Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (876)

Search Parameters:
Keywords = multi-harmonic

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
24 pages, 1024 KB  
Review
Artificial Intelligence in Glioma Diagnosis: A Narrative Review of Radiomics and Deep Learning for Tumor Classification and Molecular Profiling Across Positron Emission Tomography and Magnetic Resonance Imaging
by Rafail C. Christodoulou, Rafael Pitsillos, Platon S. Papageorgiou, Vasileia Petrou, Georgios Vamvouras, Ludwing Rivera, Sokratis G. Papageorgiou, Elena E. Solomou and Michalis F. Georgiou
Eng 2025, 6(10), 262; https://doi.org/10.3390/eng6100262 - 3 Oct 2025
Abstract
Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January [...] Read more.
Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article
Show Figures

Figure 1

15 pages, 4024 KB  
Article
Comparative Analysis of Efficiency and Harmonic Generation in Multiport Converters: Study of Two Operating Conditions
by Francisco J. Arizaga, Juan M. Ramírez, Janeth A. Alcalá, Julio C. Rosas-Caro and Armando G. Rojas-Hernández
World Electr. Veh. J. 2025, 16(10), 566; https://doi.org/10.3390/wevj16100566 - 2 Oct 2025
Abstract
This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, [...] Read more.
This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article
(This article belongs to the Section Power Electronics Components)
Show Figures

Figure 1

28 pages, 1003 KB  
Article
A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories
by Olga Tsave, Alexandra Kosvyra, Dimitrios T. Filos, Dimitris Th. Fotopoulos and Ioanna Chouvarda
Cancers 2025, 17(19), 3213; https://doi.org/10.3390/cancers17193213 - 1 Oct 2025
Abstract
Background/Objectives: Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of [...] Read more.
Background/Objectives: Cancer remains a leading global cause of death, with breast, lung, colorectal, and prostate cancers being among the most prevalent. The integration of Artificial Intelligence (AI) into cancer imaging research offers opportunities for earlier diagnosis and personalized treatment. However, the effectiveness of AI models depends critically on the quality, standardization, and fairness of the input data. The EU-funded INCISIVE project aimed to create a federated, pan-European repository of imaging and clinical data for cancer cases, with a key objective to develop a robust framework for pre-validating data prior to its use in AI development. Methods: We propose a data validation framework to assess clinical (meta)data and imaging data across five dimensions: completeness, validity, consistency, integrity, and fairness. The framework includes procedures for deduplication, annotation verification, DICOM metadata analysis, and anonymization compliance. Results: The pre-validation process identified key data quality issues, such as missing clinical information, inconsistent formatting, and subgroup imbalances, while also demonstrating the added value of structured data entry and standardized protocols. Conclusions: This structured framework addresses common challenges in curating large-scale, multimodal medical data. By applying this approach, the INCISIVE project ensures data quality, interoperability, and equity, providing a transferable model for future health data repositories supporting AI research in oncology. Full article
(This article belongs to the Section Methods and Technologies Development)
51 pages, 1102 KB  
Article
Genetic Parameters, Prediction of Genotypic Values, and Forage Stability in Paspalum nicorae Parodi Ecotypes via REML/BLUP
by Diógenes Cecchin Silveira, Annamaria Mills, Júlio Antoniolli, Victor Schneider de Ávila, Maria Eduarda Pagani Sangineto, Juliana Medianeira Machado, Roberto Luis Weiler, André Pich Brunes, Carine Simioni and Miguel Dall’Agnol
Genes 2025, 16(10), 1164; https://doi.org/10.3390/genes16101164 - 1 Oct 2025
Abstract
Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on [...] Read more.
Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on its genetic variability and agronomic potential. This study aimed to estimate genetic parameters, predict genotypic values, and identify superior ecotypes with desirable forage traits, integrating stability and adaptability analyses. Methods: A total of 84 ecotypes were evaluated over three consecutive years for twelve morphological and forage-related traits. Genetic parameters, genotypic values, and selection gains were estimated using mixed models (REML/BLUP). Stability was assessed through harmonic means of genotypic performance, and the multi-trait genotype–ideotype distance index (MGIDI) was applied to identify ecotypes with balanced performance across traits. Results: Substantial genetic variability was detected for most traits, particularly those related to biomass accumulation, such as total dry matter, the number of tillers, fresh matter, and leaf dry matter. These traits exhibited medium to high heritability and strong potential for selection. Ecotype N3.10 consistently showed superior performance across productivity traits while other ecotypes, such as N4.14 and N1.09, stood out for quality-related attributes and cold tolerance, respectively. The application of the MGIDI index enabled the identification of 17 ecotypes with balanced multi-trait performance, supporting the simultaneous selection for productivity, quality, and adaptability. Comparisons with P. notatum suggest that P. nicorae harbors competitive genetic potential, despite its lower level of domestication. Conclusions: The integration of REML/BLUP analyses, stability parameters, and ideotype-based multi-trait selection provided a robust framework for identifying elite P. nicorae ecotypes. These findings reinforce the strategic importance of this species as a valuable genetic resource for the development of adapted and productive forage cultivars in subtropical environments. Full article
(This article belongs to the Special Issue Genetics and Breeding of Forage)
15 pages, 2700 KB  
Article
Research on High-Resolution Image Harmonization Method Based on Multi-Scale and Global Feature Guidance
by Rui Li, Dan Zhang, Shengling Geng and Mingquan Zhou
Appl. Sci. 2025, 15(19), 10573; https://doi.org/10.3390/app151910573 - 30 Sep 2025
Abstract
During the image compositing process, there may be inconsistencies in tone and illumination between the foreground and background, leading to poor visual quality and low realism in the composite images. To address these issues, image harmonization techniques can be employed. This paper proposes [...] Read more.
During the image compositing process, there may be inconsistencies in tone and illumination between the foreground and background, leading to poor visual quality and low realism in the composite images. To address these issues, image harmonization techniques can be employed. This paper proposes an image harmonization method based on multi-scale and global feature guidance (MSGF). In general, images captured in different scenes may exhibit inconsistencies in lighting after composition. The goal of image harmonization is to adjust the foreground illumination to match that of the background. Traditional methods often attempt to blend pixels directly, which can result in unrealistic outcomes. The proposed approach combines multi-scale feature extraction with global feature guidance, forming the MSGF framework. The experiment was conducted on the iHarmony4 dataset. Comparative experiments showed that MSGF achieved the best performance on three subset indicators, including HCOCO. Ablation studies demonstrated the effectiveness of the proposed module. Efficiency evaluation results indicated that it took 0.01s and had 20.9 million parameters, outperforming comparative methods and effectively achieving high-quality image harmonization. Full article
Show Figures

