Hasil untuk "Vocational guidance. Career development"

Marc Schneider, Renato Loureiro, Torbjørn Cunis et al.

Trajectory prediction of aerial vehicles is a key requirement in applications ranging from missile guidance to UAV collision avoidance. While most prediction methods assume deterministic target motion, real-world targets often exhibit stochastic behaviors such as evasive maneuvers or random gliding patterns. This paper introduces a probabilistic framework based on Conditional Normalizing Flows (CNFs) to model and predict such stochastic dynamics directly from trajectory data. The learned model generates probability distributions of future target positions conditioned on initial states and dynamic parameters, enabling efficient sampling and exact density evaluation. To provide deterministic surrogates compatible with existing guidance and planning algorithms, sampled trajectories are clustered using a time series k-means approach, yielding a set of representative "virtual target" trajectories. The method is target-agnostic, computationally efficient, and requires only trajectory data for training, making it suitable as a drop-in replacement for deterministic predictors. Simulated scenarios with maneuvering and ballistic targets demonstrate that the proposed approach bridges the gap between deterministic assumptions and stochastic reality, advancing guidance and control algorithms for autonomous vehicles.

Yuta Tomokiyo, Keita Nishimoto, Kimitaka Asatani et al.

Researchers are no longer limited to producing knowledge; in today's complex world, they also address societal challenges by engaging in policymaking. Although involvement in policymaking has expanded, direct empirical evidence of its career benefits remains underexplored. Prior survey-based studies suggest potential advantages-such as broader professional networks and enhanced opportunities-yet raise concerns about insufficient institutional support. Here, we examine the 2021 WHO global air quality guideline-a science-based regulatory guideline-as a case study. To evaluate the impact of guideline development on research outcomes, we match guideline researchers with a control group of peers sharing similar research topics and prior performance. Our analysis reveals that guideline researchers attain higher future citation counts in both academic and policy domains. New collaborations formed during development yield publications with higher citation impact and the disruptive index. Moreover, about half the guideline's references are derived from guideline researchers' papers, highlighting their central role in shaping the evidence base. These results provide empirical support for the career benefits of policy engagement. Our findings indicate that engaging in international guideline development offers tangible career incentives for researchers, and that institutions can enhance research impact and promote innovative scientific progress by actively supporting their researchers' participation in such initiatives.

Ziyi Wang, Ziwen Zeng, Yuan Li et al.

Career exploration is uncertain, requiring decisions with limited information and unpredictable outcomes. While generative AI offers new opportunities for career guidance, most systems rely on linear chat interfaces that produce overly comprehensive and idealized suggestions, overlooking the non-linear and effortful nature of real-world trajectories. We present CareerPooler, a generative AI-powered system that employs a pool-table metaphor to simulate career development as a spatial and narrative interaction. Users strike balls representing milestones, skills, and random events, where hints, collisions, and rebounds embody decision-making under uncertainty. In a within-subjects study with 24 participants, CareerPooler significantly improved engagement, information gain, satisfaction, and career clarity compared to a chatbot baseline. Qualitative findings show that spatial-narrative interaction fosters experience-based learning, resilience through setbacks, and reduced psychological burden. Our findings contribute to the design of AI-assisted career exploration systems and more broadly suggest that visually grounded analogical interactions can make generative systems engaging and satisfying.

Denis Rychkovskiy

We introduce CADE 2.5 (Comfy Adaptive Detail Enhancer), a sampler-level guidance stack for SD/SDXL latent diffusion models. The central module, ZeResFDG, unifies (i) frequency-decoupled guidance that reweights low- and high-frequency components of the guidance signal, (ii) energy rescaling that matches the per-sample magnitude of the guided prediction to the positive branch, and (iii) zero-projection that removes the component parallel to the unconditional direction. A lightweight spectral EMA with hysteresis switches between a conservative and a detail-seeking mode as structure crystallizes during sampling. Across SD/SDXL samplers, ZeResFDG improves sharpness, prompt adherence, and artifact control at moderate guidance scales without any retraining. In addition, we employ a training-free inference-time stabilizer, QSilk Micrograin Stabilizer (quantile clamp + depth/edge-gated micro-detail injection), which improves robustness and yields natural high-frequency micro-texture at high resolutions with negligible overhead. For completeness we note that the same rule is compatible with alternative parameterizations (e.g., velocity), which we briefly discuss in the Appendix; however, this paper focuses on SD/SDXL latent diffusion models.

