Management Module Research Articles

K-HEALTHinAIR Open Access Platform: exploring user-profiles

Abstract Background There is a lack of awareness and accessible knowledge of IAQ effects on citizens’ health. The K-HEALTHinAIR project is working to provide a one-stop-shop for IAQ knowledge. With this aim, partners are co-designing with representatives of the quadruple helix a unique platform to allocate data, information, and relevant resources to research and foster IAQ in Europe. This platform is envisioned to store in an accessible and friendly manner datasets of concentrations of key indoor air pollutants and health parameters, standardized Key Performance Indicators, indoor location characterization information, IAQ recommendations and regulations and analytical models. Methods First, a stakeholders mapping was performed in all project pilot sites to identify the platform potential users. Second, partners identified with stakeholders which information, data and resources of the project would be of interest and how to present them. Findings were summarized in user-profiles according to the quadruple helix (academia, public administration, industry, and general society). Results A set of resources had been identified for each user profile. Each of them will be adapted and formatted according to the users’ needs before uploading it to the platform. The platform will allow users to access information according to their interest in an accessible a friendly manner. In this sense, a dashboard will be included in the website of the project for accessing the different modules of the platform: data management module and knowledge sharing module. The platform is being co-designed and will be tested in the project scenarios with representatives of the quadruple helix. Conclusions The K-HEALTHinAIR Open Access platform aims to be the one-stop-shop for IAQ knowledge.

A Switchable Linear Regulator Implemented in GaAs pHEMT for WiFi Application

ABSTRACTCompared to silicon‐based complementary metal oxide semiconductor (CMOS), second‐generation semiconductor materials, such as gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT), offer significant advantages for radio frequency (RF) applications. As communication systems become more functional, designing highly integrated on‐chip transceiver systems presents a substantial challenge, where the voltage regulator as part of the power management module is indispensable. Due to lattice defects and other issues, achieving P‐channel metal oxide semiconductor (PMOS) in GaAs pHEMT is difficult, necessitating new circuit architectures to fulfill traditional linear regulator functions. To realize wireless fidelity (WiFi) transceiver systems on chip (SoC) using compound semiconductors for superior RF performance, this paper proposes a switchable linear regulator based on a 0.25 μm GaAs pHEMT process. The proposed linear regulator consumes a quiescent current of 440 μA in no‐load conditions and regulates a 5 V supply voltage down to 2.6 V output voltage. Measured results indicate that the designed linear regulator features a current load capacity of 20 mA, a load regulation rate of 5.8 mV/mA, and a line regulation rate of 1.8 mV/V. The proposed linear regulator circuit has been applied to WiFi transceiver systems, occupying a chip area of 0.054 mm2.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform

Abstract. The quantity and diversity of Earth spatiotemporal big data have significantly increased in recent years, providing the potential to comprehensively analyse complex spatiotemporal problems from new perspectives. However, integrating, managing, and applying multi-source heterogeneous Earth spatiotemporal big data remains a challenge. To address this issue, this study proposes a cloud-based spatiotemporal computing platform, the Open Geospatial Engine (OGE), for the unified organization and joint analysis of Earth spatiotemporal big data in multiple dimensions and scales. The framework of this platform comprises three modules: data management, computing engine, and service interface. The data management module adopts the GeoCube model to effectively integrate and manage multi-source heterogeneous spatiotemporal data through multi-dimensional aligned tiles. The computing engine module seamlessly maps the GeoCube model to the cloud environment by extending Spark RDD, making the GeoCube model capable of high-performance distributed computing. Following OGC specifications, the service interface module integrates and shares data, operators, and spatiotemporal analysis models through extensible APIs in an interactive development environment. Combined with a series of high-performance optimization techniques, OGE simplifies data queries and the construction of complex analytical applications by integrating these three modules. The applicability of OGE is demonstrated by case studies involving multi-dimensional queries and joint analysis of long-time-series and heterogeneous spatiotemporal data.

