Abstract: Lung cancer is one of the leading causes of cancer-related mortality worldwide, and neoadjuvant therapy has become a cornerstone in the management of resectable non-small cell lung cancer (NSCLC). Growing evidence suggests that smoking history is an important clinical and biological factor that influences response to neoadjuvant treatment. This review synthesizes epidemiologic, translational, and clinical evidence, including findings from landmark clinical trials. It aims to evaluate the efficacy of neo-adjuvant chemotherapy, immunotherapy, targeted therapy, and multimodal treatment strategies according to smoking status. Furthermore, this review examines the molecular and immunologic mechanisms underlying the differential treatment responses observed among current, former, and never-smokers. Ultimately, there is an increasing need to conduct prospective smoking-stratified studies and integrate smoking history into medical practice to develop more personalized neoadjuvant therapies for patients with lung cancer.

Abstract: This study investigates linear and non-linear effects of financial sector development on macroeconomic volatility in 45 SSA countries from 1996 to 2021, using the 2SGMM and FGLS estimators. The empirical analysis shows that financial sector development can significantly dampen exchange rate volatility, affirming financial market liberalization theory. However, the effect of financial sector development is nuanced and sensitive to the vagaries of sample composition, econometric modeling strategy, and variable selection. Also, a non-linear, U-shaped relationship is found between financial sector development and macroeconomic volatility indicators, whereas an inverted U-shaped effect is observed when credit to the private sector is used. Furthermore, the study's control variables are shown to influence macroeconomic volatility to varying degrees of statistical significance and with varying signs of the coefficients. Therefore, policymakers should strive to achieve optimal thresholds for macroeconomic stability while focusing on fiscal sustainability, economic diversification, and improvements in institutional quality, among others.

Abstract: Importance: Social media platforms are increasingly used to obtain health information, but the quality of information about iron deficiency and the relationship between creator credentials, information quality, and viewer engagement is unclear. Objective: To evaluate the quality and viewer experience of social media content about iron deficiency and compare videos created by healthcare and non-health care creators. Design, Setting, and Participants: In this cross-sectional analysis, 100 TikTok videos about iron deficiency were evaluated. Videos were categorized according to creator type, including health-care and non-health-care creators. Two reviewers independently assessed videos using DISCERN-based measures of overall quality, relevance or accuracy, and bias. Viewer experience was evaluated using the Armstrong Viewer Assessment (AVA). Reviewer scores were averaged for analysis. The dataset also included likes, comments, and views. Main Outcomes and Measures: Mean DISCERN and AVA scores were compared between health-care and non-health-care creators using two-sided Welch two-sample t tests. P values were adjusted for 7 comparisons using the Bonferroni method. Results: Health-care creators received significantly higher DISCERN scores than non-health-care creators for overall quality (mean [SD], 2.81 [1.05] vs 1.57 [0.77]; Bonferroni-adjusted P < .001), relevance or accuracy (3.29 [1.08] vs 1.90 [0.90]; adjusted P < .001), and bias (1.74 [0.78] vs 1.21 [0.38]; adjusted P = .006). AVA viewer-experience scores were also higher for health-care creators (2.34 [0.69] vs 1.98 [0.84]), butthe difference was not significant after correction for multiple comparisons (adjusted P = .19). Conclusions and Relevance: Iron deficiency content produced by health-care creators were of significantly higher information quality than content produced by non-health-care creators. Viewer- experience did not differ significantly after multiple-comparison correction. These findings highlight the potential value of health-care professional participation in social media health communication and suggest that creator credentials may be an important indicator of information quality for individuals seeking health information online.

