Search Stage Research Articles

The Effectiveness of Obstetric Emergency Interventions in Enhancing Mother and Fetal Well-Being: A Systematic

An obstetric emergency is a condition that can threaten the life of a pregnant woman and the fetus, which occurs during pregnancy, childbirth and the puerperium. This review aimed to analyze the effectiveness of emergency obstetric interventions based on previous studies. The study was a systematic review carried out in seven stages through three databases from Ebsco, Pubmed and Proquest. The keywords used were (pregnant women) AND (emergency intervention) AND (nursing OR nurse) NOT (systematic review). The inclusion criteria in this study were: full-text, peer review, English, articles for the last five years, subject: nursing, nurses, emergency medical care, nursing care, emergency services, and document type: articles. Based on the search database, 10,496 papers were obtained, 733 documents that matched the inclusion criteria and seven papers that fit the theme were obtained. The result that based on the initial stage of the literature search, four interventions were adequate, including ACLS training, multidisciplinary management, making coloured ribbons for premature pregnancy detection, and maternal near-miss detection tools. Interventions with less effective based on statistical test results were supportive care, home visits with IPV (intimate partner violence) interventions, and administration of CPR, defibrillation, ETT insertion, and administration of epinephrine. Nurses still carry out a few emergency developmental interventions. It is necessary to develop engagement interventions related to maternal emergencies to improve the welfare of mothers and babies. Further study is needed to strengthen the evidence base of innovative interventions for specific obstetric emergencies.

Practices of psychological and pedagogical support of future teachers’ personal and professional development in the conditions of distance learning

The article highlights the intermediate results of the research "Psychological and pedagogical support of future teachers’ personal and professional development in the conditions of new educational standards’ implementation in Ukraine". Various theoretical aspects as well as adapted and original practices are characterized. The experience of the authors regarding the implementation of the components - value-worldview, process-activity, emotional-resource during the motivational-cognitive, constructive-active, reflective-prognostic, value-identification stages is highlighted. Effective author's practices are presented - adaptation trainings, portfolio of personal and professional growth in the context of performance of work functions; formation of end-to-end skills of future teachers by means of digital technologies and tools (GDocs, GMeet, Zoom, Canva, MindMeister, Mentimeter, digital boards); adaptation of active learning technologies in the distance learning format (case-technologies, game, project technologies, SWOT analysis). A complex of technologies (role-playing games, training technologies, art-therapy) is presented, which contribute to the development of the emotional intelligence of future teachers in the context of the formation of professional competencies - psychological, emotional-ethical, reflective, psycho-emotional. The research was carried out in an interdisciplinary scientific field, which requires next stages of the scientific search for correction, generalization of the results of psychological and pedagogical support of future teachers’ personal and professional development.

Advanced strategies on update mechanism of tree-seed algorithm for function optimization and engineering design problems

Tree-seed algorithm (TSA) is one of the meta-heuristic algorithms proposed recently. It has a simple but effective structure. However, for complex high-dimensional functions and constrained optimization problems, TSA has the defects of slow convergence rate and low optimization accuracy. To overcome these issues, an improved tree-seed algorithm (SATSA) based on Nelder-Mead simplex method is proposed. Firstly, appropriately increase the value of the control parameters that balance exploration and exploitation, thereby improving the local search ability of TSA, and introduce an adaptive stochastic component of Gaussian distribution in the seed generation formula of the local search stage. Then, set the maximum retention times of each tree, for trees that exceed the maximum retention times in continuous iterations, increase the number of seeds produced by this tree, then use a new equation based on the global optimal location to generate seeds, thereby speeding up the convergence of the algorithm. Finally, after all trees produce seeds, introduce the improvement strategy with Nelder-Mead simplex method into some poor quality solutions, so that the solution with poor quality is searched deeply in the neighborhood, speeding up the process of moving the overall solution to the optimal solution. Its convergence is proved by theoretical analysis, and certifies that the time complexity of SATSA is not increased compared to TSA. The simulation results of 6 advanced algorithms on CEC2017 test suite and 10 challenging engineering constrained optimization problems demonstrate that the three strategies proposed in this study are feasibility and effectiveness.

