The aim of this review is to assess the mode of action and role of antioxidants as protection from heavy metal stress in roots, mycorrhizal fungi and mycorrhizae. Based on their chemical and physical properties three different molecular mechanisms of heavy metal toxicity can be distinguished: (a) production of reactive oxygen species by autoxidation and Fenton reaction; this reaction is typical for transition metals such as iron or copper, (b) blocking of essential functional groups in biomolecules, this reaction has mainly been reported for non-redox-reactive heavy metals such as cadmium and mercury, (c) displacement of essential metal ions from biomolecules; the latter reaction occurs with different kinds of heavy metals. Transition metals cause oxidative injury in plant tissue, but a literature survey did not provide evidence that this stress could be alleviated by increased levels of antioxidative systems. The reason may be that transition metals initiate hydroxyl radical production, which can not be controlled by antioxidants. Exposure of plants to non-redox reactive metals also resulted in oxidative stress as indicated by lipid peroxidation, H(2)O(2) accumulation, and an oxidative burst. Cadmium and some other metals caused a transient depletion of GSH and an inhibition of antioxidative enzymes, especially of glutathione reductase. Assessment of antioxidative capacities by metabolic modelling suggested that the reported diminution of antioxidants was sufficient to cause H(2)O(2) accumulation. The depletion of GSH is apparently a critical step in cadmium sensitivity since plants with improved capacities for GSH synthesis displayed higher Cd tolerance. Available data suggest that cadmium, when not detoxified rapidly enough, may trigger, via the disturbance of the redox control of the cell, a sequence of reactions leading to growth inhibition, stimulation of secondary metabolism, lignification, and finally cell death. This view is in contrast to the idea that cadmium results in unspecific necrosis. Plants in certain mycorrhizal associations are less sensitive to cadmium stress than non-mycorrhizal plants. Data about antioxidative systems in mycorrhizal fungi in pure culture and in symbiosis are scarce. The present results indicate that mycorrhization stimulated the phenolic defence system in the Paxillus-Pinus mycorrhizal symbiosis. Cadmium-induced changes in mycorrhizal roots were absent or smaller than those in non-mycorrhizal roots. These observations suggest that although changes in rhizospheric conditions were perceived by the root part of the symbiosis, the typical Cd-induced stress responses of phenolics were buffered. It is not known whether mycorrhization protected roots from Cd-induced injury by preventing access of cadmium to sensitive extra- or intracellular sites, or by excreted or intrinsic metal-chelators, or by other defence systems. It is possible that mycorrhizal fungi provide protection via GSH since higher concentrations of this thiol were found in pure cultures of the fungi than in bare roots. The development of stress-tolerant plant-mycorrhizal associations may be a promising new strategy for phytoremediation and soil amelioration measures.
Function-correcting codes (FCCs) are designed to provide error protection for the value of a function computed on the data. Existing work typically focuses solely on protecting the function value and not the underlying data. In this work, we propose a general framework that offers protection for both the data and the function values. Since protecting the data inherently contributes to protecting the function value, we focus on scenarios where the function value requires stronger protection than the data itself. We first introduce a more general approach and a framework for function-correcting codes that incorporates data protection along with protection of function values. A two-step construction procedure for such codes is proposed, and bounds on the optimal redundancy of general FCCs with data protection are reported. Using these results, we exhibit examples that show that data protection can be added to existing FCCs without increasing redundancy. Using our two-step construction procedure, we present explicit constructions of FCCs with data protection for specific families of functions, such as locally bounded functions and the Hamming weight function. We associate a graph calle
3D Gaussian Splatting (3DGS) has become a mainstream representation for real-time 3D scene synthesis, enabling applications in virtual and augmented reality, robotics, and 3D content creation. Its rising commercial value and explicit parametric structure raise emerging intellectual property (IP) protection concerns, prompting a surge of research on 3DGS IP protection. However, current progress remains fragmented, lacking a unified view of the underlying mechanisms, protection paradigms, and robustness challenges. To address this gap, we present the first systematic survey on 3DGS IP protection and introduce a bottom-up framework that examines (i) underlying Gaussian-based perturbation mechanisms, (ii) passive and active protection paradigms, and (iii) robustness threats under emerging generative AI era, revealing gaps in technical foundations and robustness characterization and indicating opportunities for deeper investigation. Finally, we outline six research directions across robustness, efficiency, and protection paradigms, offering a roadmap toward reliable and trustworthy IP protection for 3DGS assets.
