of interest, such that the MCQ keeps its heading towards the local exploration direction where it will find maximum information about the surroundings. The proposed three layer cost based global re-position point selection assists in rapidly redirecting the MCQ to previously partially seen areas that could lead to more unexplored part of the lava tube. The Martian fully simulated mission presented in this article takes into consideration the fidelity of physics of Mars condition in terms of thin atmosphere, low surface pressure and low gravity of the planet, while proves the efficiency of the proposed scheme in exploring an area that is particularly challenging due to the subterranean-like environment. The proposed exploration-planning framework is also validated in simulation by comparing, Validerad;2023;Nivå 2;2023-04-20 (hanlid)

Objective: The COVID-19 pandemic posed new challenges for integrated health care worldwide. Our study aimed to describe newly implemented structures and procedures of psychosocial consultation and liaison (CL) services in Europe and beyond, and to highlight emerging needs for co-operation. Methods: Cross-sectional online survey from June to October 2021, using a self-developed 25-item questionnaire in four language versions (English, French, Italian, German). Dissemination was via national professional societies, working groups, and heads of CL services. Results: Of the participating 259 CL services from Europe, Iran, and parts of Canada, 222 reported COVID-19 related psychosocial care (COVID-psyCare) in their hospital. Among these, 86.5% indicated that specific COVID-psyCare co-operation structures had been established. 50.8% provided specific COVID-psyCare for patients, 38.2% for relatives, and 77.0% for staff. Over half of the time resources were invested for patients. About a quarter of the time was used for staff, and these interventions, typically associated with the liaison function of CL services, were reported as most useful. Concerning emerging needs, 58.1% of the CL services providing COVID-psyCare expressed wishes for mutual information exchange and support, and 64.0% suggested specific changes or improvements that they considered essential for the future. Conclusion: Over 80% of participating CL services established specific structures to provide COVID-psyCare for patients, their relatives, or staff. Mostly, resources were committed to patient care and specific interventions were largely implemented for staff support. Future development of COVID-psyCare warrants intensified intra- and inter-institutional exchange and co-operation.

We address two critical environmental and technical problems for the integration of subsurface salt cavern hydrogen storage with 100% renewable electricity. First, the storage/production rate of hydrogen must match the unpredictable pattern of renewable electricity supply and (more predictable) demand for electricity by creating and strategically locating enough salt caverns. Secondly, creating and maintaining so many salt caverns requires large volumes of fresh/seawater. We develop two static and dynamic models for Denmark as a successful case of wind power development, considering the surplus energy and demand forecasts. The static model predicts the minimum amount of hydrogen needed for balancing the average annual supply and demand of electricity and fresh water necessary for the construction of required salt caverns. The model considers all the round-trip exergy losses of electricity-H2-electricity in the electrolyzers, fuel cells, compressors, and pipelines. The dynamic model considers the variable supply from wind farms and user demand over time; We also include the effect of the inertia of the electrolyzers, fuel cells, and compressors, and technical constraints, e.g. salt cavern pressure and pipeline flow capacity, to design sufficient storage sites that can dynamically balance the fluctuating supply of renewables and variable user demand. The static model predicts a realistic volume of salt caverns for storing the surplus green hydrogen; however, in the absence of small-scale storage solutions (batteries), we show that the number of required caverns and injection/production wells become unrealistically high, with high energy demand and cost for maintenance water treatment.

The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek’s disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.

The induction clutch is a device that acts like a friction clutch but there is no abrasion and it is more accurate. Because electric, solar, and fuel car convertible to boats, vertical aircraft flying supersonic has sealed body, for this reason, a friction clutch cannot be used, because the friction clutch does not work well and due to friction, it creates a bad smell and needs that the produced gas is exited from the vehicle. Part of the new induction clutch has electronic control and another part is mechanical control. Because the electric, solar, and fuel car convertible to the boat, vertical aircraft flying supersonic must be light, an induction clutch will be used because this new induction clutch has several functions and it causes the removal of several parts in this vehicle. I made a version of this clutch in small dimensions with difficulty and I did the necessary tests and drew input and output diagrams. In this article, the new induction clutch is compared with a simple induction clutch and friction clutch, and magnetic clutch.

