Background: The ability to adjust to career transitions is becoming increasingly important because of rapid technological developments, which alter labour markets and workplace demands.
Objectives: Within the framework of the career construction model of adaptation, we examined the relationships among core self-evaluation (adaptivity readiness), career adaptability (adaptability resources), and subjective career success (adaptation result).
Methods: For this purpose, we used structural equation modelling (SEM), a cross-sectional design and sampled 242 employees from a higher education institution in South Africa.
Results: The results showed that core self-evaluation is a powerful and reliable predictor of subjective job success across the eight categories. The growth and development subdimension was significantly impacted by career adaptability, but the impact on subjective career success was lower than expected.
Conclusion: We found that psychological resources, especially core self-evaluation qualities such as emotional stability, self-efficacy and self-esteem, influence employees’ views of purposeful and a happy workplace. We recommend that adaptive readiness be developed through person-centred interventions or psychological therapies for boosting resilience and job satisfaction. By shifting the focus to internal psychological resources influencing career outcomes, especially in the context of higher education in South Africa, the study makes a theoretical and a practical contribution to career development literature. Recommendations for future research include exploring behavioural responses and longitudinal dynamics of career adaptation.
Contribution: This study extends the career construction model by empirically validating the mediating role of career adaptability between core self-evaluation and subjective career success. It contributes to the understanding of how internal psychological resources shape adaptive career outcomes in higher education, offering evidence-based guidance for employee development and organisational career interventions.
Vocational guidance. Career development, Social Sciences
We propose Bayesian Univariate-Guided Sparse Regression (BUGS), a novel global-local shrinkage framework that incorporates marginal association information directly into the prior through a continuous modulation of shrinkage. Unlike existing approaches that treat predictors symmetrically or rely on post hoc screening, BUGS embeds univariate guidance within the nonlinear variance structure of a regularized horseshoe prior, inducing adaptive shrinkage that enhances signal-noise separation. We establish theoretical guarantees including prior concentration, posterior contraction, and guidance-induced shrinkage separation, while demonstrating robustness under uninformative guidance. To enable scalability in ultra-high dimensions, we develop BUGS-Active, an active-set MCMC approximation that restricts local updates to a data-adaptive subset A_n, reducing per-iteration complexity from O(p) to O(|A_n|) while preserving key theoretical properties such as sure screening and contraction. Empirically, the proposed framework achieves strong signal recovery together with substantially improved control of false discovery rates relative to existing methods. BUGS-Active scales to dimensions up to p = 1,000,000, and is applied to a DNA methylation study with n=1051 subjects and approximately 850,000 CpG sites, yielding accurate prediction and interpretable sparse selection. These results establish marginally guided shrinkage as a powerful and scalable paradigm for high-dimensional Bayesian inference.
Many everyday tasks rely on external tutorials such as manuals and videos, requiring users to constantly switch between reading instructions and performing actions, which disrupts workflow and increases cognitive load. Augmented reality (AR) enables in-situ guidance, while recent advances in large language models (LLMs) and vision-language models (VLMs) make it possible to automatically generate such guidance. However, existing AI-powered AR tutorial systems primarily focus on physical procedural tasks and provide limited support for hybrid physical and virtual workspaces. To address this gap, we conduct a formative study of cross-reality tasks and identify key requirements for state awareness and cross-reality coordination. We present JARVIS, a VLM-driven AR instruction system that generates contextual, step-by-step guidance from a single prompt, with real-time state verification and adaptive visual feedback. To inform the system design, we conducted a formative study to understand guidance needs across cross-reality tasks, which we categorize into four types, real-to-real (R2R), real-to-virtual (R2V), virtual-to-real (V2R), and virtual-to-virtual (V2V). A within-subjects study (N=14) across four domains shows JARVIS improves usability, workload, success rate, and visualization effectiveness over baselines.