Abstract: Within the framework of the ‘Pairwise Interference Model’ of light–matter interaction, it is postulated that light particles follow definite trajectories, and thus, for example, in the double-slit experiment, a particle can pass through only one of the two slits. On this premise, the interference of two photons is examined in greater detail. It is hypothesized that the interference of a photon pair requires the involvement of matter and, initially, that both molecular excitations and photons oscillate at the same frequency and that the continuity of the oscillation is not disrupted by absorption and emission. The molecular excitations in the light source are supposed to oscillate synchronously. Both photons arrive at the same point on the detector but may travel along different paths and therefore arrive at different times. Under these conditions, their phase difference, φ(2) – φ(1), is always zero. Consequently, no interference pattern can form on the detector screen and therefore the assumptions made are incorrect. Alternatively, one may assume that only the photon oscillates, whereas the excitation stores the phase, φ, possessed by the photon at the instant of absorption. In this case, φ(2) – φ(1) corresponds to the difference in travel times modulo the oscillation period, and an interference pattern can form. However, this assumption is problematic for some reasons: the nature of the matter-based memory remains unclear; according to the special theory of relativity, no time elapses for a particle travelling at the speed of light, meaning that it cannot oscillate; and the assumption contradicts Bohr’s correspondence principle. In a classical wave, the phase information propagates away from the source at the wave velocity without changing. This leads to the alternative possibility that the excitations oscillate, whereas the photons do not. Instead, the photons transport information about the phase of the excitation at the instant of emission. As can be shown, in this case, φ(2) – φ(1) likewise corresponds to the difference in travel times modulo the oscillation period. However, this hypothesis contradicts the de Broglie postulate, according to which all particles exhibit wave properties, and the relation ε = hν must therefore be interpreted differently. It also raises the question of how the dimensions of a laser, specifically the mirror separation, can influence the wavelength of the amplified light if a photon has no wavelength. Using replicator dynamics and the ‘Pairwise Interference Model’ of light–matter interaction, a simulation demonstrates that, in a simplified laser model, assuming that photons do not oscillate whereas excitations do, self-organisation gives rise to a phase pattern in the sequence of photons within the emitted light beam that corresponds to the pattern obtained by discretising a classical wave. Thus, coherent light can exhibit wave properties even if photons themselves do not. Finally, the possibility of how incoherent light acquires wave properties is briefly discussed.

Abstract: Marketing strategy has always run on representations of the consumer: segments, personas, focus-group transcripts, survey panels. Large language models (LLMs) introduce a new representation — synthetic consumers: prompted model agents that answer surveys, react to concepts, simulate word of mouth and role-play purchase decisions at negligible marginal cost and near-zero elapsed time. Vendors now market such simulations as substitutes for early-stage consumer research, and strategy teams are using them to pre-test positioning, pricing framings and creative concepts before spending on human respondents. This conceptual paper examines what synthetic consumers can and cannot legitimately do for marketing strategy. We distinguish four uses — exploration, pre-testing, augmentation of human samples, and substitution — and argue that validity risk rises sharply across them. Drawing on the automation–augmentation paradox and on the psychometric requirements of construct validity, we identify five validity threats specific to LLM respondents: training-data echo, persona stereotyping, sycophantic and demand-characteristic responses, temporal freezing, and the absence of stakes. We propose a validation protocol — benchmark-against-humans, holdout concepts, adversarial prompting, and disclosure — under which synthetic consumers can be used defensibly in strategy work, and three propositions for empirical research. The paper's central claim is deliberately narrow: synthetic consumers are a powerful instrument for generating and screening hypotheses about consumers, and a dangerous instrument for confirming them.

Abstract: Congenital caudal malformations in domestic animals are traditionally reported as Perosomus elumbis (PE), but the associated spinal cord abnormalities are poorly characterised. This study describes the terminal spinal cord in five pigs with severe caudal malformations, comparing the findings with those of human caudal regression syndrome (CRS). Four formalin-fixed specimens (P1–P4) underwent radiography, computed tomography (CT) and magnetic resonance imaging (MRI); one specimen (P5) was examined by anatomical dissection. All cases showed terminal spinal cord anomalies. The three pigs with complete lumbosacrococcygeal agenesis (P1–P3) had a high spinal cord termination at T12–T15. This resembled Pang group 1 of human CRS, and was either abrupt (P1) or with a bulbous end (P3), or elongated and rod-shaped (P2). In P2, the spinal cord and dural sac protruded through a vertebral closure defect covered by skin, which is suggestive of tethering. The two pigs that retained lumbar or sacral segments had lesions that were located more caudally: a gradually tapering termination at L3 (P4) and lumbosacral spinal dysraphism (P5). In P3 and P4, caudally projecting structures were interpreted as cerebrospinal fluid (CSF)-filled sleeves around paired nerve roots. Four pigs exhibited a Buddha-like posture of the pelvic limbs. This is the first MRI-based description of terminal spinal cord anomalies in porcine PE, supporting a comparative neuroanatomical approach to these malformations.

