bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating ice forces on a bridge pier using field observations and a deformation model
Authors: Rødtang Einar , Alfredsen Knut , Høyland Knut
Published: October 2023

This technical note qualitatively and quantitatively describes the ice run that took place in Sokna river, Trøndelag, Norway on 23 January 2020. Metrological and hydrological data are described. Stranded ice floe size distribution data are presented and analysed. It is shown that the maximal ice floe dimension is well described by generalized exponential (shape parameter = 2.77, scale parameter = 1.42) or log-logistic distributions (alpha = 3.57, lambda = 1.15). A custom-built steel load panel had been mounted on Sokna bridge preceding the ice run; however, due to technical difficulties no forces were recorded. Hindcast calculations using Solidworks based on the permanent deflections experienced by this load panel have been carried out to estimate ice forces experienced by Sokna bridge. These hindcast calculations conclude that the real peak ice force experienced by the 0.8 m diameter cylindrical bridge piers must have been within the range 1.1 to 5.5 MN. This range is compared to predictions made by standards for predicting ice forces on bridge piers. This comparison reveals that the Norwegian standard, the lower bound of the SNiP standard, and the upper bound of the CSA S6:19 standard are consistent with this range, while the Swedish and Finnish standards predict too low forces.
Trophic ecology of juvenile lean and siscowet lake charr (Salvelinus namaycush) in Lake Superior: assessing for potential competition
Authors: Gerig Brandon S. , Sitar Shawn P. , Otte Will F. , Yule Daniel L. , Swanson Heidi K. , Bronte Charles R. , Carl Dray , Blankenheim Joshua
Published: October 2023

We investigated the spatial overlap, diet, isotopic niche, and growth of juvenile lean and siscowet lake charr ( Salvelinus namaycush) in Lake Superior to address concerns of potential competition with implications to the study of resource polymorphism. Catch data revealed the greatest levels of sympatry in waters from 40 to 60 m. Juvenile lean and siscowet diet changed ontogenetically with Mysis being the dominant prey item for the smallest lake charr but differentiating with onset of piscivory. As ecotypes increased in size, lean diets became dominated by pelagic prey, whereas siscowets had equal proportions of benthic and pelagic prey. Isotopic niche overlap declined between ecotypes coincident with siscowet lake charr shifting to deeper habitats around 400 mm. Lean and siscowet exhibited different growth trajectories. However, length at age-4 declined in parallel for both ecotypes with no trend in condition suggesting that lake charr growth is sensitive to prey biomass and unlikely related to competition. Our findings indicate minimal evidence of competition and support the concept that multiple sympatric ecotypes of lake charr in Lake Superior are maintained by resource polymorphism.
Topological classes of black holes in de-Sitter spacetime
Authors: Du Yongbin , Zhang Xiangdong
Published: October 2023

In this paper, we investigate the topological number of de-Sitter black hole solutions with different charges (q) and rotational (a) parameters. By using generalized free energy and Duan’s$$\phi $$ϕ-mapping topological current theory, we find that the topological numbers of black holes can still be classified into three types. In addition, we interestingly found the topological classes for de-Sitter (dS) spacetime with distinct horizons, i.e., black hole event horizon and cosmological horizon, will be different. Moreover, we also investigate topological classifications of dS black hole solutions in higher dimensions with or without the Gauss-Bonnet term.
Phase transitions in a Φ4 matrix model on a curved noncommutative space
Authors: Prekrat Dragan , Ranković Dragana , Todorović-Vasović Neli Kristina , Kováčik Samuel , Tekel Juraj
Published: October 2023

In this contribution, we summarize our recent studies of the phase structure of the Grosse–Wulkenhaar model and its connection to renormalizability. Its action contains a special term that couples the field to the curvature of the noncommutative background space. We first analyze the numerically obtained phase diagram of the model and its three phases: the ordered, the disordered, and the noncommutative stripe phase. Afterward, we discuss the analytical derivation of the effective action and the ordered-to-stripe transition line, and how the obtained expression successfully explains the curvature-induced shift of the triple point compared to the model without curvature. This shift also causes the removal of the stripe phase and makes the model renormalizable.
Probing dark matter using free leptons: PKMUON
Authors: Ruzi Alim , Zhou Chen , Sun Xiaohu , Wang Dayong , Wang Siguang , Ban Yong , Mao Yajun , Li Qite , Li Qiang
Published: October 2023