Kicki Häggblom, Jessica Aspfors, Pia Nyman-Kurkiala
With support from guidance counselors and teachers, young people are expected to not only acquire the knowledge and skills to handle their future choices of studies and employment but to also take an active role in developing their careers and designing their lives. Finland has been acknowledged as having one of the most professionalized school-based career education systems, with a long tradition of guidance counseling taught in class as a school subject in addition to support provided individually or in groups. A qualified guidance counselor is responsible in both cases. The formal foundation for career education is in place, which sets expectations regarding students’ career learning. This article aims at exploring career learning in Finnish general upper secondary education (GUS) through university students’ reflections on their experiences of transitioning from GUS to higher education. A pedagogical perspective is applied using the principles of educative teaching as the theoretical approach. The data consist of written career stories produced during the 2022–23 academic year by university students (N = 19) enrolled in specialization studies required to acquire these qualifications as guidance counselors in Finland. The data are interpreted using reflexive thematic analysis. The metaphor of career as a journey is used to generate three themes: (a) the tour guidance paradox, (b) destination is subject to change, and (c) souvenirs are rare. The findings show the informants recognize career development as a lifelong process. To support an individual’s career learning process, discussion of a strong pedagogical approach to career education is needed. Abstrakt Med hjälp och stöd av vägledare och lärare förväntas unga inte bara skaffa sig kunskaper och färdigheter för att hantera sina framtida studie- och yrkesval, utan också aktivt forma sina karriärer och sina liv. Finland är känt för sitt professionaliserade skolbaserade system för karriärvägledning, med en lång tradition av ett schemalagt skolämne som undervisas i klassrummet samt individuell och gruppbaserad elev- och studiehandledning. En behörig studiehandledare ansvarar för båda formerna. Dessa formella grunder för karriärvägledning skapar i sin tur förväntningar på elevernas karriärlärande. Den här artikeln syftar till att utforska karriärlärande genom universitetsstuderandes retrospektiva reflektioner över sina erfarenheter av övergången från gymnasieutbildning till högre utbildning. Ett pedagogiskt perspektiv tillämpas med principer för bildande undervisning (educative teaching) som teoretisk ansats. Datamaterialet består av karriärberättelser skrivna av universitetsstuderande (N = 19) under läsåret 2022–2023. Studerandena var inskrivna på specialiseringsstudier för att bli behöriga elev- och studiehandledare i Finland. Datamaterialet har analyserats genom reflexiv tematisk analys. Metaforen “karriär som en resa” användes för att skapa tre teman: (a) resan är en vägledningsparadox, (b) destinationen kan förändras och (c) souvenirer är sällsynta. Resultaten visar att informanterna ser karriärutveckling som en livslång process. För att stödja en individs karriärlärandeprocess behövs en diskussion om ett starkt pedagogiskt grepp i karriärvägledning. Nyckelord: karriärlärande; karriärvägledning; bildande undervisning
Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tune the generator to reduce the probability of being filtered, but this can be suboptimal: it often pushes the model toward producing samples near the classifier's decision boundary, increasing both false positives and false negatives. We propose Boundary Guidance, a reinforcement learning fine-tuning method that explicitly steers generation away from the classifier's margin. On a benchmark of jailbreak, ambiguous, and longcontext prompts, Boundary Guidance improves both the safety and the utility of outputs, as judged by LLM-as-a-Judge evaluations. Comprehensive ablations across model scales and reward designs demonstrate the robustness of our approach.
Alice Yang, Michael Beasley, Catherine Taylor
et al.
Augmented and mixed reality (MR) systems have the potential to improve surgical precision by overlaying digital guidance directly onto the operative field. This paper presents a novel MR guidance system using the Magic Leap head-mounted display to assist surgeons in executing precise scalpel movements during liver surgery. The system projects holographic cues onto a patient-specific 3D-printed liver phantom, guiding resection along a predetermined path. We describe the system design, including preoperative modeling, registration of virtual content to the phantom, and real-time visualization through the Magic Leap device. In a controlled phantom study, surgical trainees performed resection tasks with and without MR guidance. Quantitative results demonstrated that MR guidance improved cutting accuracy (mean deviation from planned path was reduced from 5.0 mm without AR to 2.0 mm with AR guidance) and efficiency (mean task time decreased from 55 s to 32 s). These improvements of approximately 60% in accuracy and 40% in speed underscore the potential benefit of MR in surgical navigation. Participants reported that the Magic Leap visualization enhanced depth perception and confidence in locating tumor boundaries. This work provides a comprehensive evaluation of an MR-assisted surgical guidance approach, highlighting its feasibility on a realistic organ phantom. We discuss the technical challenges (registration accuracy, line-of-sight, user ergonomics) and outline future steps toward clinical translation. The results suggest that Magic Leap-based MR guidance can significantly augment a surgeon's performance in delicate resection tasks, paving the way for safer and more precise liver surgery.
