bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks
Authors: Schreiber Sarah , Hewitt Danielle , Seymour Ben , Yoshida Wako
Published: August 2024

Bayesian statistics are popular in human cognitive neuroscience research because they can incorporate prior knowledge. Although well established for retrospective analysis, the application of Bayesian methods to prospective analysis is less well developed, especially when used in combination with computational model-based analysis of behavioural data. It is therefore important to establish effective methods for testing and optimising experimental designs for these purposes. One potential framework for a prospective approach is Bayes factor design analysis (BFDA), which can be used alongside latent variable modelling to evaluate and visualise the distribution of Bayes factors for a given experimental design. This paper provides a tutorial-style analysis combining BFDA with latent variable modelling to evaluate exploration-exploitation trade-offs in the binary multi-armed bandit task (MAB). This is a particularly tricky example of human decision-making with which to investigate the feasibility of differentiating latent variables between groups as a function of different design parameters. We examined how sample size, number of games per participant and effect size affect the strength of evidence supporting a difference in means between two groups. To further assess how these parameters affect experimental results, metrics of error were evaluated. Using simulations, we demonstrated how BFDA can be combined with latent variable modelling to evaluate and optimise parameter estimation of exploration in the MAB task, allowing effective inference of the mean degree of random exploration in a population, as well as between groups. However, BFDA indicated that, even with large samples and effect sizes, there may be some circumstances where there is a high likelihood of errors and a low probability of detecting evidence in favour of a difference when comparing random exploration between two groups performing the bandit task. In summary, we show how BFDA can prospectively inform design and power of human behavioural tasks.
OUCRU Outbreak Advisory Board in the COVID-19 Pandemic in Viet Nam
Authors: Ngo Phan Bao Tran , Lawson Katrina , Le Van Tan , van Doorn H Rogier , Thwaites Guy
Published: August 2024

Background Engagement between researchers and policy stakeholders plays a key role in facilitating collaboration and using research to inform health policies in Vietnam. As a leading health research institute in the region, the Oxford University Clinical Research Unit (OUCRU) has been working with policy stakeholders for health impacts since its establishment in 1991. However, such efforts have mainly been fragmented and faced multiple barriers from both sides, including time constraints, lack of support, resources and relationships, and negative perceptions, among others. Methods To promote more systemic engagement, in 2020, we established the OUCRU Outbreak Advisory Board (OAB) in response to COVID-19 with the intention of continuing after the pandemic to advise on general epidemic preparedness and response. The purpose of the OAB was to provide a platform for knowledge exchange and discussion on issues related to COVID-19, and to inform OUCRU's COVID-19 research program. It also aimed to contribute to broader outbreak management. Results Through quarterly online meetings, the OAB provided all members a forum to discuss, share, and understand each other’s activities and priorities in the context of infectious disease outbreaks in Vietnam. The OAB played a role in facilitating more effective communication between OUCRU researchers and policy stakeholders in Vietnam. Conclusions The model of an outbreak advisory board offers an inexpensive, relatively simple way to engage with policy stakeholders, and can be easily replicated by other programs. Despite its limitations, the OAB has contributed to ongoing communication efforts between OUCRU researchers and policy stakeholders in Vietnam. It helped ensure that the organization’s research response to the pandemic was relevant, timely, and locally driven and set the foundation for a more comprehensive discussion forum on research and policy related to infectious diseases, namely the OUCRU Infectious Diseases Research and Policy Discussion Forum (OUCRU IDDF).
Case Report: Soft tissue infection with Burkholderia thailandensis capsular variant: case report from the Lao PDR
Authors: Vannachone Souphaphone , Luangraj Manophab , Dance David , Chantratita Narisara , Saiprom Natnaree , Seng Rathanin , Tandhavanant Sarunporn , Rattanavong Sayaphet , Simpson Andrew , Roberts Tamalee
Published: August 2024

