Search Results (356)

As the global transportation sector accelerates toward net-zero targets, the rapid deployment of alternative fuels like hydrogen, ammonia, and batteries introduces complex and novel safety challenges. This study systematically maps the intellectual structure of safety and risk research on zero-emission transportation systems to evaluate field maturity and identify critical knowledge gaps. We conducted a comprehensive bibliometric analysis of 151 core publications retrieved from the Web of Science from 2000 to 2025. By integrating quantitative performance analysis with qualitative science mapping techniques, the results identify that the domain is nascent and rapidly expanding, and a distinct inflection in publication occurred in 2020. However, science mapping reveals a fragmented intellectual structure. Among the four identified research clusters, two dominant streams emerge as the primary drivers of the field. The first is a “motor theme” focused on lithium-ion battery reliability and thermal runaway, while the second is a “basic theme” focused on hydrogen dispersion and toxicity risks. The analysis exposes a blind spot regarding the lack of cross-modal research addressing the physical safety interactions between different fuel systems operating in the shared infrastructure. Finally, this study proposes a future research agenda focusing on gathering real-world accident data and using system-theoretic approaches to manage integrated alternative fuel risks. Full article

Theologians have a moral responsibility to attend to the grave disorder in which the language of morality currently suffers. I argue that the healing of this disorder involves a morally prescribable disloyalty to the semblance of the virtue of charity. In this paper, I will review the moral blind spots and ethical inconsistencies in how the semblance of the virtue of charity is confused with the actual virtue of charity and is thus actualized inappropriately in today’s society. I will then investigate the virtue of solidarity as a prescription to help repair the potentiality of what the virtue of charity ought to be and look like. In looking to solidarity, I will turn to Ada María Isasi-Díaz’ epistemological concept of lo cotidiano as a means of practicing accountability in virtue theory and virtue practice. First, I will explore mujerista theology as an approach and why it is important to ethically attend to the voices on the margins; then, I will investigate the concept of lo cotidiano as a methodology that centers a commitment of responsibility to the other; lastly, I will turn to the virtue of solidarity and argue how attending to solidarity enhances our call to charity in an authentic and accountable way (A version of this paper was presented at the 2025 Convention of the Catholic Theological Society of America (CTSA) in Portland, Oregon). Full article

Diffusion-weighted imaging (DWI) has been increasingly utilized in the emergent evaluation of acute ischemic stroke (AIS) patients. DWI enhances sensitivity and specificity and enables the use of delayed reperfusion treatments in selected cases. However, DWI is not devoid of limitations. DWI-negative AIS is not uncommon in clinical practice and is reported in up to 1 of 4 AIS patients. We reviewed the relevant literature and searched the PubMed and Google Scholar databases for studies reporting on DWI-negative AIS prevalence during the 2021–2025 time period. Additionally, we included cases from our practice to highlight key points. DWI-negative AIS prevalence was 16% in one meta-analysis and ranged from 6.9% to 23.2% in identified studies that met our inclusion criteria. The biological, pathophysiological, technical, epidemiological and clinical factors that contribute to DWI-negative stroke are presented in detail. Overall, the application of diffusion imaging modalities for stroke is not bereft of blind spots despite enhanced sensitivity. Over-reliance on advanced neuroimaging and unfamiliarity with its limitations predispose DWI to errors in AIS assessment. Awareness of the predisposing factors, treatment effect, and prognosis guides appropriate decision-making, promoting good outcomes. Prospective appropriately designed trials should address the lingering questions identified, such as the association between time of imaging and DWI negativity. Full article