M. I. Aldoshina

Introduction. In modern domestic research on Russian psychological and pedagogical classes and ways of pre-professional career guidance of high school students who are inclined to teaching, there is a historical correlation between the emergence of pedagogical classes in Russian education (2nd half of the 19th century) and the formation of a system of vocational and pedagogical education. The existing educational practice of continuous professional and pedagogical development of a graduate is carried out within the framework of several models organized on the basis of schools, universities and organizations of further vocational education. Features of the organization and pedagogical support of psychological and pedagogical classes are determined by different model practices of their formalization, which led to the manifestation of the characteristics of students, the educational environment of the university and the characteristics of the professional competence of university teachers of pedagogical support for different categories of students.Materials and Methods. When writing the article, a theoretical analysis of domestic and foreign literature was used, the study of scientific articles and publications on the topic; generalization and systematization of the results of domestic and foreign research; content analysis, questioning and statistical verification of experimental data and their interpretation.Results. The history of the formation of educational practice in psychological and pedagogical classes in Russia and the practice of their functioning at the present stage of the Russian history of education are considered. The given general theoretical provisions and author's calculations are illustrated by experimental data on the interpretation by students of modern classical and pedagogical universities of the functions and specifics of professional and pedagogical activity in psychological and pedagogical classes on five scales of the author's questionnaire: personal-need, sign-symbolic, spatial-professional, value-normative and socio-behavioral.Discussion and Conclusions. The processes observed by modern researchers with the preprofessional training of high school students focused on pedagogical activity are taken into account in various model practices of organizing modern specialized pedagogical classes, which are taken into account in the professional and pedagogical education of the training of future teachers accompanying them, including the characteristics of students, the characteristics of the educational environment of the university and the competence of interaction with gifted children of teachers.

Oludele Olagoke Ogunlade, Olusola Joseph Adesina

Vocational aspiration is an important behavioural process a human being is expected to ponder on ever before career decision is made. This study examined the influence of psycho‐demographic and psychological variables on vocational aspirations among 250 adolescents in Ibadan, Nigeria. Employing a correlational design, the research assessed the effects of four demographic factors (age, gender, religious affiliation, and parental education) and two psychological constructs (self‐concept and motivation) on vocational aspiration. Data were collected via a researcher‐developed instrument with established reliability (α = 0.67–0.76). Descriptive statistics characterized the sample; Pearson’s correlation revealed significant positive relationships between vocational aspiration and both self‐concept (r = 0.602, p < 0.01) and motivation (r = 0.685, p < 0.01). One‐way ANOVAs and t‑tests indicated no significant differences in vocational aspiration across demographic groups (p > .05 for all four variables). Multiple regression analysis demonstrated that self‐concept (β = 0.305, p < 0.001) and motivation (β = 0.506, p < 0.001) jointly explained 53% of the variance in vocational aspiration (F(2, 247) = 139.64, p < 0.001). The findings suggest that intrinsic psychological factors are more salient predictors of career aspirations than demographic characteristics within this context. Implications include the development of culturally responsive career guidance interventions that prioritize self‐concept enhancement and motivational support to foster adaptive vocational decision‐making among adolescents.

Jessica L. Rosenberg, Nancy Holincheck, Kathryn Fernández et al.