Background Burkholderia thailandensis is an environmental bacteria closely related to Burkholderia pseudomallei that rarely causes infection in humans. Some environmental isolates have shown to express a capsular polysaccharide known as B. thailandensis capsular variant (BTCV), but human infection has not previously been reported. Although B. thailandednisis has been identified in environmental samples in Laos before, there have not been any human cases reported. Case A 44-year-old man presented to a district hospital in Laos with a short history of fever and pain in his left foot. Physical examination identified a deep soft-tissue abscess in his left foot and an elevated white blood count. A deep pus sample was taken and melioidosis was suspected from preliminary laboratory tests. The patient was initially started on cloxacillin, ceftriaxone and metronidazole, and was then changed to ceftazidime treatment following local melioidosis treatment guidelines. Laboratory methods A deep pus sample was sent to Mahosot Hospital microbiology laboratory where a mixed infection was identified including Burkholderia sp. Conventional identification tests and API 20NE were inconclusive, and the B. pseudomallei-specific latex agglutination was positive. The isolate then underwent a Burkholderia species specific PCR which identified the isolate as B. thailandensis. The isolate was sent for sequencing on the Illumina NovaSeq 6000 system and multi-locus sequence typing analysis identified the isolate had the same sequence type (ST696) as B. thailandensis E555, a strain which expresses a B. pseudomallei-like capsular polysaccharide. Conclusion This is the first report of human infection with B. thailandensis in Laos, and the first report of any human infection with the B. thailandensis capsular variant. Due to the potential for laboratory tests to incorrectly identify this bacteria, staff in endemic areas for B. thailandensis and B. pseudomallei should be aware and ensure that appropriate confirmatory methods are used to differentiate between the species.
Research and Application of a Dual Filtering Music Hybrid Recommendation Model Based on Cat Boost Algorithm and DCN
Authors: Hou Juncai
Published: August 2024

With the increase of Internet users, the traditional music recommendation model can not meet the increasing personalized needs of users. The single deep cross network model has some defects in music recommendation, such as poor stability and inability to process complex data. To overcome the shortcomings of existing models, a new hybrid music recommendation model combining CatBoost algorithm and deep cross network is constructed to improve the recommendation performance and better meet the individual needs of users. Then the performance of the hybrid model is compared with other algorithms. The results showed that the accuracy of the proposed hybrid algorithm was up to 92.7%, which was superior to the comparison algorithm. In comparison with other single model and hybrid model, it is found that the proposed model was more than 0.05% higher than other models in the four indices of AUC area, accuracy, precision and recall. The above results showed that the proposed hybrid music recommendation model could efficiently process data information and provide users with accurate personalized music recommendation. This study not only promoted the development of music consumption and creation, but also found that the CatBoost-DCN hybrid model was significantly effective in improving recommendation performance. This finding provides a more efficient recommendation strategy for music platforms and has far-reaching significance for improving user experience and satisfaction.
Review of Automated Test Case Generation, Optimization, and Prioritization using UML Diagrams: Trends, Limitations, and Future Directions
Authors: Kongarana Srinivasa Rao , Rao A. Ananda , Raju P Radhika
Published: August 2024

This systematic literature review examines the effectiveness of automated test case generation, optimization, and prioritization methods based on Unified Modeling Language (UML) diagrams. The review summarizes the methods, main contributions, and limitations, and suggests areas for future research. This paper examines various optimization algorithms, model-based testing methods, and UML diagram validation methods to determine how well they perform. The review highlights some issues with the current situation, such as the fact that it only examines a few types of UML diagrams and does not go into great detail about how they work or compare to other diagrams. However, it also suggests ways in which these issues could be addressed in future research. Some of the suggested directions include researching different modeling languages and devising solutions to handle the complexity of system models. Model-based testing should also be combined with optimization and prioritization methods to increase the flexibility and usefulness of research in this field. This article makes no direct comparisons to UML diagrams, but it does provide a thorough discussion of the current state of the art and a list of strategic priorities to advance the field of automated test case generation, optimization, and prioritization. These reviews are useful for both researchers and practitioners because they demonstrate how things are currently done and how they should be done in the future.
DiffCRNN: A Novel Approach for Detecting Sound Events in Smart Home Systems Using Diffusion-based Convolutional Recurrent Neural Network
Authors: Al Dabel Maryam M.
Published: August 2024

This paper presents a latent diffusion model and convolutional recurrent neural network for detecting sound event, fusing advantages of different networks together to advance security applications and smart home systems. The proposed approach underwent initial training using extensive datasets and subsequently applied transfer learning to adapt to the desired task to effectively mitigate the challenge of limited data availability. It employs the latent diffusion model to get a discrete representation that is compressed from the mel-spectrogram of audio. Subsequently a convolutional neural network (CNN) is linked as the front-end of recurrent neural network (RNN) which produces a feature map. After that, an attention module predicts attention maps in temporal-spectral dimensions level, from the feature map. The input spectrogram is subsequently multiplied with the generated attention maps for adaptive feature refinement. Finally, trainable scalar weights aggregate the fine-tuned features from the back-end RNN. The experimental findings show that the proposed method performs better compared to the state-of-art using three datasets: the DCASE2016-SED, DCASE2017-SED and URBAN-SED. In experiments on the first dataset, DCASE2016-SED, the performance of the approach reached a peak in F1 of 66.2% and ER of 0.42. Using the second dataset, DCASE2017-SED, the results indicate that the F1 and ER achieved 68.1% and 0.40, respectively. Further investigation with the third dataset, URBAN-SED, demonstrates that our proposed approach significantly outperforms existing  alternatives as 74.3% and 0.44 for the F1 and ER.
Hybrid Electric Vehicle Energy Management Strategy based on Genetic Algorithm
Authors: Luo Yingzhe , Fan Chaoxiong
Published: August 2024