Stereo optical measurement techniques, such as digital image correlation (DIC), are widely used in 3D deformation measurement as non-contact, full-field measurement methods, in which stereo calibration is a crucial step. However, current stereo calibration methods lack intuitive optimal pose guidance, leading to inefficiency and suboptimal accuracy in deformation measurements. The aim of this study is to develop an interactive calibration framework that automatically generates the next optimal pose, enabling high-accuracy stereo calibration for 3D deformation measurement. We propose a pose optimization method that introduces joint optimization of relative and absolute extrinsic parameters, with the minimization of the covariance matrix trace adopted as the loss function to solve for the next optimal pose. Integrated with this method is a user-friendly graphical interface, which guides even non-expert users to capture qualified calibration images. Our proposed method demonstrates superior efficiency (requiring fewer images) and accuracy (demonstrating lower measurement errors) compared to random pose, while maintaining robustness across varying FOVs. In the thermal deformation measurement tests on an S-shaped specimen, the results exhibit high agreement with finite element analysis (FEA) simulations in both deformation magnitude and evolutionary trends. We present a pose guidance method for high-precision stereo calibration in 3D deformation measurement. The simulation experiments, real-world experiments, and thermal deformation measurement applications all demonstrate the significant application potential of our proposed method in the field of 3D deformation measurement. Keywords: Stereo calibration, Optimal pose guidance, 3D deformation measurement, Digital image correlation
We introduce FLOWER, a novel conditioning method designed for speech restoration that integrates Gaussian guidance into generative frameworks. By transforming clean speech into a predefined prior distribution (e.g., Gaussian distribution) using a normalizing flow network, FLOWER extracts critical information to guide generative models. This guidance is incorporated into each block of the generative network, enabling precise restoration control. Experimental results demonstrate the effectiveness of FLOWER in improving performance across various general speech restoration tasks.
Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samples of prompt-guided diffusion models remains a challenge, particularly when the prompts span a broad semantic spectrum and the diversity of generated data needs to be evaluated in a prompt-aware fashion across semantically similar prompts. Recent methods have introduced guidance via diversity measures to encourage more varied generations. In this work, we extend the diversity measure-based approaches by proposing the Scalable Prompt-Aware Rény Kernel Entropy Diversity Guidance (SPARKE) method for prompt-aware diversity guidance. SPARKE utilizes conditional entropy for diversity guidance, which dynamically conditions diversity measurement on similar prompts and enables prompt-aware diversity control. While the entropy-based guidance approach enhances prompt-aware diversity, its reliance on the matrix-based entropy scores poses computational challenges in large-scale generation settings. To address this, we focus on the special case of Conditional latent RKE Score Guidance, reducing entropy computation and gradient-based optimization complexity from the $O(n^3)$ of general entropy measures to $O(n)$. The reduced computational complexity allows for diversity-guided sampling over potentially thousands of generation rounds on different prompts. We numerically test the SPARKE method on several text-to-image diffusion models, demonstrating that the proposed method improves the prompt-aware diversity of the generated data without incurring significant computational costs. We release our code on the project page: https://mjalali.github.io/SPARKE
Personalizing diffusion models to specific users or concepts remains challenging, particularly when only a few reference images are available. Existing methods such as DreamBooth and Textual Inversion often overfit to limited data, causing misalignment between generated images and text prompts when attempting to balance identity fidelity with prompt adherence. While Direct Consistency Optimization (DCO) with its consistency-guided sampling partially alleviates this issue, it still struggles with complex or stylized prompts. In this paper, we propose a parallel rescaling technique for personalized diffusion models. Our approach explicitly decomposes the consistency guidance signal into parallel and orthogonal components relative to classifier free guidance (CFG). By rescaling the parallel component, we minimize disruptive interference with CFG while preserving the subject's identity. Unlike prior personalization methods, our technique does not require additional training data or expensive annotations. Extensive experiments show improved prompt alignment and visual fidelity compared to baseline methods, even on challenging stylized prompts. These findings highlight the potential of parallel rescaled guidance to yield more stable and accurate personalization for diverse user inputs.