Abstract: Ashwagandha (Withania somnifera) has been investigated as a potential ergogenic supplement, but its effects on sprint and change-of-direction performance in competitive handball players remain unclear. This randomized, placebo-controlled study examined the effects of eight weeks of supplementation with 600 mg/day of standardized Withania somnifera root extract containing 5% withanolides in 24 male national second-division handball players (Ashwagandha, n = 12; placebo, n = 12). Participants maintained their habitual training, and resting heart rate, blood pressure, handgrip strength, Squat Jump (SJ), Countermovement Jump (CMJ), Modified Agility Test (MAT), and 10- and 20-m sprint performance were assessed before and after the intervention. Mixed-design repeated-measures analyses revealed significant Group × Time interactions for resting heart rate, MAT performance, and 10- and 20-m sprint times. The Ashwagandha group (AG Group) showed lower resting heart rate and improved change-of-direction and sprint performance compared with baseline, whereas no significant Group × Time interactions were observed for body mass, body mass index (BMI), blood pressure, handgrip strength, SJ, or CMJ. These preliminary findings suggest that eight weeks of Ashwagandha supplementation may improve selected sprint and change-of-direction outcomes in competitive male handball players. Larger trials with prespecified primary outcomes, tighter training-load monitoring, independently verified extracts, and appropriate multiplicity control are warranted.

Abstract: Plantation forestry is increasingly constrained by conventional inventory methods that are labour intensive, costly, spatially sparse, and conducted too infrequently to support timely, site-specific management. Over the past two decades, rapid advances in remote sensing have fundamentally transformed this paradigm. Modern sensing systems—including satellites, unmanned aerial vehicles (UAVs), airborne, terrestrial, and mobile laser scanning, and high-precision positioning technologies—can now provide complementary observations of forest structure, condition, and change across multiple spatial scales. At the same time, developments in photogrammetry, light detection and ranging (LiDAR), computer vision, machine learning, and artificial intelligence have shifted remote sensing beyond the production of maps and three-dimensional datasets towards the automated extraction of meaningful forest information. Individual trees, canopy and stem structure, survival, stocking, health, and productivity can now all be quantified objectively and repeatedly, providing the foundation for predictive models of establishment success, growth, stocking failure, and operational risk. Consequently, remote sensing is evolving from a measurement technology into an integrated decision-support framework capable of generating spatial decision intelligence through prediction, uncertainty quantification, scenario evaluation, and actionable management recommendations, while providing an increasingly capable foundation for automation and autonomous systems. This feature article examines how integrated remote sensing has evolved from a collection of measurement technologies into a comprehensive framework for operational decision support in plantation forestry. It reviews advances in integrated satellite, aerial, and terrestrial remote sensing, automated information extraction, predictive modelling, artificial intelligence, and precision silviculture, while considering the scientific, operational, regulatory, and data-governance challenges associated with their deployment. Finally, it examines how digital forest twins, autonomous systems, and technologies adapted from Agriculture 4.0 provide a technological roadmap towards an emerging Forestry 4.0 paradigm.

Abstract: In the papers we reviewed, the two modelling lineages of sleep rarely meet. Mechanistic models simulate inputs such as light or caffeine with other people’s parameters; most learned models estimate or forecast the state and take no input. We define a sleep world model: a probabilistic model of one person’s sleep in which dated events, the sleeper’s actions and assigned interventions drive a latent SleepState of physiological and mental leaves, sleep-regulation models can inform the transition as priors, and instruments and the sleeper observe the night through separate observation models. Of the 62 learned models among the papers placed on our map, 56 only estimate or forecast. Few placed papers score a model’s predicted sleep contrast against an assigned one; in a nap study given in fixed order, a two-process model with parameters fixed beforehand overestimated slow-wave activity after the nap. No paper reviewed compares, on sleep data, a learned transition with and without a mechanistic prior. Assigned emotional or cognitive manipulations shift recorded sleep onset by minutes at group level, but an influence of a mental leaf on one person’s transitions is not shown, and every dose–response relation found is a group average. We propose a capability ladder, an evidence ladder that reserves “effect” for contrasts between inputs assigned at random or otherwise independently of the state, a minimum data protocol, six benchmark tasks and a staged roadmap. Until a person-fitted model’s predicted contrast is scored against that person’s replicated, randomised contrast, its simulated branches remain scenarios.