Text-to-image (T2I) generation, a core component of generative artificial intelligence(AI), is increasingly important for creative industries and human–computer interaction. Despite impressive progress in realism and diversity, diffusion models still exhibit critical security blind spots particularly in the Transformer key-value mapping mechanism that underpins cross-modal alignment. Existing backdoor attacks often rely on large-scale data poisoning or extensive fine-tuning, leading to low efficiency and limited stealth. To address these challenges, we propose two efficient backdoor attack methods AttnBackdoor and SemBackdoor grounded in the Transformer’s key-value storage principle. AttnBackdoor injects precise mappings between trigger prompts and target instances by fine-tuning the key-value projection matrices in U-Net cross-attention layers (≈5% of parameters). SemBackdoor establishes semantic-level mappings by editing the text encoder’s MLP projection matrix (≈0.3% of parameters). Both approaches achieve high attack success rates (>90%), with SemBackdoor reaching 98.6% and AttnBackdoor 97.2%. They also reduce parameter updates and training time by 1–2 orders of magnitude compared to prior work while preserving benign generation quality. Our findings reveal dual vulnerabilities at visual and semantic levels and provide a foundation for developing next generation defenses for secure generative AI. Full article

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems. Full article

This paper proposes a dual path mixed coupling wireless power transfer (DPMPT) coupler as a four-port structure for near-field wireless power transfer in drone and unmanned aerial vehicles. The DPMPT coupler integrates orthogonal double-D coils and 8-plates to realize mixed inductive–capacitive coupling at 6.78 MHz without additional lumped matching networks. A four-port equivalent model is developed by classifying the mutual networks into three coupling types and representing them with a transmission-matrix formulation fitted to three-dimensional full-wave simulations. The model is used to identify the main coupling paths and to evaluate the effect of rotation and lateral/diagonal misalignment on power-transfer characteristics. Simulation results at a transfer distance of 70 mm show a maximum transmission coefficient of about 0.82 at 6.78 MHz and high robustness against rotation. When switch-based port selection is applied on the transmit side, blind spots associated with pose variations that cause an abrupt drop in transmission characteristics are significantly reduced, demonstrating that the DPMPT coupler with switch control provides an effective structural basis for enhancing alignment tolerance in mixed coupling wireless power transfer systems. Full article

The accurate simulative reconstruction of blind spot accidents requires innovative simulation methods. The objective of this paper is to analyze the avoidability of a specific blind spot accident and assess the impact of various parameters as if an active turning assistant had been installed in the truck. Additionally, it proposes a novel adaptation of the turning assistant system, along with an adapted simulation model tailored for drawbar trailers. The analyses presented in this paper were performed using PC-Crash accident simulation software, applying the “Active Safety” module. After performing a simulation of an accident involving a right-turning truck with a center axle trailer and a pedestrian, the avoidability of the accident was examined by simulating the scenario as if the truck involved in the accident had been equipped with an active turning assistant system. Subsequently, a parameter analysis was conducted to analyze the effect of changes in the active turning assistant’s parameters and changes in the pedestrian’s direction of entry on the avoidability of the accident. In doing so, we determined the parameters for the worst-case (collision) and the best-case (no collision) scenarios. Finally, an adaptation and further development of the active turning assistant, along with a corresponding simulation method for drawbar trailers, are proposed. Full article

Cell line misidentification, first exposed when HeLa cells were shown to contaminate dozens of “unique” cultures, now compromises roughly one in five lines and renders thousands of papers potentially unreliable, propagating unreliable data through hundreds of thousands of citations. The financial fallout is vast with irreproducible research linked to faulty cell stocks costing the United States an estimated $28 billion each year. Today, authentication is rapid, cheap and highly accurate. Modern 24-plex short tandem repeat (STR) kits, analyzed by six-dye capillary electrophoresis and benchmarked against public databases, verify a culture in half a day for less than €40, lowering the probability of mistaken identity to less than 10–15. Complementary SNP panels, low-pass genome sequencing, digital PCR and nascent methylation “age clocks” close remaining blind spots such as aneuploidy or mixed-species co-cultures. Monte-Carlo modeling shows that even at a contamination risk of 0.07% routine STR testing yields a five-year return on investment above 3000% for a mid-size lab. Reflecting this evidence, ANSI/ATCC standards, NIH and Horizon Europe grants, major journals and FDA/EMA guidelines now encourage, recommend, or make authentication mandatory. This review discusses the historical roots and economic losses resulting from cell misidentification and contamination and offers a pragmatic roadmap to prevent working with falsified cell lines. It is further discussed that FAIR-compliant data archiving and integration of STR workflows into laboratory data management systems will allow laboratories to shift from sporadic testing of cell quality to continuous, artificial intelligence-supported assessments. Full article

