Hardware intellectual property (IP) in the globalized integrated circuit (IC) supply chain is exposed to a wide range of confidentiality and integrity attacks by untrusted third-party entities. Existing IP-level countermeasures, such as logic locking, hardware obfuscation, camouflaging, and redaction, have aimed at addressing these them. In particular, hardware redaction has emerged as a robust approach for IP protection against confidentiality attacks, including reverse engineering. We note that existing IP protection approaches, including the ones based on hardware redaction, tend to leave behind structural artifacts that can be exploited by adversaries to bypass protections or predict unlocking keys, using the knowledge of known designs, akin to a known-plaintext attack (KPA) in cryptography. In this work, we present CIPHR, a robust fine-grain hardware redaction methodology inspired by the cryptographic property of indistinguishability. The proposed approach utilizes novel heuristic-driven randomization to introduce significant structural transformations into the redacted designs. We employ structural analysis metrics to evaluate the security achieved by CIPHR compared to various state-of-the-art IP protection techniques. Multiple open-source benchmark designs are used to demonstrate that fine-grain redaction in CIPHR is robust, scalable, and indistinguishable against structural attacks.
Charul Rajput, B. Sundar Rajan, Ragnar Freij-Hollanti
et al.
In this paper, we consider the recently introduced concept of \emph{function-correcting codes (FCCs) with data protection}, which provide a certain level of error protection for the data and a higher level of protection for a desired function on the data. These codes are denoted by $(f\!:\!d_d,d_f)$-FCC, where $d_d$ is the minimum distance of the code and $d_f$ denotes the minimum distance between those codewords that correspond to different function values of a function $f:\mathbb{F}_q^k \to \mathrm{Im}(f)$, with $d_f \geq d_d$. We use a distance graph on a code based on the pairwise distances of its codewords, and show conditions under which a code cannot work as a \emph{strict} $(f\!:\!d_d,d_f)$-FCC, that is, code for which $d_f > d_d$. We then consider some well-known classes of codes, such as perfect codes and maximum distance separable (MDS) codes, and show that they cannot be used as \emph{strict} $(f\!:\!d_d,d_f)$-FCCs.
T. Polkowski, A. Gruszczyńska, A. Gruszczyńska
et al.
<p>Investigating climatic and environmental changes during past interglacials is crucial to improve our understanding of the mechanisms that govern changes related to current global warming. Among the numerous proxies that can be used to reconstruct past environmental and climatic conditions, pollen allows quantitative reconstructions of annual, warmest month and coldest month air temperatures as well as precipitation sums, and Chironomidae larvae are widely used to infer past summer air temperature. Chironomidae have mostly been used for reconstructing Holocene and Late Weichselian summer temperatures whilst there are only four sites in Europe with chironomid-based summer air temperature reconstructions for the Late Pleistocene and no such records for any Middle Pleistocene warm period as of the writing of this paper. In this study we present the first quantitative palaeoclimate reconstruction for the post-Holsteinian (Marine Isotope Stage – (MIS) 11b) in Central Europe based on both pollen and fossil chironomid remains preserved in palaeolake sediments recovered from Krępa, southeastern Poland. Besides being used for the palaeoclimatic reconstruction, pollen analysis provides the biostratigraphic framework and a broader perspective of climate development at the end of Holsteinian Interglacial. Fossil Chironomidae assemblages at Krępa consist mainly of oligotrophic and mesotrophic taxa (e.g. <i>Corynocera ambigua, Chironomus anthracinus-</i>type) while eutrophic taxa (e.g. <i>Chironomus plumosus-</i>type) are less abundant. The chironomid-based summer temperature reconstruction indicates July air temperatures between 15.3 and 20.1 °C during the early post-Holsteinian, while pollen-based temperature reconstructions (using MAT and WA-PLS methods) indicate temperature values from 15 to 19 °C. Pollen-derived mean temperature of the coldest month (MTCO) and mean annual precipitation sum vary from <span class="inline-formula">−13.2</span> to <span class="inline-formula">−9.6</span> °C and between 500 and 900 mm respectively. In any case, results from Krępa prove that conducting Chironomidae analysis is feasible for periods as early as the Middle Pleistocene, improving our understanding of the mechanisms that control present-day climatic and environmental changes.</p>
Decarbonization through the use of low-carbon fuel such as bio-compressed natural gas (bioCNG) in transport sector could play a major role in the transition to net-zero, climate change mitigation and circular economy. This study investigates the environmental sustainability potential and social cost-benefits of bioCNG as a transport fuel for South African provinces. A net-zero emission technique was adopted for evaluating environmental sustainability while the cost-benefit of social externalities was modelled using willingness-to-pay technique. Additionally, sensitivity analysis was performed to look at how various assumed variables affected the study's overall outcome. The findings show that, under the current scenario of waste landfilling, an estimated 5869.809 ktonCO2eq/year of carbon emissions would be emitted into the environment. However, utilizing the generated waste for bioCNG production, a total of 2341 million kg of bioCNG is produced which is capable of filling >460 million 5 kg gas cylinders for automobile application with an estimated emission mitigation of 1377.63 ktonsCO2eq/year which corresponds to 0.29 % of 2020 South African emission portfolio. Considering all the emission pathways, the estimated resultant social cost-benefit savings of 10.41 billion USD/year was obtained which is equivalent to 28 % of the 2024/2025 of South African national budget for the combined expenditure on social protection and healthcare. This research provides an insight into the role that the waste-to-bioCNG supply chain plays in achieving SDGs 3, 11, and 13. The proposed methodology could be extended to other countries particularly in Sub-Saharan Africa with similar waste composition and economic activities.
Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses a significant challenge, necessitating robust privacy protection mechanisms. This paper introduces a novel theoretical framework to decipher the intricate interplay between attack and protection complexities in privacy-preserving FL. We formally define "Attack Complexity" as the minimum computational and data resources an adversary requires to reconstruct private data below a given error threshold, and "Protection Complexity" as the expected distortion introduced by privacy mechanisms. Leveraging Maximum Bayesian Privacy (MBP), we derive tight theoretical bounds for protection complexity, demonstrating its scaling with model dimensionality and privacy budget. Furthermore, we establish comprehensive bounds for attack complexity, revealing its dependence on privacy leakage, gradient distortion, model dimension, and the chosen privacy level. Our findings quantitatively illuminate the fundamental trade-offs between privacy guarantees, system utility, and the effort required for both attacking and defending. This framework provides critical insights for designing more secure and efficient federated learning systems.
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally complete for arbitrary programs, it is basically designed for algebraic operations on encrypted data of predetermined shape, such as a polynomial ring. Thus, a non-tailored combination of HE and the system can yield very inefficient performance, and many previous HE-based face template protection methods are hundreds of times slower than plain systems without protection. In this study, we propose IDFace, a new HE-based secure and efficient face identification method with template protection. IDFace is designed on the basis of two novel techniques for efficient searching on a (homomorphically encrypted) biometric database with an angular metric. The first technique is a template representation transformation that sharply reduces the unit cost for the matching test. The second is a space-efficient encoding that reduces wasted space from the encryption algorithm, thus saving the number of operations on encrypted templates. Through experiments, we show that IDFace can identify a face template from among a database of 1M encrypted templates in 126ms, showing only 2X overhead compared to the identification over plaintexts.
We theoretically propose a tunable implementation of symmetry-protected topological phases in a synthetic superlattice, taking advantage of the long coherence time and exquisite spectral resolutions offered by gravity-tilted optical lattice clocks. We describe a protocol similar to Rabi spectroscopy that can be used to probe the distinct topological properties of our system. We then demonstrate how the sensitivity of clocks and interferometers can be improved by the protection to unwanted experimental imperfections offered by the underlying topological robustness. The proposed implementation opens a path to exploit the unique opportunities offered by symmetry-protected topological phases in state-of-the-art quantum sensors.
Philip Wiese, Victor Kartsch, Marco Guermandi
et al.
The widespread adoption of Internet of Things (IoT) technologies has significantly advanced environmental monitoring (EM) by enabling cost-effective and scalable sensing solutions. Concurrently, machine learning (ML) and artificial intelligence (AI) are introducing powerful tools for the efficient and accurate analysis of complex environmental data. However, current IoT platforms for environmental sensing are typically limited to a narrow set of sensors, preventing a comprehensive assessment of environmental conditions and lacking sufficient computational capabilities to support the deployment of advanced ML and AI algorithms on the edge. To overcome these limitations, we introduce a compact (17x38 mm2), multi-modal, MCU-based environmental IoT node integrating 11 sensors, including CO2 concentration, volatile organic compounds (VOCs), light intensity, UV radiation, pressure, temperature, humidity, visual sensing via an RGB camera, and precise geolocation through a GNSS module. It features GAP9, a parallel ultra-low-power system-on-chip, enabling real-time, energy-efficient edge processing of advanced ML models directly on-device. We implemented a YOLOv5-based occupancy detection pipeline (0.3 M parameters, 42 MOP per inference), demonstrating 42% energy savings over raw data streaming. Additionally, we present a smart indoor air quality (IAQ) monitoring setup that combines occupancy detection with adaptive sample rates, achieving operational times of up to 143 h on a single compact 600 mAh, 3.7 V battery. Our platform lays the groundwork for innovative applications such as predictive indoor IAQ, enabling efficient AI-driven on-edge forecasting for energy-efficient and autonomous, proactive pollution-mitigation control strategies
Integrated Sensing and Communications (ISAC) is poised to become one of the defining capabilities of the sixth generation (6G) wireless communications systems, enabling the network infrastructure to jointly support high-throughput communications and situational awareness. While recent advances have explored ISAC for both human-centric applications and environmental monitoring, existing research remains fragmented across these domains. This paper provides the first unified review of ISAC-enabled sensing for both human activities and environment, focusing on signal-level mechanisms, sensing features, and real-world feasibility. We begin by characterising how diverse physical phenomena, ranging from human vital sign and motion to precipitation and flood dynamics, impact wireless signal propagation, producing measurable signatures in channel state information (CSI), Doppler profiles, and signal statistics. A comprehensive analysis is then presented across two domains: human sensing applications including localisation, activity recognition, and vital sign monitoring; and environmental sensing for rainfall, soil moisture, and water level. Experimental results from Long-Term Evolution (LTE) sensing under non-line-of-sight (NLOS) conditions are incorporated to highlight the feasibility in infrastructure-limited scenarios. Open challenges in signal fusion, domain adaptation, and generalisable sensing architectures are discussed to facilitate future research toward scalable and autonomous ISAC.
