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Purpose The purpose of this paper is to propose a decision support model to prioritize equity crowdfunding (ECF) evaluation criteria under an uncertain environment. Design/methodology/approach The proposed decision support model first identifies a holistic list of evaluation criteria and subcriteria. These criteria are then analyzed using the analytic hierarchy process (AHP) method in an interval-valued intuitionistic fuzzy (IVIF) environment to identify the relative importance attached by crowdfunding investors to five sets of evaluation criteria (fundraiser, platform, project, campaign and investment characteristics) and their associated subcriteria. The proposed decision support model and ECF evaluation criteria were empirically examined using a real-life case study from January to February 2023. Findings The case study illustrated that the decision support model enhanced fairness and transparency in the prioritization of ECF evaluation criteria. Project characteristics were the most important criterion, followed by fundraiser characteristics and investment characteristics. These results can serve as a benchmark to help crowd investors choose ventures more wisely and make better investment decisions. Originality/value The tasks of modeling and prioritizing ECF evaluation criteria are relatively rarely addressed in the literature, especially under uncertainty. This study is one of the first attempts to use the AHP to explore ECF evaluation criteria in an IVIF environment; in particular, it sheds light on the importance that crowd investors attach to criteria related to fundraiser, platform, project, campaign and investment characteristics.

PurposeThis paper aims to adopt a practice-theory, “site ontology” perspective to understand how venture capitalists (VCs) add value to their portfolio companies (PCs).Design/methodology/approachThe empirical research involves a field ethnographic study of a VC firm in Dubai, focused on revealing what constitutes value and what VCs do to add this value to their PCs.FindingsValue adding is a profoundly social, embedded process interconnected with other ecosystem actors, investment practices and organizations. The value adding threads of VC activity are part of a holistic configuration of practices that span the investment lifecycle and different levels within the firm.Originality/valueThis research contributes a rich account of the social, symbolic nature of VC activity, depicting the everyday activities that comprise value adding practices. It is among the first to introduce practice theory to the VC context and open up a new conversation about its social ontology.

Burnout is an occupational stress syndrome that gives rise to emotional exhaustion (EE) depersonalization (DP) and reduced personal accomplishment (PA). Increasing rates of burnout among health care professionals has been reported globally. Saudi Arabia appears to be among the highest in prevalence with reports of higher than 70%. Medical residents in training are the highest group at risk. The literature has repeatedly linked burnout among residents with poor academic performance on training exams, impaired quality of life, career choice regret and intentions to abandon medicine. In this study, we explore the factors that contribute to resident burnout, their experiences with burnout and how they choose to mitigate it. A qualitative design was used to conduct this study in the city of Riyadh, Saudi Arabia. A total of 14 residents from surgical and non-surgical programs were interviewed through in-depth interviews. Interpretive thematic analysis was used in coding and generated coding templates. Categories were repetitively reviewed and revised, expanding to include new data as it emerged and collapsing to remove redundant codes. Categories were organized into the final themes and sub-themes. All participants demonstrated a shared thread of shame in reaching the level of burnout. Three main interlinked themes were identified: Burnout stigma cycle, amalgamated causes of burnout and self-coping with burnout. One of the concerning findings in our study is the participants' pursuit of self-coping strategies and the avoidance of formal help, creating a cycle of suffering in silence. The literature has repeatedly reported high levels of burnout among residents in training. This study has added another dimension to those findings through the exploration of residents actual accounts and appears to link burnout with suboptimal training and working conditions. We have highlighted the pivotal role stigma and shame play in completely preventing residents from seeking professional help.

Autism spectrum disorder (ASD) is a progressively prevalent neurodevelopmental disorder with substantial clinical heterogeneity. Despite the considerable interest in dietary interventions, no consensus has been reached regarding the optimal nutritional therapy. The present study aimed to investigate the possible positive effect of goat’s milk (GM) compared to cow’s milk (CM) on ASD autistic features in a valproic acid (VPA; 600 mg/kg)-induced white albino rat model of autism. All tests were conducted on rats that were divided into four groups (n = 15/group): control with goat milk (GM) treatment, control with cow milk (CM) treatment, autistic with goat milk (GM) treatment, and autistic with cow milk treatment. The casein levels were also measured in GM and CM. Social behavior was assessed by three-chambered sociability to test social interaction after the intervention. After 15 days of intervention, selected biomarkers, such as glutathione (GSH), thiobarbituric acid reactive substance (TBARS), interleukin-6 (IL-6), neurotransmitter dopamine (DA), serotonin (5-hydroxytryptamine, 5-HT), and glutamate (GLU), were measured in blood serum and brain homogenates. The results showed a significant positive effect on social interaction in the VPA rat ASD model fed GM. Blood serum and brain samples showed a positive increase in TBARS in the VPA rat model fed GM, but brain and serum serotonin levels were lower in both VPA-GM and VPA-CM groups. Dopamine in serum was also lower in the VPA-CM group than in the VPA-GM group. IL-6 levels were slightly lower in the VPA-GM group than in the VPA-CM group. In comparison with cow’s milk, goat’s milk was effective in ameliorating the neurotoxic effects of VPA. Goat’s milk may be considered a suitable source of dairy for children diagnosed with ASD. Autistic children with allergies to cow’s milk could possibly convert to goat’s milk. Nevertheless, more in-depth studies and clinical trials are recommended.

Chronic venous insufficiency (CVI) and lymphedema are progressive conditions that significantly impact wound healing and patient outcomes. Venous hypertension and impaired lymphatic drainage lead to edema, inflammation, and tissue damage, creating an environment conducive to ulceration. Nurses play a crucial role in identifying and managing these conditions through evidence-based strategies. Compression therapy is a cornerstone of treatment, reducing edema and promoting wound healing. Wound bed preparation, including debridement and moisture balance, is essential for optimizing healing outcomes. Lymphedema management involves complete decongestive therapy (CDT), consisting of manual lymphatic drainage, compression, decongestive exercises, and patient education. Lifestyle modifications, such as maintaining a healthy weight and engaging in regular exercise, are important adjuncts to treatment. Surgical interventions, including endovascular procedures for CVI and advanced surgical techniques for lymphedema, offer promising outcomes in selected cases. Nurses must assess patients holistically, considering factors such as arterial perfusion, infection, nutrition, and psychosocial well-being. By addressing the underlying pathophysiology, implementing appropriate interventions, and providing patient education, nurses can significantly improve wound healing rates, prevent recurrence, and enhance quality of life for individuals affected by CVI and lymphedema.

Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., Persona and System Prompts); (3) Alignment (e.g., Cultural, Safety, Ethics, and Fairness); and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic-centric LLMs and applications while providing concrete recommendations for future efforts in Arabic post-training dataset development.

The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it challenging to measure actual progress and to mitigate data contamination. Here, we aim to bridge these gaps. In particular, we introduce BALSAM, a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation. It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation. We envision BALSAM as a unifying platform that sets standards and promotes collaborative research to advance Arabic LLM capabilities.