Rapid digitization across government services, financial platforms, and telecommunications has intensified the collection and processing of large scale personal data in Bangladesh. In response, the state has introduced multiple regulatory instruments, including the Personal Data Protection Ordinance, the Cyber Security Ordinance, and the National Data Governance Ordinance in 2025. While these initiatives signal an emerging legal regime for data protection, little scholarly work examines how these frameworks operate collectively in practice. This paper presents a legal and institutional analysis of Bangladeshs emerging data protection regime through a systematic review of these three ordinances. Through this review, the paper provides an integrated mapping of Bangladeshs evolving data protection framework and identifies key legal and institutional barriers that undermine the effective protection of citizens personal data. Our findings reveal that this emerging regime is constrained by limited institutional independence, uneven regulatory capacity, and the misaligned legal assumption of individualized, autonomous data subjects. Furthermore, these frameworks invisibilize prevalent soc
Wikipedia, the Web's largest encyclopedia, frequently faces content disputes or malicious users seeking to subvert its integrity. Administrators can mitigate such disruptions by enforcing "page protection" that selectively limits contributions to specific articles to help prevent the degradation of content. However, this practice contradicts one of Wikipedia's fundamental principles$-$that it is open to all contributors$-$and may hinder further improvement of the encyclopedia. In this paper, we examine the effect of page protection on article quality to better understand whether and when page protections are warranted. Using decade-long data on page protections from the English Wikipedia, we conduct a quasi-experimental study analyzing pages that received "requests for page protection"$-$written appeals submitted by Wikipedia editors to administrators to impose page protections. We match pages that indeed received page protection with similar pages that did not and quantify the causal effect of the interventions on a well-established measure of article quality. Our findings indicate that the effect of page protection on article quality depends on the characteristics of the page pri
This technical article explores comprehensive strategies for integrating Electrostatic Discharge (ESD) protection diodes and termination resistors in LowVoltage Differential Signaling (LVDS) designs. The article examines critical aspects of protection mechanisms, design considerations, impedance matching, and placement optimization techniques. Through detailed analysis of layout considerations and advanced design strategies, the article presents solutions for common integration challenges. It emphasizes the importance of signal integrity maintenance and protection effectiveness while providing practical guidelines for implementing robust LVDS systems. Various methodologies for performance optimization and validation are discussed, offering designers a thorough framework for creating reliable high-speed digital systems that balance protection requirements with signal integrity demands.
The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local measurements. Machine learning (ML) offers a data-driven alternative for centralized fault classification (FC) and fault localization (FL), enabling faster and more adaptive decision-making. However, practical deployment critically depends on robustness. Protection algorithms must remain reliable even when confronted with missing, noisy, or degraded sensor data. This work introduces a unified framework for systematically evaluating the robustness of ML models in power system protection. High-fidelity EMT simulations are used to model realistic degradation scenarios, including sensor outages, reduced sampling rates, and transient communication losses. The framework provides a consistent methodology for benchmarking models, quantifying the impact of limited observability, and identifying critical measurement channels required for resilient operation. Results show that FC remains highly stable under most degradation types but drops by about 13% under single-phase loss, while FL is more sensitive overall, wit
Mobile spin qubit architectures promise flexible connectivity for efficient quantum error correction and relaxed device layout constraints, but their viability rests on preserving spin coherence during transport. While shuttling transforms spatial disorder into time-dependent noise, its net impact on spin coherence remains an open question. Here we demonstrate systematic noise mitigation during spin shuttling in a linear $^{28}$Si/SiGe quantum dot device. First, by passively reducing magnetic field gradients, we minimize charge-noise coupling to the spin and double the spatially averaged dephasing time $T_2^*(x_n)$ from $4.4$ to $8.5\,μ\text{s}$. Next, we exploit motional narrowing by periodically shuttling the qubit, achieving a further enhancement in coherence time up to $T_{2}^{*,sh} = 11.5\,μ\text{s}$. Finally, we incorporate dynamical decoupling techniques while periodically shuttling over distances exceeding $200\,\text{nm}$, reaching $T_\text{2}^{H,sh}= 32\,μ\text{s}$. For the same setup, we demonstrate that dressed-state shuttling provides robust protection against low-frequency noise with a decay time $T_R^{\text{sh}} = 21\,μ\text{s}$, without the overhead of pulsed contro