Tools for Fulfilling Legal Requirements of Biodiversity Specimens: Permit/Contract & Term Typologies

The multitude of legal requirements for working with biodiversity specimens is challenging and time consuming for researchers and biodiversity research institutions, including universities, museums, governmental and private institutions managing natural science collections (e.g., biobanks, preserved and living collections). This challenge significantly increased with the adoption of national laws and processes on access to genetic resources and benefit-sharing developed by provider countries in response to the Convention on Biological Diversity, Nagoya Protocol, and other concerns. Legal requirements often include a complex sequence of requests to different national authorities, response times and fees. However, obtaining the necessary documents is only the first step. Legal documentation must be managed for the full life-cycle of biodiversity specimens. In 2016, the Global Genome Biodiversity Network (GGBN) released a data standard and controlled vocabularies for permits and loans. From 2019 to 2023, GGBN led a SYNTHESYS+ project task group to review and expand them. The main outcomes are two typologies contributing to some of the core elements from the GGBN vocabularies for permits and loans. Other elements of the GGBN metadata are not addressed. The Biodiversity Permit/Contract Typology and the Typology of Legal/Contractual Terms for Biodiversity Specimens (see Schiller et al. 2024b) can serve as internationally standardised classification systems for managing permit and contract documents, and promote an internationally understandable exchange of information on terms within such documents. Their application should enable people from various backgrounds to quickly recognise what documents and legal conditions (i.e., permissions, prohibitions, restrictions, obligations) apply to a given biodiversity specimen. Details were presented at the 2024 SPNHC-TDWG joint conference (Suppl. material 1). The Biodiversity Permit and Contract Typology classifies legal and contractual documents based on their purpose. It consists of seven document categories and 38 document types (Fig. 1). We realised that documents with similar purposes, issued by different countries, may contain different terms (Fig. 2), hence document types alone fail to give sufficient information on what is allowed. Therefore, a second typology (Terms Typology), with a set of 87 catchphrases was created (Schiller et al. 2024a). These short phrases describe the essence of a specific term in a document, indicating what is allowed, required or forbidden. Both typologies are very flexible: users can choose whether they use only document categories, only document types, only catchphrases, or a mixture of them. Harvard University’s Museum of Comparative Zoology (MCZ) was the first institution to implement the typologies in its collection management digital infrastructure, MCZbase (Fig. 3). It is linking legal and contractual documents to collection objects via transactions and categorizing them using the Permit and Contract Typology. This allows rapid assessments of their applicability to accessions and loan shipments. Permissions and Rights records also include text fields to allow the addition of information on use restrictions, negotiated benefits, and any benefits provided. The Terms Typology will pave the way for the automation of collection transactions (i.e., machine-actionability) for swift decisions on a large scale. Rights management modules integrated into collections management systems or GGBN could automatically compute and arrive at decision proposals (Fig. 4). The need for repeatedly going through documents to understand stated policies and their practical consequences in the case at hand will become a situation of the past. Such automatically computed proposals can support collection staff in efficiently evaluating associated rights and drawing conclusions. The proposals will always require the review by collection staff, who carry the responsibility for the final decision and action. Our proof-of-concept exemplar developed for the future DiSSCo Loans module, employing the Open Digital Rights Language ontology, illustrates that the typology allows machine-actionability. Moving forward, one of our aims is to develop an internationally recognised standard within Biodiversity Information Standards (TDWG), based on the typologies and prepare it for ratification.

Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment

To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.

Measuring IT Governance in ERP Systems: a COBIT 2019 Evaluation of SAP Material Management Module

Information technology systems such as Enterprise Resource Planning (ERP) are crucial for companies to meet their business needs. However, as companies grow, challenges arise, particularly in the Material Management (MM) module within SAP. This study aims to identify issues such as data inaccuracies and material duplication during the interfacing process with SAP MM and assess IT governance capabilities using the COBIT 2019 framework. The methods used include interviews and literature review, focusing on the domains of APO12 (Managed Risk), BAI03 (Managed Solutions Identification & Build), and DSS03 (Managed Problems). The results show that APO12 reached capability level 4, while BAI03 and DSS03 are at level 3, with a target of level 4. Recommendations include enhancing proactive system design evaluation, developing systematic procedures to address data issues, and implementing permanent solutions to prevent data duplication and inaccuracies during interfacing.