A genetically optimized fuzzy control algorithm is used to realize the parallel hybrid system’s real-time energy distribution between the engine and the electric motor. This paper first adopts fuzzy control to improve the robustness and real-time performance of the entire system. Then, a membership function optimization method based on a genetic algorithm is proposed by simulating the working state of CYCUDDS. Simulation experiments show that compared with unoptimized fuzzy control, the improved fuzzy controller can improve the vehicle’s fuel economy and prevent continuous battery discharge, thereby improving battery endurance.
Data Protection and Privacy Protection of Advertising based on Cloud Computing Platform
Authors: Chen Zhishe
Published: August 2024

This paper uses the hybrid leapfrog algorithm to mine user information in encrypted advertisements effectively and intelligently. This method handles the nonlinearity of the original data by mapping it into kernel space. The representation of the original ciphertext in the kernel space is obtained by sparsely reconstructing the encrypted original advertising data. Build a corresponding scoring mechanism and select the best advertising data characteristics. The selected data were clustered using the data fuzzy clustering method based on the improved hybrid leapfrog. Set the adjustment coefficient to improve the local optimization performance of hybrid frog leaping. This algorithm uses the tightness and separation in genetic algorithms and constructs a fitness function to determine the clustering critical value. This enables the practical, intelligent mining of homomorphic passwords with privacy protection. Experimental results show that the method proposed in this article can effectively improve the convergence speed and accuracy of clustering. Improve Blowfish by combining multi-threading, sharing encryption and other methods. This enables encryption and decryption of large amounts of model data. The research of this project has very important research value in improving the security performance and effectiveness of cryptographic algorithms.
The Employment of Carbon Nanotubes in Biomedical Applications
Authors: A. Rida Jafaar Fahad
Published: August 2024

Carbon nanotubes (CNTs), a prominent application of nanotechnology, find extensive use across various fields. Their electrical and optical characteristics, which are affected by the manufacturing process and any impurities introduced during production, are crucial in establishing their suitability for use. This research focuses on the utilization of carbon nanotubes in medical applications, exploring their properties both as electrical conductors and semiconductors, comparable to silicon used in precision medical equipment and devices. When functioning as electrical conductors, CNTs exhibit characteristics similar to traditional conductive materials. This property is harnessed in medical applications, particularly in targeted cancer treatments that minimize impact on healthy cells. CNTs’ efficient conduction of electrical current makes them valuable components in medical devices and equipment. Furthermore, CNTs showcase semiconductor properties akin to silicon. This characteristic is crucial for developing advanced medical equipment, enabling accurate diagnostics and medical imaging. The semiconductor behavior allows the creation of intricate medical devices with enhanced precision. The research underscores the significance of CNTs in shaping the future of medical technology, especially when integrated with artificial intelligence applications. The ability of CNTs to function both as conductors and semiconductors highlights their versatility in the medical field, promising advancements in healthcare technologies. Their use holds potential for targeted cancer treatments, accurate diagnostics, medical imaging, and enhanced performance through integration with artificial intelligence.
Sports Event Data Management System and Its Application in Competition Organization
Authors: Li Zhenyu
Published: August 2024

Traditional sports competition data management systems have poor security and reliability in management results. The author proposes a new sports competition data management system based on a network platform. Integrating internal and external services to handle system business, designing pages using JSP dynamic page technology. Utilize network platforms to transmit data, and verify the transmission of data on the network platform through hardware and software firewalls. Realize data transmission between the system and the client through the designed network platform. Analyzed data backup and recovery methods to ensure data security. Track important business and operations of the system through the log tracking layer, and provide UML modeling for competition data management. The experimental results show that the recovery speed of the system is significantly faster than SSH and MDA systems, indicating strong data recovery performance of the system. It has been proven that the competition data management results of the designed system are reliable and the system performance is strong.
The Application of Intelligent Robots and Deep Learning in the Construction Management Platform System of Construction Engineering
Authors: Zhou Yandong
Published: August 2024

