Variety Of Topologies Research Articles

Lorawan-Based RSSI-Trilateration Model for Node Location: A Simulation Integrating Flora and Omnet++

Abstract This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

Linear to circular conversion in the polarized radio emission of a magnetar

AbstractRadio emission from magnetars provides a unique probe of the relativistic, magnetized plasma within the near-field environment of these ultra-magnetic neutron stars. The transmitted waves can undergo birefringent and dispersive propagation effects that result in frequency-dependent conversions of linear to circularly polarized radiation and vice versa, thus necessitating classification when relating the measured polarization to the intrinsic properties of neutron star and fast radio burst emission sites. We report the detection of such behaviour in 0.7–4 GHz observations of the P = 5.54 s radio magnetar XTE J1810−197 following its 2018 outburst. The phenomenon is restricted to a narrow range of pulse phase centred around the magnetic meridian. Its temporal evolution is closely coupled to large-scale variations in magnetic topology that originate from either plastic motion of an active region on the magnetar surface or free precession of the neutron star crust. Our model of the effect deviates from simple theoretical expectations for radio waves propagating through a magnetized plasma. Birefringent self-coupling between the transmitted wave modes, line-of-sight variations in the magnetic field direction and differences in particle charge or energy distributions above the magnetic pole are explored as possible explanations. We discuss potential links between the immediate magneto-ionic environments of magnetars and those of fast radio burst progenitors.

Collective Synthesis of Highly Oxygenated (Furano)germacranolides Derived from Elephantopus mollis and Elephantopus tomentosus.

Germacranolides, secondary metabolites produced by plants, have garnered academic and industrial interest due to their diverse and complex topology as well as a wide array of pharmacological activities. Molephantin, a highly oxygenated germacranolide isolated from medicinal plants, Elephantopus mollis and Elephantopus tomentosus, has exhibited antitumor, inflammatory, and leishmanicidal activities. Its chemical structure is based on a highly strained ten-membered macrocyclic backbone with an (E,Z)-dienone moiety, which is fused with an α-methylene-γ-butyrolactone and adorned with four successive stereogenic centers. Herein, we report the first synthesis of molephantin in 12 steps starting from readily available building blocks. The synthesis features the highly diastereoselective intermolecular Barbier allylation of the β,γ-unsaturated aldehyde with optically active 3-bromomethyl-5H-furan-2-one intermediate and ensuing Nozaki-Hiyama-Kishi (NHK) macrocyclization for the construction of the highly oxygenated ten-membered macrocyclic framework. This synthetic route enabled access to another germacranolide congener, tomenphantopin F. Furthermore, cycloisomerization of molephantin into 2-deethoxy-2β-hydroxyphantomolin could be facilitated by irradiation with ultraviolet A light (λmax=370 nm), which opened a versatile and concise access to the related furanogermacranolides such as EM-2, phantomolin, 2-O-demethyltomenphantopin C, and tomenphantopin C.

Collective Synthesis of Highly Oxygenated (Furano)germacranolides Derived from Elephantopus mollis and Elephantopus tomentosus

AbstractGermacranolides, secondary metabolites produced by plants, have garnered academic and industrial interest due to their diverse and complex topology as well as a wide array of pharmacological activities. Molephantin, a highly oxygenated germacranolide isolated from medicinal plants, Elephantopus mollis and Elephantopus tomentosus, has exhibited antitumor, inflammatory, and leishmanicidal activities. Its chemical structure is based on a highly strained ten‐membered macrocyclic backbone with an (E,Z)‐dienone moiety, which is fused with an α‐methylene‐γ‐butyrolactone and adorned with four successive stereogenic centers. Herein, we report the first synthesis of molephantin in 12 steps starting from readily available building blocks. The synthesis features the highly diastereoselective intermolecular Barbier allylation of the β,γ‐unsaturated aldehyde with optically active 3‐bromomethyl‐5H‐furan‐2‐one intermediate and ensuing Nozaki–Hiyama–Kishi (NHK) macrocyclization for the construction of the highly oxygenated ten‐membered macrocyclic framework. This synthetic route enabled access to another germacranolide congener, tomenphantopin F. Furthermore, cycloisomerization of molephantin into 2‐deethoxy‐2β‐hydroxyphantomolin could be facilitated by irradiation with ultraviolet A light (λmax=370 nm), which opened a versatile and concise access to the related furanogermacranolides such as EM‐2, phantomolin, 2‐O‐demethyltomenphantopin C, and tomenphantopin C.