Ethnoscience as the Policy Implementation of Kurikulum Merdeka in Science Learning: A Systematic Literature Review

The Kurikulum Merdeka policy has been implemented extensively in Indonesia since 2022, with one of its focuses being the integration of culture in teaching and learning. Several studies have been conducted on the integration of ethnosciences in science education. However, so far, there have been very few studies that attempt to review the results of the analysis of these articles. Therefore, this study aims to analyze a number of articles related to research on the topic of ethnosciences in science education as an implementation of the Kurikulum Merdeka policy, in order to classify them based on predetermined categories. This study utilizes the systematic literature review (SLR) method, which includes the stages of data search and article collection, article screening, assessment of the quality and suitability of scientific articles, and analysis and data classification. Based on these stages, six scientific articles were obtained, published in six SINTA-accredited journals with a focus on education and published between 2022 and 2023. The results of the quality assessment indicate that four out of the six articles meet the criteria for suitability. Thus, these articles can be considered as sources of information related to research on the topic of ethnosciences in science education as an implementation of the Kurikulum Merdeka policy. The classification has been conducted based on Indonesian culture integrated into science education, educational levels, subjects, and forms of integration of ethnosciences in science education.

An improved two-archive artificial bee colony algorithm for many-objective optimization

Artificial bee colony (ABC) algorithm has shown good performance on many optimization problems. However, these problems mainly focus on single-objective and ordinary multi-objective optimization problems (MOPs). For many-objective optimization problems (MaOPs), ABC encounters some difficulties. The selection pressure based on Pareto-dominance degrades severely. It is hard to balance convergence and population diversity. To help ABC solve MaOPs, this paper proposes an improved two-archive many-objective ABC (called MaOABC-TA) algorithm. Inspired by the improved two-archive (Two_Arch2) method, MaOABC-TA uses two archives namely convergence archive (CA) and diversity archive (DA) to promote convergence and diversity. Based on CA and DA, three different search strategies are designed to strengthen convergence or diversity in different search stages. In addition, a new probability selection strategy is proposed to choose solutions with good diversity. To verify the performance of MaOABC-TA, it is compared with 10 many-objective evolutionary algorithms (MaOEAs) and 3 many-objective ABCs on DTLZ and MaF benchmark sets with 3, 5, 8, and 15 objectives. Two performance indicators including inverted generational distance (IGD) and hypervolume (HV) and utilized. Experimental results show that MaOABC-TA is more competitive than the compared algorithms in term of the IGD and HV values.

Dynamic decisions between sellers and consumers in online second-hand trading platforms: Evidence from C2C transactions

Dynamic decisions have become pivotal strategies for sellers and consumers in C2C second-hand product market. This study instroduces a two-stage pricing model and a consumer choice model, aiming to scrutinize the dynamic pricing behaviors of individual sellers and their subsequent impact on consumer choice. Leveraging data from the Xianyu platform, akin to eBay, we identify two fundamental dynamics that underscore successful pricing strategies. Seller pricing behaviors for second-hand products are subject to a number of influences, encompassing the duration since release, consumer engagement with product features, emotive descriptions, and feedback from market information. Our investigation unveils that sellers tend to recalibrate prices in the second phase if their second-hand products remain unsold during the initial phase, leveraging additional insights amassed during the first period. This adjustment is particularly pronounced if the price adjustment in the second phase is influenced by the time since the product's release. Moreover, the extra information gleaned during the initial phase affects the range of price adjustments. Notably, our research uncovers that consumers exhibit heightened attentiveness to product features and optimistic emotional descriptions in the search stage. Conversely, during the purchasing stage, product prices and the time since release emerge as more pivotal determinants. However, prevailing price adjustment practices predominantly center around markdowns or markups, with the time since release dampening purchasing inclinations. Our findings disclose that among these practices, only price markdowns hold the power to effectively stimulate purchase decisions, particularly when influenced by the duration since release. Based on these insights, we suggest that sellers should refrain from concentrating solely on pricing adjustments for their second-hand products. Instead, they would benefit from incorporating the additional market insights gained from the time since the product’s release and the feedback from consumers to enhance the quality of dynamic pricing decisions.