We propose a new method to detect sub-GeV dark matter, through their scatterings from free leptons and the resulting kinematic shifts. Especially, such an experiment can detect dark matter interacting solely with muons. The experiment proposed here is for directly probing the muonphilic dark matter in a model-independent way. Its complementarity with the muon on the target proposal is similar to e.g. XENON/PandaX and ATLAS/CMS on the dark matter searches. Moreover, our proposal can work better for relatively heavy dark matter such as those in the sub-GeV region. We start with a small device of a size around 0.1–1[Formula: see text]m, using atmospheric muons to set up a prototype. Within only one year of operation, the sensitivity on the cross-section of dark matter scattering with muons can already reach [Formula: see text] for the dark matter mass [Formula: see text] (10, 1000)[Formula: see text]MeV. We can then interface the device with a high-intensity muon beam of [Formula: see text]/bunch. Within one year, the sensitivity can reach [Formula: see text] for [Formula: see text] (10,[Formula: see text]1000)[Formula: see text]MeV.
Numerical Studies on Seismic Response of Structural Systems Using a Response-Spectra Based Intensity Measure
Authors: Mohan Pasupuleti Naga , Chatterjee Aritra
Published: October 2023

This paper proposes a novel intensity measure (IM) based on the geometric mean of acceleration response spectral ordinates to assess the probabilistic performance of structures subjected to seismic loading. Instead of relying solely on the fundamental period, the proposed IM is evaluated across a fixed period range for all structural systems, which allows consideration of higher mode effects and period changes due to nonlinearity. The proposed seismic IM is evaluated using two established indices sufficiency and efficiency. Sufficiency quantifies the independence of an engineering demand parameter at a specific intensity level with ground motion characteristics such as seismic magnitude [Formula: see text] and distance from site to fault plane [Formula: see text]. It is calculated by linear regression analysis, or using gradient-based relative sufficiency measures. On the other hand, efficiency is measured as dispersion across ground motions at a given intensity level for any physical response quantity. It helps to reduce computational demand for failure probability assessment by considering a smaller number of records compared to an inefficient IM for similar confidence levels. The effectiveness of the proposed IM along with 10 other IMs is demonstrated on single degree of freedom systems with various fundamental periods by performing nonlinear time history analysis using a far-field ground motion record set. The study is also extended to five degree of freedom lumped mass stick models, 2D models (4-, 8-, and 12-story archetype steel frames), and 3D reinforced concrete shear wall building model. The results indicate that the proposed IM limits dispersion to within 10% for long-time period structures, and demonstrates improved sufficiency across different structural systems. For example, gradient of the proposed IM with respect to magnitude [Formula: see text] and site-to-source distance [Formula: see text] for a 12-story steel frame is reduced by 42.9% and 94%, respectively, compared to spectral acceleration at fundamental time period. Potential application of this research lies in efficiently conducting seismic reliability assessment and design for structural systems.
On centralizers and pseudo-multidegree functions for non-commutative rings having PBW bases
Authors: Niño Arturo , Reyes Armando
Published: October 2023

In this paper, we introduce a notion of pseudo-multidegree function on non-commutative rings having Poincaré–Birkhoff–Witt (PBW) bases with the aim of establishing a criterion to determine whether the centralizer of an element has finite dimension over the non-commutative ring. We illustrate our results with remarkable families of algebras studied in the literature.
Tracking Object’s Pose via Dynamic Tactile Interaction
Authors: Lin Qiguang , Yan Chaojie , Li Qiang , Ling Yonggen , Lee Wangwei , Zheng Yu , Wan Zhaoliang , Huang Bidan , Liu Xiaofeng
Published: October 2023

It is a challenging task to localize and track an in-hand object in robotic domain. Researchers were mainly using the vision as major modality for extracting object’s pose. The vision approaches are fragile when the object is occluded by the robotic arm and hand. To this end, we propose a tactile-based DTI-Tracker (tracking object’s pose via Dynamic Tactile Interaction) approach and formalize the object’s tracking as a filter problem. An Extended Kalman Filter (EKF) is used to estimate the in-hand object pose exploiting the high spatial resolution tactile feedback. Given the initial estimation error, the proposed approach rapidly converges the estimation result to the real pose and the statistic evaluation shows the robustness of the proposed approach. We evaluate this method in physics simulation and real multi-fingered grasping setup while the object is static and movable. The proposed method is a potential tool to foster future research on dexterous manipulation using multifingered robotic hand.
On additive decompositions of primitive elements in finite fields
Authors: She Yue-Feng , Wu Hai-Liang
Published: October 2023

In this paper, we study several topics on additive decompositions of primitive elements in finite fields. Also we refine some bounds obtained by Dartyge and Sárközy as well as Shparlinski.
Robust Trajectory Tracking for a Control-Surface-Free Tilt-Rotor Convertible UAV
Authors: Mimouni Mohamed Zakaria , Araar Oualid , Oudda Abdelkader , Haddad Moussa
Published: October 2023