While an advanced emergency braking system (AEBS) significantly improves the safety of a heavy goods vehicle (HGV), current implementations face limitations including inadequate scenario coverage for vulnerable road users (VRUs), overriding driver control and limited human–machine collaboration mechanisms, and an insufficient consideration of blind spot challenges in HGVs. To improve the adaptability of the AEBS for HGVs, this study proposes and validates a novel 2D AEBS control algorithm incorporating driver’s control and 3D visibility of VRUs. The proposed algorithm is designed to firstly identify the motion state scenarios based on the spatial relationship between the HGV and VRU. Then, based on the scenario classification result, the proposed algorithm determines whether the HGV needs to brake in the current scenario according to the 2D time to collision for both entities to reach the potential collision area while maintaining their current speeds. Finally, for situations requiring braking, it evaluates whether safety can be ensured under three conditions: the ego vehicle in free driving, the ego vehicle under driver-controlled braking (considering the 3D visibility of the VRU), and the ego vehicle under 2D AEBS-controlled braking. According to the test results, the proposed algorithm can deal with the VRU crossing scenario and leverage the driver’s control capabilities while utilizing AEBS as a safety net function. Full article

Large language models are increasingly used in code generation and developer tools, yet their robustness to ethically problematic natural language embedded in source code is underexplored. In this work, we study content-safety vulnerabilities arising from ethically inappropriate language placed in non-functional code regions (e.g., comments or identifiers), rather than traditional functional security vulnerabilities such as exploitable program logic. In real-world and educational settings, programmers may include inappropriate expressions in identifiers, comments, or print statements that are operationally inert but ethically concerning. We present Code Redteaming, an adversarial evaluation framework that probes models’ sensitivity to such linguistic content. Our benchmark spans Python and C and applies sentence-level and token-level perturbations across natural-language-bearing surfaces, evaluating 18 models from 1B to 70B parameters. Experiments reveal inconsistent scaling trends and substantial variance across injection types and surfaces, highlighting blind spots in current safety filters. These findings motivate input-sensitive safety evaluations and stronger defenses for code-focused LLM applications. Full article

Background: Memory B cells (B_mem) facilitate the generation of renewed and rapid antigen-specific antibody responses long after the initial antigen exposure, at a time when circulating serum antibodies may have declined. As the generation and/or recruitment of B_mem is at the core of most vaccination strategies, the assessment of antigen-specific B_mem is highly informative for forecasting and profiling the elicited B cell immune response. Methods: The two prevalent techniques used to detect antigen-specific B_mem cells at single-cell resolution are probe-based flow cytometry and B cell ImmunoSpot, while the measurement of B cell-derived antibodies in culture supernatants of stimulated B cells offers a semi-quantitative alternative. To the best of our knowledge, a direct side-by-side comparison of these assay systems has not yet been reported using the same starting PBMC material in a blinded fashion to test all three assays simultaneously. Results: These three assay systems were run in parallel to detect SARS-CoV-2 Wuhan-1 strain Spike-specific IgG⁺ B_mem in peripheral blood mononuclear cell (PBMC) samples obtained from well-defined cohorts comprising pre-COVID-19 era “naïve” individuals (negative controls), individuals shortly after recovery from a PCR-verified SARS-CoV-2 infection (positive controls), and a cohort of donor PBMCs isolated in 2024 (the experimental group). Each assay was able to discern Spike-exposed individuals from naïve , with ImmunoSpot suggesting superior sensitivity and specificity. ImmunoSpot and flow cytometry results were closely correlated. Conclusions: The study demonstrates that all three assays are suited for the detection of specific B_mem in antigen-primed individuals when such B_mem occur in the mid- to high-frequency range, and that they broadly concur. Strengths and weaknesses of the three test systems are discussed. Full article