In order to solve the problem of duplicate data entry between construction management platform systems in construction engineering, the author proposes to apply RPA intelligent process robots to replace manual data collection, operation, entry, and verification. The design of the system is divided into overall architecture, instruction program loading process, human-machine interaction system level services, and other levels. An end-to-end procedural instruction transmission control method is adopted, establish a low-level control command output module for the online calibration system of the flight path, using a basic service architecture system, implement human-machine interactive control of the online calibration system for flight paths on the B/S architecture system. Build a record controller module for the inspection trajectory correction of RPA intelligent process robots, and perform feedback control during the trajectory correction process in LOG-CONTROL-BLOCK, using the RPA feedback correction algorithm, achieve adaptive correction and error feedback tracking of the inspection trajectory of RPA intelligent process robots. Implement calibration system development and design in an integrated DSP (Digital Signal Processing) information processing platform. The experimental results show that good economic benefits have been achieved through application, and the problem of duplicate data entry between the employer’s IFS system and the ENPOWER document management system has been solved, greatly reducing error rates and personnel costs. It can replace manual data collection, entry, verification, and business operations; It has the characteristics of low error rate, low cost, high accuracy, compliance, and 24/7 standby; In 2021, 108000 yuan was saved, in 2022, 432000 yuan was saved, and in 2023, 432000 yuan was saved, demonstrating the labor hour cost savings achieved by utilizing RPA intelligent process robots.
Application of Software Robots and Deep Learning in Real time Processing of E-commerce Orders
Authors: Niu Wenbo , Hu Yibo , Zhang Wei
Published: August 2024

The development of e-commerce faces many problems, among which the real-time information exchange between customers and websites is the most urgent. To study the application of software robots in the real-time processing of e-commerce orders. According to the working mechanism of ALICE, JAVAPROGRAMD, AIML technology, database technology, knowledge base principle, Rule of inference, and reasoning strategies are used to design the structure, workflow, knowledge processing flow, and Inference engine supporting human-computer automatic negotiation of the software robot. Establishing a rule base is the acquisition, induction, and organization of knowledge related to order processing. It requires learning and analyzing the problems and related materials to be solved, simulating the actual process of order processing, and extracting useful processing rules and processes from it. The rules in the system are not organized in a simple list form, but have a certain hierarchical structure. This way, when using these rules for reasoning, the hierarchy is clear and it is easy to modify and add rules. The hierarchical structure of the rules referred to here is to simulate the thinking activities of the human brain in the process of order processing, and organize them in a certain way. Based on the basic rules of order processing, the process of order processing, the rational performance of the order and the possibility of necessary events, so as to realize the structure of the reasoning machine. Expands the application of Chatbot ALICE, and provides a new tool for real-time online negotiation and negotiation of e-commerce order processing. The core part of the e-commerce order processing software robot is the human-machine automatic interaction module. Database module, knowledge base module and rule base module provide the necessary data, knowledge and rule basis for its realization, while the implementation of reasoning machine provides the possibility for the intelligence and dynamic interaction of human-machine automatic interaction module.
Leveraging Emotions in Student Feedback to Improve Course Content and Delivery
Authors: Wani Abid Hussain
Published: August 2024

Emotions play a vital role in almost all the activities we perform, including learning. In fact, the success of any learning system is largely dependent upon its ability to deliver the course content in such a form so as to meet the learning requirements of the target audience. Learning Systems can be tailored to effectively utilize the feedback from learners to improve the course content, and thus the feedback can prove to be a valuable asset. There is an increased demand for focusing on a learner-centric approach to content delivery. In this study we attempt at detecting different learning-relevant emotions from the feedback for a course so as to enable course designers to incorporate the type of content that matches a learners requirements. Rather than taking into account six basic emotions (sadness, happiness, fear, anger, surprise and disgust) we consider interest, engagement, confusion, frustration, disappointment, boredom, hopefulness and satisfaction emotions for the purpose of our study since they are more relevant in a learning setup. We employed a supervised algorithm, Support Vector Machine, for affect detection from the textual feedback in our experiments.
Optimization Algorithm for Green Environment Design Based on Artificial Intelligence
Authors: Qian Kai
Published: August 2024

In order to build energy-efficient commercial buildings in bustling urban centers and utilize passive means suchas natural ventilation and natural lighting as much as possible to improve indoor environmental quality, the author proposes agreen environment design optimization algorithm based on artificial intelligence. Taking Building A as an example, simulationtechnology was used to optimize the performance of the enclosure structure, anti condensation in the glass atrium, and naturalventilation during the transition season. The sunlight and shadow simulation software BSAT was used to simulate the mutualocclusion and self occlusion of the building. It was found that external rolling shutters were not required for shading the facadebetween axes L2-L5 and 3-9. Using DeST-C, the energy consumption and investment of south facing, east facing, and west facingenvelope structures were compared when using different types of glass. Combining economic efficiency and energy-saving effects,the optimal southbound enclosure structure scheme for this building is to use Low-e membrane coated hollow double glass with tenhorizontal louvers for external shading; It is recommended to adopt the scheme of hollow double glass (Low-e membrane) or hollowdouble glass (Lowe membrane)+external roller shutter in the east-west direction. It is worth noting that for east-west orientedglass, the Lowe film should be low permeability, mainly to improve the thermal performance in winter and reduce the radiationheat gain in summer. Using DeST-C for simulation 2 calculation, it was found that when the thermal performance of windowsincreased from 3.0W/(m2. K), shading coefficient 0.7 to 2.0W/(m2 K), and shading coefficient 0.5, the maximum cold and heatload of the building and the cumulative consumption of cold and heat throughout the year were significantly reduced, providingan effective reference for solving problems.
Research on the Application of MOOCs Based on Reinforcement Learning in College English Teaching
Authors: Gu Yu
Published: August 2024