Convertible UAVs unlock a new range of applications, by combining the flight features of vertical take-off and landing (VTOL) and fixed-wing UAVs. Tilt-rotor UAV (TRUAV) is a popular category, in which switching from one flight mode to another is achieved by tilting some or all the rotors. In this work, we consider a new TRUAV design that does not include any control surfaces. This design, which we named control-surface-free TRUAV (CSF-TRUAV), exploits only propellers to control the drone’s position and attitude in both VTOL and fixed-wing modes. We also consider a control scheme that, unlike existing works, uses a single controller to handle both flight modes. This makes the transition from VTOL to cruise mode no longer an issue. This control scheme is implemented using a sliding mode controller (SMC), and validated on the full nonlinear model of the CSF-TRUAV, including all coupling and aerodynamic effects. The obtained results show the incapacity of a first-order SMC in dealing with the aerodynamic forces and moments, which act as external perturbations if they are not accurately estimated and fed to the controller. To deal with this issue, a super-twisting SMC (ST-SMC) is designed. The ST-SMC was capable of accurate trajectory tracking in both VTOL and fixed-wing modes.
A Novel Supply Chain-Based Framework for the Healthcare Industry: A Survey Paper
Authors: Satav Sandip D.
Published: October 2023

In recent years, the healthcare industry has faced numerous challenges in ensuring efficient and reliable supply chain management. The emergence of novel supply chain-based frameworks has presented promising solutions to these challenges. This survey paper aims to explore and analyze the latest advancements in supply chain frameworks specifically tailored for the healthcare industry. We review the existing literature, identify key trends, and highlight the potential impact of these frameworks on improving patient care, optimizing inventory management, enhancing operational efficiency, and reducing costs. Additionally, we discuss the challenges and opportunities associated with the implementation of these frameworks and suggest future research directions.
Formally Verifying Optimizations with Block Simulations
Authors: Gourdin Léo , Bonneau Benjamin , Boulmé Sylvain , Monniaux David , Bérard Alexandre
Published: October 2023

CompCert (ACM Software System Award 2021) is the first industrial-strength compiler with a mechanically checked proof of correctness. Yet, CompCert remains a moderately optimizing C compiler. Indeed, some optimizations of “gcc ‍-O1” such as
Lazy Code Motion
(LCM) or
Strength Reduction
(SR) were still missing: developing these efficient optimizations together with their formal proofs remained a challenge.

Cyril Six et al. have developed efficient formally verified translation validators for certifying the results of superblock schedulers and peephole optimizations. We revisit and generalize their approach into a framework (integrated into CompCert) able to validate many more optimizations: an enhanced superblock scheduler, but also
Dead Code Elimination
(DCE),
Constant Propagation
(CP), and more noticeably, LCM and SR. In contrast to other approaches to translation validation, we co-design our untrusted optimizations and their validators. Our optimizations provide hints, in the forms of
invariants
or
CFG morphisms
, that help keep the formally verified validators both simple and efficient. Such designs seem applicable beyond CompCert.
Secure RDTs: Enforcing Access Control Policies for Offline Available JSON Data
Authors: Renaux Thierry , Van den Vonder Sam , De Meuter Wolfgang
Published: October 2023

Replicated Data Types (RDTs) are a type of data structure that can be replicated over a network, where each replica can be kept (eventually) consistent with the other replicas. They are used in applications with intermittent network connectivity, since local (offline) edits can later be merged with the other replicas. Applications that want to use RDTs often have an inherent security component that restricts data access for certain clients. However, access control for RDTs is difficult to enforce for clients that are not running within a secure environment, e.g., web applications where the client-side software can be freely tampered with. In essence, an application cannot prevent a client from reading data which they are not supposed to read, and any malicious changes will also affect well-behaved clients.

This paper proposes Secure RDTs (SRDTs), a data type that specifies role-based access control for offline-available JSON data. In brief, a trusted application server specifies a security policy based on roles with read and write privileges for certain fields of an SRDT. The server enforces read privileges by projecting the data and security policy to omit any non-readable fields for the user's given role, and it acts as an intermediary to enforce write privileges. The approach is presented as an operational semantics engineered in PLT Redex, which is validated by formal proofs and randomised testing in Redex to ensure that the formal specification is secure.
AST vs. Bytecode: Interpreters in the Age of Meta-Compilation
Authors: Larose Octave , Kaleba Sophie , Burchell Humphrey , Marr Stefan
Published: October 2023

Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interpreter. Systems such as RPython and GraalVM provide components such as a garbage collector and just-in-time compiler in a language-agnostic manner, greatly reducing implementation effort.

However, meta-compilation-based language implementations still need to improve further to reach the low memory use and fast warmup behavior that custom-built systems provide. A key element in this endeavor is interpreter performance. Folklore tells us that bytecode interpreters are superior to abstract-syntax-tree (AST) interpreters both in terms of memory use and run-time performance.

This work assesses the trade-offs between AST and bytecode interpreters to verify common assumptions and whether they hold
in the context of meta-compilation systems. We implemented four interpreters, each an AST and a bytecode one using RPython and GraalVM. We keep the difference between the interpreters as small as feasible to be able to evaluate interpreter performance, peak performance, warmup, memory use, and the impact of individual optimizations.

