Search Results (30)

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised four thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, (3) Application of interactive teaching strategies, and (4) Lesson planning and transfer. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, colored-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Participants (N = 37) were predominantly female (95%) and represented multiple generations of X, Y, and Z. Mid- and end-of-workshop reflection prompts were embedded within Sessions 2 and 4, with participants recording their responses on colored papers, which were then compiled into a single Word document for thematic analysis. Results: Thematic analysis of 59 mid- and end-workshop reflections revealed six interconnected themes, grouped into three categories: (1) engagement and experiential learning, (2) practical applicability and generational awareness, and (3) facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design. Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings. Full article

Due to the high mobility, flexibility, and low cost, unmanned aerial vehicles (UAVs) can provide an efficient way for provisioning data communication and computing offloading services for massive Internet of Things (IoT) devices, especially in remote areas with limited infrastructure. However, current transmission schemes for unmanned aerial vehicle-assisted Internet of Things (UAV-IoT) predominantly employ polling scheduling, thus not fully exploiting the potential multiuser diversity gains offered by a vast number of IoT nodes. Furthermore, conventional opportunistic scheduling (OS) or opportunistic beamforming techniques are predominantly designed for downlink transmission scenarios. When applied directly to uplink IoT data transmission, these methods can incur excessive uplink training overhead. To address these issues, this paper first proposes a low-overhead multi-UAV uplink OS framework based on channel reciprocity. To avoid explicit massive uplink channel estimation, two scheduling criteria are designed: minimum downlink interference (MDI) and the maximum downlink signal-to-interference-plus-noise ratio (MD-SINR). Second, for a dual-UAV deployment scenario over Rayleigh block fading channels, we derive closed-form expressions for both the average sum rate and the asymptotic sum rate based on the MDI criterion. A degrees-of-freedom (DoF) analysis demonstrates that when the number of sensors, K, scales as ${\rho }^{\alpha }$, the system can achieve a total of $2\alpha$ DoF, where $\alpha \in \left[0,1\right]$ is the user-scaling factor and $\rho$ is the transmitted signal-to-noise ratio (SNR). Third, for a three-UAV deployment scenario, the Gamma distribution is employed to approximate the uplink interference, thereby yielding a tractable expression for the average sum rate. Simulations confirm the accuracy of the performance analysis for both dual- and three-UAV deployments. The normalized error between theoretical and simulation results falls below 1% for K > 30. Furthermore, the impact of fading severity on the system’s sum rate and DoF performance is systematically evaluated via simulations under Nakagami-m fading channels. The results indicate that more severe fading (a smaller m) yields greater multiuser diversity gain. Both the theoretical and simulation results consistently show that within the medium-to-high SNR regime, the dual-UAV deployment outperforms both the single-UAV and three-UAV schemes in both Rayleigh and Nakagami-m channels. This study provides a theoretical foundation for the adaptive deployment and scheduling design of UAV-assisted IoT uplink systems under various fading environments. Full article

Optimising the learning rate is essential for efficient neural network training, but static methods can cause overshooting or undershooting, while adaptive techniques like ADAM often struggle to balance exploration and exploitation. We introduce the Polling Method, an ensemble-based optimisation approach that dynamically selects the most effective learning rate at each step, improving convergence and mitigating issues inherent in traditional optimisation strategies. By evaluating base models with varying learning rates at each epoch, the method adaptively balances exploration and exploitation without being constrained by predefined functions or gradient noise. This study details the theoretical foundation, implementation, and integration of the Polling Method with the ADAM optimiser, demonstrating its effectiveness in Artificial Neural Networks and Bayesian variational inference. The results demonstrate that Polling Method-ADAM reduces absolute error by 50% compared to ADAM alone, while also accelerating convergence. In Bayesian optimisation, it reduces the mean gradient shift from 0.85 to 0.7835 over 500 iterations, indicating improved stability in high-dimensional problems. By introducing adaptive learning rate selection within training, the Polling Method enhances optimisation efficiency while mitigating noise accumulation. This framework provides a computationally efficient, flexible alternative for deep learning applications, offering significant improvements over traditional optimisers and a potential breakthrough in neural network training strategies. Full article

Gene editing technologies, particularly CRISPR/Cas9, have revolutionized livestock genetics. They enable precise, efficient, and inheritable genome modifications. This review summarizes recent advances in the application of gene editing in livestock. We focus on six key areas: enhancement of disease resistance, improvement of growth performance and meat production traits, modification of milk composition, regulation of reproductive traits, adaptation to environmental stress, and promotion of animal welfare. For example, they have played an important role in improving mastitis resistance in cows, enhancing meat production performance in pigs, increasing milk yield in goats, and producing polled cows. Despite rapid progress, practical implementation in animal breeding still faces challenges. These include off-target effects, low embryo editing efficiency, delivery limitations, and ethical as well as regulatory constraints. Future directions emphasize the development of advanced editing tools, multiplex trait integration, and harmonized public policy. With continued innovation and responsible oversight, gene editing holds great promise for sustainable animal agriculture and global food security. Full article