This PhD thesis discusses how European law could improve privacy protection in the area of behavioural targeting. Behavioural targeting, also referred to as online profiling, involves monitoring people's online behaviour, and using the collected information to show people individually targeted advertisements. To protect privacy in the area of behavioural targeting, the EU lawmaker mainly relies on the consent requirement for the use of tracking technologies in the e-Privacy Directive, and on general data protection law. With informed consent requirements, the law aims to empower people to make choices in their best interests. But behavioural studies cast doubt on the effectiveness of the empowerment approach as a privacy protection measure. Many people click "I agree" to any statement that is presented to them. Therefore, to mitigate privacy problems such as chilling effects, this study argues for a combined approach of protecting and empowering the individual. Compared to the current approach, the lawmaker should focus more on protecting people. The PhD thesis is a legal study, but it also incorporates insights from other disciplines, such as computer science, behavioural economic
Smart grids are critical cyber-physical systems that are vital to our energy future. Smart grids' fault resilience is dependent on the use of advanced protection systems that can reliably adapt to changing conditions within the grid. The vast amount of operational data generated and collected in smart grids can be used to develop these protection systems. However, given the safety-criticality of protection, the algorithms used to analyze this data must be stable, transparent, and easily interpretable to ensure the reliability of the protection decisions. Additionally, the protection decisions must be fast, selective, simple, and reliable. To address these challenges, this paper proposes a data-driven protection strategy, based on Gaussian Discriminant Analysis, for fault detection and isolation. This strategy minimizes the communication requirements for time-inverse relays, facilitates their coordination, and optimizes their settings. The interpretability of the protection decisions is a key focus of this paper. The method is demonstrated by showing how it can protect the medium-voltage CIGRE network as it transitions between islanded and grid-connected modes, and radial and mesh t
Disaggregated memory leverages recent technology advances in high-density, byte-addressable non-volatile memory and high-performance interconnects to provide a large memory pool shared across multiple compute nodes. Due to higher memory density, memory errors may become more frequent. Unfortunately, tolerating memory errors through existing memory-error protection techniques becomes impractical due to increasing storage cost. This work proposes replication-aware memory-error protection to improve storage efficiency of protection in data-centric applications that already rely on memory replication for performance and availability. It lets such applications lower protection storage cost by weakening the protection of each individual replica, but still realize a strong protection target by relying on the collective protection conferred by multiple replicas.
Contrary to the conventional view that noise is detrimental, we show that mixed noise can protect entanglement in a two-atom-cavity system. Specifically, the leakage of the cavity and the stochastic atom-cavity couplings are modeled as two types of noises. From the analytical derivation of the dynamical equations, the mechanism of the entanglement protection is revealed as the high-frequency(HF) noise in the atom-cavity couplings could suppress the decoherence caused by the cavity leakage, thus protect the entanglement. We investigate the entanglement protection induced by mixed noise constructed from diverse noise types, including the Ornstein-Uhlenbeck noise, flicker noise, and telegraph noise. Numerical simulations demonstrate that entanglement protection depends critically on the proportion of HF components in the power spectral density of the mixed noise. Our work establishes that enhanced HF components are essential for effective noise-assisted entanglement protection, offering key insights for noise engineering in practical open quantum systems.
Availability attacks, or unlearnable examples, are defensive techniques that allow data owners to modify their datasets in ways that prevent unauthorized machine learning models from learning effectively while maintaining the data's intended functionality. It has led to the release of popular black-box tools (e.g., APIs) for users to upload personal data and receive protected counterparts. In this work, we show that such black-box protections can be substantially compromised if a small set of unprotected in-distribution data is available. Specifically, we propose a novel threat model of protection leakage, where an adversary can (1) easily acquire (unprotected, protected) pairs by querying the black-box protections with a small unprotected dataset; and (2) train a diffusion bridge model to build a mapping between unprotected and protected data. This mapping, termed BridgePure, can effectively remove the protection from any previously unseen data within the same distribution. BridgePure demonstrates superior purification performance on classification and style mimicry tasks, exposing critical vulnerabilities in black-box data protection. We suggest that practitioners implement multi
In this paper we investigate an optimal control problem involving a toy model for the protection on a crop field. Precisely, we consider a protection on a crop field and we want to place intervention zones represented by a control, in order to maximise the protection on the field during a given period. Using a relaxation method, we prove that there exists a control which maximises the protection and, moreover, it must be a bang-bang control. Furthermore, with additional assumptions on the crop field geometry, some results on the shape of the optimal intervention are proved using comparison results for elliptic equations via Schwarz and Steiner symmetrizations. Finally, some numerical simulations are performed in order to illustrate those results.