Our results show that both systems indeed reach performance close to Node.js/V8. Looking at interpreter-only performance,
our AST interpreters are on par with, or even slightly faster than their bytecode counterparts. After just-in-time compilation, the results are roughly on par. This means bytecode interpreters do not have their widely assumed performance advantage. However, we can confirm that bytecodes are more compact in memory than ASTs, which becomes relevant for larger applications. However, for smaller applications, we noticed that bytecode interpreters allocate more memory because boxing avoidance is not as applicable, and because the bytecode interpreter structure requires memory, e.g., for a reified stack.

Our results show AST interpreters to be competitive on top of meta-compilation systems. Together with possible engineering benefits, they should thus not be discounted so easily in favor of bytecode interpreters.
Rhombus: A New Spin on Macros without All the Parentheses
Authors: Flatt Matthew , Allred Taylor , Angle Nia , De Gabrielle Stephen , Findler Robert Bruce , Firth Jack , Gopinathan Kiran , Greenman Ben , Kasivajhula Siddhartha , Knauth Alex , McCarthy Jay , Phillips Sam , Porncharoenwase Sorawee , Søgaard Jens Axel , Tobin-Hochstadt Sam
Published: October 2023

Rhombus is a new language that is built on Racket. It offers the same kind of language extensibility as Racket itself, but using traditional (infix) notation. Although Rhombus is far from the first language to support Lisp-style macros without Lisp-style parentheses, Rhombus offers a novel synthesis of macro technology that is practical and expressive. A key element is the use of multiple binding spaces for context-specific sublanguages. For example, expressions and pattern-matching forms can use the same operators with different meanings and without creating conflicts. Context-sensitive bindings, in turn, facilitate a language design that reduces the notational distance between the core language and macro facilities. For example, repetitions can be defined and used in binding and expression contexts generally, which enables a smoother transition from programming to metaprogramming. Finally, since handling static information (such as types) is also a necessary part of growing macros beyond Lisp, Rhombus includes support in its expansion protocol for communicating static information among bindings and expressions. The Rhombus implementation demonstrates that all of these pieces can work together in a coherent and user-friendly language.
Mat2Stencil: A Modular Matrix-Based DSL for Explicit and Implicit Matrix-Free PDE Solvers on Structured Grid
Authors: Cao Huanqi , Tang Shizhi , Zhu Qianchao , Yu Bowen , Chen Wenguang
Published: October 2023

Partial differential equation (PDE) solvers are extensively utilized across numerous scientific and engineering fields. However, achieving high performance and scalability often necessitates intricate and low-level programming, particularly when leveraging deterministic sparsity patterns in structured grids.

In this paper, we propose an innovative domain-specific language (DSL), Mat2Stencil, with its compiler, for PDE solvers on structured grids. Mat2Stencil introduces a structured sparse matrix abstraction, facilitating modular, flexible, and easy-to-use expression of solvers across a broad spectrum, encompassing components such as Jacobi or Gauss-Seidel preconditioners, incomplete LU or Cholesky decompositions, and multigrid methods built upon them. Our DSL compiler subsequently generates matrix-free code consisting of generalized stencils through multi-stage programming. The code allows spatial loop-carried dependence in the form of quasi-affine loops, in addition to the Jacobi-style stencil’s embarrassingly parallel on spatial dimensions. We further propose a novel automatic parallelization technique for the spatially dependent loops, which offers a compile-time deterministic task partitioning for threading, calculates necessary inter-thread synchronization automatically, and generates an efficient multi-threaded implementation with fine-grained synchronization.

Implementing 4 benchmarking programs, 3 of them being the pseudo-applications in NAS Parallel Benchmarks with 6.3% lines of code and 1 being matrix-free High Performance Conjugate Gradients with 16.4% lines of code, we achieve up to 1.67× and on average 1.03× performance compared to manual implementations.
Asparagus: Automated Synthesis of Parametric Gas Upper-Bounds for Smart Contracts
Authors: Cai Zhuo , Farokhnia Soroush , Goharshady Amir Kafshdar , Hitarth S.
Published: October 2023

Modern programmable blockchains have built-in support for smart contracts, i.e. ‍programs that are stored on the blockchain and whose state is subject to consensus. After a smart contract is deployed on the blockchain, anyone on the network can interact with it and call its functions by creating transactions. The blockchain protocol is then used to reach a consensus about the order of the transactions and, as a direct corollary, the state of every smart contract. Reaching such consensus necessarily requires every node on the network to execute all function calls. Thus, an attacker can perform DoS by creating expensive transactions and function calls that use considerable or even possibly infinite time and space. To avoid this, following Ethereum, virtually all programmable blockchains have introduced the concept of “gas”. A fixed hard-coded gas cost is assigned to every atomic operation and the user who calls a function has to pay for its total gas usage. This technique ensures that the protocol is not vulnerable to DoS attacks, but it has also had significant unintended consequences. Out-of-gas errors, i.e. ‍when a user misunderestimates the gas usage of their function call and does not allocate enough gas, are a major source of security vulnerabilities in Ethereum.