To address the inadequacies of traditional Advanced Orbiting Systems (AOS) multiplexing algorithms in accommodating the networked and diverse transmission demands of space data, this paper proposes an efficient network AOS integrated multiplexing algorithm based on elastic time slots. The AOS network traffic is categorized into three types based on its characteristics, and a strongly scalable AOS integrated multiplexing model is established, which consists of a packet multiplexing layer, a virtual channel multiplexing layer, and a decision-making layer. For synchronous services, an isochronous frame generation algorithm and a periodic polling virtual channel scheduling algorithm are employed to meet the periodic transmission requirements. For asynchronous non-real-time services, a high-efficiency frame generation algorithm and a uniform queue length virtual channel scheduling algorithm are utilized to satisfy the high-efficiency transmission requirements. For asynchronous real-time services, an adaptive frame generation algorithm based on traffic prediction and a virtual channel scheduling algorithm based on comprehensive channel state are proposed. These algorithms optimize frame generation efficiency and dynamically calculate optimal scheduling results based on virtual channel scheduling status, transmission frame scheduling status, virtual channel priority status, and traffic prediction status, thereby meeting the high dynamics, low latency, and high efficiency transmission requirements. Additionally, a slot preemption-based elastic time slot scheduling strategy is proposed at the decision layer, which dynamically adjusts and optimizes the time slot allocation for the three types of traffic based on the current service request status and time slot occupancy status. Simulation results show that the proposed algorithm not only achieves lower average delay, fewer frame residuals, and higher transmission efficiency, but also maintains high stability under different working conditions, effectively meeting the transmission requirements of various types of space network traffic. Full article

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments. Full article

Repeated exposure to water treadmill (WT) exercise could elicit kinematic responses reflecting adaptation to WT exercise. The study’s aim was to compare the responses of a group of sport horses to a standardised WT exercise test (WT_SET) carried out at three time points, week 0 (n = 48), week 20 (n = 38), and week 40 (n = 29), throughout a normal training programme incorporating WT exercise. Horses were recruited from the existing client populations of two commercial water treadmill venues for the purpose of this longitudinal, observational study. Limb, back, poll, wither, and pelvic kinematics were measured during the WT_SET using videography, optical motion capture, and inertial motion sensors. Forelimb and hindlimb protraction increased (p < 0.001 for both), and forelimb and hindlimb retraction decreased (p < 0.001 for both) at week 40 compared to week 0. Caudal thoracic flexion–extension and lateral bend ranges of movement were greater at week 40 compared to week 0 (p < 0.001 and p = 0.009, respectively). Increased training speed was associated with increased craniocaudal poll movement (p = 0.021), decreased forelimb protraction (p = 0.008), and increased forelimb retraction (p = 0.021). In addition to characteristic changes in kinematics due to increasing water depth, regular WT exercise resulted in kinematic adaptation to movement in water. Factors such as the frequency of WT sessions and the type of session used with respect to depth and speed were seen to influence the nature of the adaptation. The results suggest that WT exercise sessions could be designed in accordance with specific training goals when used within a normal sport horse training programme. Full article

Beef is a major global source of protein, playing an essential role in the human diet. The worldwide production and consumption of beef continue to rise, reflecting a significant trend. However, despite the critical importance of beef cattle resources in agriculture, the diversity of cattle breeds faces severe challenges, with many breeds at risk of extinction. The initiation of the Beef Cattle Genome Project is crucial. By constructing a high-precision functional annotation map of their genome, it becomes possible to analyze the genetic mechanisms underlying important traits in beef cattle, laying a solid foundation for breeding more efficient and productive cattle breeds. This review details advances in genome sequencing and assembly technologies, iterative upgrades of the beef cattle reference genome, and its application in pan-genome research. Additionally, it summarizes relevant studies on the discovery of functional genes associated with key traits in beef cattle, such as growth, meat quality, reproduction, polled traits, disease resistance, and environmental adaptability. Finally, the review explores the potential of telomere-to-telomere (T2T) genome assembly, structural variations (SVs), and multi-omics techniques in future beef cattle genetic breeding. These advancements collectively offer promising avenues for enhancing beef cattle breeding and improving genetic traits. Full article