BRalu Profile: the procurement strategy

Learning outcomes After completion of the case study, students will be able to understand BRalu Profile’s product range, customer base and historical developments; analyze how BRalu Profile’s procurement strategy evolved and its impact on business profitability; calculate and compare procurement costs and evaluate their role in decision-making for different suppliers; examine how market conditions (includes domestic and international dynamics) and pricing strategies influence procurement choices; assess the pros and cons of different procurement options and make informed recommendations based on supply chain principles; and identify potential procurement risks (e.g. currency exchange rates and supplier reliability) and propose strategies to mitigate them. Case overview/synopsis This case study explored the challenges faced by BRalu Profile, a prominent aluminum profile products company based in Ahmedabad, India. It focused on the critical task of supplier evaluation, selection and the complexities of maintaining relationships with existing suppliers. This case study delved into the intricate dynamics of procurement decisions within the supply chain and their direct impact on the firm’s overall performance. It also emphasized the supply chain’s susceptibility to disruptions and their consequences on company operations and supplier selection criteria. The protagonist, Dhaval Choladiya, had to navigate the complexities of cost-benefit analysis to identify the most suitable supplier, maximizing the firm’s net benefit while considering nonpricing parameters. This case study revealed the critical importance of maintaining strong supplier relationships in a competitive market and offered insights into the complexities of sourcing. Complexity academic level This case study is suitable for an undergraduate or graduate-level course on strategic sourcing or supply chain management or a risk management module in operations, strategy or finance course (e.g. how to deal with input cost fluctuations). Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

Evaluation of TNAU Integrated pest management (IPM) module and farmers practices against maize fall army worm, Spodoptera frugiperda (J.E Smith) at Madurai in Tamil Nadu

Evaluation of TNAU Integrated pest management (IPM) module and farmers practices against maize fall army worm, Spodoptera frugiperda (J.E Smith) at Madurai in Tamil Nadu

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

Enhancing yield and economic benefits through sustainable pest management in Okra cultivation.

Okra (Abelmoschus esculentus) is a prominent vegetable crop in Asia, confronting persistent threats from pests such as leafhoppers, whiteflies, and shoot and fruit borers. Conventional chemical control methods, despite their adverse ecological effects, remain the primary approach for pest management. Indiscriminate chemical use has led to reduced biodiversity among natural predators and the disruption of food webs in ecosystems. To address these challenges, this study assessed the efficacy of integrated (IM) and biointensive (BM) pest management modules in comparison to conventional chemical methods (CM) for mitigating insect damage to okra leaves and fruits, and subsequently, their impact on okra yield. Our result revealed that the BM exhibited the least effectiveness but outperformed untreated control plots significantly. In contrast, both IM and CM significantly reduced damage from sap-sucking insects and borer pests. Notably, plots treated with the chemical module found decreased populations of natural enemies. The IM demonstrated the lowest fruit infestation rate (5.06%), yielding the highest crop production (8.97 t ha-1), along with the maximum net return (Indian Rupees: 44,245) and incremental cost-benefit ratio (3.31). Thus, the study suggested that the implementation of integrated pest management practices can result in higher okra yields and greater economic benefits. These findings shed light on the potential of sustainable agricultural practices as a safer and more economically viable alternative to chemical-intensive pest control in okra cultivation.

