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Space exploration has become an integral part of modern society, and since its early days in the 1960s, software has grown in importance, becoming indispensable for spaceflight. However, software is boon and bane: while it enables unprecedented functionality and cost reductions and can even save spacecraft, its importance and fragility also make it a potential Achilles heel for critical systems. Throughout the history of spaceflight, numerous accidents with significant detrimental impacts on mission objectives and safety have been attributed to software, although unequivocal attribution is sometimes difficult. In this Entry, we examine over two dozen software-related mishaps in spaceflight from a software engineering perspective, focusing on major incidents and not claiming completeness. This Entry article contextualizes the role of software in space exploration and aims to preserve the lessons learned from these mishaps. Such knowledge is crucial for ensuring future success in space endeavors. Finally, we explore prospects for the increasingly software-dependent future of spaceflight.

Textured gradient microstructures can be engineered by tailoring the molten pool cooling rate during additive manufacturing (AM). Here we consider the design of grain structures and crystallographic orientations by controlling the solidification strategy during AM by directed energy deposition (DED). In this paper the textures generated by open loop (fixed scan speed of 100, 200 and 300 mm/min) and closed loop adaptive control to achieve cooling rates of 500, 1100 and 1750 °C/s were compared using electron back scatter diffraction (EBSD). The cooling rate was determined key to eliminating the microstructural gradients or anisotropy for DED parts or to engineer functionality along the desired path. The solidified macro-textures along the building direction were significantly affected by the formation of columnar grains, their growth direction, and morphological transition to equiaxed grains as a result of rapid cooling.

Summary: Cell cycle progression is regulated by the orderly balance between kinase and phosphatase activities. PP2A phosphatase holoenzymes containing the B55 family of regulatory B subunits function as major CDK1-counteracting phosphatases during mitotic exit in mammals. However, the identification of the specific mitotic roles of these PP2A-B55 complexes has been hindered by the existence of multiple B55 isoforms. Here, through the generation of loss-of-function genetic mouse models for the two ubiquitous B55 isoforms (B55α and B55δ), we report that PP2A-B55α and PP2A-B55δ complexes display overlapping roles in controlling the dynamics of proper chromosome individualization and clustering during mitosis. In the absence of PP2A-B55 activity, mitotic cells display increased chromosome individualization in the presence of enhanced phosphorylation and perichromosomal loading of Ki-67. These data provide experimental evidence for a regulatory mechanism by which the balance between kinase and PP2A-B55 phosphatase activity controls the Ki-67-mediated spatial organization of the mass of chromosomes during mitosis.

This research work involves the advanced microstructural characterization and corrosion resistance of hybrid nanocomposites produced by friction stir-induced reaction in an Al–Mg alloy/TiO2 system tested by potentiodynamic and spectroscopy analyses in the form of polarization and impedance. The impact of TiO2 content and submerged cooling medium were assessed on the subsequent electrochemical behavior of the composites. The electrochemical polarization measurements reveal a substantial variation in the corrosion performance correlated to the chemistry of nanocomposites and aluminum matrix grain structure, depending on friction stirring parameters. The heterogeneity of reinforcing nanoparticles at high fractions can increase the corrosion rate in the stirred region by more than 140 times compared to the primary alloy. This is attributed to secondary phase interfaces and nanoparticle clustering at the grain boundaries, promoting galvanic and pitting corrosion phenomena. In contrast, submerged reactive methods employing a cooling medium led to the formation of ultra-fine/nano-sized cellular structured in-situ hybrid nanocomposites with superior corrosion resistance and an excellent combination of mechanical and chemical properties.