Abstract: Background. Elevated total circulating vitamin B12 is a nonspecific biochemical finding that may reflect supplementation, altered cobalamin-binding proteins, liver or kidney disease, malignancy, or analytical interference. “Idiopathic” elevation should describe a finding that remains unexplained after an appropriate assessment, rather than an established disease entity. Methods. This narrative review integrates verified clinical studies, laboratory investigations, and recent systematic reviews, with targeted updates through October 2026. It distinguishes association from causation and evidence on initially unexplained elevation from evidence in established disease. Findings. In a French cohort, solid cancer developed within five years in 20.8% of patients with persistent vitamin B12 elevation, compared with 6.0% with transient elevation and 3.8% with consistently normal values; the adjusted hazard ratio for persistent elevation was 5.90 (95% confidence interval, 2.79–12.45). These estimates arose from a selected clinical population and are not screening probabilities for the general population. A 2025 mortality meta-analysis included 28 studies, 69,610 participants, and 15,815 deaths, but heterogeneity limited universal prognostic interpretation. Recent laboratory studies also support considering immunoglobulin-associated vitamin B12 in selected cases, while the specificity of polyethylene glycol precipitation remains disputed. Elevated total vitamin B12 does not exclude clinically significant deficiency. Conclusions. Evaluation should verify exposure and measurement, identify common organ-specific causes, and pursue malignancy-directed investigations according to clinical findings and persistence. Follow-up intervals and the intensity of surveillance remain unvalidated. Neither treatment directed solely at lowering vitamin B12 nor indiscriminate cancer imaging is supported by the available evidence.

Abstract: Spray drying with carbohydrate, gum and protein wall materials is the dominant route for converting botanical extracts into powders for supplements and functional foods. Most studies, and recent reviews of wall materials, judge success by encapsulation yield, encapsulation efficiency and particle morphology measured immediately after drying. For products sold in hot and very humid regions, where stability testing is performed at 30 degrees Celsius and 75 per cent relative humidity, these measures say little about shelf life. This narrative review reframes wall-material selection around storage performance. It explains how water sorption and the glass transition determine stickiness, caking and chemical degradation; compares maltodextrins, gum arabic, starches and proteins by their stability-relevant properties, including halal and supply considerations; and examines what encapsulation efficiency, moisture content and flow indices do and do not show. A recent elderberry study is used as a worked example: its preferred powder had a Carr index of about 27 and a Hausner ratio of 1.38, classed as poor flow by pharmacopoeial criteria, and no storage test was performed. A testing sequence combining sorption isotherms, glass transition measurement and climatic-zone storage in the final pack is proposed, with a minimum reporting standard.

Abstract: The rapid growth and heterogeneity of Internet of Things (IoT) networks have increased the attack surface of modern networks and their exposure to diverse and evolving cyber threats. To cope with this change, the need for intrusion detection systems capable of identifying both known and previously unseen attacks has emerged. This paper proposes AEFS-Stack IDS, an advanced hybrid deep learning and machine learning framework for intrusion detection and threat mitigation in IoT networks. The proposed framework combines Deep Learning (DL) and Machine Learning (ML) architectures by integrating feature selection, autoencoder-based anomaly detection, and stacked classification to reduce the feature space, identify anomalous traffic, and classify known attack categories. In addition to this, a mitigation module is further incorporated to respond to normal, known malicious, and anomalous traffic based on the detection outcome. The framework is evaluated using the UNSW-NB15 and ToN-IoT benchmark datasets, with performance assessed using accuracy, precision, recall, F1-score, and computational measures. Experimental results show that AEFS-Stack IDS achieves 97.43% accuracy, 100% precision, and an F1-score of 98.70% on UNSW-NB15, while RF-CNN-LSTM achieves 99.90% accuracy, 100% precision, and an F1-score of 100% on ToN-IoT. The results demonstrate that integrating feature reduction, anomaly detection, heterogeneous classification, and threat mitigation within a unified framework are effective for real-time intrusion detection in resource-constrained IoT environments.

Abstract: During interpersonal interaction, understanding others’ actions requires the brain to continuously integrate information as behavior unfolds. Yet an action may deviate from its expected course and subsequently be corrected, raising the question of whether neural representations reflect only its eventual outcome or also retain information about how it developed. Here, we recorded electroencephalography from 20 expert table tennis players and 21 novices observing dyadic actions involving uninterrupted success, successfully corrected deviations, or goal-disrupting deviations. These conditions allowed us to distinguish information about an action’s preceding course from its eventual outcome. We combined time-resolved neural decoding, temporal generalization, and representational similarity analysis to characterize the differentiation and organization of unfolding actions. Neural activity differentiated the three action courses, with motor expertise selectively modulating their neural separability and temporal generalization. Critically, in experts, neural representational geometry corresponded to the shared preceding deviation of corrected and goal-disrupting actions despite their different outcomes. This correspondence remained significant after controlling for competing theoretical models and low-level visual and motion similarity. These findings suggest that neural representations of others’ actions incorporate information about their preceding course beyond their immediate observable state, highlighting how the brain preserves action history during unfolding dyadic actions.