Self-powered temperature-changing system driven by wind energy

Research on outdoor, mobile, and self-powered temperature-control devices has always been highly regarded. These devices can reduce energy consumption for cooling and heating, and they have broad market prospects. On this basis, a rotary disc-shaped triboelectric nanogenerator (TENG) with a maximum open-circuit voltage of 6913 V, a maximum short-circuit current of 85 μA, and a maximum transferred charge of 1.3 μC was prepared. We synthesized a ferroelectric ceramic composed of 0.15PbTiO3–0.85PbSc0.5Ta0.5O3 (0.15PT–0.85PST), which exhibited excellent electrothermal effects at room temperature. By quenching, the electrothermal effect (Δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta$$\\end{document}Tmax) and energy harvesting properties of the device were 1.574 K and 0.542 J/cm3, respectively. Then, for the first time, we proposed a self-powered temperature quantification control system with a rotary disc-shaped TENG. This device effectively harnessed wind and water energy, in addition to other types of energy. The system consisted of energy collecting cups, a rotating disc-shaped FEP–rabbit fur TENG, a circuit management module, and a ferroelectric ceramic chip array. Through the circuit management module, the system converted external wind energy into a high-voltage electric field at the two ends of the 0.15PT–0.85PST ceramic chip to fully stimulate the electrothermal effect. At a speed of 200 rpm, the temperature change in the insulated cup within 276 s was 0.49 K, and the volume of the insulated cup was 300 times greater than that of the 0.15PT–0.85PST ceramic chip. Compared with the results reported in previous work, the cooling and heating times were both reduced by 31%, and the temperature changes for both cooling and heating increased by 81%. Moreover, the heating and cooling temperatures of the device optimized on this basis were increased to 1.19 K and 0.93 K, respectively. The great improvement in the temperature variation performance confirmed the great potential of the device for commercialization. This research could serve as a reference for reducing energy consumption for cooling and heating, and it meets the international energy policies of carbon dioxide emission peaking and carbon neutrality.

Student perspectives on designing and implementing an interprofessional education module in chronic disease management at primary care level in Vietnam

PurposeThis study explored students' perspectives on designing and implementing the new clinical interprofessional education (IPE) module for chronic disease management at the primary care level in the Vietnamese context.Design/methodology/approachStudents from seven different university-level healthcare programmes participated in a cross-sectional survey Course Experience Questionnaire-based study. Additionally, two open-ended questions were presented to gather qualitative data, mapping student perspectives. Statistical analyses and thematic analyses were performed.FindingsThe results show that students agree with quality statements about IPE design features, such as good teaching, clear goals and standards, appropriate workload, appropriate assessment and a blended learning approach. Answers to the open-ended comments pointed out the strengths of the IPE module in providing opportunities for interprofessional learning, improving interprofessional collaboration competencies, real-life interprofessional collaboration practice and continuous feedback from tutors. Students also reported weaknesses in the IPE module, including a lack of feedback on the care plan and logistics of the IPE module.Originality/valueStudents positively recognise key design features of the IPE module, combining classroom activities with standardised patient simulations, clinical practice and home visits. Chronic disease management at the primary care level is a suitable context for training students to work interprofessionally. The strengths and weaknesses identified by students could help in the redesign and future implementation of the IPE module in the Vietnamese context. They could inspire practices in Southeast Asian medical education and beyond.

Development of Family Empowerment Model in Diabetes Management Among Type 2 DM Patients

Background: Diabetes management has not been optimally implemented by patients, considering the high incidence of diabetes complications. Therefore, family empowerment is important for diabetes management. Objective: This study aims to determine factors related to diabetes management, inhibiting, and supporting factors for diabetes management to develop a family-based educational model in diabetes management through expert testing.Methods: This research used Mixed Methods design with an Explanatory Sequential Design approach. A cross-sectional approach to 207 people with DM selection using a multistage sampling technique. Data was collected by questionnaire, in-depth interview, and Delphi study. Data analysis using chi-square and logistic regression. The development of family empowerment model is based on the theory Ladder of Empowerment, Social Cohesion, and Family Support. Results: Research results showed that good knowledge of patients, good family empowerment, and absence of DM risk factors are related to diabetes management. Model development and expert testing (Delphi Study) of the model find that a family-based diabetes management module is needed. Conclusion: A good family-based diabetes management module includes aspects of Self-Instructional, Self-Contained, Stand Alone, Adaptive, and User-Friendly aspects. This module is expected to improve diabetes management for patients and their families so that diabetes complications can be prevented and the patient's quality of life increases.