Managing school administrative tasks can consume substantial time and effort. Every semester, teachers find themselves occupied in repetitive manual tasks such as attendance tracking, disciplinary documentation, and assignment grading. These sometimes could take longer time than preparation for teaching. In this research, we proposed an Artificial Intelligence (AI) approach to handle this problem. In the era of Industry 4.0, AI has been managed to be personal assistance and do human repetitive tasks efficiently. However, the implementation of AI in Indonesia especially for educational institutions is still rare. Therefore, we have developed an innovative AI-driven attendance and uniform detection system by implementing computer vision models. Computer vision is a field of AI that equips machines with the ability to interpret and understand visual information from images and videos, enabling them to classify images and detect objects. The results show that computer vision has successfully facilitated swift and accurate detections for this task. We have also incorporated a timestamp to provide information about the time when students arrive at school. Subsequently, all the recorded data will be saved and organized within the school’s database. As a result, teachers are liberated from the tedium of manual data entry and can redirect their efforts toward pedagogical materials and instructional strategies.

This study highlights the scientific legacy and impact of Dr Saeed-Ul Hassan’s research on the world of science. He was a remarkable researcher in the fields of scientometrics, altmetrics, artificial intelligence, and data science, as evidenced by the Eugene Garfield Awards he received for innovation in citation analysis in 2017 and 2022. Based on datasets retrieved from the Scopus database for the years 2009–2022, he collaborated with 157 researchers from 22 different nations and his research findings are published in reputable journals and conferences. In the last five years of his life, his scholarly publications and scientific impact significantly increased. His significant attention to Open Access publications and high levels of worldwide collaboration have greatly contributed to his impact in the world of science. The results of altmetric studies indicate that Dr SU Hassan’s scientific publications are widely accepted by researchers on Twitter and Mendeley. The majority of individuals that tweeted and read his publications were from European and American nations. His research had the greatest impact on experts in the fields of scientometrics, computer science and social sciences, according to the classification of the readers of his research articles.

We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked with planning a path over a horizon such that the accumulated area swept out by its sensor footprint is maximized. Because this problem exhibits a diminishing returns property known as submodularity, we choose to formulate it as a tree-based sequential decision making process. This formulation allows us to evaluate the effects of the robot's actions on future world coverage states, while simultaneously accounting for traversability risk and the dynamic constraints of the robot. To quickly find near-optimal solutions, we propose an effective approximation to the coverage sensor model which adapts to the local environment. Our method was extensively tested across various complex environments and served as the local exploration algorithm for a competing entry in the DARPA Subterranean Challenge.

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations., Comment: to be published

In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. %In this work, We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments., Comment: 6 pages, Submitted to ICRA 2023 - Contains Appendix

IntroductionIncreasing agricultural production is necessary due to the growing population in order to ensure food security. Also, a large part of agricultural lands in arid and semi-arid climates face the limitation of carbon storage and nutrients. This limitation is more visible in coarse textured soils with low clay content, due to the inability to supply elements required for plant growth and the ability to retain water in the soil. Therefore, modifying the physical, chemical and biological characteristics of the soil is inevitable to achieve sustainable agriculture. In the meantime, with the high use of chemical fertilizers along with the excessive tillage activities while destroying the soil, management costs increase. Soil degradation is an increasing worldwide threat to the sustainability of agriculture. The use of organic amendments like biochar may prove a key for sustainable agriculture, as it could keep the carbon pool in soil over the long term, thus improving soil fertility and crop productivity, mitigating global climate change, and finally enhancing soil physicochemical quality. The ability of biochar to enhance the physical and mechanical properties of soils is dependent on the characteristics of biochar including its particle size, application rate, feedstock type, and pyrolysis conditions. This study aimed to assess the effect of applying different application rates, feedstock, and particle sizes of biochar on the soil’s physical and mechanical properties in sandy loam soil. Materials and MethodsThe study was conducted in a research greenhouse of Shiraz University in 2019. To investigate the effect of biochar feedstock, application rate, and particle size on soil bulk density, aggregate stability (mean weight diameter), penetration resistance, and shear strength in sandy loam soil, using two feedstock types (palm leaf biochar and lemon peel biochar) were pyrolyzed at a temperature of 500 °C for 3 h. Each biochar was fractioned by dry sieving into t