Elastic Shape Analysis of Tree-Like 3D Objects Using Extended SRVF Representation.

How can one analyze detailed 3D biological objects, such as neuronal and botanical trees, that exhibit complex geometrical and topological variation? In this paper, we develop a novel mathematical framework for representing, comparing, and computing geodesic deformations between the shapes of such tree-like 3D objects. A hierarchical organization of subtrees characterizes these objects - each subtree has a main branch with some side branches attached - and one needs to match these structures across objects for meaningful comparisons. We propose a novel representation that extends the Square-Root Velocity Function (SRVF), initially developed for Euclidean curves, to tree-shaped 3D objects. We then define a new metric that quantifies the bending, stretching, and branch sliding needed to deform one tree-shaped object into the other. Compared to the current metrics such as the Quotient Euclidean Distance (QED) and the Tree Edit Distance (TED), the proposed representation and metric capture the full elasticity of the branches (i.e., bending and stretching) as well as the topological variations (i.e., branch death/birth and sliding). It completely avoids the shrinkage that results from the edge collapse and node split operations of the QED and TED metrics. We demonstrate the utility of this framework in comparing, matching, and computing geodesics between biological objects such as neuronal and botanical trees. We also demonstrate its application to various shape analysis tasks such as (i) symmetry analysis and symmetrization of tree-shaped 3D objects, (ii) computing summary statistics (means and modes of variations) of populations of tree-shaped 3D objects, (iii) fitting parametric probability distributions to such populations, and (iv) finally synthesizing novel tree-shaped 3D objects through random sampling from estimated probability distributions.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson’s disease

Objective. Dopaminergic treatment is effective for Parkinson’s disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy. Approach. This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed. Results. The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions. Significance. The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

An Adaptive Coordinated Wide-Area Backup Protection Algorithm for Network Topology Variability

An Adaptive Coordinated Wide-Area Backup Protection Algorithm for Network Topology Variability

Phylogenomic analysis of Stylops reveals the evolutionary history of a Holarctic Strepsiptera radiation parasitizing wild bees

Holarctic Stylops is the largest genus of the enigmatic insect order Strepsiptera, twisted winged parasites. Members of Stylops are obligate endoparasites of Andrena mining bees and exhibit extreme sexual dimorphism typical of Strepsiptera. So far, molecular studies on Stylops have focused on questions on species delimitation. Here, we utilize the power of whole genome sequencing to infer the phylogeny of this morphologically challenging genus from thousands of loci. We use a species tree method, concatenated maximum likelihood analysis and Bayesian analysis with a relaxed clock model to reconstruct the phylogeny of 46 Stylops species, estimate divergence times, evaluate topological consistency across methods and infer the root position. Furthermore, the biogeographical history and coevolutionary patterns with host species are assessed. All methods recovered a well resolved topology with close to all nodes maximally supported and only a handful of minor topological variations. Based on the result, we find that included species can be divided into 12 species groups, seven of them including only Palaearctic species, three Nearctic and two were geographically mixed. We find a strongly supported root position between a clade formed by the spreta, thwaitesi and gwynanae species groups and the remaining species and that the sister group of Stylops is Eurystylops or Eurystylops + Kinzelbachus. Our results indicate that Stylops originated in the Western Palaearctic or Western Palaearctic and Nearctic in the early Neogene or late Paleogene, with four independent dispersal events to the Nearctic. Cophylogenetic analyses indicate that the diversification of Stylops has been shaped by both significant coevolution with the mining bee hosts and host-shifting. The well resolved and strongly supported phylogeny will provide a valuable phylogenetic basis for further studies into the fascinating world of Strepsipterans.