One approach to using prior experience in robot motion planning is to store solutions to previously seen problems in a database of paths. Methods that use such databases are characterized by how they query for a path and how they use queries given a new problem. In this work we present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways. First, we use the database to compute a heuristic for determining which nodes of a search tree to expand, in contrast to prior work which generally pastes the (possibly transformed) queried path or uses it to bias a sampling distribution. We demonstrate that this makes our method more easily composable with other search methods by dynamically interleaving exploration according to a baseline algorithm with exploitation of the database guidance. Second, in contrast to other methods that treat the database as a single fixed prior, our database (and thus our queried heuristic) updates as we search the implicitly defined robot configuration space. We experimentally demonstrate the effectiveness of PDG in a variety of explicitly defined environment distributions in simulation.
Abstract Social cognitive career theory accepts that an individual’s career journey occurs through the interaction of multiple factors. The career development process includes internal or external factors’ positive or negative effects. This study aims to examine the mediating role of career and talent development self-efficacy, which expresses the belief about a person’s ability, in the relationship between two variables related to parents (adolescent–parent career adaptation—career-related parental support), which is an important factor in the career development of secondary school students. The model was tested with data collected from 652 secondary school students. The results showed that career and ability self-efficacy had a partial mediating role between career-related parental support and adolescent–parent career adjustment. Suggestions are presented to increase adolescent–parent harmony.
This article is based on an empirical study conducted between February and May 2023, employing methods such as online questionnaire surveys, content analysis of school websites, and secondary analysis of regulatory documents in the field of education in Russia and France. These documents include materials from organizations, centers, and other structures addressing issues related to vocational guidance, employment, training, and the development of student mobility in secondary schools and colleges. The work highlights the scientific problem of considering students’ personal interests, inclinations, and abilities as a critical factor in selecting a university training program. The study revealed differences between Russian and French applicants in terms of the importance attributed to institutional, social, and personal factors when choosing a university course. Both Russian and French applicants place equal emphasis on institutional factors when deciding on a course of study. However, French applicants prioritize personal motives, such as self-realization, self-development, interesting future work, and the potential to build a successful career. Conversely, Russian applicants are more influenced by social factors, including entrance exam results, the prestige of the university, the potential to secure a well-paying job after graduation, and future high earnings. French applicants tend to make more balanced and informed decisions, leveraging their existing abilities to secure fulfilling future employment, achieve self-actualization, and develop successful careers.
نیروی انسانی، به عنوان سرمایۀ با ارزش سازمانها مطرح است که انتخاب، نگهداری و حفظ آن برای هر سازمانی ضروری و با اهمیت است که این مهم با استفاده از پرسشنامههای شغلی میسر خواهد شد. به همین منظور پژوهش حاضر با هدف بررسی روایی و پایایی پرسشنامه تیپشنای شغلی پانیکار انجام شده. روش: برای بررسی اولیه پرسشنامه، نسخه اولیه پرسشنامه بر روی یک نمونه 50 نفری (25 نفر دختر، 25 نفر پسر متوسطه دوم) اجرا شد. سپس پرسشنامه در اختیار 10 تا از متخصصین در حوزه روانشناسی و روانسنجی (7 روانشناس و 3 روانسنج) قرار گرفت و روایی محتوایی و صوری آن نیز بررسی شد. سپس برای بررسی تحلیل عاملی تأییدی از میان افراد بین 14 تا 24 سال 600 نفر به صورت روش نمونهگیری در دسترس انتخاب و به پرسشنامه تیپشنای شغلی پانیکار پاسخ دادند. یافتهها: در مطالعه حاضر، بعد از تحلیل دادهها توسط روش روایی محتوایی و تحلیل عاملی تأییدی، در نهایت یک پرسشنامه 312 سؤالی به دست آمد که 8 تیپ شغلی را از هم مجزا میکند و مؤلفههای هر تیپ نیز از نقطه اتکا، لذت، برنامهریزی، هویت، ریسک، انگیزه و چارچوب تشکیل شده است. نتایج حاصل از اعتبارسنجی براساس آلفای کرونباخ نیز 77/0 به دست آمد که مقداری مناسب است. نتیجهگیری: از نتایج این مطالعه، چنین حاصل میشود که این پرسشنامه، ابزاری قابل اعتماد و روا برای ارزیابی تیپهای شغلی در محل کار است.
