Next Issue
Volume 11, April
Previous Issue
Volume 10, December
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
1.9
Journals
Inventions
Volume 11
Issue 1
share announcement

Inventions, Volume 11, Issue 1 (February 2026) – 19 articles

Cover Story (view full-size image): This paper introduces a compact, hermetically sealed screw-type shredder for controlled fragmentation of fully cured photopolymer resin, addressing a key challenge in additive-manufacturing waste management. Unlike gravity-dependent systems, the device operates reliably in both terrestrial and microgravity environments, enabling future closed-loop recycling of SLA materials in space. Dual counter-rotating screw shafts with carbide cutting inserts are combined with a pressurized co₂-assisted cooling and evacuation circuit to ensure particle confinement, thermal control, and forced material transport. Performance estimates suggest reduced cutting temperatures, lower power consumption, and extended tool life, positioning the system as an enabling technology for sustainable in situ photopolymer recycling. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
38 pages, 5653 KB  
Article
Tracing Innovation Pathways
by Luigi Assom, Aron Larsson and Alessandro Chiolerio
Inventions 2026, 11(1), 19; https://doi.org/10.3390/inventions11010019 - 16 Feb 2026
Viewed by 815
Abstract
Evaluating innovation and optimising its role in the inventions is fundamental for applied research, that requires planning the use of available resources. Traditional assessment approaches often miss to capture how innovation stagnates between the ideation and prototyping phases (the Valley of Death), and [...] Read more.
Evaluating innovation and optimising its role in the inventions is fundamental for applied research, that requires planning the use of available resources. Traditional assessment approaches often miss to capture how innovation stagnates between the ideation and prototyping phases (the Valley of Death), and to learn how innovation emerges from intermediate-steps contributed by individuals. This paper focuses on tracing innovation as an approach enabling mapping of pathways of intermediate-steps and opportunities for valorising unplanned outcomes. We adopt a qualitative case study to explore how innovation pathways can be conceptualised through technological readiness levels. The operational settings of an EU-funded project defined the boundaries of the study. A network analysis explored relationships among themes that emerged from respondents involved in the activities, following an inductive approach to derive themes from data. Findings indicate that intermediate innovation steps, including failures, are viewed as cumulative contributions to novelty. Their documentation is seen as an investment for unlocking latent value embedded in distributed knowledge. Within this scope, we outline a blockchain-based knowledge graph as a proof-of-concept for tracing cumulative contributions, identifying breakthroughs leading to technological maturity and supporting generation of hypothesis grounded on experimental trials. As a result, we suggest that paths recombining prior knowledge into novelty encode latent value that can be interpreted as a function of the network topology, and propose a conceptual framework for analysing value by means of information theory metrics applicable to innovation graphs. Full article
(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)
►▼ Show Figures

Graphical abstract

29 pages, 5707 KB  
Article
An ANN-Based MPPT and Power Control Strategy for DFIG Wind Energy Systems with Real-Time Validation
by Hamid Chojaa, Kawtar Tifidat, Aziz Derouich, Mishari Metab Almalki and Mahmoud A. Mossa
Inventions 2026, 11(1), 18; https://doi.org/10.3390/inventions11010018 - 15 Feb 2026
Viewed by 620
Abstract
Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of [...] Read more.
Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of a DFIG by continuously tracking the maximum power point under fluctuating wind conditions. Two independent control schemes are developed for the decoupled regulation of active and reactive power in a grid-connected DFIG wind turbine. The first scheme is based on conventional field-oriented control using proportional integral regulators (FOC–PI), while the second employs an Artificial Neural Network Controller (ANNC). The effectiveness of both controllers is evaluated through MATLAB/Simulink 2020 Version simulations of a 1.5 MW DFIG-based wind energy conversion system and experimentally validated using a real wind profile implemented on an eZdsp TMS320F28335 digital signal processor. The proposed control approach achieves low output ripple, a steady-state error below 0.16%, total harmonic distortion of 0.38%, and a limited overshoot of 5%. The obtained results confirm the robustness and reliability of the implemented control strategies in enhancing power capture and improving overall system stability under variable wind conditions. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 3rd Edition)
►▼ Show Figures

