Search Results (26)

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

Food security (FS) is influenced by primarily financial but also sociodemographic factors. Identification of correlates of food insecurity (FI) is a crucial issue in the context of achieving sustainable development goals. The aims of the study were: (1) to recognize FI in the selected Central and Eastern European (CEE) countries, (2) to examine common socioeconomic and demographic characteristics for FI. The analysis used the set of eight-item FI indicators adopted by the Food and Agriculture Organization, applying the Gallup World Poll survey data from 2017 to 2019. Multinomial logistic regressions were used to examine FI at mild and moderate or severe levels compared with FS. Differences in the profiles of FI were observed in analyzed countries: Poland, Lithuania and Slovakia. Lithuanians experienced the lowest FS, and Slovaks the highest. The FI status was associated with education, gender, age, household composition and income. It was found that the impact of these factors was not the same in the examined countries. Differences in profiles of FI in CEE countries indicate the need to analyze the problem individually for each country. Identifying groups particularly vulnerable to FI may allow appropriate targeting of instruments counteracting FI and adapt them to people with different characteristics. Full article

The deployment of new 5G services and future demands for 6G make it necessary to increase the performance of access networks. This challenge has prompted the development of new standardization proposals for Passive Optical access Networks (PONs) that offer greater bandwidth, greater reach and a higher rate of aggregation of users per fiber, being Time- and Wavelength-Division Multiplexing (TWDM) a promising technological solution for increasing the capacity by up to 40 Gbps by using several wavelengths. This solution introduces tunable transceivers into the Optical Network Units (ONUs) for switching from one wavelength to the other, thus addressing the ever-increasing bandwidth demands in residential broadband and mobile fronthaul networks based on Fiber to the Home (FTTH) technology. This adds complexity and sources of inefficiency, such as the laser tuning time (LTT) delay, which is often ignored when evaluating the performance of Dynamic Bandwidth Allocation (DBA) mechanisms. We present a novel DBA algorithm that dynamically handles the allocation of bandwidth and switches the ONUs’ lasers from one wavelength to the other while taking LTT into consideration. To optimize the packet delay, we introduce a scheduling mechanism that follows the Longest Processing Time first (LPT) scheduling discipline, which is implemented over the Interleaved Polling with Adaptive Cycle Time (IPACT) DBA. We also provide quality of service (QoS) differentiation by introducing the Max-Min Weighted Fair Share Queuing principle (WFQ) into the algorithm. The performance of our algorithm is evaluated through simulations against the original IPACT algorithm, which we have extended to support multi-wavelengths. With the introduction of LPT, we obtain an improved performance of up to 73% reduction in queue delay over IPACT while achieving QoS differentiation with WFQ. Full article

Brief face-to-face self-confidence workshops were effective in reducing depression among the public. Technological advances have enabled traditional face-to-face interventions to be adapted using unique technology-mediated platforms. This article details the formative development of a self-confidence web-based seminar (webinar) intervention for workplace depression. The first section discusses a qualitative study that explores the feasibility and acceptability of adapting the self-confidence workshops into a webinar platform on employees in the workplace. The second section describes the systematic development of this new webinar intervention informed by the qualitative study findings, a published systematic review, and previous face-to-face self-confidence workshops. The qualitative study involves three focus groups (n = 10) conducted in a small organization. Three themes were identified relevant to the running of the new self-confidence webinars in the workplace: personal (content, time and duration preference, features of the webinar, individual participation, personalization), interpersonal (stigma from others, engagement with participants/presenter, moderated interaction), and organizational (endorsement from management, work demand). For the intervention development, the format, structure, features, and content of the self-confidence webinar intervention are described. Features such as file sharing, virtual whiteboard, live chat, and poll are explained with the intervention primarily based on cognitive behavior therapy and coping flexibility concepts. Full article

The unprecedented situation of the COVID-19 pandemic has generated radical transformations of the Romanian education system, forcing teachers as well as students to adapt in a short time to new social conditions and to the online learning process. The paper analyzes the sustainability of the e-learning system implemented in Romania during the pandemic, and it is based on an opinion poll based on a questionnaire developed on three levels of schooling (middle school, high school, and university), analyzed from three perspectives, teachers–students–parents, and identifying the possible psychological effects on students, resulting from the corroboration of social isolation with the online continuation of the educational process. Although before the pandemic the e-learning system was rarely used by both students and teachers, the research results indicate that students have accepted online learning, even if they find it less attractive than the traditional education system. From the teacher–student–parent perspective, e-learning is an effective sustainable learning solution in current and future conditions, but it requires good collaboration between parents and teachers, careful monitoring of children’s/students’ behavior to identify and combat possible effects determined by the changing way of learning and of social realities. Full article