Hasil untuk "Vocational guidance. Career development"

Aidan Gleich, Scott C. Schmidler

We address the problem of accurate, training-free guidance for conditional generation in trained diffusion models. Existing methods typically rely on point-estimates to approximate the posterior score, often resulting in biased approximations that fail to capture multimodality inherent to the reverse process of diffusion models. We propose a sequential Monte Carlo (SMC) framework that constructs an unbiased estimator of $p_θ(y|x_t)$ by integrating over the full denoising distribution via Monte Carlo approximation. To ensure computational tractability, we incorporate variance-reduction schemes based on Multi-Level Monte Carlo (MLMC). Our approach achieves new state-of-the-art results for training-free guidance on CIFAR-10 class-conditional generation, achieving $95.6\%$ accuracy with $3\times$ lower cost-per-success than baselines. On ImageNet, our algorithm achieves $1.5\times$ cost-per-success advantage over existing methods.

Yeongmin Kim, Donghyeok Shin, Byeonghu Na et al.

Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies a test-time scaling method that enables sampling from regions with higher human-aligned reward values. Existing gradient guidance methods approximate the expected future reward (EFR) at an intermediate particle $\mathbf{x}_t$ using a Taylor approximation, but this approximation at each time step incurs high computational cost due to sequential neural backpropagation. We show that the EFR at any $\mathbf{x}_t$ can be computed using only marginal samples from a pre-trained diffusion model. The proposed EFR formulation detaches the neural dependency between $\mathbf{x}_t$ and the EFR, enabling closed-form guidance computation without neural backpropagation. To further improve efficiency, we introduce lookahead sampling to collect marginal samples. For final sample generation, we use an accurate solver that guides particles toward high-reward lookahead samples. We refer to this sampling scheme as LiDAR sampling. LiDAR achieves substantial performance improvements using only three samples with a 3-step lookahead solver, exhibiting steep performance gains as lookahead accuracy and sample count increase; notably, it reaches the same GenEval performance as the latest gradient guidance method for SDXL with a 9.5x speedup.

Asriyana Asriyana, DYP. Sugiharto, Sunawan Sunawan et al.

This study explores how cultural and religious values shape the career search efficacy of vocational high school students (SMK) in Banda Aceh, Indonesia. Using a qualitative exploratory-descriptive approach, data were collected through semi-structured interviews and focus group discussions (FGDs) involving students and guidance counselors. The findings reveal that career search efficacy is deeply embedded within Acehnese cultural norms, including Islamic principles, familial decision-making (musyawarah), and informal social interactions such as coffee shop discussions. The analysis identified four key dimensions of culturally influenced career search efficacy: career exploration, interview efficacy, networking efficacy, and personal exploration. These dimensions illustrate that students' career development is not only an individual process but also one that is shaped by community values and spiritual beliefs. However, the contextual nature of the findings and the limited participant scope suggest that further research is needed using mixed methods and broader samples to generalize results. This study contributes to the development of culturally responsive career guidance services and supports the integration of local wisdom into educational practices.

الهام ابراهیمی, شایان ملکیان, وحید سلیمی بنی

هدف: با تحول الگوهای اشتغال در دنیای معاصر، درک موفقیت شغلی دیگر صرفاً بر مبنای مسیرهای شغلی خطی و سازمان‌محور تعریف نمی‌شود، بلکه نیازمند الگوهای خودراهبر و منعطف است. در این میان، بازآفرینی مسیر شغلی به عنوان فرآیندی آگاهانه برای بازطراحی اهداف، نقش‌ها، مهارت‌ها و محیط کاری، نقشی کلیدی در شکل‌دهی به تجربه و ادراک موفقیت شغلی دارد. هدف این پژوهش بررسی تأثیر بازآفرینی مسیر شغلی بر ادراک موفقیت شغلی با درنظرگرفتن نقش میانجی ادراک استخدام‌پذیری و نقش تعدیل‌گر حمایت سازمانی ادراک‌شده است. روش: برای تحقق هدف پژوهش از روش پیمایش و مدلسازی معادلات ساختاری استفاده شد. پرسشنامه پژوهش میان 497 نفر از کارکنان شرکت‌های کارگزاری بازار سرمایه ایران به صورت تصادفی توزیع شد. نرم‌افزار اسمارت پی‌ال اس 3 برای تحلیل داده‌ها مورد استفاده قرار گرفت. یافته‌ها: یافته‌های پژوهش نشان داد، بازآفرینی مسیر شغلی تأثیر مثبتی بر ادراک موفقیت شغلی دارد و این اثر از طریق ادراک استخدام‌پذیری میانجی‌گری می‌شود. به‌علاوه، حمایت سازمانی ادراک‌شده در این رابطه نقش تعدیل‌گر ایفا می‌کند. نتیجه‌گیری: ادراک موفقیت شغلی مفهومی است که هم از طریق عاملیت فردی و تسهیل مداخلاتی مانند بازآفرینی مسیر شغلی توسط خودِ فرد حاصل می‌شود و هم تابع حمایت‌های سازمانی است.