We focus on the well-studied problem of automatically finding upper-bounds on the gas usage of a smart contract. This is a classical problem in the blockchain community and has also been extensively studied by researchers in programming languages and verification. In this work, we provide a novel approach using theorems from polyhedral geometry and real algebraic geometry, namely Farkas’ Lemma, Handelman’s Theorem, and Putinar’s Positivstellensatz, to automatically synthesize linear and polynomial parametric bounds for the gas usage of smart contracts. Our approach is the first to provide completeness guarantees for the synthesis of such parametric upper-bounds. Moreover, our theoretical results are independent of the underlying consensus protocol and can be applied to smart contracts written in any language and run on any blockchain.

As a proof of concept, we also provide a tool, called “Asparagus” that implements our algorithms for Ethereum contracts written in Solidity. Finally, we provide extensive experimental results over 24,188 real-world smart contracts that are currently deployed on the Ethereum blockchain. We compare Asparagus against GASTAP, which is the only previous tool that could provide parametric bounds, and show that our method significantly outperforms it, both in terms of applicability and the tightness of the resulting bounds. More specifically, our approach can handle 80.56% of the functions (126,269 out of 156,735) in comparison with GASTAP’s 58.62%. Additionally, even on the benchmarks where both approaches successfully synthesize a bound, our bound is tighter in 97.85% of the cases.
A Pretty Expressive Printer
Authors: Porncharoenwase Sorawee , Pombrio Justin , Torlak Emina
Published: October 2023

Pretty printers make trade-offs between the
expressiveness
of their pretty printing language, the
optimality
objective that they minimize when choosing between different ways to lay out a document, and the
performance
of their algorithm. This paper presents a new pretty printer, Π

e

, that is strictly more expressive than all pretty printers in the literature and provably minimizes an optimality objective. Furthermore, the time complexity of Π

e

is better than many existing pretty printers. When choosing among different ways to lay out a document, Π

e

consults a user-supplied
cost factory
, which determines the optimality objective, giving Π

e

a unique degree of flexibility. We use the Lean theorem prover to verify the correctness (validity and optimality) of Π

e

, and implement Π

e

concretely as a pretty printer that we call PrettyExpressive. To evaluate our pretty printer against others, we develop a formal framework for reasoning about the expressiveness of pretty printing languages, and survey pretty printers in the literature, comparing their expressiveness, optimality, worst-case time complexity, and practical running time. Our evaluation shows that PrettyExpressive is efficient and effective at producing optimal layouts. PrettyExpressive has also seen real-world adoption: it serves as a foundation of a code formatter for Racket.
Mobius: Synthesizing Relational Queries with Recursive and Invented Predicates
Authors: Thakkar Aalok , Sands Nathaniel , Petrou George , Alur Rajeev , Naik Mayur , Raghothaman Mukund
Published: October 2023

Synthesizing relational queries from data is challenging in the presence of recursion and invented predicates. We propose a fully automated approach to synthesize such queries. Our approach comprises of two steps: it first synthesizes a non-recursive query consistent with the given data, and then identifies recursion schemes in it and thereby generalizes to arbitrary data. This generalization is achieved by an iterative predicate unification procedure which exploits the notion of data provenance to accelerate convergence. In each iteration of the procedure, a constraint solver proposes a candidate query, and a query evaluator checks if the proposed program is consistent with the given data. The data provenance for a failed query allows us to construct additional constraints for the constraint solver and refine the search. We have implemented our approach in a tool named Mobius. On a suite of 21 challenging recursive query synthesis tasks, Mobius outperforms three state-of-the-art baselines Gensynth, ILASP, and Popper, both in terms of runtime and accuracy. We also demonstrate that the synthesized queries generalize well to unseen data.
Quantifying and Mitigating Cache Side Channel Leakage with Differential Set
Authors: Ma Cong , Wu Dinghao , Tan Gang , Kandemir Mahmut Taylan , Zhang Danfeng
Published: October 2023

Cache side-channel attacks leverage secret-dependent footprints in CPU cache to steal confidential information, such as encryption keys. Due to the lack of a proper abstraction for reasoning about cache side channels, existing static program analysis tools that can quantify or mitigate cache side channels are built on very different kinds of abstractions. As a consequence, it is hard to bridge advances in quantification and mitigation research. Moreover, existing abstractions lead to imprecise results. In this paper, we present a novel abstraction, called differential set, for analyzing cache side channels at compile time. A distinguishing feature of differential sets is that it allows compositional and precise reasoning about cache side channels. Moreover, it is the first abstraction that carries sufficient information for both side channel quantification and mitigation. Based on this new abstraction, we develop a static analysis tool DSA that automatically quantifies and mitigates cache side channel leakage at the same time. Experimental evaluation on a set of commonly used benchmarks shows that DSA can produce more precise leakage bound as well as mitigated code with fewer memory footprints, when compared with state-of-the-art tools that only quantify or mitigate cache side channel leakage.
Compositional Verification of Efficient Masking Countermeasures against Side-Channel Attacks
Authors: Gao Pengfei , Zhang Yedi , Song Fu , Chen Taolue , Standaert Francois-Xavier
Published: October 2023