Figure 1

22 pages, 1249 KB  
Systematic Review
Radiomics vs. Deep Learning in Autism Classification Using Brain MRI: A Systematic Review
by Katerina Nalentzi, Georgios S. Ioannidis, Haralabos Bougias, Sotirios Bisdas, Myrsini Balafouta, Cleo Sgouropoulou, Michail E. Klontzas, Kostas Marias and Periklis Papavasileiou
Appl. Sci. 2025, 15(19), 10551; https://doi.org/10.3390/app151910551 - 29 Sep 2025
Abstract
Autism diagnosis through magnetic resonance imaging (MRI) has advanced significantly with the application of artificial intelligence (AI). This systematic review examines three computational paradigms: radiomics-based machine learning (ML), deep learning (DL), and hybrid models combining both. Across 49 studies (2011–2025), radiomics methods relying [...] Read more.
Autism diagnosis through magnetic resonance imaging (MRI) has advanced significantly with the application of artificial intelligence (AI). This systematic review examines three computational paradigms: radiomics-based machine learning (ML), deep learning (DL), and hybrid models combining both. Across 49 studies (2011–2025), radiomics methods relying on classical classifiers (i.e., SVM, Random Forest) achieved moderate accuracies (61–89%) and offered strong interpretability. DL models, particularly convolutional and recurrent neural networks applied to resting-state functional MRI, reached higher accuracies (up to 98.2%) but were hampered by limited transparency and generalizability. Hybrid models combining handcrafted radiomic features with learned DL representations via dual or fused architectures demonstrated promising balances of performance and interpretability but remain underexplored. A persistent limitation across all approaches is the lack of external validation and harmonization in multi-site studies, which affects robustness. Future pipelines should include standardized preprocessing, multimodal integration, and explainable AI frameworks to enhance clinical viability. This review underscores the complementary strengths of each methodological approach, with hybrid approaches appearing to be a promising middle ground of improved classification performance and enhanced interpretability. Full article
Show Figures

Figure 1

15 pages, 855 KB  
Article
Integrating Fitbit Wearables and Self-Reported Surveys for Machine Learning-Based State–Trait Anxiety Prediction
by Archana Velu, Jayroop Ramesh, Abdullah Ahmed, Sandipan Ganguly, Raafat Aburukba, Assim Sagahyroon and Fadi Aloul
Appl. Sci. 2025, 15(19), 10519; https://doi.org/10.3390/app151910519 - 28 Sep 2025
Abstract
Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait [...] Read more.
Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO2Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth, 2nd Edition)
Show Figures

Figure 1

34 pages, 4877 KB  
Article
Climate-Adaptive Residential Demand Response Integration with Power Quality-Aware Distributed Generation Systems: A Comprehensive Multi-Objective Optimization Framework for Smart Home Energy Management
by Mahmoud Kiasari and Hamed Aly
Electronics 2025, 14(19), 3846; https://doi.org/10.3390/electronics14193846 - 28 Sep 2025
Abstract
Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective [...] Read more.
Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article
(This article belongs to the Special Issue Energy Technologies in Electronics and Electrical Engineering)
Show Figures

Figure 1

27 pages, 616 KB  
Article
Assessing the Risk of Earnings Management Through the Lens of Individual Moral Philosophy: Insights from Accounting Professionals
by Anna Misztal and Michał Comporek
Risks 2025, 13(10), 184; https://doi.org/10.3390/risks13100184 - 25 Sep 2025
Abstract
This study explores how individual moral philosophies influence accountants’ ethical perceptions of earnings management risk, addressing the broader question of how moral reasoning interacts with the cultural environment in shaping financial reporting decisions. Although accounting standards such as IFRS/IAS aim to harmonize reporting, [...] Read more.
This study explores how individual moral philosophies influence accountants’ ethical perceptions of earnings management risk, addressing the broader question of how moral reasoning interacts with the cultural environment in shaping financial reporting decisions. Although accounting standards such as IFRS/IAS aim to harmonize reporting, cultural, and institutional factors can lead professionals to interpret and apply them differently, making ethical perceptions context-dependent. Building on positive accounting theory and Forsyth’s model of personal moral philosophy, we conducted a scenario-based survey among Polish accounting professionals, using an extended set of earnings management scenarios developed by Bruns and Merchant and modified by Jooste. Our results indicate that subjectivists demonstrate greater ethical sensitivity to earnings-altering behavior, while absolutists exhibit the least. We also examined ethical evaluations across different types of earnings management practices, including income-increasing versus income-decreasing, accrual-based versus real earnings management, and multi-year versus single-year manipulations. Understanding how different moral orientations influence the perception of managerial interventions in reported figures can help executives foster an organizational culture that promotes the provision of reliable and accurate information to stakeholders. Study limitations include sample size and scope, suggesting the need for future research incorporating broader demographics and contextual variables. Full article
Show Figures

Figure 1

32 pages, 7470 KB  
Article
Consensus-Guided Construction of H5N1-Specific and Universal Influenza a Multiepitope Vaccines
by Marco Palma
Biology 2025, 14(10), 1327; https://doi.org/10.3390/biology14101327 - 25 Sep 2025
Abstract
Background/Objectives: Influenza A viruses—including highly pathogenic H5N1—remain a global threat due to rapid evolution, zoonoses, and pandemic potential. Strain-specific vaccines targeting variable antigens often yield limited, short-lived immunity. The HA receptor-binding domain (RBD), a functionally constrained and immunologically relevant region, is a promising [...] Read more.
Background/Objectives: Influenza A viruses—including highly pathogenic H5N1—remain a global threat due to rapid evolution, zoonoses, and pandemic potential. Strain-specific vaccines targeting variable antigens often yield limited, short-lived immunity. The HA receptor-binding domain (RBD), a functionally constrained and immunologically relevant region, is a promising target for broad and subtype-focused vaccines. We aimed to design multiepitope constructs targeting conserved HA-RBD and adjacent domains to elicit robust, durable, cross-protective responses. Methods: Extensive sequence analyses (>20,000 H5N1 and >190,000 influenza A sequences) were used to derive consensus sequences. Three HA-based candidates were developed: (i) EpitoCore-HA-VX, a multi-epitope construct containing CTL, HTL, and B-cell epitopes from the H5N1 HA-RBD; (ii) StructiRBD-HA-VX, incorporating a conformationally preserved RBD segment; and (iii) FusiCon-HA-VX, targeting the conserved HA fusion peptide shared across subtypes. Two external HA comparators—a 400-aa HA fragment and the literature-reported HA-13–263-Fd-His—were analyzed under the same pipeline. The workflow predicted epitopes; evaluated antigenicity, allergenicity, toxicity, conservation, and HLA coverage; generated AlphaFold models; performed TLR2/TLR4 docking with pyDockWEB; and carried out interface analysis with PDBsum; and C-ImmSim simulations. Results: Models suggested stable, energetically favorable TLR2/TLR4 interfaces supported by substantial binding surfaces and complementary electrostatic/desolvation profiles. Distinct docking patterns indicated receptor-binding flexibility. Immune simulations predicted strong humoral responses with modeled memory formation and, for the H5N1-focused designs, cytotoxic T-cell activity. All candidates and comparators were predicted to be antigenic, non-allergenic, and non-toxic, with combined HLA coverage approaching global breadth. Conclusions: This study compares three design strategies within a harmonized framework—epitope collation, structure-preserved RBD, and fusion-peptide targeting—while benchmarking against two HA comparators. EpitoCore-HA-VX and StructiRBD-HA-VX showed promise against diverse H5N1 isolates, whereas FusiCon-HA-VX supported cross-subtype coverage. As these findings are model-based, they should be interpreted qualitatively; nonetheless, the integrated, structure-guided approach provides an adaptable path for advancing targeted H5N1 and broader influenza A vaccine concepts. Full article
Show Figures

Figure 1

15 pages, 4149 KB  
Article
A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification
by Junzhi Li, Xin Ning and Yong Wang
Atmosphere 2025, 16(10), 1120; https://doi.org/10.3390/atmos16101120 - 24 Sep 2025
Viewed by 44
Abstract
Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite [...] Read more.
Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article
Show Figures

Graphical abstract

24 pages, 3764 KB  
Article
The Evaluation of the Effect of Power Circuit Configuration Changes on the Level of Harmonics Generated by the Hoisting Machine Drive System
by Tomasz Siostrzonek and Zbigniew Mikoś
Energies 2025, 18(19), 5043; https://doi.org/10.3390/en18195043 - 23 Sep 2025
Viewed by 140
Abstract
The quality of electrical energy in mining plants is still a topic that is not directly linked to occupational safety and the efficiency of the mining process. Modernisation of hoisting machines should be carried out, taking into account the impact of such a [...] Read more.
The quality of electrical energy in mining plants is still a topic that is not directly linked to occupational safety and the efficiency of the mining process. Modernisation of hoisting machines should be carried out, taking into account the impact of such a drive on the mine’s power grid. A hoisting machine is one of the largest consumers of electricity in this grid and, as such, can pose a real threat to its proper functioning. The aim of the study was to determine the impact of the hoisting machine drive system on the mine network after a thorough modernisation process. Measurements were taken before and after the process. The assessment was carried out in two aspects, i.e., the measurement results obtained after the modernisation were compared with the applicable regulations. The second approach was to compare the results before and after modernisation. In the authors’ opinion, this second approach is a way of preventing potential phenomena that may occur in the network as a result of the operation of the power electronic system and should complement the analysis described in the regulations. The assessment of the current operation of the system in relation to the previous one makes it possible to evaluate the correctness of the solution applied and may provide guidelines for further steps in the event of a failure or unsafe events. Full article
(This article belongs to the Special Issue Technology for Analysis and Control of Power Quality)
Show Figures