In the field of teaching college English, this study explores the integration of reinforcement learning concepts with Massive Open Online Courses (MOOCs). ”LearnFlex,” the suggested framework, is intended to support an environment that is dynamic and flexible for learning. By offering thorough English language courses and utilizing reinforcement learning techniques  LearnFlex leverages the inherent benefits of MOOCs to customize and enhance the learning process for every student. This study’s main goal is to assess how well LearnFlex works in the context of teaching college English to improve student performance, engagement, and general satisfaction. Through the integration of educational technology, machine learning, and pedagogical methodologies, LearnFlex aims to offer significant insights that support the ongoing development of efficient and customized online learning. The study contributes to the larger objective of improving teaching strategies by utilizing cutting-edge technologies to build a learning ecosystem that is more adaptable and focused on the needs of students. This study aims to provide insights for future improvements in online education, specifically in the area of language training, by conducting a thorough examination of LearnFlex’s effects.
Improving Data Security and Scalability in Healthcare System using Blockchain Technology
Authors: K R Rohini , Subramanian Rajakumar , Soman Geetha
Published: August 2024

The wearable tech revolution and the proliferation of Internet of Things (IoT) gadgets have created exciting new opportunities for remote patient monitoring. Healthcare providers are increasingly utilizing wearable technologies to expedite the process of diagnosis and treatment. The healthcare and research fields have been substantially impacted by emerging technologies. On the other hand, there are legitimate worries regarding the security of data transfers and transaction recording upon using these technologies. Healthcare departments need easy data interchange for interoperability. Protecting data security and integrity is crucial when exchanging information with authorized parties. In many existing solutions, patients’ sensitive data is collected and stored in smart healthcare systems. Scalability, breach or unauthorized access to this data can compromise privacy. The adoption of blockchain technology is one way to safeguard patient privacy in healthcare. Also, by facilitating the safe and secure exchange of data, blockchain technology is revolutionizing the healthcare industry by making traditional methods of diagnosis and treatment more reliable. However, blockchain has serious problems with its highly limited scalability. This article suggests a new method “SHORTBLOCKS” depending on blockchain technology for the safe administration and analysis of data. To circumvent the security-scalability issue and achieve high throughput, this study employs the newly introduced protocol “SHORTBLOCKS”, which extends blockchain concept to a direct acyclic graph of blocks. The proposed system uses both a private and a public blockchain that are created on the new protocol. In order to analyze patient health data, a system using smart contracts and a private blockchain is built. The system logs the occurrence to the public blockchain in the case that the smart contract raises an alarm due to an unusual reading. This will fix the scalability issue with initial blockchain as well as the security and privacy issues involved in remote patient monitoring. Simulation results shows the performance of the proposed system by comparing with existing solutions, SPECTRE protocol and GHOSTDAG protocol.
Optimizing Waste Reduction in Manufacturing Processes Utilizing IoT Data with Machine Learning Approach for Sustainable Production
Authors: Altarazi Faisal
Published: August 2024

Sustainable manufacturing with the Internet of Things (IoT) reduces environmental impacts, conserves natural resources, saves energy, and improves worker, community, and consumer safety while maintaining economic viability. IoT’s network of sensors and intelligent devices collects and analyzes data throughout the production lifecycle, enabling organizations to fulfil sustainability objectives and adopt more efficient, less wasteful operations. Waste management and reduction measures are thefocus of sustainable manufacturing research. Improvements are needed to simplify waste management and reduce production waste. Thus, in this study, we introduce an innovative machine learning technology called ”EcoEfficientNet”, developed to tackle this problem. Our study addresses the issue of waste in manufacturing processes. EcoEfficientNet employs sophisticated deep learning algorithms to analyze complex production data, allowing it to identify significant patterns and determine specific areas where waste can be significantly minimized. EcoEfficientNet’s approach to waste reduction in manufacturing processes revolves around three main strategies: data-driven analysis, optimization recommendations, and adaptable learning for continual enhancement. EcoEfficientNet’s success lies in its capacity for perpetual learning, enabling it to adapt to novel data and evolve alongside production settings. An extensive case study of a particular manufacturing process is carried out to assess the efficiency of EcoEfficientNet and provide helpful perspectives into the model’s effectiveness. By incorporating this method into the manufacturing process, organizations have seen a decrease in waste generation of up to 30%, demonstrating the applicability and efficacy of machine learning in improving sustainable manufacturing processes. The key to EcoEfficientNet’s success is its ability to engage in continuous learning, allowing it to adjust to new data and develop in tandem with operational environments.
Application of Physical Modeling and Virtual Simulation Technology in Measuring the Performance of Subway Train Tracking and Operation
Authors: Wang Xiuxuan , Liang Hongwei
Published: August 2024