The article develops an impact-resilient aerial robot (s-ARQ) equipped with a compliant arm to sense contacts and reduce collision impact and featuring a real-time contact force estimator and a non-linear motion controller to handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions. Further, a new collision-inclusive planning method that aims to prioritize contacts to facilitate aerial robot navigation in cluttered environments is proposed. A range of simulated and physical experiments demonstrate key benefits of the robot and the contact-prioritized (CP) planner. Experimental results show that the compliant robot has only a $4\%$ weight increase but around $40\%$ impact reduction in drop tests and wall collision tests. s-ARQ can handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions at $3.0$ m/s with a success rate of $100\%$. Our proposed compliant robot and contact-prioritized planning method can accelerate computation time while having shorter trajectory time and larger clearances compared to A$^\ast$ and RRT$^\ast$ planners with velocity constraints. Online planning tests in partially-known environments further demonstrate the preliminary feasibility of our method to apply in practical use cases., Comment: To appear in TMECH/AIM FS. Video https://youtu.be/Ks1tMMtjfGg

In this paper, first the full dynamics of aerial transportation of a rigid body with arbitrary number of quadrotors is derived. Then a control strategy is proposed to convey the nonuniform rigid body appropriately to the desired trajectory. In the dynamical model of this transportation system, not only the load is considered as a nonuniform and non-homogeneous rigid body but also mass, flexibility, and tension of the cables are considered. Each cable is modeled as successive masses, springs, and dampers where each mass, spring, and damper has 4 degrees of freedom (DOF). The Euler-Lagrange equations are used to derive the motion equation. The control strategy includes three loops of attitude control, formation control, and navigation control. The sliding mode control is designed based on multi-agent systems for the formation control where the controller is proven to be asymptotically stable. The navigation control loop, based on the load states, guarantees that the load reaches the desired location. Finally, numerical examples and simulations are presented to verify the appropriate operation of the proposed system for transporting both homogeneous and non-homogeneous bodies by spreading quadrotors according to mass distribution of the body.

Studies conducted on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples are considered atypical of the world's population and may not accurately represent human behavior. In this study, we aim to quantify the extent to which the ACM FAccT conference, the leading venue in exploring Artificial Intelligence (AI) systems' fairness, accountability, and transparency, relies on WEIRD samples. We collected and analyzed 128 papers published between 2018 and 2022, accounting for 30.8% of the overall proceedings published at FAccT in those years (excluding abstracts, tutorials, and papers without human-subject studies or clear country attribution for the participants). We found that 84% of the analyzed papers were exclusively based on participants from Western countries, particularly exclusively from the U.S. (63%). Only researchers who undertook the effort to collect data about local participants through interviews or surveys added diversity to an otherwise U.S.-centric view of science. Therefore, we suggest that researchers collect data from under-represented populations to obtain an inclusive worldview. To achieve this goal, scientific communities should champion data collection from such populations and enforce transparent reporting of data biases., Comment: To appear at ACM FAccT 2023

Modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modeling problems. An off-road vehicle encounters highly complex and difficult-to-model terrain/vehicle interactions, as well as having complex vehicle dynamics of its own. These complexities can create challenges for effective high-speed control and planning. In this paper, we introduce a framework for multistep dynamics prediction that explicitly handles the accumulation of modeling error and remains scalable for sampling-based controllers. Our method uses a specially-initialized Long Short-Term Memory (LSTM) over a limited time horizon as the learned component in a hybrid model to predict the dynamics of a 4-person seating all-terrain vehicle (Polaris S4 1000 RZR) in two distinct environments. By only having the LSTM predict over a fixed time horizon, we negate the need for long term stability that is often a challenge when training recurrent neural networks. Our framework is flexible as it only requires odometry information for labels. Through extensive experimentation, we show that our method is able to predict millions of possible trajectories in real-time, with a time horizon of five seconds in challenging off road driving scenarios.