Topology-Accelerated and Selective Cascade Depolymerization of Architecturally Complex Polyesters.

Despite considerable recent advances already made in developing chemically circular polymers (CPs), the current framework predominantly focuses on CPs with linear-chain structures of different monomer types. As polymer properties are determined by not only composition but also topology, manipulating the topology of the single-monomer-based CP systems from linear-chain structures to architecturally complex polymers could potentially modulate the resulting polymer properties without changing the chemical composition, thereby advancing the concept of monomaterial product design. To that end, here, we introduce a chemically circular hyperbranched polyester (HBPE), synthesized by a mixed chain-growth and step-growth polymerization of a rationally designed bicyclic lactone with a pendent hydroxyl group (BiLOH). This HBPE exhibits full chemical recyclability despite its architectural complexity, showing quantitative selectivity for regeneration of BiLOH, via a unique cascade depolymerization mechanism. Moreover, distinct differences in materials properties and performance arising from topological variations between HBPE, hb-PBiLOH, and its linear analogue, l-PBiLOH, have been revealed where generally the branched structure led to more favorable interchain interactions, and topology-amplified optical activity has also been observed for chiral (1S, 4S, 5S)-hb-PBiLOH. More intriguingly, depolymerization of l-PBiLOH proceeds through an unexpected, initial topological transformation to the HBPE polymer, followed by the faster cascade depolymerization pathway adopted by hb-PBiLOH. Overall, these results demonstrate that CP design can go beyond typical linear polymers, and rationally redesigned, architecturally complex polymers for their unique properties may synergistically impart advantages in topology-augmented depolymerization acceleration and selectivity for exclusive monomer regeneration.

Abstract 5792: Rapid and efficient generation of panels of T cell engaging (TCE) multispecific antibodies via complementary technologies

Abstract A goal of immunotherapy is to induce an anti-tumor response that is potent and specific. However, there is increasing understanding that currently approved monospecific checkpoint inhibitors are not effective for all patients and/or indications. Thus, investigators are increasingly pursuing multispecific antibody treatments in an attempt to enhance clinical outcomes. These treatments may either take the form of mixtures (administered simultaneously or in series) of multiple monospecific antibodies, or as single entity, multispecific antibodies (‘multispecifics’). In the case of multispecifics, the large number of possible topologies and complexity of manufacture necessitate the development and application of a robust set of complementary technologies. We have developed an exemplary set of such technologies. Herein, we demonstrate the ability to direct desired antibody chain pairing (HC-HC and HC-LC), isolate and engineer single-domain antibodies, as well as generate large (>100) panels of multispecific antibodies with diverse topologies from a limited number of input molecules. These technologies can be leveraged in combination with our affinity and developability-optimized TCE αCD3 and αCD28 antibody panels to achieve desired potency without the need for additional discovery or engineering. Citation Format: Cory Ahonen, Nathan Sharkey, Melissa Durkin, Andrew Avery, Michael Battles, Beth Sharkey, Kyle Barlow, Arvind Sivasubramanian, Caitlin Stein, Bradley Lunde, Kevin Schutz, Bianka Prinz, Rob Pejchal, Paul Widboom, Eric Krauland. Rapid and efficient generation of panels of T cell engaging (TCE) multispecific antibodies via complementary technologies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5792.