Background: Career guidance and counselling in secondary schools are crucial for young adolescents as they embark on the initial stages of shaping their post-school studies and career goals. Initiating career guidance as early as Grade 9 is imperative to ensure alignment between chosen subjects and future career aspirations. The study was conducted with Grade 12 learners at a resource-constrained high school in Cape Town, Western Cape.
Objectives: Utilising the Systems Theory Framework (STF), we sought to explore Grade 12 learners’ perceptions of the effectiveness of career guidance and counselling and its significance in shaping future career choices.
Method: The study used a qualitative methodology. Semi-structured online interviews were used to gather the data, which were then analysed thematically. The study consisted of a sample size of 16 Grade 12 participants.
Results: The findings indicated that learners found career guidance and counselling effective for deciding future occupations, with a preference for opportunities such as job shadowing and interactions with experts in the field of interest. However, limited access to vocational resources and a lack of structured classroom guidance led learners to conduct their own research.
Conclusion: The study recommends that life orientation teachers actively engage with organisations and relevant stakeholders in providing career guidance and counselling to learners and ensure that pupils have sufficient access to career resources for effective career guidance.
Contribution: These findings are a valuable resource for professionals and policymakers seeking to improve high school learners’ educational experiences, providing a framework for future scholars delving into the dynamic field of career counselling.
Vocational guidance. Career development, Social Sciences
چکیدههدف: این پژوهش کاربردی، با هدف واکاوی عمیقتر و دقیقتر تجارب و نگرش متخصصین حوزهی ارزیابی شایستگی نسبت به ارزیابی شایستگی حضوری و مجازی طرح ریزی شد. روش: پژوهش به روش کیفی از نوع پدیدار شناسی توصیفی با استفاده از نمونهگیری هدفمند با جامعهی هدف مدیران سازمانها و ارزیابان کانونهای ارزیابی، اجرا شد. ابزار پژوهش، مصاحبهی عمیق بود که با رسیدن به مجموع 14 مصاحبه، دادههای پژوهش به حالت اشباع رسید و نهایتا دادههای جمعآوری شده، به روش کلایزی تحلیل شدند.یافتهها: نتایج حاصل از مصاحبهها در 5 خوشه اصلی شامل: محدودیت های ارزیابی حضوری، پیشرانهی اصلی بهرهگیری از تکنولوژی- ورود تدریجی تکنولوژی به فرایند ارزیابی، با آیندهای رو به رشد- تسهیلگری تکنولوژی با تنوع بخشی به روشها- چالشهای بازطراحی فرایندها متناسب با تکنولوژی- استفاده از تکنولوژی در دست ارزیاب انسانی، راه غلبه بر بسیاری از محدودیتها و در 15 خوشه فرعی دستهبندی شدند.نتیجهگیری: بیشترین تاکید بر استفادهی تلفیقی از هر دو نوع رویکرد، بهصورت((تکنولوژی بهعنوان ابزار در دست ارزیاب)) بود و برتری مطلقی برای روشهای تکنولوژی محور نسبت به سایر روشها، وجود ندارد. شرکتکنندگان، ترویج استفاده از تکنولوژی در ارزیابی را امری غیرقابل اجتناب دانسته و بر لزوم بازطراحی فرایندهای سازمانی متناسب با تکنولوژی، تاکید کردند.
María Dóra Björnsdóttir, Sif Einarsdottir, Guðmundur Bjarni Arnkelsson
et al.