In recent years, the intelligence of various parts of the home has become one of the essential features of any modern home. One of these parts is the intelligence lighting system that personalizes the light for each person. This paper proposes an intelligent system based on machine learning that personalizes lighting in the instant future location of a recognized user, inferred by trajectory prediction. Our proposed system consists of the following modules: (I) human detection to detect and localize the person in each given video frame, (II) face recognition to identify the detected person, (III) human tracking to track the person in the sequence of video frames and (IV) trajectory prediction to forecast the future location of the user in the environment using Inverse Reinforcement Learning. The proposed method provides a unique profile for each person, including specifications, face images, and custom lighting settings. This profile is used in the lighting adjustment process. Unlike other methods that consider constant lighting for every person, our system can apply each 'person's desired lighting in terms of color and light intensity without direct user intervention. Therefore, the lighting is adjusted with higher speed and better efficiency. In addition, the predicted trajectory path makes the proposed system apply the desired lighting, creating more pleasant and comfortable conditions for the home residents. In the experimental results, the system applied the desired lighting in an average time of 1.4 seconds from the moment of entry, as well as a performance of 22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP in human tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50 MinFDE5 in trajectory prediction.

In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame. The local detection is then transformed into the inertial frame and used to set references for a Nonlinear Model Predictive Controller (NMPC) for reactive tracking of humans or objects in SubT environments. The proposed novel fusion uses intensity filtering and K-means clustering on the LiDAR point cloud and frequency filtering and connectivity clustering on the events induced in an event camera by the returning LiDAR beams. The centroids of the clusters in the event camera and LiDAR streams are then paired to localize reflective markers present on safety vests and signs in SubT environments. The efficacy of the proposed scheme has been experimentally validated in a real SubT environment (a mine) with a Pioneer 3AT mobile robot. The experimental results show real-time performance for human detection and the NMPC-based controller allows for reactive tracking of a human or object of interest, even in complete darkness., Comment: Accepted at IFAC World Congress 2023

To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion policies. We propose the Semantic Belief Graph (SBG), a geometric- and semantic-based representation of a robot's probabilistic roadmap in the environment. The SBG nodes comprise of the robot geometric state and the semantic-knowledge of the terrains in the environment. The SBG edges represent local semantic-based controllers that drive the robot between the nodes or invoke an information gathering action to reduce semantic belief uncertainty. We formulate a semantic-based planning problem on SBG that produces a policy for the robot to safely navigate to the target location with minimal traversal time. We analyze our method in simulation and present real-world results with a legged robotic platform navigating multi-level outdoor environments.

Background: Genetic diversity in human leukocyte antigen (HLA) alleles across populations is a significant risk factor for drug-induced severe cutaneous adverse reactions (SCARs), e.g., carbamazepine (CBZ)- and lamotrigine (LTG)-induced StevensJohnson syndrome (SJS), and toxic epidermal necrolysis (TEN). The present study aimed to investigate the frequency of different HLA alleles in Iranian patients with CBZ- and LTG-induced SJS/TEN. Methods:A case-control study was conducted from 2011 to 2018 at various hospitals affiliated with Shiraz University of Medical Sciences (Shiraz, Iran). A total of 31 patients receiving anticonvulsant drugs (CZB or LTG) were recruited and divided into two groups. The drug-induced group (n=14) included hospitalized patients due to CBZ- or LTG-induced SJS/TEN. The drug-tolerant group (n=17) included individuals receiving CBZ or LTG for at least three months with no adverse effects. In addition, 46 healthy individuals (control group) were recruited. The frequency of HLA-A, -B, and -DRB1 alleles in patients with CZB- or LTG-induced SJS/TEN was investigated. HLA typing was performed using the allele-specific polymerase chain reaction method. The Chi square test and Fisher’s exact test were used to determine a potential association between SJS/TEN and HLA alleles. P Results: CBZ- or LTG-induced SJS/TEN was not significantly associated with HLA alleles. However, HLA-DRB1*01 showed a significantly higher frequency in patients with CBZ-induced SJS/TEN than the CBZ-tolerant patients (30% vs. 9%, P=0.07). Conclusion: Overall, no significant association was found between CBZ- or LTG-induced SJS/TEN and HLA alleles. Further largescale studies are required to substantiate our findings.