Cross co-teaching for semi-supervised medical image segmentation

Excellent performance has been achieved on semi-supervised medical image segmentation, but existing algorithms perform relatively poorly for objects with variable topologies and weak boundaries. In this paper, we propose a novel cross co-teaching framework, called Cross-structure-task Collaborative Teaching (CroCT), which not only can effectively handle variable topologies, but also strengthens the learning for weak boundaries of unlabeled data. Specifically, a new cross-structure-task collaborative teaching mechanism is developed based on our designed “E-Net” structure composed of a shared encoder and two decoder branches with distinct learning paradigms, which asks these two branches to regress topology-aware signed distance functions and densely-predicted segmentation masks for each other. Powered by the collaboration across different structural biases and sequence-related tasks, our CroCT can extract more discriminative yet complementary representations from abundant raw medical data to promote the consistency learning generalization, further boosting the performance for tackling highly diverse shapes and topological changes intra-/inter-slices. Besides, it guarantees the diversities from multi-levels, i.e., structure and task perspectives, to preclude prediction uncertainty. In addition, a novel adaptive boundary enhancing (ABE) module is proposed to introduce compact annularly enhanced boundary features into semi-supervised training, which significantly improves weak boundary perception ability for unlabeled data while facilitating collaborative teaching for efficiently propagating complementary knowledge across different branches. The extensive experiments on three challenging medical benchmarks, employing different labeled settings, demonstrate the superiority of our CroCT over recent state-of-the-art competitors.

Experimental and numerical investigation of porous bleed control for supersonic/subsonic flows, and shock-wave/boundary-layer interactions

This paper presents a comprehensive experimental and numerical investigation into the control of boundary layers using porous bleed systems. The study focuses on both supersonic and subsonic flow regimes, as well as on the control of shock-wave/boundary-layer interactions. By simplifying this complex problem into two separate scenarios, the research establishes the necessity of individually characterizing bleed systems for both supersonic and subsonic flow regimes due to variations in the flow topology within the bleed holes. In supersonic conditions, a strong agreement between experimental and numerical results validates the effectiveness of porous bleed in controlling boundary layers. Notably, three-dimensional effects are experimentally demonstrated, and variations of the boundary-layer profiles along the span are the first time experimentally proven. The study extends its scope to subsonic flows, revealing that while boundary-layer bleeding enhances flow momentum near the wall, mass removal induces a decrease in momentum in the outer boundary layer and external flow. The research also explores shock-wave/boundary-layer interaction control, achieving a remarkable alignment between simulations and experiments. The findings endorse the use of Reynolds-Averaged Navier-Stokes simulations for studying porous bleed systems in various flow conditions, providing valuable insights to enhance bleed models, especially in the context of shock-wave/boundary-layer interaction control.

A novel geometric predictive algorithm for assessing compressive elastic modulus in MEX additive processes, based on part nonlinearities and layers stiffness, validated with PETG and PLA materials

MEX (Material Extrusion) is an intrusive technological process that inherently induces alterations in the elastic and mechanical parameters of plastic materials. Manufacturers provide initial mechanical parameters for plastic filaments, which undergo modifications during MEX manufacturing, influenced by intrinsic manufacturing factors such as temperature and pressure changes, as well as geometric and technological parameters of the 3D additive process. These factors, compounded by the inherent geometric nonlinearities in plastic components, directly impact the post-manufacture mechanical and elastic properties of the material. Presently, material characterization in MEX manufacturing relies on manual experimental testing, necessitating new tests for any variation in manufacturing parameters. In this scenario of mechanical uncertainty, rigorously validating component behavior involves costly experimental trials. Intending to solve the problems of MEX components manufacturing, the paper presents an innovative methodology based on the use of a new predictive algorithm created by the researchers capable of obtaining the elastic modulus of a plastic material manufactured with MEX and its mechanical behavior in the elastic zone under compressive loads. The predictive algorithm only needs as input the compressive elastic modulus of the isotropic plastic material filament and the manufacturing parameters of the MEX process. The smart developed algorithm calculates the stiffness of each layer considering the number of holes in the projected area. The innovative predictive algorithm has been experimentally and numerically validated using PETG (Polyethylene Terephthalate Glycol) material and PLA (Polylactic Acid) on test specimens and on a case study of variable topology. The results show deviations from [0.2%–4.3%] for PETG and [0.4%] for PLA concerning the experimental tests and [1.1%–13.5%], to the numerical analyses. In this line, the presented algorithm greatly improves the results obtained by the simulation software since this software currently can not consider the geometric and technological parameters associated with the 3D manufacturing process of the component. The predictive algorithm is valid for each print pattern and manufacturing direction. The new algorithm improves the existing state of the art significantly since this algorithm extends its utility to most plastic polymer materials suitable for MEX 3D printing, provided that the mechanical and elastic properties of the filament are known. Its versatility extends to complex component geometries subjected to uniaxial compression loads, eliminating the need for mechanical analysis software or expensive experimental validations.