This quasi-experimental field study evaluated the short- and long-term effects of two short theoretically based career interventions, Icelandic Developmental-focused Intervention (IDI) and Cognitive Information Processing intervention (CIP), with upper secondary school students. In Iceland drop out is high and normative graduation rates low in higher education, indicating that student’s need support in managing transitions. The participants split in the IDI, CIP, and control groups were in their last year of upper secondary schools, age 17 to 28 years, 60% were female. Career indecision, career thoughts, career decision self-efficacy, and global life satisfaction were assessed at pre-test (N = 468), post-test (N = 336), and follow-up (N = 225). At post-test, MANCOVA analysis showed statistically significant difference between the groups as expected. Pairwise comparisons revealed larger difference scores for the CIP group in career decision self-efficacy as compared to the control group, as well as in global life satisfaction as compared to the IDI group. Short-term effects were detected on all the outcome measures, except negative career thoughts (CTI). Short, focused, structured and theoretically founded intervention can have impact at transition points. At the one-year follow-up, MANCOVA analyses did not indicate any statistically significant difference in all measures between groups. Lack of effectiveness for the developmentally based intervention and long-term impact suggests that students need career education from an early age to support career development. The results can support policy makers and practitioners in Iceland in providing effective career interventions in upper secondary education and through the school system. Útdráttur Íslenskir háskólanemar hætta frekar námi eða útskrifast seint í samanburði við nágrannalöndin. Vitað er að náms- og starfsráðgjöf dregur úr brotthvarfi og ýtir undir farsælt náms- og starfsval. Rannsókn með hálf-tilraunasnið var framkvæmd til að meta skammtíma- og langtíma áhrif tveggja stuttra starfsþróunarinngripa á lokaári framhaldsskólans. Inngripin eru byggð á þekktum kenningum, annars vegar með áherslu á starfsferilsþróun (e. Icelandic developmental-focused Intervention – IDI), og hins vegar ákvarðanatöku (e. cognitive information processing theory – CIP). Þátttakendur voru á aldrinum 17–28 ára og 60% konur. Þau voru skráð í svokallaða lok áfanga í framhaldsskóla og var skipt í þrjá hópa IDI, CPI og samanburðarhóp. Þátttakendur svöruðu fjórum spurningalistum einni viku fyrir (forpróf N = 468) og einni viku eftir (eftirpróf N = 336) inngripstímabil og við eins árs eftirfylgd (N = 225). Margbreytusamvikagreining sýndi marktækan mun á hópunum eins og búist var við viku eftir inngrip. Paraður samanburður á milli hópa benti til að þátttakendur í CIP hóp hefðu meira sjálfstraust við ákvarðanir um nám og störf heldur en þátttakendur í samanburðarhóp og meiri almenna lífsánægju en IDI hópur. Ekki komu fram tölfræðilega marktækur munur á hópunum ári eftir inngripin. Skammtímaáhrif komur fram á öllum matsþáttum nema hugsunum um störf. Framhaldsskólanemar virðast þurfa kenningarlega grunduð og skipulagðari inngrip í náms og starfsferil en lok áfangar hafa boðið uppá hingað til. Kerfisbundin náms- og starfsráðgjöf eða fræðsla í gegnum allt skólakerfið myndi styðja nemendur við að verða sjálfstæðari í náms- og starfsvali og auðvelda næstu skref. Lykilorð: Starfsþróunarinngrip; árangur; skammtímaáhrif; langtímaáhrif
LeoWu TomyEnrique, Xiangcheng Du, Kangliang Liu
et al.
Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on resource-limited platforms. SGENet contains two branches: super-resolution branch and semantic guidance branch. We apply a lightweight pre-trained recognizer as a semantic extractor to enhance the understanding of text information. Meanwhile, we design the visual-semantic alignment module to achieve bidirectional alignment between image features and semantics, resulting in the generation of highquality prior guidance. We conduct extensive experiments on benchmark dataset, and the proposed SGENet achieves excellent performance with fewer computational costs. Code is available at https://github.com/SijieLiu518/SGENet
Physical skill acquisition, from sports techniques to surgical procedures, requires instruction and feedback. In the absence of a human expert, Physical Task Guidance (PTG) systems can offer a promising alternative. These systems integrate Artificial Intelligence (AI) and Mixed Reality (MR) to provide realtime feedback and guidance as users practice and learn skills using physical tools and objects. However, designing PTG systems presents challenges beyond engineering complexities. The intricate interplay between users, AI, MR interfaces, and the physical environment creates unique interaction design hurdles. To address these challenges, we present an interaction design toolkit derived from our analysis of PTG prototypes developed by eight student teams during a 10-week-long graduate course. The toolkit comprises Design Considerations, Design Patterns, and an Interaction Canvas. Our evaluation suggests that the toolkit can serve as a valuable resource for practitioners designing PTG systems and researchers developing new tools for human-AI interaction design.
In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.