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks. Full article

Light Detection and Ranging (LiDAR) is fundamental to autonomous driving and robotics, as it provides reliable 3D geometric information. However, snowfall introduces numerous spurious reflections that corrupt range measurements and severely degrade downstream perception. Existing desnowing techniques either rely on handcrafted filtering rules that fail under varying snow densities, or require paired snowy–clean scans, which are nearly impossible to collect in real-world scenarios. Self-supervised LiDAR desnowing approaches address these challenges by projecting raw 3D point clouds into 2D range images and jointly training a point reconstruction network (PR-Net) and a reconstruction difficulty network (RD-Net). Nevertheless, these methods remain limited by their reliance on the outdated Noise2Void training paradigm, which restricts reconstruction quality. In this paper, we redesign PR-Net with a blind-spot architecture to overcome the limitation. Specifically, we introduce a 3D-KNN encoder that aggregates neighborhood features directly in Euclidean 3D space, ensuring geometrically consistent representations. Additionally, we integrate residual state-space blocks (RSSB) to capture long-range contextual dependencies with linear computational complexity. Extensive experiments on both synthetic and real-world datasets, including SnowyKITTI and WADS, demonstrate that our method outperforms state-of-the-art self-supervised desnowing approaches by up to 0.06 IoU while maintaining high computational efficiency. Full article

In recent years, intelligent driving has attracted more and more attention due to its potential to revolutionize transportation safety and efficiency, emerging as a disruptive technology that reshapes the future landscape of transportation. Environmental perception serves as the primary and fundamental cornerstone of intelligent driving systems. To address the intrinsic blind spots in environmental perception, this paper presents a vehicle collaborative perception approach based on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) semantic communication. Specifically, a Transformer-based semantic segmentation technique is proposed for application to images acquired from surrounding vehicles and ground-based cameras. Subsequently, the generated semantic segmentation maps are transmitted via V2V/V2I communication. In the receiver, a semantic-guided image reconstruction technique based on Generative Adversarial Networks (GANs) is developed to generate images with high realism. The generated Image images can be further fused with locally perceived data, facilitating intelligent collaborative perception. This method achieves effective elimination of blind spots. Furthermore, as only semantic segmentation maps—with a data size significantly smaller than that of raw images—are transmitted instead of the latter, it exhibits excellent adaptability to the dynamically time-varying characteristics of V2V/V2I channels. Even in poor channel condition, the proposed method maintains high reliability and real-time performance. Full article

Study program recommendations are pivotal for the accreditation of study programs. In Germany, one of the most important recommendations used in the accreditation of computing curricula is published by the Gesellschaft der Informatik e.V. (GI), the largest German professional society of computer science. This work investigates the difference between reality at institutions and the GI recommendations. Systematically gathered syllabi of Northern Germany computing curricula have been coded both deductively and inductively according to Mayring’s qualitative content analysis (QCA) method. The descriptions of 197 mandatory courses belonging to 13 program descriptions were analyzed. In addition to the 17 subject areas already described in the GI recommendations, four new subject areas have been identified that can be considered widespread. On the other hand, four subject areas from the GI recommendations could not be found as part of the mandatory curriculum. The study identified a notable divergence between current study programs and the current GI recommendations. However, as only mandatory syllabi were investigated, this study contains some blind spots with regard to electives and study specializations as well as with regard to a regional selection bias. Secondary findings concern the handling of learning outcomes in German syllabus descriptions and the GI recommendations themselves. Full article