Emerging digital technologies and consumer decision-making in retail sector: Towards an integrative conceptual framework

This paper explores the influence of digital technologies on the consumer decision-making in retail sector with two online survey-based studies. Study 1 identifies unique attributes of six digital technologies, including two current (Internet and Mobile Platform) and four emerging (Artificial Intelligence, Augmented, Mixed and Virtual Reality) technologies. Study two focuses on older consumers to understand their decision-making process when shopping for products or services using new digital technologies. We extend the AISAS (Awareness, Interest, Search, Action, and Sharing) model to show that with digital technologies, consumer decision journey is no longer linear. For example, attention can lead directly to action, without going through the interest or search stages. Similarly, purchase can lead to sharing that may lead to loyalty and psychological engagement, and reinforce attention. We found no significant difference in these effects between older and younger consumers. Besides providing useful insights about consumer decision-making process with emerging digital technologies for future academic researchers, these results also give useful ideas to marketing practitioners interested in introducing these emerging technologies to deliver superior value to their customers.

THE STRUCTURE AND FEATURES OF DIAGNOSTIC THINKING WHEN MAKING A PSYCHOLOGICAL AND PEDAGOGICAL DIAGNOSIS

The article characterises the structure of diagnostic thinking of a specialist in the process of solving professional problems. The features of this type of thinking, characteristic of educational psychologists, are outlined, the main stages of the diagnostic search are highlighted. The main strategies of diagnostic thinking used by teachers and psychologists are described - traditional, hypothetical and informative. A comparative analysis of the logic of the diagnostic search of teachers and educational psychologists is given. It is shown that teachers and educational psychologists more often use the traditional and informative strategies of diagnostic search and less often the hypothetical one. At the same time, in the process of solving diagnostic problems, teachers choose an informative strategy, while educational psychologists choose a traditional one. The choice of a specific diagnostic search strategy by specialists depends on the availability of knowledge about the structure of the object of psychodiagnostics, as well as on the system of signs and symptoms that characterise problem situations.

Differential Evolution with Group-Based Competitive Control Parameter Setting for Numerical Optimization

Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such as premature convergence and stagnation. In order to alleviate them, this paper presents a novel DE variant by designing a new mutation operator (named “DE/current-to-pbest_id/1”) and a new control parameter setting. In the new operator, the fitness value of the individual is adopted to determine the chosen scope of its guider among the population. Meanwhile, a group-based competitive control parameter setting is presented to ensure the various search potentials of the population and the adaptivity of the algorithm. In this setting, the whole population is randomly divided into multiple equivalent groups, the control parameters for each group are independently generated based on its location information, and the worst location information among all groups is competitively updated with the current successful parameters. Moreover, a piecewise population size reduction mechanism is further devised to enhance the exploration and exploitation of the algorithm at the early and later evolution stages, respectively. Differing from the previous DE versions, the proposed method adaptively adjusts the search capability of each individual, simultaneously utilizes multiple pieces of successful parameter information to generate the control parameters, and has different speeds to reduce the population size at different search stages. Then it could achieve the well trade-off of exploration and exploitation. Finally, the performance of the proposed algorithm is measured by comparing with five well-known DE variants and five typical non-DE algorithms on the IEEE CEC 2017 test suite. Numerical results show that the proposed method is a more promising optimizer.

PENGEMBANGAN MEDIA PEMBELAJARAN PAI BERBASIS VIDEO PADA MATERI SURAH AL-FIIL

The development of this media stems from a lack of understanding of Muslim students at SD No. 3 Jimbaran regarding short surah material, especially surah Al-Fīl. The purpose of this study was to determine the feasibility of instructional video media for learning PAI material for surah Al-Fīl. The method used is Research and Development (R&D) using the ADDIE model with data search stages through observation and preparation of questionnaire instruments. The results of the validation of material experts with a percentage of 99.2%, design experts with a percentage of 86.07%, media experts with a percentage of 91.30%, individual tests with a percentage of 83.33%, small group tests with a percentage of 84.99% and group tests large with a percentage of 84.18%. Based on the average of the three experts, the results of the percentage were 92.19% in the very feasible category and the average of the three groups of students got the percentage of 84.16% in the very feasible category. It can be concluded from the results of these percentages that the product developed is very suitable to be used as a medium for learning PAI.