Masking is one of the most effective countermeasures for securely implementing cryptographic algorithms against power side-channel attacks, the design of which however turns out to be intricate and error-prone. While techniques have been proposed to rigorously verify implementations of cryptographic algorithms, currently they are limited in scalability. To address this issue, compositional approaches have been investigated, but insofar they fail to prove the security of recent efficient implementations. To fill this gap, we propose a novel compositional verification approach. In particular, we introduce two new language-level security notions based on which we propose composition strategies and verification algorithms. Our approach is able to prove efficient implementations, which cannot be done by prior compositional approaches. We implement our approach as a tool CONVINCE and conduct extensive experiments to confirm its efficacy. We also use CONVINCE to further explore the design space of the AES Sbox with least refreshing by replacing its implementation for finite-field multiplication with more efficient counterparts. We automatically prove leakage-freeness of these new versions. As a result, we can effectively reduce 1,600 randomness and 3,200 XOR-operations of the state-of-the-art AES implementation.
Aspect-level Information Discrepancies across Heterogeneous Vulnerability Reports: Severity, Types and Detection Methods
Authors: Sun Jiamou , Xing Zhenchang , Xia Xin , Lu Qinghua , Xu Xiwei , Zhu Liming
Published: October 2023

Vulnerable third-party libraries pose significant threats to software applications that reuse these libraries. At an industry scale of reuse, manual analysis of third-party library vulnerabilities can be easily overwhelmed by the sheer number of vulnerabilities continually collected from diverse sources for thousands of reused libraries. Our study of four large-scale, actively maintained vulnerability databases (NVD, IBM X-Force, ExploitDB, and Openwall) reveals the wide presence of information discrepancies, in terms of seven vulnerability aspects, i.e., product, version, component, vulnerability type, root cause, attack vector, and impact, between the reports for the same vulnerability from heterogeneous sources. It would be beneficial to integrate and cross-validate multi-source vulnerability information, but it demands automatic aspect extraction and aspect discrepancy detection. In this work, we experimented with a wide range of NLP methods to extract named entities (e.g., product) and free-form phrases (e.g., root cause) from textual vulnerability reports and to detect semantically different aspect mentions between the reports. Our experiments confirm the feasibility of applying NLP methods to automate aspect-level vulnerability analysis and identify the need for domain customization of general NLP methods. Based on our findings, we propose a discrepancy-aware, aspect-level vulnerability knowledge graph and a KG-based web portal that integrates diversified vulnerability key aspect information from heterogeneous vulnerability databases. Our conducted user study proves the usefulness of our web portal. Our study opens the door to new types of vulnerability integration and management, such as vulnerability portraits of a product and explainable prediction of silent vulnerabilities.
IMPRINT: Interactional Dynamics-aware Motion Prediction in Teams using Multimodal Context
Authors: Yasar Mohammad Samin , Islam Md Mofijul , Iqbal Tariq
Published: October 2023

Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, in this work, we introduce IMPRINT, a multi-agent motion prediction framework that models the interactional dynamics and incorporates the multimodal context (e.g., data from RGB and depth sensors and skeleton joint positions) to accurately predict the motion of all the agents in a team. In IMPRINT, we propose an Interaction module that can extract the intra-agent and inter-agent dynamics before fusing them to obtain the interactional dynamics. Furthermore, we propose a Multimodal Context module that incorporates multimodal context information to improve multi-agent motion prediction. We evaluated IMPRINT by comparing its performance on human-human and human-robot team scenarios against state-of-the-art methods. The results suggest that IMPRINT outperformed all other methods over all evaluated temporal horizons. Additionally, we provide an interpretation of how IMPRINT incorporates the multimodal context information from all the modalities during multi-agent motion prediction. The superior performance of IMPRINT provides a promising direction to integrate motion prediction with robot perception and enable safe and effective human-robot collaboration.
Introduction to the Theme “Pharmacological Individuality: New Insights and Strategies for Personalized and Precise Drug Treatment”
Authors: Meyer Urs A. , Amara Susan G. , Blaschke Terrence F. , Insel Paul A.
Published: October 2023

The reviews in Volume 64 of the Annual Review of Pharmacology and Toxicology cover diverse topics. A common theme in many of the reviews is the interindividual variability in the clinical response to drugs. Highlighted areas include emerging developments in pharmacogenomics that can predict the personal risk for drug inefficacy and/or adverse drug reactions. Other reviews focus on the use of circulating biomarkers to define drug metabolism phenotypes and the effect of circadian regulation on drug response. Another emerging technology, digital twins that model individual patients, is used to generate computational simulations of drug effects and identify optimal personalized treatments. Another variable that may affect clinical outcomes, the nocebo response (an adverse reaction to a placebo), complicates clinical trials. These reviews further document that pharmacological individuality is an essential component of the concepts of personalized medicine and precision medicine and will likely have an important impact on patient care.
Zeolite Imidazolate Framework-Based Platform for the Electrochemical Detection of Epinephrine
Authors: Soosaimanickam Charlin , Sakthivel Arunkumar , Murugavel Kathiresan , Alwarappan Subbiah
Published: October 2023