Teng Wang, Haojun Jiang, Yuxuan Wang et al.

Echocardiography is a critical tool for detecting heart diseases, yet its steep operational difficulty causes a shortage of skilled personnel. Probe guidance systems, which assist in acquiring high-quality images, offer a promising solution to lower this operational barrier. However, robust probe guidance remains challenging due to significant individual variability. This variability manifests as differences in low-level features within two-dimensional (2D) images, which complicates image feature understanding, and differences in individual three-dimensional (3D) structures, which poses challenges for precise navigation. To address these challenges, we first propose leveraging the robust image representations learned by ultrasound foundation models from vast datasets. Yet, applying these models to probe navigation is non-trivial due to their lack of understanding of individual 3D structures. To this end, we meticulously design a Vision-Action Adapter (VA-Adapter) to online inject the capability of understanding individual 3D structures. Specifically, by embedding the VA-Adapter into the foundation model's image encoder, the model can infer cardiac anatomy from historical vision-action sequences, mimicking the cognitive process of a sonographer. Extensive experiments on a dataset with over 1.31M samples demonstrate that the VA-Adapter outperforms strong probe guidance models while requiring approximately 33 times fewer trained parameters.

Mengting Wei, Yante Li, Tuomas Varanka et al.

In this study, we propose a method for video face reenactment that integrates a 3D face parametric model into a latent diffusion framework, aiming to improve shape consistency and motion control in existing video-based face generation approaches. Our approach employs the FLAME (Faces Learned with an Articulated Model and Expressions) model as the 3D face parametric representation, providing a unified framework for modeling face expressions and head pose. This not only enables precise extraction of motion features from driving videos, but also contributes to the faithful preservation of face shape and geometry. Specifically, we enhance the latent diffusion model with rich 3D expression and detailed pose information by incorporating depth maps, normal maps, and rendering maps derived from FLAME sequences. These maps serve as motion guidance and are encoded into the denoising UNet through a specifically designed Geometric Guidance Encoder (GGE). A multi-layer feature fusion module with integrated self-attention mechanisms is used to combine facial appearance and motion latent features within the spatial domain. By utilizing the 3D face parametric model as motion guidance, our method enables parametric alignment of face identity between the reference image and the motion captured from the driving video. Experimental results on benchmark datasets show that our method excels at generating high-quality face animations with precise expression and head pose variation modeling. In addition, it demonstrates strong generalization performance on out-of-domain images. Code is publicly available at https://github.com/weimengting/MagicPortrait.

Marc J. Fischer, Jeffrey Potts, Gabriel Urreola et al.

Augmented Reality (AR) surgical navigation systems are emerging as the next generation of intraoperative surgical guidance, promising to overcome limitations of traditional navigation systems. However, known issues with AR depth perception due to vergence-accommodation conflict and occlusion handling limitations of the currently commercially available display technology present acute challenges in surgical settings where precision is paramount. This study presents a novel methodology for utilizing AR guidance to register anatomical targets and provide real-time instrument navigation using placement of simulated external ventricular drain catheters on a phantom model as the clinical scenario. The system registers target positions to the patient through a novel surface tracing method and uses real-time infrared tool tracking to aid in catheter placement, relying only on the onboard sensors of the Microsoft HoloLens 2. A group of intended users performed the procedure of simulated insertions under two AR guidance conditions: static in-situ visualization, where planned trajectories are overlaid directly onto the patient anatomy, and real-time tool-tracking guidance, where live feedback of the catheter's pose is provided relative to the plan. Following the insertion tests, computed tomography scans of the phantom models were acquired, allowing for evaluation of insertion accuracy, target deviation, angular error, and depth precision. System Usability Scale surveys assessed user experience and cognitive workload. Tool-tracking guidance improved performance metrics across all accuracy measures and was preferred by users in subjective evaluations. A free copy of this paper and all supplemental materials are available at https://bit.ly/45l89Hq.