Abstract: Cut-off lows (COLs) are recurrent synoptic-scale systems over southern Africa and are frequently associated with high-impact weather, yet their propagation and persistence remain challenging to predict. This study investigates the dynamical structure and evolution of two representative COL events using reanalysis data, with emphasis on geopotential height, sea-level pressure, and three-dimensional potential vorticity (PV) diagnostics. Synoptic identification and PV-based analyses are used to characterise the vertical structure, cold-core development, jet interaction, and propagation of systems. Both events exhibit classical COL characteristics, including closed mid-tropospheric circulation, vertically coherent PV anomalies, and pronounced cold-core structures, but differ substantially in their propagation behaviour. The first event exhibits a deeper and more vertically stacked PV anomaly with limited interaction with the upper-level jet, resulting in quasi-stationary behaviour and prolonged persistence. In contrast, the second event exhibits a more symmetric and comparatively shallower PV structure that remains dynamically coupled to the mid- to upper-tropospheric jet, promoting enhanced downstream advection and a more progressive evolution. Vertical cross-section analyses indicate that the depth and coherence of the cold core, together with the degree of jet–PV interaction, are important factors in modulating COLs propagation and longevity. These results demonstrate the importance of a three-dimensional PV perspective for understanding COLs dynamics over southern Africa and highlight how differences in large-scale dynamical coupling can influence their propagation and predictability.

Abstract: Mixed methods research (MMR) has become increasingly important in substance use disorder (SUD) research because it combines quantitative and qualitative approaches to capture the biological, psychological, social, and structural dimensions of addiction. However, the value of MMR depends on effectively integrating these approaches throughout the research process. This critical review examines major integration challenges in mixed-methods SUD research and identifies methodological strategies to address them. The review also examines how power asymmetries and researcher positionality shape integration decisions and how longitudinal designs introduce distinct integration challenges. Collectively, these approaches enhance the credibility, coherence, and interpretability of integrated findings. The review argues that integration should be treated not as a final stage of analysis but as a methodological commitment embedded throughout study design, sampling, data collection, analysis, and reporting. By synthesizing current methodological guidance, this essay seeks to advance rigorous mixed-methods research that informs evidence-based policies and interventions for substance use disorders.

Abstract: Question-answering recipe recommendation tasks lack datasets with ground-truth labels. Existing methods often generate datasets from simple combinations of ingredient or tag constraints, leading to unnatural expressions and unreasonable constraints that are hard to reflect users’ real preferences. This paper proposes a construction method for a benchmark-style QA dataset for natural-language recipe recommendation. Driven by real user behavior, our method integrates external user query and review data to extract fine-grained preference signals by LLMs and maps them onto a knowledge-graphbased tag schema, thereby constructing QA data consisting of preference and ingredient constraints. Besides, a greedy strategy for constraint selection is also used to control the answer size, while tagbased re-ranking is applied to improve data quality. The generated dataset RecipeADQA contains natural-language questions, structured constraints, and answer sets, resulting in 128,465 instances. Experiments compare RecipeADQA with baseline datasets from two perspectives: data quality and retrieval-recommendation performance. The results show that ours outperforms existing methods on the same recipe knowledge graph in coverage, higher question quality in naturalness and constraint rationality, and better queryability. Moreover, we design a noise-augmented retrieve-and-rerank model, in which a lightweight parser first retrieves candidate recipes, and a small language model then selects and ranks relevant recipes from a noisy candidate pool. The model trained on RecipeADQA outperforms the same model trained on the baseline datasets. The relevant construction code and data have been open-sourced at a GitHub repository https://github.com/MorimoLab/RecipeADQA.