This study investigates the profound and multifaceted impacts of Artificial Intelligence (AI) and geopolitical developments on global dynamics by 2040. Utilising a Delphi process coupled with probabilistic modelling, the research constructs detailed scenarios that reveal the cascading effects of these emerging forces across economic, societal, and security domains. The findings underscore the transformative potential of AI, predicting significant shifts in employment patterns, regulatory challenges, and societal structures. Specifically, the study forecasts a high probability of AI-induced unemployment reaching 40–50%, alongside the rapid evolution of AI technologies, outpacing existing governance frameworks, which could exacerbate economic inequalities and societal fragmentation. Simultaneously, the study examines the critical role of geopolitical developments, identifying increased nationalisation, the expansion of conflicts such as the Russia–Ukraine war, and the strategic manoeuvres of major powers like China and Israel as key factors that will shape the future global landscape. The research highlights a worrying lack of preparedness among governments and societies, with a 10% probability of their being equipped to manage the complex risks posed by these developments. This low level of readiness is further complicated by the short-term orientation prevalent in Western businesses, which prioritise immediate returns over long-term strategic planning, thereby undermining the capacity to respond effectively to these global challenges. The study calls for urgent, forward-looking policies and international cooperation to address the risks and opportunities associated with AI and geopolitical shifts. It emphasises the need for proactive governance, cross-sector collaboration, and robust regulatory frameworks to ensure that the benefits of technological and geopolitical advancements are harnessed without compromising global stability or societal well-being. As the world stands on the brink of unprecedented change, the findings of this study provide a crucial roadmap for navigating the uncertainties of the future.

One of the challenges in the semiconductor industry is to find new barrier materials and copper (Cu) alternative solutions in interconnects. In this work, we focused to find alternative diffusion barrier materials. Different binary (CoMo, CoRu, CoTa, CoW, MoRu, RuTa, RuW) and ternary (CoMoTa, CoRuTa, MoRuTa) metal alloys were evaluated theoretically with the Miedema model to find the amorphous phase composition range. Afterward, thin films of the alloys with various compositions were deposited by magnetron sputtering and theoretical values were compared to the experimental results. From the experimental measurements, which included grazing incidence x-ray diffraction analysis and resistivity measurements, suitable binary and ternary alloys were chosen for diffusion analysis. By annealing thin film stacks at temperatures ranging from 500 to 675 ° C, diffusion was induced and detected by x-ray photoelectron spectroscopy depth profiles. Seventeen alloys were evaluated by their diffusion barrier effectiveness, and five of those, which include Ru 60 Ta 40 , Ru 45 W 55 , Mo 47 Ru 53 , Mo 36 Ru 50 Ta 14 , and Co 40 Mo 35 Ta 25 , showed excellent barrier properties against copper diffusion. Furthermore, all of the stated materials have a lower resistivity than TaN. Last, the adhesion of the best performing alloys to SiCOH and Cu was evaluated by the modified edge lift-off test. Only Ru 45 W 55 had reasonable adhesion at both interfaces. The other materials showed low adhesion strength to Cu, which would make an adhesion promoter (liner), such as cobalt, necessary for the integration. [ABSTRACT FROM AUTHOR]

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

The authors conducted a case study of a first year Communication discipline specific seminar class to determine how the use of a common reader for the course affected students' self-efficacy. Using focus groups and an end of semester storytelling assignment, the authors collected data that revealed three themes: choice for participation, identity struggles, and college expectations. Whether or not the students read the common reader ("Paper Towns" by John Green) and whether or not they took a leadership role in preparing the public research presentation based on the reader, the focus group discussions allowed students to discuss self-identity issues, as well as their own college expectations. Feeling positive about their major, choice to live in the residence halls, friends, and programs of which they are a part helped the participating students feel they had made a competent choice. In assessing the themes that emerged, student self-efficacy was demonstrated in all three areas of self-determination theory: competence, relatedness, and autonomy.

Abstract Genome browsers facilitate integrated analysis of multiple genomics datasets yet visualize only a few regions at a time and lack statistical functions for extracting meaningful information. We present HiCognition, a visual exploration and machine-learning tool based on a new genomic region set concept, enabling detection of patterns and associations between 3D chromosome conformation and collections of 1D genomics profiles of any type. By revealing how transcription and cohesion subunit isoforms contribute to chromosome conformation, we showcase how the flexible user interface and machine learning tools of HiCognition help to understand the relationship between the structure and function of the genome.