Hongyu Yan, Zijun Li, Kunming Luo et al.

Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce SymmCompletion, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.

Ludovic Petitdemange, Salomé Nashed

Blind and Visually Impaired (BVI) Individuals face significant challenges in science due to the discipline's reliance on visual elements such as graphs, diagrams, and laboratory work. Traditional learning materials, such as Braille and large-print textbooks, are often scarce or delayed, while practical experiments are rarely adapted for accessibility. Additionally, mainstream educators lack the training to effectively support BVI students, and Teachers for the Visually Impaired (TVIs) often lack scientific expertise. As a result, BVI individuals remain underrepresented in scientific jobs, reinforcing a cycle of exclusion. However, technological advancements and inclusive initiatives are opening new opportunities. Outreach programs aim to make science engaging and accessible for BVI individuals through multi-sensory learning experiences. Hands-on involvement in these activities fosters confidence and interest in scientific careers. Beyond sparking interest, equipping BVI students with the right tools and skills is crucial for their academic success. Early exposure to assistive technologies enables BVI students to navigate scientific studies independently. Artificial Intelligence (AI) tools further enhance accessibility by converting visual data into descriptive text and providing interactive assistance. Several learning sessions demonstrated the effectiveness of these interventions, with participants successfully integrating into university-level science programs. Educating BVI and their teachers on these tools and good pratices is the aim of our project AccesSciencesDV. Research careers offer promising opportunities for BVI, especially in computational fields. By leveraging coding, data analysis, and AI-driven tools, BVI researchers can conduct high-level scientific work without relying on direct visual observations. The presence of BVI scientists enriches research environments.

Minghao Fu, Guo-Hua Wang, Xiaohao Chen et al.

Recent advances in text-to-image synthesis largely benefit from sophisticated sampling strategies and classifier-free guidance (CFG) to ensure high-quality generation. However, CFG's reliance on two forward passes, especially when combined with intricate sampling algorithms, results in prohibitively high inference costs. To address this, we introduce TeEFusion (Text Embeddings Fusion), a novel and efficient distillation method that directly incorporates the guidance magnitude into the text embeddings and distills the teacher model's complex sampling strategy. By simply fusing conditional and unconditional text embeddings using linear operations, TeEFusion reconstructs the desired guidance without adding extra parameters, simultaneously enabling the student model to learn from the teacher's output produced via its sophisticated sampling approach. Extensive experiments on state-of-the-art models such as SD3 demonstrate that our method allows the student to closely mimic the teacher's performance with a far simpler and more efficient sampling strategy. Consequently, the student model achieves inference speeds up to 6$\times$ faster than the teacher model, while maintaining image quality at levels comparable to those obtained through the teacher's complex sampling approach. The code is publicly available at https://github.com/AIDC-AI/TeEFusion.