Figure 1

14 pages, 3727 KB  
Article
Preparation and Performance of High-Thermal-Conductivity Composite Materials for Online Monitoring Equipment of Ultra-High Voltage Bushings
by Jie Zhang, Longgang Guo, Lin Li, Jian Qin, Zhiqiang Zhang and Zefeng Yang
Inventions 2026, 11(1), 17; https://doi.org/10.3390/inventions11010017 - 12 Feb 2026
Viewed by 883
Abstract
In response to thermal failure risks in ultra-high voltage (UHV) bushing online monitoring devices and maintenance equipment—caused by high heat generation of electronic components and the intrinsically low thermal conductivity of conventional resin encapsulation materials—this study proposes a novel modification strategy based on [...] Read more.
In response to thermal failure risks in ultra-high voltage (UHV) bushing online monitoring devices and maintenance equipment—caused by high heat generation of electronic components and the intrinsically low thermal conductivity of conventional resin encapsulation materials—this study proposes a novel modification strategy based on flash Joule heating (FJH). Distinct from conventional interface modification methods, the proposed approach enables cross-scale, in situ microsoldering between multi-walled carbon nanotubes (MWCNTs) and carbon fibers (CFs), constructing a multiscale reinforcement network with integrated thermal transport and mechanical load transfer pathways. The transient ultra-high-temperature thermal shock generated by FJH not only effectively removes inert impurities on CF surfaces but also drives carbon structural reconstruction, enabling graphitic-level welding of MWCNTs onto the fiber surface. This micro-welded architecture fundamentally differs from traditional filler dispersion or interface coating strategies, which often suffer from the trade-off between interfacial thermal transport and mechanical bonding. By contrast, the FJH-induced carbon–carbon bonded nodes form a continuous conductive and load-bearing network at the micro–nano scale. Characterizations using scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) confirm successful in situ welding of MWCNTs onto CF surfaces. Meanwhile, FJH treatment effectively removes oxygen-containing functional groups and surface impurities. Analysis of carbon bonding evolution indicates that the welding efficiency reaches its maximum at 90 V. Macroscopic performance tests demonstrate that, compared with epoxy resin, the thermal conductivity of the multiscale reinforced system increases by approximately 168%, while the mechanical strength improves by 62.72%. This study provides new theoretical insights and technical pathways for the development of next-generation polymer composite materials with both high thermal conductivity and high mechanical strength. Full article
(This article belongs to the Special Issue Mechanics of Composite Materials: Strength, Deformation, and Failure Analysis)
►▼ Show Figures

Figure 1

27 pages, 2612 KB  
Article
Quantitative Evaluation Method for Source-Load Complementarity and System Regulation Capacity Across Multi-Time Scales
by Xiaoyan Hu, Keteng Jiang, Zikai Fan, Borui Liao, Bingjie Li, Zesen Li, Yi Ge and Hu Li
Inventions 2026, 11(1), 16; https://doi.org/10.3390/inventions11010016 - 11 Feb 2026
Viewed by 349
Abstract
Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support [...] Read more.
Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes. Full article
(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)
►▼ Show Figures

Figure 1

33 pages, 1911 KB  
Review
Conceptual Architecture of a Trustworthy Wind and Photovoltaic Power Forecasting System: A Systematic Review and Design
by Pavel V. Matrenin, Irina F. Iumanova and Alexandra I. Khalyasmaa
Inventions 2026, 11(1), 15; https://doi.org/10.3390/inventions11010015 - 5 Feb 2026
Viewed by 753
Abstract
Accurate and trustworthy forecasting of wind and photovoltaic power generation is essential for the reliable operation and planning of modern power systems. Although recent machine-learning-based forecasting solutions increasingly incorporate elements of trustworthy artificial intelligence, such as explainability, uncertainty quantification, robustness, drift monitoring, and [...] Read more.
Accurate and trustworthy forecasting of wind and photovoltaic power generation is essential for the reliable operation and planning of modern power systems. Although recent machine-learning-based forecasting solutions increasingly incorporate elements of trustworthy artificial intelligence, such as explainability, uncertainty quantification, robustness, drift monitoring, and machine learning operations, these components are typically introduced in a fragmented manner and remain weakly integrated at the architectural level, which limits their applicability in real operational environments. This paper presents a systematic review of 59 peer-reviewed journal articles published between 2019 and 2025, conducted in accordance with the PRISMA 2020 guidelines. The review includes studies focused on wind and photovoltaic power forecasting that report system architectures, frameworks, or end-to-end pipelines incorporating at least one trust-related attribute. The literature search was performed using Scopus, IEEE Xplore, MDPI, and ScienceDirect. Using a narrative and architectural synthesis, the review identifies six structural gaps hindering industrial deployment: the absence of semantic data models, shallow model-centric explainability, drift monitoring without governance mechanisms, lack of automated model lifecycle management, insufficient robustness to real-world data defects, and the absence of integrated end-to-end architectures. The evidence base is limited by the heterogeneity of architectural descriptions and the predominantly qualitative nature of reported implementations. Based on these findings, a high-level reference architecture for a trustworthy AI-based forecasting system is proposed. The architecture formalizes trustworthiness as a system-level property and integrates semantic, technological, and functional trust layers within a unified data and model lifecycle, supporting reproducible, interpretable, and operationally reliable forecasting for both wind and photovoltaic power plants. Full article
(This article belongs to the Special Issue Emerging Trends and Innovations in Renewable Energy)
►▼ Show Figures