The purpose of the paper is to characterize the outcomes of the COVID-19 pandemic for farms and resilience activities performed by farmers in response to the economic crisis caused by the COVID-19 pandemic in the context of building rural area resilience. Research was carried out in all 87 urban–rural communes in Poland and focused on special determinants of rural resilience such as connections between small cities and rural areas, as well as the location of the territorial unit (peripheral versus non-peripheral). The purpose of the survey was to poll local government representatives on the outcomes of the COVID-19 pandemic for farms and identify resilience activities performed by farmers in response to the economic crisis caused by the COVID-19 pandemic. Empirical research was performed from September to October 2021. The results emphasize the significance of the diversification of farms and networks for strategies for coping with the COVID-19 crisis. The research revealed processes wherein farms adapted, even if to a small extent, to crisis conditions. The COVID-19 pandemic brought new challenges, at the same time stimulating innovative responses in communities and businesses in rural areas. This study also confirms the role of ITC solutions in the process of adaptation to the crisis and implies a need to strengthen local links between the rural area and the city, especially those relevant to peripheral areas. Local government authorities play a crucial role in this process. Full article

In the digital age, maintaining election integrity is critical, especially in Africa, where the security of electronic elections is often questioned. This study presents a blockchain-based vote counting and validation (BBVV) system developed using a mixed methods approach that combines stakeholder questionnaires to capture system specification and randomized historical election data analysis, following the Design Science Research strategy. Using the theory of the Byzantine General Problem, the BBVV protocol is proposed, which provides an accurate local count of votes at polling stations before national aggregation. The system was tested with randomized historical election data on the Algorand blockchain TestNet and confirmed that a local consensus on the vote count could be reached before it is added to the national tally on the blockchain. Our results show that in the cases where consensus was reached, this was the instance in only about 5% of the voting scenarios, with only 10% of the total vote being considered valid due to the strict consensus requirements. In addition, significant discrepancies were found between officials, with no consensus reached in 95% of cases which was due to the rogue values generated by a randomized dataset. The performance of the BBVV system was evaluated using transaction metrics, saturation, throughput, traffic, and latency to assess its efficiency, scalability, and reliability. The results suggest that blockchain technology can significantly improve the integrity of elections by ensuring a transparent, secure, and accurate vote-counting process. Future work will focus on improving the adaptability and scalability of the BBVV system for different electoral situations. Full article

To maximize the performance of IoT devices in edge computing, an adaptive polling technique that efficiently and accurately searches for the workload-optimized polling interval is required. In this paper, we propose NetAP-ML, which utilizes a machine learning technique to shrink the search space for finding an optimal polling interval. NetAP-ML is able to minimize the performance degradation in the search process and find a more accurate polling interval with the random forest regression algorithm. We implement and evaluate NetAP-ML in a Linux system. Our experimental setup consists of a various number of virtual machines (2–4) and threads (1–5). We demonstrate that NetAP-ML provides up to 23% higher bandwidth than the state-of-the-art technique. Full article

The timeliness of order deliveries seriously impacts customers’ evaluation of logistics services and, hence, has increasingly received attention. Due to the diverse and large quantities of orders under the background of electronic commerce, how to pick orders efficiently while also adapting these features has become one of the most challenging problems for distribution centers. However, previous studies have not accounted for the differences in the stochastic characteristics of order generation, which may lead to asymmetric optimization problems. With this in mind, a new asymmetric polling-based model that assumes the varying stochastic characteristics to analyze such order picking systems is put forward. In addition, two important indicators of the system, mean queue length (MQL) and mean waiting time (MWT), are derived by using probability-generating functions and the embedded Markov chain. Then, simulation experiments and a comparison of the numerical and theoretical results are conducted, showing the usefulness and practicalities of the proposed model. Finally, the paper discusses the characteristics of the novel order picking system and the influence of the MQL and MWT on it. Full article

A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV). Full article

This article presents the process of adaptation and validation, and the resulting psychometric properties, of the “Questionnaire of Barriers Perceived” (QBP). The scale identifies whether a student’s perceptions and expectations are mediated by stereotypes or roles associated with gender through the study of their professional aspirations, fear of negative judgement, and perceptions/awareness of gender roles of men and women. Two descriptive studies were conducted via a cross-sectional poll. The questionnaire was administered first to 240 students and then to a total of 1044 student from all the degrees studied at the Faculty of Education at the university at which the study took place. The data were subjected to item content analysis, descriptive analysis, analysis of internal consistency, study of the relationship between variables, correlational analysis, and an exploratory and confirmatory factorial analysis. The results showed that the scale had a high goodness-of-fit index, as well as validity and reliability. The dimensions that the model comprised were found to be interrelated and coherent with the theoretical structure considered in the initial version of the instrument. The resulting questionnaire presented sufficient validity and reliability to be used in other contexts and studies of the same nature. Full article

Virtualization is a core technology for cloud computing, server consolidation and multi-platform support. However, there is a concern regarding performance degradation due to the duplicated I/O stacks virtualization environments. In this paper, we propose a new I/O framework, we refer to it as Direct-Virtio, that manipulates storage directly, which makes it feasible to avoid the duplicated overhead. In addition, we devise two novel mechanisms, called vectored I/O and adaptive polling, to process multiple I/O requests collectively and to check I/O completion efficiently. Real implementation-based evaluation shows that our proposal can enhance performance for both micro and macro benchmarks. Full article