ناصر سیف اللهی

چکیده هرگونه سیاست‌گذاری در زمینه بهبود فضای کسب‌وکار به ویژه در مرحله شروع کسب‌وکار می‌تواند نقش مهمی در کارآفرینی کشاورزی ایفا کند. هدف پژوهش حاضر بررسی اثر بهبود فضای کسب‌وکار بر کارآفرینی روستایی با نقش میانجی قابلیت بازاریابی بود. نوع پژوهش از نظر هدف کاربردی و به لحاظ ماهیت روش کار توصیفی‌ـ همبستگی بود. جامعه آماری تحقیق اعضای تعاونی‌های روستایی استان اردبیل با ۸۱ شرکت تعاونی روستایی و تعداد اعضای ۸۹۲۰۸ نفر بود. حجم نمونه با استفاده از جدول کرجسی- مورگان به تعداد 384 نفر تعیین شد و نمونه‌ها به روش نمونه‌گیری تصادفی انتخاب شدند. جهت تحلیل فرضیه‏ها از روش مدل‌سازی معادلات ساختاری استفاده ‌شد. داده‏ها با استفاده از نرم‌افزار اس‌پی‌اس‌اس و اسمارت پی ‌ال ‌اس تجزیه‌وتحلیل گردید. بر اساس یافته‏های پژوهش، بهبود فضای کسب‌وکار بر کارآفرینی کشاورزی تأثیر مثبت و معنا‌دار می‌گذارد. قابلیت بازاریابی نیز بین بهبود فضای کسب‌وکار و کارآفرینی کشاورزی نقش میانجی ایفا می‏کند. بر اساس نتایج این تحقیق، کارآفرینان میبایستی بر اهمیت قابلیت‌های بازاریابی و خواسته‌ها و پیشنهادهای مشتریان در موفقیت کسب‌وکار خود توجه ویژه مبذول نمایند. همچنین برای شروع کسب‌وکار خود شرایط فضای کسب‌وکار را از ابعاد مختلف بررسی و مدنظر داشته باشند.

Kimberly Mora-Castro

Para este ensayo se plantea que los largometrajes pueden ser observados desde la mirada del análisis transaccional (AT), aunque no hayan sido pensados desde este enfoque; y que con el análisis dado estos podrían servir como recurso de apoyo en el quehacer de la persona profesional en Orientación. Por lo que, se busca visibilizar cómo puede ser analizado el filme El principito, a partir de premisas del AT, para la potenciación del uso de este tipo de materiales en las intervenciones que se realizan desde la profesión de Orientación. Al usar como base la teoría del AT, propuesta por Eric Berne, con conceptualizaciones como el análisis estructural y funcional de la personalidad, las transacciones, los juegos psicológicos y el guion de vida. Concluyendo que el AT permite analizar el ser humano desde diferentes realidades donde se encuentre, lo que le facilita a la persona profesional en Orientación unirse a diferentes procesos de desarrollo personal, como el autoconocimiento. Asimismo, se determina que los materiales cinematográficos tienen el potencial para ser analizadas desde el AT. También que, al analizar una película, como El Principito, se pueden identificar una serie de premisas del AT; que le abren la posibilidad a la persona orientadora de utilizarla, y así emplear su creatividad en la construcción de procesos de trabajo diversos que se apeguen a los principios de esta disciplina.

Yunran Di, Haotian Shi, Weihua Zhang et al.

Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the interaction between expressways and arterial networks and achieve orderly coordination between them. This study employs the multi-class cell transmission model (CTM) combined with the macroscopic fundamental diagram (MFD) to model the traffic dynamics of expressway systems and arterial subregions, enabling vehicle path tracking across these two systems. Consequently, a comprehensive traffic transmission model suitable for mixed road networks has been integrated. Utilizing the SUMO software, a simulation platform for the mixed road network is established, and the average trip lengths within the model have been calibrated. Based on the proposed traffic model, this study constructs a route guidance model for mixed road networks and develops an integrated model predictive control (MPC) strategy that merges route guidance, perimeter control, and ramp metering to address the challenges of mixed road networks' traffic flow control. A case study of a scenario in which a bidirectional expressway connects two subregions is conducted, and the results validate the effectiveness of the proposed cooperative guidance and control (CGC) method in reducing overall congestion in mixed road networks.

Bhargav Jha, Shaunak Bopardikar, Alexander Von Moll et al.

This paper presents joint motion planning of a vehicle with an attached rotating turret. The turret has a limited range as well as the field of view. The objective is capture a maneuvering target such that at the terminal time it is withing the field-of-view and range limits. Catering to it, we present a minimum effort guidance law that commensurate for the turn rate abilities of the vehicle and the turret. The guidance law is obtained using linearization about the collision triangle and admits an analytical solution. Simulation results are presented to exemplify the cooperation between the turret and the vehicle.

Zixiao Wang, Junwu Weng, Mengyuan Liu et al.