The percentage of women receiving bachelors degrees in physics in the U.S. lags well behind that of men, and women leave the major at higher rates. Achieving equity in physics will mean that women stay in physics at the same rates as men, but this will require changes in the culture and support structures. A strong sense of belonging can lead to higher retention rates so interventions meant to increase dimensions of physics identity (interest, recognition, performance, and competence) may increase persistence overall and increase women's retention differentially. We describe our model in which mentorship, an understanding of career options (career conceptualization), and leadership are inputs into the development of these dimensions of physics identity. This paper includes preliminary results from a qualitative study that aims to better understand how career conceptualization, leadership, and mentorship contribute to the development of physics identity and belonging. We report results from a survey of 15 undergraduate physics students which was followed up by interviews with 5 of those students. The students were from a small private liberal arts college in the midwest region of the U.S. and a large public university in the southeast region of the U.S. classified as a Hispanic-serving institution (HSI). With respect to mentorship, we found that it could provide critical support for students' engagement in the physics community. Leadership experiences have not previously been positioned as an important input into identity, yet we found that they helped women in physics feel more confident, contributing to their recognition of themselves as physics people. While the data on how career conceptualization contributed to the building of identity is limited, there are some connections to recognition and competence, and it will be an interesting avenue of future exploration.

Sakir Hossain Faruque, Sharun Akter Khushbu, Sharmin Akter

A career is a crucial aspect for any person to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, and skill-related activities. In this research, we collected essential information from Computer Science (CS) and Software Engineering (SWE) students to train a machine learning (ML) model that predicts career paths based on students' career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. Additionally, the research helps individual CS and SWE students find suitable jobs that match their skills, interests, and skill-related activities.

Yurui Huang, Xuesen Cheng, Chaolin Tian et al.

This study aims to investigate the influence of cross-border recruitment program in China, which confers scientists with a 'talent hat' including a startup package comprising significant bonuses, pay, and funding, on their future performance and career development. By curating a unique dataset from China's 10-year talent recruitment program, we employed multiple matching designs to quantify the effects of the cross-border recruitment with 'talent hat' on early career STEM scholars. Our findings indicate that the cross-border talents perform better than their comparable contenders who move without talent hats and those who do not move, given equivalent scientific performance before relocation. Moreover, we observed that scholars in experimental fields derive greater benefits from the talent program than those in non-experimental fields. Finally, we investigated how the changes in scientific environment of scientists affect their future performance. We found that talents who reassembled their collaboration network with new collaborators in new institutions after job replacement experienced significant improvements in their academic performance. However, shifting research directions entails risks, which results in a subsequent decrease of future productivity and citation impact following the relocation. This study has significant implications for young scientists, research institutions, and governments concerning cultivating cross-border talents.

Alex D. Hayes, Ryan J. Caverly

This paper presents an estimation and control framework that enables the targeted reentry of a drag-modulated spacecraft in the presence of atmospheric density uncertainty. In particular, an extended Kalman filter (EKF) is used to estimate the in-flight density errors relative to the atmospheric density used to generate the nominal guidance trajectory. This information is leveraged within a model predictive control (MPC) strategy to improve tracking performance, reduce control effort, and increase robustness to actuator saturation compared to the state-of-the-art approach. The estimation and control framework is tested in a Monte Carlo simulation campaign with historical space weather data. These simulation efforts demonstrate that the proposed framework is able to stay within 100 km of the guidance trajectory at all points in time for 98.4% of cases. The remaining 1.6% of cases were pushed away from the guidance by large density errors, many due to significant solar storms and flares, that could not physically be compensated for by the drag control device. For the successful cases, the proposed framework was able to guide the spacecraft to the desired location at the entry interface altitude with a mean error of 12.1 km and 99.7% of cases below 100 km.

Seungyeop Han, Byeong-Un Jo, Koki Ho

This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing conditions. A nonlinear control law is applied to effectively track the reference velocity vector within a finite time, and its robustness against disturbances is studied. Furthermore, the guidance law is expanded to incorporate ground collision avoidance by considering the shape of the gravity turn trajectory. The proposed method's fuel efficiency, robustness, and practicality are demonstrated through comprehensive numerical simulations, and its performance is compared with existing methods.