Hyperparameter optimization method based on dynamic Bayesian with sliding balance mechanism in neural network for cloud computing

Hyperparameter optimization (HPO) of deep neural networks plays an important role of performance and efficiency of detection networks. Especially for cloud computing, automatic HPO can greatly reduce the network deployment cost by taking advantage of the computing power. Benefiting from its global-optimal search ability and simple requirements, Bayesian optimization has become the mainstream optimization method in recent years. However, in a non-ideal environment, Bayesian method still suffers from the following shortcomings: (1) when search resource is limited, it can only achieve inferior suboptimal results; (2) the acquisition mechanism cannot effectively balance the exploration of parameter space and the exploitation of historical data in different search stages. In this paper, we focused on the limited resources and big data provided by the cloud computing platform, took the anchor boxes of target detection networks as the research object, employed search resource as a restraint condition, and designed a dynamic Bayesian HPO method based on sliding balance mechanism. The dynamism of our method is mainly reflected in two aspects: (1) A dynamic evaluation model is proposed which uses the cross-validation mechanism to evaluate the surrogate model library and select the best model in real time; (2) A sliding balance mechanism is designed based on resource constraints to seek a balance between exploration and exploitation. We firstly augment the recommended samples of probability of improvement acquisition function by using k-nearest neighbor method, then introduce Hausdorff distance to measure the exploration value and match sampling strategy with resource utilization, which makes it slide smoothly with resource consumption to establish a dynamic balance of exploration to exploitation. The provided experiments show that our method can quickly and stably obtain better results under the same resource constraints compared with mature methods like BOHB.Graphical

The Effect of Quantum Teaching Method on Motivation and Arabic Language Learning Outcomes (Literature Study)

This study analyzes the effectiveness of the Quantum Teaching method in improving motivation and learning outcomes in Arabic. Researchers use descriptive qualitative research methods with a systematic approach to conduct data analysis in a simplified approach. Data was collected through database search tools and literature search stages using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. Article search strategies are carried out through international databases, such as Open Knowledge Maps, ResearchGate, and Google Scholar. The summary of this literature review is done using an analysis matrix. The results showed that applying the Quantum Teaching method significantly improved students' Arabic proficiency and learning outcomes. Quantum Teaching effectively improves student learning outcomes, motivation, and engagement. This method creates a comfortable, enjoyable learning environment and strengthens the interaction between teachers and students. In addition, Quantum Teaching also improves students' memory and learning potential.

Studying health anxiety related attentional bias during online health information seeking: Impacts of stages and task types

Seeking online health information may reinforce the anxiety of those who are already overly anxious about their health. This study explored how people with health anxiety may behave differently in terms of their attentional biases when seeking health information online. We conducted an eye-tracking experiment with 17 participants in the high health anxious group and 17 participants in the low health anxious group, who performed three types of information-seeking tasks (factual, interpretive, and exploratory) on a Chinese health website. We observed that both groups mainly allocated their attention to the stages of evaluating the list of search results and synthesizing information to make health decisions. They showed similar attention tracks at the earlier search stages and health anxiety was found to associate with attentional biases towards certain website stimuli. However, the high health anxious group showed more active eye movements than their low health anxious counterparts. Attentional biases from the high health anxious group mainly occurred at the later stage of processing rather than the initial orientation stages. As for task types, the high health anxious group presented more extensive attentional biases when performing the interpretive task, compared to the explorative and factual tasks. The findings provide novel insights into the attentional biases of people with health anxiety as they search online for health information, which have implications on designing more effective information interventions for vulnerable groups of health information consumers. The findings can also help clinicians interpret patients’ anxiety-related sensations and provide intervening recommendations for clients in use of online health information.