Numerous well-annotated human key-point datasets are publicly available to date. However, annotating human poses for newly collected images is still a costly and time-consuming progress. Pose distributions from different datasets share similar pose hinge-structure priors with different geometric transformations, such as pivot orientation, joint rotation, and bone length ratio. The difference between Pose distributions is essentially the difference between the transformation distributions. Inspired by this fact, we propose a method to calibrate a pre-trained pose generator in which the pose prior has already been learned to an adapted one following a new pose distribution. We treat the representation of human pose joint coordinates as skeleton image and transfer a pre-trained pose annotation generator with only a few annotation guidance. By fine-tuning a limited number of linear layers that closely related to the pose transformation, the adapted generator is able to produce any number of pose annotations that are similar to the target poses. We evaluate our proposed method, FlexPose, on several cross-dataset settings both qualitatively and quantitatively, which demonstrates that our approach achieves state-of-the-art performance compared to the existing generative-model-based transfer learning methods when given limited annotation guidance.

Shenhao Zhu, Junming Leo Chen, Zuozhuo Dai et al.

In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques. The methodology utilizes the SMPL(Skinned Multi-Person Linear) model as the 3D human parametric model to establish a unified representation of body shape and pose. This facilitates the accurate capture of intricate human geometry and motion characteristics from source videos. Specifically, we incorporate rendered depth images, normal maps, and semantic maps obtained from SMPL sequences, alongside skeleton-based motion guidance, to enrich the conditions to the latent diffusion model with comprehensive 3D shape and detailed pose attributes. A multi-layer motion fusion module, integrating self-attention mechanisms, is employed to fuse the shape and motion latent representations in the spatial domain. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Experimental evaluations conducted on benchmark datasets demonstrate the methodology's superior ability to generate high-quality human animations that accurately capture both pose and shape variations. Furthermore, our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset. Project page: https://fudan-generative-vision.github.io/champ.

Leon Pietschmann, Paul-David Zuercher, Erik Bubík et al.

The combination of Visual Guidance and Extended Reality (XR) technology holds the potential to greatly improve the performance of human workforces in numerous areas, particularly industrial environments. Focusing on virtual assembly tasks and making use of different forms of supportive visualisations, this study investigates the potential of XR Visual Guidance. Set in a web-based immersive environment, our results draw from a heterogeneous pool of 199 participants. This research is designed to significantly differ from previous exploratory studies, which yielded conflicting results on user performance and associated human factors. Our results clearly show the advantages of XR Visual Guidance based on an over 50\% reduction in task completion times and mistakes made; this may further be enhanced and refined using specific frameworks and other forms of visualisations/Visual Guidance. Discussing the role of other factors, such as cognitive load, motivation, and usability, this paper also seeks to provide concrete avenues for future research and practical takeaways for practitioners.

A. Atlaskirov

International expert councils single out the transformations taking place in the labor markets under the influence of the achievements of information and technological progress as one of the most significant risks of sustainable development. Experts note that the process of division of labor between people and artificial intelligence will lead to the release of significant labor resources. These processes are fraught with serious risks for social stability in the Kabardino-Balkarian Republic. The purpose of the presented work is to study the labor attitudes of graduates of higher and secondary specialized educational institutions of Kabardino-Balkaria. The conducted research showed that a systemic problem of training personnel for the regional economy is manifested in the republic. Career guidance, which, if applied effectively, can serve as one of the key factors in the socio-economic development of the region, is poorly organized. A significant part of the youth of the republic does not receive up-to-date information about potential directions for the development of the economy and the labor market, and the vocational guidance lessons themselves do not arouse their interest. This leads to the fact that many graduates do not show the desire to continue working in their profession. It was also revealed that among graduates of higher and secondary specialized educational institutions in the region there is a widespread desire, after graduation, to start an entrepreneurial activity and work for themselves, which indicates a decrease in the importance of paternalistic behavior patterns among the population of the region, especially among young people.

