The disposal of solid waste has become one of the critical issues facing governments due to its environmental impact due to the difficulty of its decomposition. Electric cable waste (ECW) is one of these wastes. Its production increased in Iraq over time due to the demolition and reconstruction of residential and commercial homes. Therefore, reusing it in other industries, such as concrete technology, is a promising solution. Limited studies have studied the utilization of these local wastes as a replacement for natural sand in the short and long term. Therefore, the aim of this study is to investigate the properties of mortar incorporating recycled ECW as a partial replacement for sand. The fine aggregate (natural sand) was replaced by weight with ECW ranging from 0 to 25 % in the step of 5 %. Flow rate, as well as mechanical properties (compressive strength, flexural strengths, and density), were executed at 7, 28, and 360 days. It was found that the best performance was obtained at a replacement ratio of 5 % of ECW with mechanical strengths close to or slightly less than the reference sample and a 17 % reduction in density. However, regarding sustainability, it is possible to produce a lightweight structural mortar with a density lower than 1700 kg/m3 and a compressive strength of 36 MPa at 360 days when replacing the natural sand with 25 % ECW.

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH2 as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10−5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10−8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10−8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

Identifying Internet-facing industrial control system (ICS) devices is important for asset inventory, vulnerability assessment, exposure measurement, and security monitoring. This dataset release supports research on network traffic fingerprinting for Internet-facing ICS devices under realistic measurement conditions. The dataset is constructed from Internet-scale ICS service discovery followed by protocol-specific probing across three commonly deployed ICS protocols: Modbus/TCP, EtherNet/IP, and S7comm. It contains anonymized network traffic, scanning logs, device information records, and protocol-specific scanner code used to document the measurement logic. The release covers 13,002 responsive Internet-facing ICS endpoints, including 2,205 labeled endpoints spanning 20 vendors, 8 device types, and 165 device models. The measurements are organized across three collections and 60 scanning rounds for each protocol. The dataset captures Internet-facing measurement characteristics that are rarely represented in testbed datasets, including long-tailed label distributions, response variability across scanning rounds, temporal variation, and scanner-location effects. These characteristics support empirical studies of device fingerprint generation, vendor/type/model identification, and robustness to Internet-facing measurement variability. All released IP addresses are anonymized using a consistent prefix-preserving transformation, allowing cross-file linkage across device information, packet captures, and scanning logs without exposing the original public endpoints.

This article presents a novel model-free distributionally robust framework for a challenging equilibrium-seeking problem (ESP) under fully unknown coupled dynamics. We consider a scenario in the ESP where the state transitions of players are governed by an unknown coupled dynamic system, and each player aims to minimize its own cost function. By predicting the stochastic distribution of player states through Gaussian process regression, we propose a novel distributionally robust approximation (DRA) that transforms the complex ESP with unknown coupled dynamic system into a solvable distributionally robust optimization problem. The gradient of the DRA's objective function is quantified, ensuring solvability. The effectiveness of the proposed DRA framework is evaluated through a nonlinear system, demonstrating comparable performance to model-based methods without requiring any dynamic model.

Crises faced by regional or national governments are usually caused by either natural or human disasters or by the actions of terrorists or other nations. Decision making in the face of a crisis is difficult because the context typically is complex, and decision makers often have insufficient time or information to thoughtfully make decisions that will manage the crisis well. As a result, they often rely on brief discussions and past experiences that omit key dimensions of the crisis, which results in selection of an inferior response alternative. This article describes concepts and procedures to guide the initial phases of planning for crises so that if and when a crisis occurs, a proactively developed framework provides a sound foundation for quickly structuring decisions and implementing more detailed analyses in advance of taking specific crisis-response actions. Case-study examples are used to illustrate three main elements of our suggested approach: identifying the main dimensions of the decisions to be faced, articulating the objectives that are to be achieved, and generating a set of potentially desirable alternatives to best achieve these objectives. Although the decision analysis and behavioral science methods that we rely on are not novel, proactive structuring of crisis decisions that likely will need to be made quickly has been given only limited attention by analysts and decision makers. Improved recognition of the importance of decision-focused proactive planning should result in better decisions when a crisis occurs, leading to a reduction in the adverse consequences for countries and their citizens. Funding: This work was supported by Ben Delo and Longview Philanthropy.

Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to channel impairments, such as noise, fading, and interference. To strengthen the reliability of intelligent transportation systems, this work improves the robustness of V2X cooperative perception under communication conditions that reflect common channel impairments. This paper proposes an Adaptive Feature Fusion Transformer (AFFormer), a Transformer-based framework that mitigates the adverse effects of corrupted features by modeling temporal, inter-agent, and spatial correlations. AFFormer introduces three key modules: Multi-Agent and Temporal Aggregation for context-aware fusion across agents and over time, Dual Spatial Attention for efficient modeling of spatial dependencies, and Uncertainty-Guided Fusion for entropy-driven refinement of fused features. A teacher-student knowledge distillation strategy further enhances robustness by aligning fused features with reliable early-collaboration supervision. AFFormer is validated on the V2XSet and DAIR-V2X datasets, where it consistently outperforms existing methods under both ideal and impaired communication conditions, demonstrating improved robustness to communication-induced feature degradation while maintaining a competitive efficiency-accuracy trade-off.

Abstract Public attitudes toward nuclear weapons remain a critical issue in international security, yet the thinking behind individuals’ support or opposition to their use is not well understood. This study examines how the American public reasons about whether to deploy nuclear weapons in a hypothetical war between the United States and Iran. Participants were asked to state their preference between continuing a ground war, deploying a nuclear strike resulting in 100,000 civilian casualties, or deploying a strike killing 2 million civilians. They then provided an open-ended answer where they described the reasons for their decision. Using Structural Topic Modeling, we identified 10 distinct patterns in participants’ thinking. Some responses emphasized concerns about deterrence or saving lives, while others focused on national security, or retaliation, among other reasons. The type of thinking participants employed was found to be related to their preceding choice, as well as to individual characteristics, such as gender, political affiliation, punitive–authoritarian–nationalist attitudes, and the influence of the relative emotional impact of the 2 bombs (i.e., the better bomb effect). These findings highlight the complexity of the thinking underlying nuclear decision making and help shed light on potential avenues for reducing the risk of a nuclear weapon being deployed again.

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km2 of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities.