Pubmedia Jurnal Penelitian Tindakan Kelas Indonesia
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”Gör och kör” i en hållbar struktur : En avrapportering av följeforskning kring framväxt och etablering av lokal operativ samverkan och myndighetsgemensamma tillsyner
No full textRapporten beskriver hur Eskilstuna kommun och Polismyndigheten har utvecklat lokal operativ samverkan (LOS) för att bekämpa systemhotande brottslighet, med särskilt fokus på välfärdsbrott och arbetslivskriminalitet. Arbetet har följts genom en lärande utvärdering mellan maj 2024 och september 2025. Syftet har varit att förstå hur samverkan formas över tid, vilka hinder och möjliggörare som finns, och att bidra med kunskap för fortsatt utveckling. Bakgrunden är att välfärdsbrottslighet är ett komplext fenomen som ofta beskrivs som ett ”wicked problem”. Det omfattar allt från bidragsfusk till folkbokföringsbrott och kräver samarbete över organisatoriska gränser eftersom det inte finns standardiserade arbetssätt eller tydliga ansvarsfördelningar. Kommunernas arbete mot denna typ av brott har intensifierats efter lagen om kommuners ansvar för brottsförebyggande arbete (SFS 2023:196), vilket har lett till nya lokala initiativ och samordnande roller. Samverkan mellan myndigheter är dock utmanande, bland annat på grund av skillnader i kultur – kommunens serviceperspektiv kontra polisens kontrollperspektiv – samt sekretesshinder och målkonflikter. Följeforskningen har byggt på intervjuer med nyckelpersoner, inspelningar från samverkansmöten och workshops. Analysen har genomförts med reflexiv tematisk metod, vilket innebär att materialet har lästs noggrant med fokus på att identifiera återkommande idéer eller mönster. Resultatet visar att arbetet har gått från ett löst organiserat initiativ till en mer strukturerad samverkansform med flera mötesforum. Dessa forum omfattar LOS styrgrupp vilket skapades som ett strategiskt forum, LOS operativ som blev navet för planering och uppföljning av tillsyner, och LOS aktionsmöten vilka infördes för detaljerad planering. Utöver dessa strukturer har ett nytt arbetssätt för myndighetsgemensamma tillsyner har etablerats. Arbetssättet vägleder hur verksamheter väljs ut utifrån kriterier som misstänkt fusk, behov av flera lagrum och felaktig folkbokföring samt hur den gemensamma tillsynen planeras och genomförs. Samtidigt som planeringen har blivit mer systematisk, kvarstår ett starkt personberoende där nyckelpersoner varit avgörande för framgång. Fokus på personer och relationsbyggande har således bidragit till en god grund för fortsatt arbete, men skapar även sårbarhet. Två idéer har präglat utvecklingen: ”tillsammans är vi starka”, som betonar gemenskap och kunskapsdelning, och ”gör och kör”, ett pragmatiskt förhållningssätt som möjliggjort snabb utveckling med mindre fokus på strukturer. Båda idéerna har varit bärande i utvecklingen av samverkan och bidragit positivt till framväxt och utveckling av myndighetsgemensamma tillsyner. Samtidigt har utmaningar uppstått, framförallt kring resursprioritering mellan LOS och ordinarie verksamhet samt skillnader i perspektiv mellan kommunens serviceinriktning och polisens brottsbekämpande fokus. Dessa skillnader har krävt kontinuerlig förhandling och anpassning
AI-driven hybrid control for hydrogen-integrated microgrids : Probabilistic energy management with vehicle-to-grid
No full textDespite the exciting potential of microgrids in future smart energy systems, they encounter significant challenges, including fluctuations in energy demand and output, as well as the unpredictable behavior of electric vehicles. This article examines the ability of microgrids to enhance the integration of renewable energy sources to achieve Zero-Energy Buildings (ZEBs) and facilitate the deployment of Vehicle-to-Grid (V2G) technologies. The designed microgrid comprises vehicles utilizing V2G technology for daily energy storage and a hydrogen cycle featuring electrolyzers and fuel cells for seasonal storage. Probability functions based on uncertainty for distance, arrival, and departure periods from charging stations are formulated to mitigate uncertainties associated with electric vehicles (EVs). A genetic algorithm is employed to optimally regulate EVs' charging and discharging range and the hydrogen cycle's dynamic configuration. The system's feasibility is evaluated for a district in Tehran, characterized by a hot semi-arid climate per the Köppen climate classification, comprising 600 EVs and 3000 residential and 55 commercial buildings. The performance of the suggested smart system is compared with traditional scenarios from techno-ecological, economic, and environmental perspectives. The findings indicate that 62.6 % of the overall energy demand is met by renewable sources (wind and solar), and the microgrid can independently fulfill the need for over 50 % of the year, owing to the implemented hybrid optimum controllers. The findings indicate that 41 % and 16 % of total renewable electricity generation are stored in hydrogen systems and electric vehicles, respectively, highlighting their significant potential for both short-term and long-term storage. Compared to the same traditional scenarios, the suggested system, with an annual energy gain of 8.9 GWh, exhibits superior performance due to its little reliance on the grid while simultaneously ensuring the happiness of electric vehicle owners and the stability of energy storage systems. The intelligent microgrid demonstrates significant efficiency, conserving over 12,600 MWh of energy and decreasing more than 8800 tons of CO2 emissions. Furthermore, this system generates a substantial financial benefit of approximately USD 468,000, highlighting its notable environmental and economic merits
Advancements and challenges of deep learning architectures for aerial image analysis : A systematic review
No full textThe rapid advancement of deep learning (DL) technologies has significantly transformed the domain of aerial image analysis. This systematic review explores the forefront of deep learning architectures specifically designed for the processing and analysis of aerial imagery. It offers a comprehensive examination of updated models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers, highlighting their unique contributions and comparative effectiveness in aerial image analysis. This review critically compares these architectures through an extensive literature survey, focusing on their impact on enhancing accuracy, computational efficiency, and overall performance in critical aerial imaging tasks, such as classification, object detection, and semantic segmentation. Additionally, it sheds light on the innovative architectural improvements that have been crucial in overcoming traditional challenges associated with aerial image processing, such as handling high-resolution data, managing diverse and changing landscapes, and ensuring real-time analysis capabilities. By synthesizing current findings and identifying prevailing trends, this review not only charts the progress in the field but also outlines future research directions, emphasizing the need for more adaptable, robust, and efficient deep-learning solutions to meet the growing demands of aerial image analysis
Anomaly detection using different types of machine learning models in the context of "smart maintenance technologies in the manufacturing industry"
No full textIndustry 4.0 presents nine technologies, including the Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, etc. The progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as IIoT, Big Data and Analytics, Cloud Computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how IIoT can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden
Attention-based fuzzy neural networks for self-supervised data annotation
No full textAnnotating vibration data from heavy-duty pumps in the mining industry is highly challenging because it demands domain knowledge, a complex inspection setup, and, in many cases, remains infeasible. A self-supervised data annotation (SSDA) framework is therefore proposed and evaluated on historical data of slurry-pump vibration signals. The framework began with the collection of heterogeneous information, followed by information fusion using an autoencoder. This was then followed by a datafication step for preprocessing and achieving a better representation of features through a feature embedding technique. As a result, redundant information was pushed into an eight-dimensional latent space, achieving a reconstruction loss of 0.0023. Furthermore, Initial data annotation was obtained by combining the Isolation Forest and Kneedle algorithms to locate a data-driven knee or threshold, and it was found to be 0.58 for predicting labels. Partial samples were labeled and considered accurate. Lastly, an attention-based fuzzy neural network (AFNN) is trained on those labels where membership functions convert each latent feature into graded truth values. At the same time, an attention layer highlights the most relevant rules. An iterative self-training loop was implemented to refine the training set and obtain labeled data with higher model confidence. Here, we also tested six baseline models and found AFNN quite impressive. After seven iterations 2780 of 2872 samples were labeled and the remaining 92 are considered uncertain, still need some review from an expert, and the AFNN model confidence was (96.8%). Statistical analysis confirmed that the model predictions were significantly associated with true labels (p<0.05) and not driven by chance.
Characterizing time-critical internet of things
No full textThe Internet of Things (IoT) is increasingly being adopted in diverse domains, many of which require strict timing constraints and predictable behavior. Despite the growing importance of timing characteristics in IoT applications, current approaches to address timing requirements are often fragmented, context-specific, and lack a unified understanding. Consequently, addressing timing aspects in IoT remains largely ad hoc and dependent on individual applications, making it challenging to generalize findings or systematically apply established solutions. The goal of this study is to provide a comprehensive understanding of how timing is defined, characterized, and measured within the IoT community. We conducted this study through a systematic and structured mix methods research approach. First, we performed a systematic review of the literature, extracting and analyzing information from 38 primary studies, selected from a rigorous process involving 1176 studies. Second, to complement the literature findings, we conducted an expert survey involving 28 respondents from academia and industry, representing a variety of roles with specialized expertise in IoT systems and timing-related issues. We identified two primary characterizations of timing within the IoT: time-criticality and predictability. Additionally, we collected and categorized 113 distinct timing metrics from literature into commonly found layers of an IoT system. The majority of the surveyed practitioners and researchers (75%) agree with our categorization and consider this research useful and relevant (71.5%). We believe that our study provides practitioners and researchers with insights into timing characteristics and metrics in IoT applications, towards the ultimate goal of standardization