مرتضی اکبری, فاطمه پروین

امروزه توسعه بخش کشاورزی بدون تکیه بر اطلاعات کارآمد و استفاده از فناوری‌های نوین امکان‌پذیر نبوده و استفاده از بستر فناوری اطلاعات جهت توسعه و به‌روز کردن اطلاعات و ابزارهای مرتبط و موردنیاز با فعالیت‌های کارآفرینانه در این بخش موجب اشتغال‌زایی و خلق ارزش‌افزوده می‌گردد. لذا پژوهش حاضر با هدف شناسایی مولفه ها توسعه کارآفرینی در بخش کشاورزی مبتنی بر فناوری اطلاعات انجام گرفت. این مطالعه از نوع کیفی (تحلیل محتوا و دلفی) می‌باشد. جهت تهیه داده‌ها ابتدا با مرور مبانی نظری و مطالعات پیشین از طریق روش تحلیل محتوا، مولفه های توسعه کارآفرینی در بخش کشاورزی مبتنی بر فناوری اطلاعات شناسایی و از طریق روش دلفی به تائید و اولویت‌بندی این مولفه ها پرداخته شد. درروش تحلیل محتوا از روش‌های ذهنی برای تحلیل داده‌ها و در روش دلفی از آمار توصیفی همچون شمارش، درصد برای تحلیل داده‌های پرسشنامه‌ها و مصاحبه‌ها استفاده ‌شد. درروش تحلیل محتوا 27 شاخص به‌عنوان عوامل توسعه‌ی کارآفرینی بخش کشاورزی استخراج شد و به مرحله دلفی راه یافتند. در سه دور دلفی نیز 13 شاخص به شاخص‌ها افزوده شد و از مجموع 40 شاخص تنها 8 شاخص مورد توافق عمومی خبرگان قرار نگرفت. نتایج پژوهش نشان داد که عوامل موثر بر توسعه فناوری اطلاعات و ارتباطات از 9 مولفه (زیرساخت حقوقی- قانونی، زیرساخت فنی، زیرساخت آموزشی- ترویجی، زیرساخت مدیریتی، زیرساخت فرهنگی) و 32 شاخص تشکیل می شود که نقش زیربنایی و بنیادی در توسعه کسب و کارهای کارآفرینانه در بخش کشاورزی دارد همچنین زمینه ساز توسعه هر چه بیشتر کارآفرینی در بخش کشاورزی می‌شود.

محسن سندگل, صادق اصغری لفمجانی, غریب فاضل نیا et al.

جوامع روستایی ارتباط تنگاتنگی با محیط پیرامون خود دارند که بسیاری از ویژگی‌های آن‌ها منجر به توسعه کارآفرینی و تقویت اقتصاد روستایی می‌گردد و بعضی دیگر، توسعه کارآفرینی را با محدودیت مواجه ساخته، پیشرفت اقتصادی را به تأخیر می-اندازند. با توجه به اثرات محدودیت‌های کالبدی و زیست‌‌محیطی بر توسعه کارآفرینی در روستاهای پیرامونی مرز و تالاب بین‌المللی هامون، هدف پژوهش حاضر، تحلیل اثرات این محدودیت‌ها در روستاهای بخش قرقری شهرستان هیرمند می‌باشد. این تحقیق از نوع توصیفی- تحلیلی است که در آن از روش اسنادی برای بررسی سوابق و تبیین مسئله و از روش پیمایشی (با ابزار مصاحبه و تکمیل پرسشنامه) برای جمع‌آوری داده‌های مورد نیاز در سطح روستا استفاده شده است. جامعه آماری پژوهش شامل سرپرستان خانوارهای ساکن در محدوده موردمطالعه می‌باشد که با توجه به تعداد خانوارهای ساکن در محدوده موردمطالعه و استفاده از فرمول کوکران، تعداد 348 نمونه برای تکمیل پرسشنامه‌های تحقیق محاسبه گردید. تجزیه‌وتحلیل داده‌ها با استفاده از روش‌های آمار توصیفی و تحلیلی، مدل ARAS و نرم‌افزارهای SPSS، Choice Expert و ArcGIS انجام گردیده است. نتایج مدل ARAS در بررسی شدت محدودیت‌های کارآفرینی در روستاهای موردمطالعه نشان می‌دهد که محدودیت‌های کالبدی در 6/58 درصد از روستاها و محدودیت‌های زیست‌محیطی نیز در 9/68 درصد از روستاها در سطح شدید یا بسیار شدید می‌باشد. همچنین بر اساس نتایج آزمون فریدمن، محدودیت‌های ناشی از وجود دیوار مرزی با میانگین 487/0 و محدودیت دسترسی به منابع آب با میانگین 393/0 به ترتیب بالاترین محدودیت‌های کالبدی و زیست‌محیطی را در بین محدودیت‌های متنوع در هر بعد به خود اختصاص می‌دهند.