Which decision-making stages matter more? Influencer’s perceived credibility, sponsorship and moderating role of trust

Purpose By acknowledging the importance of micro-influencers and all decision-making process stages, this study aims to explore the impact of perceived influencer’s credibility, impacted by the sponsorship absenteeism, on problem recognition, information search, alternative evaluation, purchase and post-purchase. Additionally, the authors investigate the moderating role of trust level on the researched relationships. Design/methodology/approach This study focuses on 111 young Croatian consumers of cosmetic products. The authors analysed data with confirmatory factor and regression analyses. Findings This study reveals positive relationships between micro-influencers’ perceived credibility and all decision-making phases. The research results also show that the lack of sponsorship positively influences perceived credibility. Furthermore, results indicate that the trust level is an essential moderator for the relationships between perceived credibility and sponsorship and information search and buying stages. Research limitations/implications Limitations include the convenience sampling method and data collection at one point while also focusing on consumers from one country. Practical implications This study provides practical implications for companies outlining the marketing activities that should be considered in all stages of the decision-making process while recognising the attractiveness of micro-influencers for the buying experience. Originality/value This study fills gaps in the literature on micro-influencers credibility in general and particularly in the cosmetics industry. In addition, the study fills the gaps in the literature considering the impact of perceived micro-influencer credibility on all five decision-making process stages.

The effectiveness of growth-friendly systems in the treatment of early onset scoliosis: a systematic review

Objective. To perform a systematic analysis of the literature evaluating the effectiveness of growth-friendly systems in the treatment of early onset scoliosis.Material and Methods. A subject search was conducted in the Google Scholar database for the terms “growing rods”, “early onset scoliosis”, “treatment”, “surgery” and “growth-friendly” using AND or OR logical operators with a search depth of 10 years. At the first stage, 824 abstracts of publications were selected. The second stage of search was carried out in accordance with the PICOS criteria, 38 abstracts of original studies, case series and reviews of surgical techniques for spinal deformity correction with preservation of growth potential were selected. The evaluation criteria were divided into four groups: general data, correction of the frontal and sagittal components of the deformity, complications and unplanned scenarios.Results. In the analyzed studies, gender equality of distribution was observed, and the average age at the time of the primary operation was 6.6 years for patients with traditional growing rods (TGR) and Schilla/Luque trolley and 4.9 years for patients with Vertical Expandable Prosthetic Titanium Rib (VEPTR). From the point of view of the magnitude of spinal deformity correction, the VEPTR systems demonstrated the minimum result (18 % correction), and the TGR and Schilla results were comparable (42.1 and 53.1 %, respectively), as well as the indicators of the dynamics of body lengthening.Conclusion. The use of VEPTR systems is associated with a high risk of complications and a somewhat lower efficiency of spinal deformity correction, however, VEPTR is indispensable in the treatment of thoracic insufficiency syndrome, as well as severe deformities of the axial skeleton in young children using non-vertebral fixation points. Growth modulation systems (Schilla and Luque trolley) showed deformity correction results similar to those of TGR.

Analysis of the influence of population distribution characteristics on swarm intelligence optimization algorithms

The distribution characteristic of populations is one of the main characteristics of population. The pattern of distribution characteristics determines the probability law of individual values. At the same time, the initialization of the population lays a foundation for the iterative process of the swarm intelligence optimization algorithm. To reveal the influence of population distribution characteristics on the swarm intelligence optimization algorithm, this paper proposes a variety of search strategies based on the populations with different distribution characteristics and analyzes the influence of population distribution characteristics on optimization process by comparing the test results of the optimization algorithm after implementing these strategies. First, a new population generator is designed that can transform the same initial population into a population with uniform and central peaking distributions. On this basis, the two kinds of populations are applied to the global and local search stages of the optimization process, and four different search strategies are formed. Among them, the global and local search strategies based on a uniformly distributed population are the traditional methods. Finally, the performance of the optimization algorithm using different search strategies is evaluated through 29 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. In addition, the algorithms are applied to solve the TSP problem. For CEC2017 in 100D, only 13 of the 29 test functions achieve the best optimization effect by using the traditional method, while the other 16 test functions achieve better search results by using the other three search strategies. The analysis shows that the population distribution characteristics have a great influence on the population optimization algorithm. The performance of the algorithm with different population distribution combination strategies is statistically superior to the traditional algorithm with a uniform distribution population, as revealed in the test functions ranging from 38.7% to 62.9% for different dimensions. By reconstructing populations with different distribution characteristics, the overall efficiency of the swarm intelligence optimization algorithm can be greatly improved.