The purpose of this paper is to study the application of virtual simulation technology in the measurement of subway train tracking performance. Firstly, the safety braking model required by virtual reconnection technology is discussed around the operation simulation and performance measurement of virtual reconnection. The two trains are combined into ”one virtual reconnection train set”, the tracking train and the head train run with relative moving block, and the two run with the front train set with moving block. Then, according to the characteristics of Metro train tracking operation, a virtual reconnection model and an improved station tracking model are proposed for the bottleneck area of the station. Finally, the proposed model is verified by numerical calculation and computer simulation modeling. The simulation results reproduce the dynamic characteristics of train flow during metro train operation. The results show that the departure interval and minimum tracking interval of the train are all 58 s. The improved station tracking model has the moderate passing capacity as the relative moving block, and the virtual reconnection model has the largest passing capacity. After the initial delay of the system, the delay recovery ability of the virtual reconnection model is the strongest.
Perancangan Kampanye Pola Hidup Rendah Gula Melalui Media Sosial Sebagai Upaya Pencegahan Obesitas Bagi Remaja Usia 16 -21 Tahun
Authors: Tri Sendyana I Nyoman , Rizkiantono Raditya Eka
Published: August 2024

Human life cannot be separated from sugar consumption, because sugar is a source of calories for the body. However, the higher consumption of sugar causes the obesity rate in Indonesian adolescents to also creep up. By looking at the long future that teenagers have, obesity here caused by sugar can have a bad impact on them, both in terms of disease and psychological aspects. Seeing this, social media is needed as a means to share information about low-sugar lifestyles to raise public awareness of the importance of low-sugar lifestyles for long-term life.
The design of this campaign was carried out through several research methods. Literature study on the topic of sugar, obesity, and social media campaigns. Existing and comparative studies on similar campaigns in Indonesia to add references and insight. Depth interviews were conducted with extreme users and nutritionists. Questionnaires were conducted to collect data regarding the target audience. Experimental studies design design concepts that include media concepts, communication concepts, and visual concepts. These concepts were then validated through user testing and interviews with expert sources.
Through these methods, an Instagram and YouTube social media campaign with 5 content segments was produced which aims to increase public awareness of the amount of hidden sugar that is consumed unknowingly through circulating food and drinks. In addition, this campaign is expected to educate the public about the importance of a low-sugar lifestyle and physical activity as a measure to overcome and prevent obesity caused by excess sugar consumption as well as provide understanding to the public about the fact that obesity in adolescence can cause diseases in old age. which can be prevented with a healthy lifestyle in adolescence.
Perancangan Kampanye Sosial Pola Asuh Sehat dalam Keluarga Muda sebagai Upaya Pencegahan Stunting
Authors: Fitriani Ajrina , Dwi Wahyurini Octaviyanti
Published: August 2024

Stunting cases in toddlers remain a persistent health problem in Indonesia for several decades. Stunting is a condition of failed growth and development in toddlers due to malnutrition starting from the womb, which leads to them having shorter or stunted stature compared to their peers. Food insecurity is not the sole cause of stunting, other factors such as inappropriate parenting practices also play a significant role. One crucial aspect to consider about it is the younger generation, as they will eventually become parents in their young families. Seeing this a social campaign is needed to promote the adoption of healthy parenting practices specifically targeted towards the younger generation as an additional information medium to reach a broader audience and increase awareness of implementing these healthy parenting practices. This campaign incorporates several research methods, including literature studies, existing studies, comparator studies, depth interviews, questionnaires, experimental studies, expert interviews, user testing, and evaluation to create the final design. Big idea used in this campaign is "Prevent Stunting Before It's Too Late," emphasizing that by applying healthy parenting practices, children can avoid the risk of stunting before it impairs their growth and development. The output of this design includes campaign videos, social media, and merchandise. This social campaign aims to supplement the limitations of previous campaigns and raise awareness among the public, especially the younger generation who are planning to get married and have children, about the importance of implementing healthy parenting practices in young families as a preventive measure against stunting.
Furthering our understanding
Authors: Lennon Roisin
Published: August 2024

