University of Wisconsin–Parkside,University of Wisconsin System

In recent times, the adoption of Artificial Intelligence (AI) has attracted heightened scrutiny from construction industry stakeholders, driven by its perceived potential to reshape project delivery. This paper summarises the findings of a pilot study with senior managers and owners of construction SMEs in selected major UK cities. Using semi-structured interviews, the paper examined how UK construction SMEs perceive and approach Artificial Intelligence (AI) adoption. The key findings revealed that AI is increasingly viewed not as an optional enhancement but as a strategic necessity for sustaining competitiveness, improving operational efficiency, enhancing safety, and advancing sustainability goals. However, the fulfilment of these benefits is constrained by entrenched challenges, including financial limitations, skills gaps, cultural resistance, technical barriers, strategic uncertainty, and security risks. The paper notes that construction industry stakeholders need to embed AI readiness into business strategy and prioritise workforce upskilling across both technical and adaptive skillsets to promote equitable and secure adoption. While policymakers should introduce targeted incentives to drive transformative changes that entrench AI in the construction industry, especially SMEs. Given the small, purposive sample, the study is exploratory, and its findings should be viewed as indicative rather than generalisable.

This article examines the United Nations’ capacity to protect human rights in the Occupied Palestinian Territory in the post–7 October 2023 phase by combining doctrinal legal analysis of the UN Charter, international human rights law and international humanitarian law with an institutional case study of UN decision-making and implementation. It analyses (i) the normative obligations and competence of key UN organs and mechanisms, (ii) the extent to which their responses generated protection outcomes, and (iii) how institutional paralysis—particularly veto politics in the Security Council—mediated the gap between legal norms and effective protection. The study triangulates primary UN documentation (Security Council/General Assembly resolutions, OHCHR/OCHA reports, and official UN communications) with peer‑reviewed scholarship and selected NGO evidence used only for corroboration. It argues that the UN’s contribution has been strongest in documentation, agenda-setting and humanitarian coordination, yet structurally weak in coercive protection and accountability, producing a persistent norm‑implementation gap.

ABSTRACT Broaching is a strategy used to discuss race, ethnicity, and culture (REC) with marginalized clients. This preliminary study outlines an empirically based model for training counseling students to adopt and engage in broaching attitudes and behaviors. This model was specifically designed for students with little to no experience discussing issues of REC. The study found the training model to be empirically significant in moving students further along the continuum of broaching styles.

In this paper, we build a data collection system for object detection and analyze the data with nested sampling. First we collect the data with a lock-in amplifier to avoid the interference in the environment. Then we construct the likelihood function with the electromagnetic reflection model. The nested sampling algorithm is adapted based on the specific likelihood function derived from the electromagnetic model. With our data collection method and analysis approach, we estimate two-dimensional locations in both the horizontal and vertical dimensions, even though the collected data is only one-dimensional.

Marine oil spills cause severe damage to ocean ecosystems and coastal economies. Timely scheduling of oil spill emergency resources is crucial for effective emergency response. Addressing the challenge of efficient resource scheduling under dynamically evolving oil spill conditions in marine environments, this paper proposes a hierarchical hybrid intelligent architecture. Firstly, it integrates an improved A*-PSO path planning module and an attention-based Bayesian Proximal Policy Optimization (SA-BPPO) reinforcement learning module to construct a 'path navigation-decision optimization' model for oil spill emergency resource scheduling. The improved A*-PSO path planning module generates initial paths and performs smoothing optimization, enabling rapid planning of efficient paths from start to end points, enhancing their feasibility and safety. Secondly, the self-attention Bayesian PPO (SA-BPPO) reinforcement learning module focuses on optimizing ship cleaning strategies. By incorporating an attention mechanism, the model’s ability to focus on key information is enhanced. The Bayesian method estimates the uncertainty of strategy outputs, allowing ships to dynamically adjust cleaning strategies according to environmental changes. Experimental results demonstrate that the proposed architecture significantly outperforms algorithms like Deep Q-Learning (DQN), Soft Actor-Critic (SAC), Ant Colony Optimization (ACO), and Dijkstra in key indicators such as cleaning time, efficiency, and path optimization. Compared to DQN, the improved A*-PSO-SA-BPPO algorithm improves average cleaning efficiency by 36\%. Compared to SAC, it improves average cleaning efficiency by 9.17\%. Compared to ACO and Dijkstra algorithms, it improves average cleaning efficiency by 36.78\% and 75\%, respectively. Ablation experiments further validate the effectiveness of each module that adding the improved A*-PSO module reduces cleaning time by 17.24\% and increases cleaning efficiency by 39.45\%.