سیدمحمدرضا حسینی نژاد ماهانی, سید علی سیادت

هدف: در سال‌های اخیر نارضایتی کارفرمایان و صاحبان مشاغل نسبت به کارآیی بیرونی نظام آموزش عالی و افزایش نرخ بیکاری فارغ‌التحصیلان، از واگرایی و ناسازواری برنامه‌های درسی دانشگاهی با اشتغال و بازارکار حکایت دارد. از این‌رو پژوهش حاضر با هدف شناسایی و اولویت‌بندی عوامل این ناسازواری انجام شده است.روش: این تحقیق با روش ترکیبی گنجانده شده در 2 فاز انجام شده است. در فاز اول ضمن مطالعه کلیه پژوهش‌های سال‌های اخیر پیرامون برنامه‌های درسی آموزش عالی و اشتغال با روش‌های کیفی فراترکیب و تحلیل محتوا، عوامل ناسازواری شناسایی و در فاز دوم با استفاده از روش کمی تحلیل سلسله مراتبی گروهی اولویت‌بندی شده‌است.یافته‌ها: یافته‌های تحقیق عوامل ناسازواری را در سه مقوله اصلی شامل عوامل فرآیندی (با زیر مقوله‌های نیازسنجی، طراحی، اجرا و ارزشیابی)، عوامل بخشی (عناصر) (با زیرمقوله‌های اساتید و مدرسان، راهبردهای یاددهی و یادگیری، اهداف، محتوا و فضا، محیط و امکانات) و عوامل نهادی (با زیر مقوله‌های تصمیم‌گیران، کمیته‌های برنامه‌ریزی درسی و سازوکار اداری) ارائه و زیرمقوله «محتوا» را به عنوان مهم‌ترین عامل ناسازواری معرفی نمود.نتیجه‌گیری: نظام آموزش عالی کشور در راستای ایجاد همنوایی بین برنامه‌های درسی و اشتغال ناگزیر به بازنگری برنامه‌های درسی با رویکرد اشتغال‌محور و توجه ویژه به عوامل شناسایی‌شده اولویت‌دار می‌باشد.

Wonjun Kang, Kevin Galim, Hyung Il Koo et al.

Recently, there have been significant improvements in the quality and performance of text-to-image generation, largely due to the impressive results attained by diffusion models. However, text-to-image diffusion models sometimes struggle to create high-fidelity content for the given input prompt. One specific issue is their difficulty in generating the precise number of objects specified in the text prompt. For example, when provided with the prompt "five apples and ten lemons on a table," images generated by diffusion models often contain an incorrect number of objects. In this paper, we present a method to improve diffusion models so that they accurately produce the correct object count based on the input prompt. We adopt a counting network that performs reference-less class-agnostic counting for any given image. We calculate the gradients of the counting network and refine the predicted noise for each step. To address the presence of multiple types of objects in the prompt, we utilize novel attention map guidance to obtain high-quality masks for each object. Finally, we guide the denoising process using the calculated gradients for each object. Through extensive experiments and evaluation, we demonstrate that the proposed method significantly enhances the fidelity of diffusion models with respect to object count. Code is available at https://github.com/furiosa-ai/counting-guidance.

Xavier Sido

The aim of this chapter is to identify and characterise the pedagogical model prescribed for this particular mathematics teaching delivered in post-primary vocational training. More specifically, the contribution investigates two major questions: what specificities, proximities, differences with the pedagogical models of mathematics teaching for elementary primary and secondary? How should this mathematics teaching be operationalised, particularly with regard to the specific characteristics of the public for whom it is intended?

Zhuokai Zhao, Harish Palani, Tianyi Liu et al.

Multimodal deep learning, especially vision-language models, have gained significant traction in recent years, greatly improving performance on many downstream tasks, including content moderation and violence detection. However, standard multimodal approaches often assume consistent modalities between training and inference, limiting applications in many real-world use cases, as some modalities may not be available during inference. While existing research mitigates this problem through reconstructing the missing modalities, they unavoidably increase unnecessary computational cost, which could be just as critical, especially for large, deployed infrastructures in industry. To this end, we propose a novel guidance network that promotes knowledge sharing during training, taking advantage of the multimodal representations to train better single-modality models to be used for inference. Real-world experiments in violence detection shows that our proposed framework trains single-modality models that significantly outperform traditionally trained counterparts, while avoiding increases in computational cost for inference.