The author of Lennon (2024) responds to the Letter to the Editor on their research article on biomechanics and fetal positioning
The symptoms, diagnosis, and management of allergic rhinitis
Authors: Daly Rachel , Rickards Emma , Woodward Kerry
Published: August 2024

Allergic rhinitis is a common disorder that is strongly linked to asthma and conjunctivitis. It is usually a long-standing condition that often goes undetected in the primary care setting

Allergic Rhinitis (AR) is a very common, under recognised problem in the UK with symptoms significantly impacting on quality of life, work and school performance, social life and is a risk factor for developing asthma. Treatment options for AR include allergen avoidance, antihistamines and intra-nasal pharmacological therapies.
Representation of the American South in North American Regional Geography Textbooks in the Twenty-First Century
Authors: McDaniel Paul
Published: August 2024

The American South’s influence on cultural, economic, and political dynamics of the
United States and broader perspectives of the South as a region continue to evolve over two
decades into the twenty-first century. For instance, the southeastern US contains Sunbelt locales
experiencing some of the fastest population growth in the country. Observers eagerly focus
attention on local and state elections in the South as the region exerts considerable sway on the
outcomes of national elections. Meanwhile, college-level regional geography courses about North
America, which include coverage of the US South, are contained within many geography
curriculums. Such courses play a role in shaping students’ perceptions of regions and are popular in
many geography programs. This paper examines how contemporary North American regional
geography textbooks, with editions published since 2010, portray the American South. How do
these texts define the American South? What major topics do these textbooks include within their
coverage of the American South and how do these topics compare and contrast across these texts?
What impactful contemporary themes remain underrepresented, particularly considering ongoing
current events in the 2020s? Findings suggest recommendations for contemporary pedagogy about
the American South within North America regional geography courses and future research
trajectories.
“We need more!”: Mexican Immigrants’ Tacit Knowledge and Mississippi’s Beef Cattle Industry
Authors: Quinonez Zepeda Juan
Published: August 2024

The United States’ deeply rooted reliance on forms of racialized and undervalued agricultural labor has long been underpinned by a common misconception that this work requires little to no skill or prior knowledge. This paper focuses on contemporary centrality of (de)valued migrant knowledge in the modern animal agriculture industry in the US South, employing research on tacit knowledge to explore the often-ignored impact immigrant knowledge has on the Mississippi beef cattle industry. Through a focus on Mexican migrants’ indispensable skills and tacit knowledge, the article emphasizes the critical role farmworkers’ knowledge plays in negotiating operating threats. Utilizing semi-structured interviews with farm owners and Mexican farmworkers, I evaluate how skill is visible in daily work, how workers obtain and transfer tacit knowledge, and how farm owners perceive this knowledge. Not only are skill and knowledge highly regarded in the cattle industry, but Mexican workers are intentionally sought out for them. Despite this, I argue, Mexican migrants’ experiences and opportunities are conditioned by the region’s persistent agricultural plantation bloc that reproduces racialized and migratory labor exploitation. Hence, I highlight a paradox whereby Mexican cattle workers are highly acknowledged for their contributions but simultaneously exploited and thus, constrained in fully leveraging their knowledge.
Plug-and-play adaptive optics microscopy with full-field correction using isoplanatic patch estimation and field segmentation
Authors: Dorn Alex , Zappe Hans , Ataman Çağlar
Published: August 2024

We discuss the implementation and performance of a plug-play adaptive optics (AO) module for commercial microscopes comprising indirect wavefront sensing, and a deformable phase plate (DPP) located directly between the objective and the turret. With the DPP at this location, the system closely resembles a pupil-AO scheme, in which effective aberration correction is only possible within the isoplanatic patch. We overcome this limitation by estimating the aberration profiles at multiple field points in parallel and correcting them in sequence to obtain a 2D array of high-quality sub-aperture images. These are then stitched together to form a corrected full-field image. To minimize the measurement time without compromising correction quality, we propose an empirical method to identify the size of the isoplanatic patch, which is both sample and system dependent. Matching the field segment size to that of the isoplanatic patch provides the best compromise between consistent correction quality across the image and measurement time. We demonstrate the performance of the developed system in a commercial microscope using synthetic samples and discuss the performance and limitations of the system.
Large beam size grating coupler in silicon-on-insulator using fully etched subwavelength gratings
Authors: Ruiz Miguel Barona , Moreno-Pozas Laureano , Ginel Pablo , Ortega-Moñux Alejandro , de Oliva-Rubio Jose , Molina-Fernandez Inigo , Wangüemert-Pérez J. Gonzalo , Halir Robert
Published: August 2024