In this study, we employed a zeolite imidazolate framework (ZIF-67) modified glassy carbon electrode (GCE) as a platform for the electrochemical detection of epinephrine. The ZIF-67 modified glassy carbon electrode (GCE) exhibited excellent electrocatalytic activity towards the detection of epinephrine in phosphate buffer (PBS, pH 7.0). The proposed ZIF-67 displayed superior electrocatalytic activity towards the oxidation of epinephrine in the 0.5−50 μM linear range with a limit of detection (LOD) of 2.1 μM, and a sensitivity of 0.06 μA μM−1 cm−2. The efficacy of the proposed biosensor was demonstrated for the direct determination of epinephrine from serum and urine samples. All these results confirmed the usefulness of ZIF-67 modified electrodes for clinical analysis.
Exploring the role of urinary extracellular vesicles in kidney physiology, aging, and disease progression
Authors: Grange Cristina , Dalmasso Alessia , Cortez Judiel John , Spokeviciute Beatrice , Bussolati Benedetta
Published: October 2023

Extracellular vesicles (EVs), membranous vesicles present in all body fluids, are considered important messengers, carrying their information over long distance and modulating the gene expression profile of recipient cells. EVs collected in urine (uEVs) are mainly originated from the apical part of urogenital tract, following the urine flow. Moreover, bacterial-derived EVs are present within urine and may reflect the composition of microbiota. Consolidated evidence has established the involvement of uEVs in renal physiology, being responsible for glomerular and tubular cross talk and among different tubular segments. uEVs may also be involved in other physiological functions such as modulation of innate immunity, coagulation, or metabolic activities. Furthermore, it has been recently remonstrated that age, sex, endurance excise, and lifestyle may influence uEV composition and release, modifying their cargo. On the other hand, uEVs appear modulators of different urogenital pathological conditions, triggering disease progression. uEVs sustain fibrosis and inflammation processes, both involved in acute and chronic kidney diseases, aging, and stone formation. The molecular signature of uEVs collected from diseased patients can be of interest for understanding kidney physiopathology and for identifying diagnostic and prognostic biomarkers.
ATP functions as a primary alarmin in allergen-induced type 2 immunity
Authors: O’Grady Scott M. , Kita Hirohito
Published: October 2023

Environmental allergens that interact with the airway epithelium can activate cellular stress pathways that lead to the release of danger signals known as alarmins. The mechanisms of alarmin release are distinct from damage-associated molecular patterns (DAMPs), which typically escape from cells after loss of plasma membrane integrity. Oxidative stress represents a form of allergen-induced cellular stress that stimulates oxidant-sensing mechanisms coupled to pathways, which facilitate alarmin mobilization and efflux across the plasma membrane. In this review, we highlight examples of alarmin release and discuss their roles in the initiation of type 2 immunity and allergic airway inflammation. In addition, we discuss the concept of alarmin amplification, where “primary” alarmins, which are directly released in response to a specific cellular stress, stimulate additional signaling pathways that lead to secretion of “secondary” alarmins that include proinflammatory cytokines, such as IL-33, as well as genomic and mitochondrial DNA that coordinate or amplify type 2 immunity. Accordingly, allergen-evoked cellular stress can elicit a hierarchy of alarmin signaling responses from the airway epithelium that trigger local innate immune reactions, impact adaptive immunity, and exacerbate diseases including asthma and other chronic inflammatory conditions that affect airway function.
Knowledge Graph‐Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness
Authors: Li Linqing , Wang Zhifeng
Published: October 2023

In the realm of online tutoring intelligent systems, e‐learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph‐based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise‐rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely, the Knowledge Graph Importance‐Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive Cognitive Diagnosis model to recommend the proper exercises. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each exercise and identifies the candidate exercise set (EC) that exhibits the highest exercise informativeness. Moreover, the exercise representation component utilizes a graph neural network to process student records. The output of the graph neural network serves as the input for exercise‐level attention and skill‐level attention, ultimately generating exercise embeddings and skill embeddings. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one‐dimensional knowledge graph into a multidimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select exercises from the candidate exercise set for the tested exercise set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two types of publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.
Aflatoxin Contamination of Maize from Small-Scale Farms Practicing Different Artisanal Control Methods in Kitui, Kenya
Authors: Kyalo Winfred Muthini , Onono Joshua Orungo , Ombui Jackson Nyarongi , Gathura Peter Baaro , Gitahi Johnson Nduhiu , Ateku Penina Afwande
Published: October 2023