رضا نیری, حاجیه رجبی فرجاد, صدیقه طوطیان

هدف: این پژوهش با هدف تعیین تاثیر اقدامات آماده‌سازی بر اشتغال‌پذیری ادراک شده با نقش میانجی هویت شغلی در بانک کشاورزی شعب غرب تهران انجام شد.روش: این تحقیق از لحاظ هدف کاربردی، به روش توصیفی- پیمایشی از نوع همبستگی اجرا گردید. جامعه آماری این پژوهش کلیه کارکنان شعب غرب تهران که تعداد آن 150نفر بود که حجم نمونه آماری با استفاده از نمونه گیری تصادفی ساده 132 نفر تعیین گردید. ابزار اصلی جمع‌آوری اطلاعات، پرسشنامه هویت شغلی (فرانکو و تاوارس،2013) با 9 سوال و پرسشنامه آمادهسازی شغلی (استامپ و هارتمن، 1983) با 24 سوال پرسشنامه اشتغال‌پذیری ادراک شده (رائول وارنولد، 2007) با 16سوال بود که روایی آن به صورت صوری و محتوایی و پایایی آن با استفاده از الفای کرونباخ محاسبه و برای متغیرهای هویت شغلی (825/0)، برنامهریزی شغلی (88/.) اشتغال‌پذیری ادراک شده (820/0) و اکتشاف شغلی (730/0) گزارش گردید. داده‌های به دست آمده با استفاده از مدل معادلات ساختاری و نرم افزار Smart PLS نسخه شماره دو تجزیه و تحلیل شد.یافته‌ها: نتایج تحقیق نشان داد اقدامات آماده‌سازی شغلی (برنامه‌ریزی شغلی و اکتشاف شغلی) بر اشتغال‌پذیری ادراک شده با نقش میانجی هویت شغلی تاثیر معنی‌داری دارد.نتیجه‌گیری: با فراهم نمودن شرایط لازم برای هویت‌بخشی به شغلی در کنار اقدامات لازم برای آماده‌سازی شغلی (برنامه‌ریزی شغلی و اکتشاف شغلی) بر اشتغال‌پذیری کارکنان اقدام نمود.

نسرین حیدری سورشجانی, فخرالسادات نصیری, سیروس قنبری

هدف: هدف پژوهش بررسی نقش جوّ سازمانی نوآورانه بر تاب‌آوری سازمانی بواسطه دانش آفرینی سازمانی بود.روش: جامعه پژوهش کلیه معلمان مقطع متوسطه دوم استان چهارمحال و بختیاری، که تعداد آنان برابر با 2687 نفر که با استفاده از روش نمونه‌گیری تصادفی طبقه‌ای نسبتی بر مبنای فرمول کوکران 337 نفر تعیین شد. روش پژوهش کمی از نوع مطالعات همبستگی و رویکرد مدل سازی معادله ساختاری کوواریانس محور است. جهت گردآوری داده‌ها از پرسشنامه‌های جوّسازمانی نوآورانه سیگل و کایمر (1978)، تاب‌آوری سازمانی پرایاگ و همکاران (2018) و دانش‌آفرینی سازمانی مدل نوناکاو تاکوچی (2018) استفاده شد. پایایی و روایی پرسشنامه‌ها با تکنیک‌های آلفای کرانباخ و تحلیل عاملی بررسی شد، مقادیر پایایی پرسشنامه‌ها تاب‌آوری سازمانی (93/0)، جوّسازمانی نوآورانه (97/0)، دانش‌آفرینی سازمانی (94/0) بود. تحلیل داده‌ها از نرم افزار Amos23 استفاده شد. یافته‌ها: نشان داد جوّسازمانی نوآورانه اثر مثبت و معنادار بر تاب‌آوری سازمانی بواسطه دانش آفرینی سازمانی در سطح 001/0 دارد. جوَسازمانی نوآورانه بر تاب‌آوری سازمانی معلمان از طریق دانش آفرینی سازمانی با اثرکل و ضریب 89/0 و با اثرغیرمستقیم و ضریب 0.73 در سطح 0.001 معنی دار بود. نتیجه‌گیری: به متولیان آموزش و پرورش پیشنهاد می‌شود به توانمندسازی معلمان و به ایجاد چشم‌انداز مشترک و ارتباطات صادقانه، و شفاف در مدرسه بپردازند.