Abstract: Accurate in-season yield prediction is critical for guiding nitrogen management in rice production, particularly under the increasingly variable weather conditions in cold regions. However, the prediction accuracy of conventional crop models is often limited by uncertainties in model inputs and the inability to capture real-time crop status. This study proposed a yield prediction framework by coupling the CERES-Rice model with re-mote sensing observations and machine learning techniques. Field experiments were conducted in Northeast China from 2014 to 2018. Spectral data collected by the Rap-idSCAN active canopy sensor were used to retrieve key agronomic parameters, including aboveground biomass (AGB), leaf area index (LAI), plant nitrogen concentration (PNC) and plant nitrogen uptake (PNU), using a Random Forest algorithm. These variables were then assimilated into the CERES-Rice model to calibrate its sensitive parameters and im-prove yield predictions. The analysis showed that updating agronomic variables using remote sensing resulted in acceptable modeling accuracy (R² > 0.7). Using PNU and AGB outperformed LAI in enhancing model performance, while using PNC was unreliable. Without assimilation, CERES-Rice gave moderate performance (R² = 0.58, RMSE = 1.3 t ha⁻¹), while coupling with PNU improved prediction accuracy(R² = 0.67, RMSE = 0.79 t ha⁻¹). This study highlights the potential of integrating active canopy sensing and ma-chine learning with crop models for dynamic yield forecasting and provides a valuable reference for optimizing nitrogen management in rice systems.

Abstract: Digital payment satisfaction is an important post-adoption outcome, yet users may evaluate payment services through several overlapping functional and relational criteria. This study investigates how perceived convenience, trust, expectation confirmation, performance expectancy, and effort expectancy are associated with satisfaction among users of Visa-enabled digital payments in Bangladesh. Drawing on expectation-confirmation theory and research on technology acceptance and digital trust, a cross-sectional analysis was conducted using 385 populated survey records from the supplied Group-6 workbook. Three-item composite scores were calculated for each construct. Ordinary least squares regression was estimated with five simultaneous predictors, and a separate one-way analysis of variance examined satisfaction across self-reported AI-feature experience categories. The regression explained 24.3% of satisfaction variance (adjusted R² = .233; F(5,379) = 24.33, p < .001). Performance expectancy (B = .179), effort expectancy (B = .167), trust (B = .160), and convenience (B = .143) showed positive conditional associations; confirmation did not meet the .05 threshold (B = .086, p = .077). Satisfaction did not significantly differ across AI-experience groups (F(3,381) = .386, p = .763). However, internal consistency estimates were below conventional thresholds for every focal three-item scale (α = .452-.526), materially limiting construct-level inference. The findings provide a transparent preliminary assessment of payment satisfaction and identify measurement validation, respondent provenance, and AI-specific operationalization as priorities before confirmatory interpretation or journal submission.

Abstract: Studies of reduced training often conclude that a lower dose maintains muscle strength and size because values after reduced training do not differ from values after resistance training. A maintained level does not show how fast the adaptation is being lost. This study proposed a three-step procedure for testing whether reduced training maintains strength and hypertrophy adaptations, applied to published group means from two resistance training trials and 8,000 simulated datasets. First, a shape check: under exponential detraining, each later equal-interval loss must be smaller. Second, if the detraining arm passes, a constraint linking its loss rate to that under reduced training. Third, a calibrated estimate of the maintenance dose, checked on a held-out training frequency. Significance was set at p ≤ 0.05. The detraining arm of older adults violated the shape check in the reported means, and the constraint then depended on an unmeasured floor. With training every other week, the calibration implied a 1.63% loss of quadriceps cross-sectional area against 5.90% reported (a result known before analysis). Leg press strength fell 5 kg, then 10 kg, over successive six-week periods, whereas exponential detraining requires shrinking losses; published spreads cannot establish this for the population. Simulated false rejection of a correct model was 3% to 8%. A maintained level of strength or muscle size does not establish that the adaptation is being retained. Testing maintenance needs measurements at the start of reduced training and at two or more equally spaced later times, with each participant's changes reported.

Abstract: The growing frequency of disasters, from hurricanes to pandemics, has strained emergency management (EM) systems, highlighting the need for an expanded workforce. Nursing students, with their clinical expertise and enthusiasm for practical experience, offer a valuable resource to enhance community resilience. This literature review synthesizes 10 peer-reviewed studies from 2015 to 2025 to explore strategies for integrating nursing students into EM programs, particularly for universities without established programs. From the perspective of a lifelong public health responder now teaching nursing students, the review identifies simulation-based training, interprofessional collaborations, community partnerships, and curriculum integration as key strategies documented in the literature, while offering some real-world individual experience. Outcomes of the strategies include improved student competencies, enhanced community preparedness, and increased professional development, though some challenges like training gaps and logistical complexities persist. Lessons learned are discussed as well as some practical steps for universities to take from scratch to achieve the title goal. Future research directions are suggested to evaluate long-term impacts and scalability, offering actionable insights, especially for educators.