In this article, we propose a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources. In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame. The local detection is then transformed into the inertial frame and used to set references for a Nonlinear Model Predictive Controller (NMPC) for reactive tracking of humans or objects in SubT environments. The proposed novel fusion uses intensity filtering and K-means clustering on the LiDAR point cloud and frequency filtering and connectivity clustering on the events induced in an event camera by the returning LiDAR beams. The centroids of the clusters in the event camera and LiDAR streams are then paired to localize reflective markers present on safety vests and signs in SubT environments. The efficacy of the proposed scheme has been experimentally validated in a real SubT environment (a mine) with a Pioneer 3AT mobile robot. The experimental results show real-time performance for human detection and the NMPC-based controller allows for reactive tracking of a human or object of interest, even in complete darkness., Full text license: CC BY-NC-ND

AI's impact has traditionally been assessed in terms of occupations. However, an occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by AI. To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact (AII) measure, we employ Deep Learning Natural Language Processing to automatically identify AI patents that impact various occupational tasks at scale. Our methodology relies on a comprehensive dataset of 19,498 task descriptions and quantifies AI's impact through analysis of 12,984 AI patents filed with the United States Patent and Trademark Office (USPTO) between 2015 and 2020. Our observations reveal that the impact of AI on occupations defies simplistic categorizations based on task complexity, challenging the conventional belief that the dichotomy between basic and advanced skills alone explains the effects of AI. Instead, the impact is intricately linked to specific skills, whether basic or advanced, associated with particular tasks. For instance, while basic skills like scanning items may be affected, others like cooking may not. Similarly, certain advanced skills, such as image analysis in radiology, may face impact, while skills involving interpersonal relationships may remain unaffected. Furthermore, the influence of AI extends beyond knowledge-centric regions. Regions in the U.S. that heavily rely on industries susceptible to AI changes, often characterized by economic inequality or a lack of economic diversification, will experience notable AI impact.

In this article, we investigate the optimal path planning for aerial load transportation in complex, dynamic, and static environments using Particle Swarm Optimization (PSO). A hierarchical optimal control system is designed for a quadrotor equipped with a cable-suspended payload, employing Euler-Lagrange equations of motion. To navigate through obstacles, an improved artificial potential field combined with the PSO algorithm is used to determine the shortest path for a virtual point, acting as a leader. This leader guides the system toward the target point while avoiding collisions with both fixed and moving obstacles. The gravitational and repulsion coefficient forces using various PSO methods are fine-tuned to achieve the best trajectory and minimize time duration. The identified point serves as the desired location for quadrotor position control, based on a sliding mode strategy. Finally, we present numerical results to demonstrate the successful transportation of the payload by the system.

The task of establishing and maintaining situational awareness in an unknown environment is a critical step to fulfil in a mission related to the field of rescue robotics. Predominantly, the problem of visual inspection of urban structures is dealt with view-planning being addressed by map-based approaches. In this article, we propose a novel approach towards effective use of Micro Aerial Vehicles (MAVs) for obtaining a 3-D shape of an unknown structure of objects utilizing a map-independent planning framework. The problem is undertaken via a bifurcated approach to address the task of executing a closer inspection of detected structures with a wider exploration strategy to identify and locate nearby structures, while being equipped with limited sensing capability. The proposed framework is evaluated experimentally in a controlled indoor environment in presence of a mock-up environment validating the efficacy of the proposed inspect-explore policy.