This paper introduces Triosecuris, a formally verified defense against Spectre BTB, RSB, and PHT that combines CET-style hardware-assisted control-flow integrity with compiler-inserted speculative load hardening (SLH). Triosecuris is based on the novel observation that in the presence of CET-style protection, we can precisely detect BTB misspeculation for indirect calls and RSB misspeculation for returns and set the SLH misspeculation flag. We formalize Triosecuris as a transformation in Rocq and provide a machine-checked proof that it achieves relative security: any transformed program running with speculation leaks no more than what the source program leaks without speculation. This strong security guarantee applies to arbitrary programs, even those not following the cryptographic constant-time programming discipline.
Generative AI (GenAI) systems and chatbots rely on vast corpora of consumer data. The use of such data for training GenAI has raised concerns around data ownership, copyright issues, and potential harm to consumers. In this work, we explore a related but less examined angle: the ownership and privacy of data originating from deceased individuals. We propose three post mortem data management principles to guide the protection of deceased individual's data, and analyze popular GenAI chatbots policies and answers to legacy requests. We plan to systematically audit consumer GenAI chatbots on their behavior regarding post-mortem data management
Fault tolerance in Deep Neural Networks (DNNs) deployed on resource-constrained systems presents unique challenges for high-accuracy applications with strict timing requirements. Memory bit-flips can severely degrade DNN accuracy, while traditional protection approaches like Triple Modular Redundancy (TMR) often sacrifice accuracy to maintain reliability, creating a three-way dilemma between reliability, accuracy, and timeliness. We introduce NAPER, a novel protection approach that addresses this challenge through ensemble learning. Unlike conventional redundancy methods, NAPER employs heterogeneous model redundancy, where diverse models collectively achieve higher accuracy than any individual model. This is complemented by an efficient fault detection mechanism and a real-time scheduler that prioritizes meeting deadlines by intelligently scheduling recovery operations without interrupting inference. Our evaluations demonstrate NAPER's superiority: 40% faster inference in both normal and fault conditions, maintained accuracy 4.2% higher than TMR-based strategies, and guaranteed uninterrupted operation even during fault recovery. NAPER effectively balances the competing demands of a
Revert protection is a feature provided by some blockchain platforms that prevents users from incurring fees for failed transactions. We study the economic implications and benefits of revert protection in the context of priority gas auctions and maximal extractable value. We develop a model in which searchers bid for a top-of-block arbitrage opportunity under varying degrees of revert protection. This model applies to a broad range of settings, including bundle auctions on L1s and priority ordering sequencing rules on L2s. We quantify, in closed form, how revert protection improves equilibrium auction revenue, market efficiency, and blockspace efficiency.
Images serve as a crucial medium for communication, presenting information in a visually engaging format that facilitates rapid comprehension of key points. Meanwhile, during transmission and storage, they contain significant sensitive information. If not managed properly, this information may be vulnerable to exploitation for personal gain, potentially infringing on privacy rights and other legal entitlements. Consequently, researchers continue to propose some approaches for preserving image privacy and publish reviews that provide comprehensive and methodical summaries of these approaches. However, existing reviews tend to categorize either by specific scenarios, or by specific privacy objectives. This classification somewhat restricts the reader's ability to grasp a holistic view of image privacy protection and poses challenges in developing a total understanding of the subject that transcends different scenarios and privacy objectives. Instead of examining image privacy protection from a single aspect, it is more desirable to consider user needs for a comprehensive understanding. To fill this gap, we conduct a systematic review of image privacy protection approaches based on pr
To maintain quality in hospital services, management strategies are fundamental. The objective of this article was to elaborate and validate the contents of an instrument for the management of hospital radiological protection. Therefore, a study was conducted in two Portuguese-speaking countries, Brazil and Portugal. Initially, a data collection instrument was created to elaborate essential items for the management of hospital radiological protection, followed by the validation of the contents of this instrument, using the Delphi technique. The validation of the instrument content was performed by a group of judges, following the steps of the Delphi technique. The questionnaire answered 33 professionals, of these, 25 Brazilians and 8 Portuguese. The affirmative statements among the professionals are related to the instructions on radiological protection for the radiodiagnostic team and the radiological protection program. It is concluded that the instrument built and validated for the management of radiological protection contributes to the organization of diagnostic imaging services and may be adapted for the management of specific services.