Figure 1

24 pages, 830 KB  
Review
Strengthening Jordan’s Laboratory Capacity for Communicable Diseases: A Comprehensive Multi-Method Mapping Toward Harmonized National Laboratories and Evidence-Informed Public Health Planning
by Dalia Kashef Zayed, Ruba A. Al-Smadi, Mohammad Almaayteh, Thekryat Al-Hjouj, Ola Hamdan, Ammar Abu Ghalyoun, Omar Alsaleh, Tariq Abu Touk, Saddam Nawaf Almaseidin, Thaira Madi, Samar Khaled Hassan, Muna Horabi, Adel Belbiesi, Tareq L. Mukattash and Ala’a B. Al-Tammemi
Int. J. Environ. Res. Public Health 2025, 22(9), 1459; https://doi.org/10.3390/ijerph22091459 - 20 Sep 2025
Viewed by 342
Abstract
Infectious diseases remain a global threat, with low- and middle-income countries disproportionately affected due to socio-economic and demographic vulnerabilities. Robust laboratory systems are critical for early detection, outbreak containment, and guiding effective interventions. This study aimed to map and evaluate Jordan’s laboratory diagnostic [...] Read more.
Infectious diseases remain a global threat, with low- and middle-income countries disproportionately affected due to socio-economic and demographic vulnerabilities. Robust laboratory systems are critical for early detection, outbreak containment, and guiding effective interventions. This study aimed to map and evaluate Jordan’s laboratory diagnostic network for communicable diseases, identify gaps, and recommend strategies to strengthen capacity, harmonization, and alignment with international standards. A multi-method approach was employed in 2023 through collaboration between the Jordan Center for Disease Control and the Health Care Accreditation Council. Data were collected via (i) a desktop review of 226 national and international documents; (ii) 20 key informant interviews with stakeholders from the public, private, military, veterinary, and academic sectors; and (iii) 23 field visits across 27 laboratories in four Jordanian governorates. Data were analyzed thematically and synthesized using the LABNET framework, which outlined ten core laboratory capacities. Findings were validated through a multi-sectoral national workshop with 90 participants. The mapping revealed the absence of a unified national laboratory strategic plan, with governance dispersed across multiple authorities and limited inter-sectoral coordination. Standard operating protocols (SOPs) existed for high-priority diseases such as T.B, HIV, influenza, and COVID-19 but were lacking or outdated for other notifiable diseases, particularly zoonoses. Quality management was inconsistent, with limited participation in external quality assurance programs and minimal accreditation uptake. Biosafety and biosecurity frameworks were fragmented and insufficiently enforced, while workforce shortages, high turnover, and limited specialized training constrained laboratory performance. Despite these challenges, Jordan demonstrated strengths including skilled laboratory staff, established reference centers, and international collaborations, which provide a platform for improvement. Jordan’s laboratory network has foundational strengths but faces systemic challenges in policy coherence, standardization, quality assurance, and workforce capacity. Addressing these gaps requires the development of a national laboratory strategic plan, strengthened legal and regulatory frameworks, enhanced quality management and accreditation, and integrated One Health coordination across human, animal, and environmental health sectors. These measures will improve diagnostic reliability, preparedness, and alignment with the global health security agenda. Full article
Show Figures

Figure 1

33 pages, 5292 KB  
Article
BESS-Enabled Smart Grid Environments: A Comprehensive Framework for Cyber Threat Classification, Cybersecurity, and Operational Resilience
by Prajwal Priyadarshan Gopinath, Kishore Balasubramanian, Rayappa David Amar Raj, Archana Pallakonda, Rama Muni Reddy Yanamala, Christian Napoli and Cristian Randieri
Technologies 2025, 13(9), 423; https://doi.org/10.3390/technologies13090423 - 20 Sep 2025
Cited by 1 | Viewed by 197
Abstract
Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity [...] Read more.
Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity and power quality features. This paper proposes a comprehensive framework that integrates machine learning for attack detection, cryptographic security, data validation, and power quality control. With the BESS-Set dataset for binary classification, Random Forest achieves more than 98.50% accuracy, while LightGBM attains more than 97.60% accuracy for multi-class classification on the resampled data. Principal Component Analysis and feature importance show vital indicators such as State of Charge and battery power. Secure communication is implemented using Elliptic Curve Cryptography and a hybrid Blowfish–RSA encryption method. Data integrity is ensured through applying anomaly detection using Z-scores and redundancy testing, and IEEE 519-2022 power quality compliance is ensured by adaptive filtering and harmonic analysis. Real-time feasibility is demonstrated through hardware implementation on a PYNQ board, thus making this framework a stable and feasible option for BESS security in smart grids. Full article
Show Figures

Figure 1

25 pages, 1397 KB  
Review
Multi-Source Data Integration and Model Coupling for Watershed Eco-Assessment Systems: Progress, Challenges, and Prospects
by Li Ma, Zihe Xu, Lina Fan, Hongxia Jia, Hao Hu and Lixin Li
Processes 2025, 13(9), 2998; https://doi.org/10.3390/pr13092998 - 19 Sep 2025
Viewed by 250
Abstract
The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. [...] Read more.
The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article
Show Figures

Figure 1

Back to TopTop