Evaluating the Effectiveness of an Online Gamified Traffic Safety Education Platform for Adolescent Motorcyclists in Pakistan

This study explores the potential of online traffic safety education for adolescent motorcyclists in Pakistan. An e-learning platform, “Route 2 School” (R2S), was developed focusing on traffic knowledge, situation awareness, risk detection, and risk management. Male students (14–18 years) who commute to school by motorcycle were divided into an experimental group (EG) and a control group (CG), both completing pre- and post-measurement questionnaires. The EG showed significant improvement in knowledge, risk detection, and risk management compared to the CG, but not in situation awareness. Participants reported increased traffic safety awareness and suggested adding more interactive elements. The R2S platform’s scores revealed better performance in risk detection and risk management modules than situation awareness. Time spent on modules varied, with situation awareness requiring the most time. Adolescents expressed satisfaction with the platform, acknowledging its role in increasing traffic awareness. This study provides initial insights into the effectiveness of online traffic safety education in Pakistan, highlighting the potential to address the lack of comprehensive traffic safety education in schools. Further research and stakeholder engagement are recommended to integrate such platforms into formal education, potentially reducing traffic-related injuries among adolescent motorcyclists in developing countries.

Multi-level exploration of auricular acupuncture: from traditional Chinese medicine theory to modern medical application.

As medical research advances and technology rapidly develops, auricular acupuncture has emerged as a point of growing interest. This paper delves into the intricate anatomy of auricular points, their significance and therapeutic principles in traditional Chinese medicine (TCM), and the underlying mechanisms of auricular acupuncture in contemporary medicine. The aim is to delve deeply into this ancient and mysterious medical tradition, unveiling its multi-layered mysteries in the field of neurostimulation. The anatomical structure of auricular points is complex and delicate, and their unique neurovascular network grants them a special status in neurostimulation therapy. Through exploration of these anatomical features, we not only comprehend the position of auricular points in TCM theory but also provide a profound foundation for their modern medical applications. Through systematic review, we synthesize insights from traditional Chinese medical theory for modern medical research. Building upon anatomical and classical theoretical foundations, we focus on the mechanisms of auricular acupuncture as a unique neurostimulation therapy. This field encompasses neuroregulation, pain management, psychological wellbeing, metabolic disorders, and immune modulation. The latest clinical research not only confirms the efficacy of auricular stimulation in alleviating pain symptoms and modulating metabolic diseases at the endocrine level but also underscores its potential role in regulating patients' psychological wellbeing. This article aims to promote a comprehensive understanding of auricular acupuncture by demonstrating its diverse applications and providing substantial evidence to support its broader adoption in clinical practice.

Leveraging Oracle HCM for Enhanced Employee Engagement

As the current corporate environment continues to evolve, employee engagement has emerged as a critical component in determining the success of an organisation. Oracle Human Capital Management (HCM) systems provide a revolutionary chance to improve employee engagement by providing a full suite of tools that are geared to handle different areas of workforce management. This opportunity presents itself as a result of the availability of these systems. In order to provide a more unified and responsive approach to employee engagement, Oracle Human Capital Management (HCM) offers an integrated platform that incorporates basic HR tasks, talent management, workforce management, and analytics. In this article, we investigate how the capabilities of Oracle Human Capital Management (HCM) may be strategically used to cultivate a staff that is more engaged and motivated. The cloud-based design of the system guarantees accessibility and real-time data processing, both of which are crucial for interactions with workers that are timely and relevant. A variety of features that may be adapted to assist engagement activities are provided by the modules that make up Oracle Human Capital Management (HCM). These modules include Employee Central, Talent Management, and Learning Management. For example, the Talent Management module helps to connect employee goals with organisational objectives, which in turn improves both work happiness and performance. While this is going on, the Learning Management module offers individualised training and development possibilities, according to the workers' requirements for professional advancement and their goals for their careers.