CodeX : Contextual Flow Tracking for Browser Extensions
No full textBrowser extensions put millions of users at risk when misusing their elevated privileges. Despite the current practices of semi-automated code vetting, privacy-violating extensions still thrive in the official stores. We propose an approach for tracking contextual flows from browser-specific sensitive sources like cookies, browsing history, bookmarks, and search terms to suspicious network sinks through network requests. We demonstrate the effectiveness of the approach by a prototype called CodeX that leverages the power of CodeQL while breaking away from the conservativeness of bug-finding flavors of the traditional CodeQL taint analysis. Applying CodeX to the extensions published on the Chrome Web Store between March 2021 and March 2024 identified 1,588 extensions with risky flows. Manual verification of 339 of those extensions resulted in flagging 212 as privacy-violating, impacting up to 3.6M users
Differential susceptibility effects of the 5-HTTLPR and MAOA genotypes on decision making under risk in the Iowa gambling task
No full textIntroduction The interplay between genetic and environmental factors, as explored through studies of gene-environment interactions (cGxE), has illuminated the complex dynamics influencing behavior and cognition, including decision-making processes. In this study, we investigated the differential susceptibility effects of the 5-HTTLPR and MAOA genotypes on decision-making under risk using the Iowa Gambling Task.Methods Data from 264 participants (138 women, 126 men) aged 18-22 years, from the 2015 wave of the Survey of Adolescent Life in V & auml;stmanland (SALVe Cohort) was analyzed. Participants provided genetic data including the MAOA and 5-HTTLPR genotypes, and completed the Iowa Gambling Task (IGT) to evaluate decision-making behavior. Parent reports, including assessments of positive parenting styles and early life stress were used for cGxE analysis.Results In a General Linear Model, significant interactions were found among males for the 5-HTTLPR, with SS/SL carriers showing higher net scores with positive parenting and lower scores with less positive parenting in relation to decision-making under risk in the IGT (trials 61-100), indicating differential susceptibility effects. Male LL carriers showed minimal fluctuation in IGT scores. Similar effects were observed for males with the MAOA S-allele. No significant interactions were found for females.Discussion In conclusion, our study indicates that the 5-HTTLPR and MAOA genes demonstrate susceptibility to environmental factors in influencing decision-making under risk among males, as assessed by the Iowa Gambling Task. We anticipate that these findings will contribute to advancing the understanding of the complex interactions between genetic and environmental factors in shaping human behavior and decision-making
Digital health literacy—a key factor in realizing the value of digital transformation in healthcare
No full textBackground: Digital health technologies and AI are transforming healthcare by improving access, optimizing care, and enabling personalized, preventive, and predictive solutions. However, digital health literacy remains a critical barrier, affecting individuals' ability to engage with digital health technologies (DHTs) and limiting progress toward digital health equity. Aims: To propose a framework that captures the complexity of digital health literacy and guides research, and to share key insights from the Improving Digital Empowerment for Active Healthy Living EU project. Results: We introduce a conceptual framework that explores digital health literacy's interactions with social determinants, providing a foundation for research, policy, and practice. Insights from the project (Improving Digital Empowerment for Active Healthy Living), involving 14 partners across 10 European countries, offer evidence-based strategies to empower individuals and promote digital inclusion. Concluding remarks: To keep pace with technological advancements, digital health literacy should be integrated into lifelong learning initiatives. Urgent research is needed to inform policies and guide interventions that enhance digital health literacy and ensure equitable digital transformation in healthcare
Pembiasaan Murāja‘ah Reflektif dalam Menguatkan Hafalan Al-Qur’an Santri Salah Satu Pondok Pesantren di Kabupaten Bekasi
Get PDFPenelitian ini bertujuan untuk menguatkan hafalan Al-Qur’an santri melalui pembiasaan murāja‘ah reflektif di salah satu pondok pesantren di Kabupaten Bekasi. Penelitian menggunakan pendekatan Penelitian Tindakan Kelas (PTK) dengan desain dua siklus yang meliputi tahap perencanaan, pelaksanaan tindakan, observasi, dan refleksi. Subjek penelitian terdiri atas tujuh santri tahfidz. Data dikumpulkan melalui observasi partisipatif, wawancara dengan guru halaqah dan santri, serta dokumentasi selama proses murāja‘ah berlangsung. Hasil penelitian menunjukkan bahwa penerapan murāja‘ah reflektif mampu meningkatkan kestabilan hafalan santri secara bertahap. Pada kondisi pra-tindakan, murāja‘ah dilakukan secara mekanis dan hafalan santri cenderung tidak stabil. Pada Siklus I, santri mulai menunjukkan kesadaran terhadap kesalahan hafalan dan keterlibatan aktif dalam proses murāja‘ah. Perbaikan tindakan pada Siklus II menghasilkan peningkatan yang lebih signifikan, ditandai dengan hafalan yang lebih stabil, berkurangnya kesalahan berulang, serta meningkatnya kemandirian santri dalam melakukan murāja‘ah. Simpulan penelitian ini menunjukkan bahwa pembiasaan murāja‘ah reflektif efektif dalam menguatkan hafalan Al-Qur’an santri dan dapat dijadikan alternatif strategi pembelajaran tahfidz di lingkungan pesantren