Software development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the space domain with its often expensive and one-of-a-kind devices, which are developed in complex multitier supply chains, the risk is even greater. This paper presents the SKQuest data set. It contains the completed responses with 190 variables from an empirical study with over 100 software experts. The data set covers distinct aspects of measuring metrics and transparency in software projects. To show what information lies in the data set, the paper investigates, and affirms, from different perspectives, the following questions: Is transparency a problem in software development projects? Is there a desire for more transparency in projects? Can metrics contribute to improving the situation? Moreover, it attempts to replicate the results of an earlier study. The main contribution of this paper is, however, the SKQuest data set that is published with this paper in CSV formatas. It is a tool that enables systematic investigations of software metrics and allows research on how they can improve the efficiency of the software lifecycle, not limited to, but particularly with respect to transparency. Consequently, the paper may serve as a starting point for future research avenues in academia and industry and help to improve existing and future standards in software development.

Introduction: Selective serotonin reuptake inhibitors (SSRIs) are the most often used medications to treat major depressive disorder (MDD). Despite their effectiveness in reducing depressive symptoms, several issues are associated with their use in MDD, such as limited improvement of anhedonia, emergence of emotional blunting, induction or exacerbation of insomnia, and sexual dysfunction. Due to its also devoid of the issues related to treatment noted with SSRIs. The aim of this 12-week non-inferiority naturalistic observation was to compare the effectiveness and tolerability of SSRIs and trazodone in extended release (XR) in MDD.Methods: A total of 186 subjects were recruited, of which 92 received trazodone XR and 94 received SSRIs. Patients were allocated to trazodone XR or SSRIs, according to the attending physician based on clinical evaluation. Assessments at baseline and weeks 2, 4, 8, and 12 were conducted to evaluate the severity of depression (Montgomery–Åsberg Depression Rating Scale, clinician- and patient-rated Quick Inventory of Depressive Symptomatology—the primary endpoints of the study), anhedonia (the Snaith–Hamilton Pleasure Scale), anxiety (the Hamilton Anxiety Rating Scale), insomnia (the Athens Insomnia Scale), and therapeutic effectiveness (the Clinical Global Impression Scale).Results: After 12 weeks, trazodone XR was more effective than SSRIs in reducing the severity of depression, anxiety, and insomnia. There was a trend for higher effectiveness of in reduction of anhedonia, which became insignificant after controlling the results for the duration of previous psychiatric treatment as a covariate. The proportion of treatment-responsive subjects in the trazodone XR group compared to SSRIs was comparable or higher. The proportion of patients achieving remission was higher in the trazodone XR arm vs. the SSRI arm.Discussion: In summary, the results indicate that trazodone XR is effective in MDD in the “real-world” setting. Its potential superiority over SSRIs in addressing particular symptomatic dimensions should be verified in future studies.

A key welding parameter to quantify in the welding process is the ratio of the heat required to melt the weld metal versus the total energy delivered to the weld, and this is referred to as the melting efficiency. It is generally expected that the productivity of the welding process is linked to this melting efficiency, with more productive processes typically having higher melting efficiency. A comparison is made between the melting efficiency in standard gas metal arc welding (GMAW) and cold wire gas metal arc welding (CW-GMAW) for the three primary transfer modes: short-circuit, globular, and spray regime. CW-GMAW specimens presented higher melting efficiency than GMAW for all transfer modes. Moreover, an increase in plate thickness in the spray transfer regime caused the melting efficiency to increase, contrary to what is expected.

The paper investigates the effect of tool eccentricity on the strain rate and microtextures produced in rapid-quenched AA6061 friction stir welds. Thermocouples are embedded in the tool and the thermal cycle are recorded via a data acquisition system. Stir zone grain refinement is observed in all samples, with the eccentric sample having the finest grains (3.18 μm). The strain rate is measured to be between 0.17 and 51.94 s−1, with the eccentric sample experiencing the highest strain rate. The pole figures indicate crystallographic textures shifted approximately 90° when tool eccentricity is employed. The aligned samples have a strong {11-2} B simple shear texture component, while the eccentric sample shows morphological changes towards a strong {11-1} A simple shear texture component.