Understanding obesity among young adults in India: A focus group study

IntroductionThe study aims to understand the knowledge and awareness about risk factors and consequences of obesity, barriers and motivators to weight loss, and potential solutions for weight management among young adults (YAs) in India. MethodSix focus group discussions were conducted with an inductive approach among obese YAs (18–30 years) recruited through snowball and purposive sampling. The principle of maximum diversity was used to build a pool of varied and extensive information regarding obesity in YAs. The discussions were audio-recorded and transcribed verbatim using NVivo 1.0 by QSR International to generate codes, subthemes, and themes from the discussions. ResultAmong the thirty recruited YAs (mean age 24.7 ± 2.84 years), nearly half were from a high-income group (56.6 %) and had postgraduate education (53.3 %). Key themes included the need for increased awareness about obesity risk factors and consequences across different societal levels. Physical appearance was the primary motivator for weight loss, followed by physical endurance and social support. Major barriers included lack of energy, time, motivation, lifestyle changes, easy access to unhealthy foods, work commitments, and workplace culture. A holistic approach involving dietary changes, physical activity, and educational and policy reforms was suggested as potential solutions. ConclusionThe challenges faced by YAs in developing countries regarding different aspects of the development and management of obesity are unique and different from the other population groups. Individualized weight management modules may be useful for managing obesity in this group.

Towards a Deep Learning-Powered Herbarium Image Analysis Platform

Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity. ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023, Sahraoui et al. 2023). Within the e-Col+project (ANR-21-ESRE-0053), we are developing multiple deep learning models aimed at identifying plant morphological traits. We have developed pipelines and models for cleaning, analyzing, and transforming herbarium images, including models for: i) detecting non-vegetal elements, such as barcodes, envelopes, labels, etc.; ii) detecting plant organs, including leaves, flowers, fruits, etc.; and iii) segmenting to recognize plant parts for image cleaning. We are also developing models for classification tasks related to various morphological traits. To validate these models, improve their generalization, and make them easily usable by end-users, deploying them within a generic platform is crucial. The generic platform called PlantAI, currently under development by the e-Col+ project, should enable easy deployment during development for testing and allow users to load annotations for new traits in order to train a model and add it to the existing catalog. The platform is based on a microservice architecture, allowing users to upload images, create custom datasets, and access various AI models for image analysis. The platform is composed of four main modules, as illustrated in Fig. 1. The first module is the collaborative workspace manager, which allows users to create projects and image datasets and invite other users to collaborate on a project. The second module is the navigation interface and dashboards. This module integrates a search engine using metadata and AI annotations, a navigation interface between projects, datasets, and specimens, as well as dashboards for analysis across datasets, specimens, and AI models. The third module is the dataset manager, which handles metadata and annotations associated with the specimens. These annotations can be produced either by expert users or by AI models. The fourth module is the AI models management module, so that models can be used to generate AI annotations of specimen. During the development lifecycle of an AI model, users can create datasets and annotate them with AI models. These annotations can be in two possible states: validated by experts and non-validated. Users collaborating on a project can indicate errors in the model predictions and leave comments to explain their evaluations. These corrections made by experts can be used to retrain the models and thus improve their performance. This platform, will be highly beneficial for botanists, enhancing the efficiency and effectiveness of biodiversity analyses from herbarium scans. We aim to provide users with a catalog of AI models through this platform and allow them to import their own datasets with their own annotations regarding traits of their choice. Users will be able to select a model from the AI model catalog and train it using their dataset. Ultimately, the model obtained from this training will be automatically deployed to be available for AI annotation. The annotations produced by this model will be automatically available in the filtering and navigation interface, thus allowing for dynamic and automatic integration of the AI annotations into the navigation interface.