Giordano d'Aloisio, Tosin Fadahunsi, Jay Choy
et al.
Background: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) - an open-source text-to-image generation model - has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. Aims: To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based approach designed to enhance the social and environmental sustainability of SD models. Method: SustainDiffusion searches the optimal combination of hyperparameters and prompt structures that can reduce gender and ethnic bias in generated images while also lowering the energy consumption required for image generation. Importantly, SustainDiffusion maintains image quality comparable to that of the original SD model. Results: We conduct a comprehensive empirical evaluation of SustainDiffusion, testing it against six different baselines using 56 different prompts. Our results demonstrate that SustainDiffusion can reduce gender bias in SD3 by 68%, ethnic bias by 59%, and energy consumption (calculated as the sum of CPU and GPU energy) by 48%. Additionally, the outcomes produced by SustainDiffusion are consistent across multiple runs and can be generalised to various prompts. Conclusions: With SustainDiffusion, we demonstrate how enhancing the social and environmental sustainability of text-to-image generation models is possible without fine-tuning or changing the model's architecture.
Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.
China’s National Key Ecological Function Zones (NKEFZs) currently represent the largest and most extensive ecological conservation policy in China, with one of the core objectives of this policy being to improve eco-environmental quality (EEQ). This study regards the establishment of NKEFZs as a quasi-natural experiment. Based on panel data from 130 counties in Sichuan Province from 2001 to 2021, a multi-period difference-in-differences (DID) model was employed to evaluate the impact of NKEFZ establishment on EEQ. The findings indicate the following: ① The establishment of NKEFZs can significantly enhance the EEQ of the covered areas, albeit as a gradual long-term process. This conclusion not only meets the parallel-trends assumption but also holds true in a series of robustness tests such as placebo tests. ② Mechanism analysis reveals that NKEFZs can enhance EEQ through the effects of optimizing land spatial allocation and upgrading industrial structure. ③ Heterogeneity analysis demonstrates that the beneficial effect of NKEFZs on EEQ varies across different functional zone types, geographic spaces and ethnic regions. Our study not only contributes to the accumulation of empirical evidence and institutional refinement in the sustainable implementation of ecological policies in China but also offers valuable insights and references for other countries in formulating policies for eco-environmental protection.
Lazarus Obed Livingstone Banda, Chigonjetso Victoria Banda, Jane Thokozani Banda
et al.
Abstract Background The Ayeyarwady Basin in Myanmar, a critical economic zone, faces severe ecological degradation due to unsustainable agricultural practices. These practices pose significant threats to human health and marine biodiversity. Environmental threats persist despite the Myanmar government’s efforts to implement biodiversity protection policies. This research explores the limited compliance with environmental protection policies among farmers in the Ayeyarwady Basin and its implications for sustainable agricultural practices and ecological conservation. Methods This research employs an exploratory phenomenological approach, utilizing semi-structured, in-depth interviews with government officials and farmers (N = 30). The data collected were subjected to thematic analysis using Atlas 23. Results Preliminary findings reveal a gap in farmers’ awareness and understanding of these policies, hindered by insufficient financing, poor communication infrastructure, and uncoordinated policy monitoring. These factors and existing unrest contribute to a top-down policy approach that neglects frontline stakeholders. The study suggests the need for clear stakeholder roles, adequate policy financing, and diverse communication strategies to effectively implement environmental policies and protect human and marine life. Conclusions Environmental policy shortcomings in Myanmar are attributable to governmental oversight and insufficient stakeholder engagement. To mitigate pollution and safeguard river basin ecosystems, the government must delineate stakeholder responsibilities, allocate appropriate policy funding, and adopt varied communication approaches with farmers. Graphical Abstract
Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities.