Brian Gaudet, Roberto Furfaro

We use reinforcement meta learning to optimize a line of sight curvature policy that increases the effectiveness of a guidance system against maneuvering targets. The policy is implemented as a recurrent neural network that maps navigation system outputs to a Euler 321 attitude representation. The attitude representation is then used to construct a direction cosine matrix that biases the observed line of sight vector. The line of sight rotation rate derived from the biased line of sight is then mapped to a commanded acceleration by the guidance system. By varying the bias as a function of navigation system outputs, the policy enhances accuracy against highly maneuvering targets. Importantly, our method does not require an estimate of target acceleration. In our experiments, we demonstrate that when our method is combined with proportional navigation, the system significantly outperforms augmented proportional navigation with perfect knowledge of target acceleration, achieving improved accuracy with less control effort against a wide range of target maneuvers.

Joshua Barrow, Kristi L. Engel, Tiffany R. Lewis et al.

In April 2020, the 2019 and 2020 American Physical Society's Division of Particles and Fields (APS DPF) Early Career Executive Committee (ECEC) members were tasked with organizing the formation of a representative body for High-Energy Physics (HEP) early career members for the Snowmass process by the DPF Executive Committee. Here, we outline the structure we developed and the process we followed to help provide context and guidance for future early career Snowmass efforts. Our organization was composed of a cross-frontier branch with committees on Inreach, Diversity Equity and Inclusion, Survey, and Long Term Organizational Planning; in addition to the Frontier Coordination branch, formed by committees responsible for liaising with each Frontier. Throughout this document, the authors reflect on the triumphs and pitfalls of a program created from nothing over a very short period of time, by people with good intentions, who had no prior experience in building such an organization. Through this exercise of reflecting, we sometimes find that we would recommend a different path to our future selves. Insomuch as there are things to find fault with, it is in the robustness of the systems we built and refined.

افسانه تاجیک, سید علی حسینی المدنی, آناهیتا خدابخشی کولایی

هدف: پژوهش حاضر با هدف مقایسه اثربخشی آموزش مبتنی بر درمان فراشناختی (MCT) و ذهن اگاهی مبتنی بر شفقت به خود بر تاب آوری در برابر استرس پرستاران در همه گیری کووید -19 بود. روش : طرح پژوهش حاضر، یک طرح نیمه آزمایشی از نوع طرح پیش‌آزمون - پس‌آزمون با گروه کنترل غیر معادل همراه با پیگیری سه ماهه بود. جامعه آماری پژوهش شامل کلیه پرستاران بیمارستان تامین اجتماعی شهرستان شهریار در سال 1399-1400بود. ۶۰ نفر از پرستاران انتخاب شدند و در سه گروه به طور تصادفی جایگرین شدند. که شامل: 20 نفر برای برنامه آموزشی مبتنی بر فراشناخت، 20 نفر برای برنامه آموزشی مبتنی بر ذهن اگاهی مبتنی بر شفقت و 20 نفر برای گروه گواه است. از پرسشنامه تاب آوری در برابر استرس کونور و دیویدسون (۲۰۰۳) برای گرد آوری داده ها استفاده شد. یافته ها: نتایج در کل نشان داد که آموزش مبتنی بر درمان فراشناخت (MCT) و ذهن اگاهی مبتنی بر شفقت به خود بر تاب آوری در برابر استرس پرستاران در همه گیری کووید -19 تاثیر دارد و تاثیر هر دو روش نیز نسبت به مرحله پیش ازمون پایدار بوده است. هرچند پایداری مداخله مبتنی بر درمان فراشناخت (MCT) بیشتر از ذهن اگاهی مبتنی بر شفقت ارزیابی شده است.