The scalar mismatch of regional governance: A comparative analysis of hierarchical structures

Self-organisation in territories leads to the emergence of patterns in urban systems that shape the interactions and dependencies between cities, resulting in a hierarchical organisation. Governance follows a hierarchical structure as well, breaking the territory into smaller units for its management. The possible mismatch between these two organisations may lead to a range of problems, ranging from inefficiencies to insufficient and uneven distribution of resources. This paper seeks to develop a methodology to explore and quantify the correspondence between the hierarchical organisation given by the structure of governance and that given by the structure of the urban systems being governed, where Chile is used as a case study. The urban hierarchical structure is defined according to the connectivity of the system given by the road network. This is extracted through a clustering algorithm defined as a percolation process on the Chilean street network, giving rise to urban clusters at different scales. These are then compared to the spatial scales of the politico-administrative system. This is achieved by using measures of pair-wise distance similarity on the dendrograms, such as the cophenetic distance, by looking at the different clustering membership using Jaccard similarity and by analysing the topological diversity defined as the structural entropy. The results show that the urban sub-national structures present high heterogeneity, while the administrative system is highly homogeneous, replicating the same structure of organisation across the national territory. Such contrasting organisational structures present administrative challenges that can give rise to poor decision-making processes and mismanagement, in addition to impairing the efficient functioning of the systems themselves. Our results can help address these challenges, informing how to rebalance such mismatches through planning and political strategies that consider the complex interdependencies of territories across scales.

Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks

Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders.

2D Conjugated Metal-Organic Frameworks: Defined Synthesis and Tailor-Made Functions.

Conspectus2D conjugated metal-organic frameworks (2D -MOFs) have emerged as a class of graphene-like materials with fully π-conjugated aromatic structures. Their unique structural characteristics provide abundant physiochemical features, including regular nanochannels, high electrical conductivity, and customizable band gaps. Recent intensive research has significantly advanced this field, yet the exploration of 2D c-MOFs with enhanced features is limited by the availability of organic linkages and topologies. Designing novel ligands is essential for the construction of new 2D c-MOFs with high crystallinity, excellent conductivity, and tailor-made functions.In this Account, we summarize our recent contributions in fine-tuning the topology of 2D c-MOFs through precise ligand design, thereby giving them fantastic structures and tailor-made functions. First, we propose the concept of replacing planar ligands by nonplanar ligands on conductive MOF skeletons. The incorporation of nonplanar ligands improves the solubility of large π-conjugated organic molecules without interfering with the interlayer π-stacking. Our investigation discovered that conjugate polycyclic aromatics-based ligands can be synthesized through in situ Scholl reactions by means of oxidative cyclodehydrogenation of a nonplanar precursor ligand during the solvothermal synthesis process. Hence, fully conjugated 2D c-MOFs can be directly synthesized from nonplanar organic ligands, simplifying and diversifying the preparation of 2D c-MOFs. Accordingly, the design flexibility of the ligands expands the topological structures and pore types. By controlling the synthesis conditions, we can successfully induce either a rhombus or a kagome topology from a nonplanar D2 symmetric ligand. Moreover, by employing a ligand engineering strategy, we incrementally increase the number of coordination functional groups on a twisted hexabenzocoronene core, resulting in the formation of three distinct symmetric hydroxyl ligands. These ligands elicit diverse target topologies and pore sizes, resulting in variances in the coordination node density on the skeletons. This, in turn, leads to differences in electron transfer abilities, ultimately causing variations in the electrical conductivity and mobility. In addition, we employ a straightforward coupling method to incorporate redox components, such as salphen and pyrazine, into nonplanar ligands, facilitating the synthesis of 2D c-MOFs with highly active centers. This strategy confers upon the resulting frameworks substantial capacity for catalysis and energy storage, offering a good platform for elucidating the structure-property relationships at the molecular level. Moreover, the well-defined synthesis of 2D c-MOFs imparts them with specific properties, particularly in the fields of electrical, electrochemical, and spintronic applications. At the end, the primary challenges facing 2D c-MOFs in achieving tailor functions and their practical applications are proposed. This account is expected to evoke new inspirations and innovative research in the field of 2D c-MOFs, especially in emerging interdisciplinary research areas.

Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts.

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

Micro-Electro-Mechanical Systems Microphones: A Brief Review Emphasizing Recent Advances in Audible Spectrum Applications.

The MEMS microphone is a representative device among the MEMS family, which has attracted substantial research interest, and those tailored for human voice have earned distinct success in commercialization. Although sustained development persists, challenges such as residual stress, environmental noise, and structural innovation are posed. To collect and summarize the recent advances in this subject, this paper presents a concise review concerning the transduction mechanism, diverse mechanical structure topologies, and effective methods of noise reduction for high-performance MEMS microphones with a dynamic range akin to the audible spectrum, aiming to provide a comprehensive and adequate analysis of this scope.

Accelerating multi-tier storage cache simulations using knee detection

Storage cache hierarchies include diverse topologies, assorted parameters and policies, and devices with varied performance characteristics. Simulation enables efficient exploration of their configuration space while avoiding expensive physical experiments. Miss Ratio Curves (MRCs) efficiently characterize the performance of a cache over a range of cache sizes, revealing “key points” for cache simulation, such as knees in the curve that immediately follow sharp cliffs. Unfortunately, there are no automated techniques for efficiently finding key points in MRCs, and the cross-application of existing knee-detection algorithms yields inaccurate results.We present a multi-stage framework that identifies key points in any MRC, for both stack-based (e.g., LRU) and more sophisticated eviction algorithms (e.g., ARC). Our approach quickly locates candidates using efficient hash-based sampling, curve simplification, knee detection, and novel post-processing filters. We introduce Z-Method, a new multi-knee detection algorithm that employs statistical outlier detection to choose promising points robustly and efficiently.We evaluated our framework against seven other knee-detection algorithms, identifying key points in multi-tier MRCs with both ARC and LRU policies for 106 diverse real-world workloads. Compared to naïve approaches, our framework reduced the total number of points needed to accurately identify the best two-tier cache hierarchies by an average factor of approximately 5.5× for ARC and 7.7× for LRU.We also show how our framework can be used to seed the initial population for evolutionary algorithms. We ran 32,616 experiments requiring over three million cache simulations, on 151 samples, from three datasets, using a diverse set of population initialization techniques, evolutionary algorithms, knee-detection algorithms, cache replacement algorithms, and stopping criteria. Our results showed an overall acceleration rate of 34% across all configurations.

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches. In this study, we developed SynerGNet, a graph neural network model designed to accurately predict the synergistic effect of drug pairs against cancer cell lines. SynerGNet utilizes cancer-specific featured graphs created by integrating heterogeneous biological features into the human protein-protein interaction network, followed by a reduction process to enhance topological diversity. Leveraging synergy data provided by AZ-DREAM Challenges, the model yields a balanced accuracy of 0.68, significantly outperforming traditional machine learning. Encouragingly, augmenting the training data with carefully constructed synthetic instances improved the balanced accuracy of SynerGNet to 0.73. Finally, the results of an independent validation conducted against DrugCombDB demonstrated that it exhibits a strong performance when applied to unseen data. SynerGNet shows a great potential in detecting drug synergy, positioning itself as a valuable tool that could contribute to the advancement of combination therapy for cancer treatment.