The long-standing, dominant approach to robotic obstacle negotiation relies on mapping environmental geometry to avoid obstacles. However, this approach does not allow for traversal of cluttered obstacles, hindering applications such as search and rescue operations through earthquake rubble and exploration across lunar and Martian rocks. To overcome this challenge, robots must further sense and utilize environmental physical interactions to control themselves to traverse obstacles. Recently, a physics-based approach has been established towards this vision. Self-propelled robots interacting with obstacles results in a potential energy landscape. On this landscape, to traverse obstacles, a robot must escape from certain landscape basins that attract it into failure modes, to reach other basins that lead to successful modes. Thus, sensing the potential energy landscape is crucial. Here, we developed new methods and performed systematic experiments to demonstrate that the potential energy landscape can be estimated by sensing environmental physical interaction. We developed a minimalistic robot capable of sensing obstacle contact forces and torques for systematic experiments over a wide range of parameter space. Surprisingly, although these forces and torques are not fully conservative, they match the potential energy landscape gradients that are conservative forces and torques, enabling an accurate estimation of the potential energy landscape. Additionally, a bio-inspired strategy further enhanced estimation accuracy. Our results provided a foundation for further refining these methods for use in free-locomoting robots. Our study is a key step in establishing a new physics-based approach for robots to traverse clustered obstacles to advance their mobility in complex, real-world environments.
Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incorporates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.
Gabrielle dos Santos Ilha, Marianne Boix, Jürgen Knödlseder
et al.
Astronomical observatories have been identified as substantial contributors to the carbon footprint of astrophysical research. Being part of the collaboration that currently develops the Medium-Sized Telescope (MST) of the Cherenkov Telescope Array, a ground-based observatory for very-high-energy gamma rays that will comprise 64 telescopes deployed on two sites, we assessed the environmental impacts of one MST on the Northern site by means of a Life Cycle Assessment. We identified resource use and climate change as the most significant impacts, being driven by telescope manufacturing and energy consumption during operations. We estimate life cycle greenhouse gas emissions of 2,660 +/- 274 tCO2 equivalent for the telescope, 44% of which arise from construction, 1% from on-site assembly and commissioning, and 55% from operations over 30 years. Environmental impacts can be reduced by using renewable energies during construction and operations, use of less electronic components and metal casting, and use of recycled materials. We propose complementing project requirements with environmental budgets as an effective measure for impact management and reductions.
Pietro Colombo, Claire Miller, Xiaochen Yang
et al.
Understanding the dynamics of climate variables is paramount for numerous sectors, like energy and environmental monitoring. This study focuses on the critical need for a precise mapping of environmental variables for national or regional monitoring networks, a task notably challenging when dealing with skewed data. To address this issue, we propose a novel data fusion approach, the \textit{warped multifidelity Gaussian process} (WMFGP). The method performs prediction using multiple time-series, accommodating varying reliability and resolutions and effectively handling skewness. In an extended simulation experiment the benefits and the limitations of the methods are explored, while as a case study, we focused on the wind speed monitored by the network of ARPA Lombardia, one of the regional environmental agencies operting in Italy. ARPA grapples with data gaps, and due to the connection between wind speed and air quality, it struggles with an effective air quality management. We illustrate the efficacy of our approach in filling the wind speed data gaps through two extensive simulation experiments. The case study provides more informative wind speed predictions crucial for predicting air pollutant concentrations, enhancing network maintenance, and advancing understanding of relevant meteorological and climatic phenomena.
Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for Integrated Sensing and Communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning based multi-node ISAC 4D environment reconstruction method with Uplink-Downlink (UL-DL) cooperation, which employs virtual aperture technology, Constant False Alarm Rate (CFAR) detection, and Mutiple Signal Classification (MUSIC) algorithm to maximize the sensing capabilities of single sensing nodes. Simultaneously, it introduces a cooperative environmental reconstruction scheme involving multi-node cooperation and Uplink-Downlink (UL-DL) cooperation to overcome the limitations of single-node sensing caused by occlusion and limited viewpoints. Furthermore, the deep learning models Attention Gate Gridding Residual Neural Network (AGGRNN) and Multi-View Sensing Fusion Network (MVSFNet) to enhance the density of sparsely reconstructed point clouds are proposed, aiming to restore as many original environmental details as possible while preserving the spatial structure of the point cloud. Additionally, we propose a multi-level fusion strategy incorporating both data-level and feature-level fusion to fully leverage the advantages of multi-node cooperation. Experimental results demonstrate that the environmental reconstruction performance of this method significantly outperforms other comparative method, enabling high-precision environmental reconstruction using ISAC system.