Special Forces Algorithm: A novel meta-heuristic method for global optimization

This paper proposes a novel swarm intelligence optimizer inspired by human behavior called the Special Forces Algorithm (SFA). SFA is inspired by the missions of modern Special Forces in real life. First of all, in the initial stage of algorithm iteration, the speed of light mechanism is proposed for simulating the UAV-assisted search of special forces, and the loss probability mechanism is proposed for simulating the loss of contact due to force majeure during the search of special forces. The algorithm can more fully explore the solution space and enhance the ability of an algorithm to jump out of a local optimum. Secondly, by simulating the combat tactics of special forces, the algorithm uses command parameters to control the three stages of large-scale search, transitional stage search and small-scale precise search, making the balance between the exploration stage and the exploitation stage more reasonable. Finally, SFA inspired by special forces operations are presented. To verify the effectiveness of the proposed algorithm, SFA is compared with 6 classical algorithms in 23 benchmark functions. In addition, the algorithm is applied to practical engineering problems. Experimental results show that SFA has shown great potential and competitive results. SFA can achieve good search performance and optimization accuracy based on a better balance of exploration and exploitation capabilities.

Aplikasi SABARSON (Si Penjaga Kualitas Rumah Sakit) Sebagai Early Warning System

Determining the quality of hospital service is about the efficiency of bed management. In some hospitals, Barber Johnson graphs are still described in Microsoft Excel, but the BOR indicator has not been displayed. SABARSON: The Guardian of Hospital Quality as an Early Warning System is a creative innovation in system design that can be used to determine the quality of a hospital by displaying a graph of the four indicators BOR, LOS, BTO, and TOI. This type of design uses the system development method with the development life cycle (SDLC) approach with the stage of problem identification, reference search, dataflow and concept preparation, color palette preparation, system design, and system implementation. The purpose of this study is to help and facilitate medical record workers in describing or displaying a Barber Johnson graph quickly and precisely. This study resulted in several interface design results such as login, BOR, TOI, LOS, BTO, report recapitulation, and Barber Johnson graphic.

Clinical case of psoriasis in an HIV-positive patient

Modern ideas about psoriasis are becoming wider, while the incidence of this dermatosis is increasing every year. Psoriasis does not seem to be just a skin pathological process, since the basis of this disease is the development of chronic systemic immune inflammation resulting from the launch of a complex cascade of immune reactions with impaired activity of T-lymphocytes and the production of various pro-inflammatory cytokines and chemokines.
 The course of psoriatic disease has its own characteristics in a patient with human immunodeficiency virus. The peculiarities of the course of psoriasis in a patient with human immunodeficiency virus require a personalized approach in therapy.
 This article describes a clinical case of psoriasis in a patient with newly diagnosed human immunodeficiency virus. The stages of the diagnostic search for HIV infection are described. To confirm the diagnosis of psoriasis, a pathohistological examination of a skin biopsy was performed, as well as additional laboratory and instrumental studies. The main features of the course of psoriatic disease in an HIV-positive patient are demonstrated: a combination of several clinical forms of the disease, severe severity, lack of necessary positive dynamics against the background of external and basic anti-inflammatory forms of therapy.

Adaptive multi-surrogate and module-based optimization algorithm for high-dimensional and computationally expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving computationally expensive optimization problems. However, the performance of SAEAs deteriorates drastically for high-dimensional problems since the accuracy of surrogates may decrease to an insupportably low level in high dimensions which profoundly affect subsequent evolution optimization. To overcome this issue, this paper proposes an adaptive multi-surrogate and module-based optimization algorithm named AMSMO. Unlike existing algorithms that adopt sole surrogate models, AMSMO develops a real-time autoselection of kernels approach for building the most promising RBF. AMSMO makes use of five modules to promote the optimization quality, including: a. global search module; b. local search module; c. semi-local search module; d. convergence module; e. restart module. Specifically, we provide a novel distance- and objective-value-based criterion for global search. The semi-local search module applies a dynamic search criterion that makes the algorithm explore at the early search stage and exploit at the final stage. A cluster-based restart criterion is proposed to address the stagnation of optimization. Several benchmark functions with dimensions varying from 30 to 100 and computationally intensive practical simulation–optimization problems are adopted to validate the proposed algorithm. The empirical experiments demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in most cases.