Aflatoxin contamination of maize is a threat to food security and public health for households that depend on farming in developing countries. The objective of this study was to determine levels of total aflatoxins in maize from farms adopting different artisanal aflatoxin control methods. A cross-sectional study was conducted with 315 maize farmers who provided maize samples for aflatoxin analysis and additional data on artisanal aflatoxin control methods applied at farm level. Maize grains were ground, and levels of aflatoxins were determined using competitive enzyme-linked immunosorbent assay. Data were analyzed by computing descriptive statistical measures, and binary logistic regression was used to determine the relationship between levels of aflatoxin in maize and artisanal control methods applied in different farms. Aflatoxin was detected in 98% of maize samples with a mean total aflatoxin level of 12.86 μg/kg which was above the maximum tolerable limits. There was a significant difference in total aflatoxin levels in maize obtained from farms which practiced minimum tillage compared to those practicing deep tillage (

p

 = 0.015). Drying maize on bare ground had a higher likelihood of aflatoxin contamination than drying maize on tarpaulin (

p

 = 0.005). One-third of maize samples had aflatoxin levels exceeding the set maximum limit, with maize samples from lowland areas having high proportions of aflatoxin-positive cases as compared to uplands. Artisanal aflatoxin control technologies such as land tillage, types of platforms for drying maize, and sources of maize seed significantly influence the level of aflatoxins in maize samples. We recommend targeted active surveillance for aflatoxins, continuous public education, and adoption of farm-level mitigation measures to reduce the impact of aflatoxin contamination in farming communities.
Fibroblast Growth Factor 21 in Gestational Diabetes Mellitus and Type 2 Diabetes Mellitus
Authors: Gawlik Katarzyna , Milewicz Tomasz , Pawlica-Gosiewska Dorota , Trznadel-Morawska Iwona , Solnica Bogdan
Published: October 2023

Objective. Women who develop GDM present a metabolic condition similar to that found in type 2 diabetes, characterized by impaired insulin response. Due to similar pathophysiologic mechanisms found between type 2DM and GDM, there is a great interest in finding markers that will lead to the understanding of a possible common origin to both diseases. The aim of this study was to determine serum FGF21 levels in 2DM and GDM and its correlation with selected metabolic parameters. Method. The study included 54 2DM patients and 52 nondiabetic individuals (control group 1) as well as 74 GDM women and 32 healthy pregnant controls (control group 2). Serum FGF21 was determined by enzyme-linked immunosorbent assay (ELISA), in all groups, and correlated with biochemical parameters of glucose metabolism and insulin resistance (HbA1c, HOMA index, TG, and HDL cholesterol). Results. FGF21 concentration was significantly higher in 2DM as compared with control group 1 (

p
<
0.01

). In the 2DM group, FGF21 was positively correlated with HOMA index (

p
=
0.022

,

R
=
0.398

). In the GDM group, the positive relationships with FGF21 were observed with glucose (

p
=
0.020

,

R
=
0.264

) and TG (

p
=
0.013

,

R
=
0.283

) while HDL-C levels were correlated negatively (

p
=
0.004

,

R
=
−
0.326

). Conclusion. Serum FGF21 levels were significantly higher in 2DM patients than those without diabetes. Moreover, serum FGF21 levels were associated with selected metabolic parameters, suggesting that it may play acrolein glucose and lipid metabolism.
Analysis of Subclinical Thyroid Dysfunction and Metabolic Abnormality in 28568 Healthy People
Authors: Xie Yan , Wang Zhixue , Chen Zongtao
Published: October 2023

We analyzed the detection rates of metabolic syndrome (MetS) and subclinical thyroid dysfunction, including subclinical hyperthyroidism (SCHyper) and subclinical hypothyroidism (SCH), in healthy people, as well as their relationship. Clinical data were collected from 28,568 healthy individuals who underwent physical examinations. The detection rates of SCHyper, SCH, and MetS, as well as in different genders and ages, were analyzed. The detection rate of SCHyper and SCH in females was significantly higher than that in males (

P

 < 0.001), but that of MetS in males was significantly higher than that in females (

P

 < 0.001). In each age group, the detection rate of SCH in females was higher than that in males (

P

 < 0.001). The detection rate of SCH was significantly different in different age groups (

P

 < 0.001). The detection rates of hyperlipidemia (

P

 < 0.001), obesity (

P

 = 0.004), hypertension (

P

 = 0.009), and hyperglycemia (

P

 < 0.001) in the female SCH group were significantly higher than those in the normal group. The detection rates of hyperlipidemia (

P

 = 0.006), obesity (

P

 = 0.04), and hypertension (

P

 = 0.04) in the male SCH group were higher than those in the normal group. The males with SCHyper were more prone to hyperlipidemia (

P

 = 0.02) and obesity (

P

 = 0.03). In addition, the female SCHyper group was not significantly different from the normal group (

P

 > 0.05). Conclusively, the detection rate of SCHyper and SCH in females is higher than that in males, which increases with age. Attention should be paid to subclinical thyroid dysfunction in elderly people, especially females. Early individualized screening and early intervention should be carried out for people with abnormal metabolism.