Several emerging applications of silicon photonics, including sensing, ranging and optical trapping, require fixed, well-collimated beams that enable interaction with targets placed centimeters away from the chip. Generating such beams without using bulk-optic lenses entails radiating beams with diameters of hundreds of microns directly from the chip. Gratings with sufficiently low strength have so far only been shown in the silicon nitride platform using specialized shallow etch steps; in silicon-on-insulator the implementation becomes much more challenging due to the increased index contrast. Here we report the first fully etched silicon-on-insulator grating capable of radiating such large beams. Using a judiciously designed double-period subwavelength structure, we experimentally demonstrate a beam diameter in excess of 350 μm, with feature sizes compatible with deep-ultraviolet lithography.
Noise Resistant Voxel scanning LiDAR
Authors: Garikapati Malvika , Huang Yuping , Sua Yong Meng , Ramanathan Jeevanandha
Published: August 2024

We demonstrate an upconversion imaging technique that transduces near infrared signals to visible for photon counting with low loss and high efficiency. By using far-detuned, 10-ns pump pulses for upconversion, fast voxel scanning is achieved with sub-centimeter ranging resolution over a designated spatial interval of 1.5~meters that is defined and fast shifted by an electronic delay line. Aided by superior noise suppression, 50×50×1000 voxels can be measured at a remarkable speed of 10 frames per second, making it suitable for fast LiDAR applications in high noise and photon-starved environments, including those for autonomous navigation and aerospace missions.
Evaluating theory of change to improve the functioning of the network for improving quality of care for maternal, newborn and child health
Authors: Dube Albert , Mwandira Kondwani , Akter Kohenour , khatun Fatama , Lemma Seblewengel , Seruwagi Gloria ,   QCN Evaluation Group, Shawar Yusra Ribhi , Djellouli Nehla , Mwakwenda Charles , English Mike , Colbourn Tim
Published: August 2024

In 2017, WHO and global partners launched ‘The Network for Improving Quality of Care for Maternal, Newborn and Child Health’ (QCN) seeking to reduce in-facility maternal and newborn deaths and stillbirth by 50% in health facilities by 2022. We explored how the QCN theory of change guided what actually happened over 2018–2022 in order to understand what worked well, what did not, and to ultimately describe the consequences of QCN activities. We applied theory of change analysis criteria to investigate how well-defined, plausible, coherent and measurable the results were, how well-defined, coherent, justifiable, realistic, sustainable and measurable the assumptions were, and how independent and sufficient the causal links were. We found that the QCN theory of change was not used in the same way across implementing countries. While the theory stipulated Leadership, Action, Learning and Accountability as the principle to guide network activity implementation other principles and varying quality improvement methods have also been used; key conditions were missing at service integration and process levels in the global theory of change for the network. Conditions such as lack of physical resources were frequently reported to be preventing adequate care, or harm patient satisfaction. Key partners and implementers were not introduced to the network theory of change early enough for them to raise critical questions about their roles and the need for, and nature of, quality of care interventions. Whilst the theory of change was created at the outset of QCN it is not clear how much it guided actual activities or any monitoring and evaluation as things progressed. Enabling countries to develop their theory of change, perhaps guided by the global framework, could improve stakeholder engagement, allow local evaluation of assumptions and addressing of challenges, and better target QCN work toward achieving its goals.
Predicting nickel concentration in soil using fractional-order derivative and visible-near-infrared spectroscopy indices
Authors: Cao Jianfei , Liu Wei , Feng Yongyu , Liu Jianhua , Ni Yuanlong
Published: August 2024

Accurate monitoring and estimation of heavy metal concentrations is an important process in the prevention and treatment of soil pollution. However, the weak correlation between spectra and heavy metals in soil makes it difficult to use spectroscopy in predicting areas with a risk of heavy metal pollution. In this paper, a method for detection of Ni in soil in eastern China using the fractional-order derivative (FOD) and spectral indices was proposed. The visible-near-infrared (Vis-NIR) spectra were preprocessed using the FOD (range: 0 to 2, interval: 0.1) to solve the problems of baseline drift and overlapping peaks in the original spectra. The product index (PI), ratio index (RI), sum index (SI), difference index (DI), normalized difference index (NDI), and brightness index (BI) were applied and compared. The results showed that the spectral detail increased as the FOD increased, and the interference of the baseline drift and overlapping peaks was eliminated as the spectral reflectance decreased. Furthermore, the FOD extracted the spectral sensitivity information more effectively and improved the correlation between the Vis-NIR spectra and the Ni concentration, and the NDI had a maximum correlation coefficient (r) of 0.803 for order 1.9. The estimation model based on the NDI dataset constructed after FOD processing had the best performance, with a validation accuracy RP2 of 0.735, RMSE of 3.848, and RPD of 2.423. In addition, this method is easy to carry out and suitable for estimating other heavy metal elements in soil.