Figure 1

20 pages, 4557 KB  
Article
Research on Characterization and Detection Methods of Photovoltaic Cell Thermal Defects Based on Temperature Derivatives
by Zhizhen Du, Kai Liu, Zhiqiang Dai, Like Fan and Guangning Wu
Inventions 2026, 11(1), 14; https://doi.org/10.3390/inventions11010014 - 4 Feb 2026
Viewed by 482
Abstract
Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term [...] Read more.
Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term field operation. Such hotspots not only reduce the power-generation efficiency and service life of PV cells but may also pose safety risks to grid-connected photovoltaic power stations. To address this problem, a squared even-order derivative (SEOD) method based on surface temperature analysis is introduced to enable the quantitative detection of thermal defects in PV cells. In this study, typical faults in PV cells, including low-resistance defects and silicon-based deep scratches, are analyzed. A simulation model is established to correlate typical faults with their equivalent volumetric heat sources, followed by experimental validation for low-resistance defects. Based on this framework, the SEOD algorithm is developed and applied to achieve high-precision localization and quantitative characterization of thermal defects in both simulation models and experimental samples. Full article
(This article belongs to the Special Issue Mechanics of Composite Materials: Strength, Deformation, and Failure Analysis)
►▼ Show Figures

Figure 1

26 pages, 6390 KB  
Article
Image Captioning Using Enhanced Cross-Modal Attention with Multi-Scale Aggregation for Social Hotspot and Public Opinion Monitoring
by Shan Jiang, Yingzhao Chen, Rilige Chaomu and Zheng Liu
Inventions 2026, 11(1), 13; https://doi.org/10.3390/inventions11010013 - 2 Feb 2026
Viewed by 717
Abstract
Large volumes of images shared on social media have made image captioning an important tool for social hotspot identification and public opinion monitoring, where accurate visual–language alignment is essential for reliable analysis. However, existing image captioning models based on BLIP-2 (Bootstrapped Language–Image Pre-training) [...] Read more.
Large volumes of images shared on social media have made image captioning an important tool for social hotspot identification and public opinion monitoring, where accurate visual–language alignment is essential for reliable analysis. However, existing image captioning models based on BLIP-2 (Bootstrapped Language–Image Pre-training) often struggle with complex, context-rich, and socially meaningful images in real-world social media scenarios, mainly due to insufficient cross-modal interaction, redundant visual token representations, and an inadequate ability to capture multi-scale semantic cues. As a result, the generated captions tend to be incomplete or less informative. To address these limitations, this paper proposes ECMA (Enhanced Cross-Modal Attention), a lightweight module integrated into the Querying Transformer (Q-Former) of BLIP-2. ECMA enhances cross-modal interaction through bidirectional attention between visual features and query tokens, enabling more effective information exchange, while a multi-scale visual aggregation strategy is introduced to model semantic representations at different levels of abstraction. In addition, a semantic residual gating mechanism is designed to suppress redundant information while preserving task-relevant features. ECMA can be seamlessly incorporated into BLIP-2 without modifying the original architecture or fine-tuning the vision encoder or the large language model, and is fully compatible with OPT (Open Pre-trained Transformer)-based variants. Experimental results on the COCO (Common Objects in Context) benchmark demonstrate consistent performance improvements, where ECMA improves the CIDEr (Consensus-based Image Description Evaluation) score from 144.6 to 146.8 and the BLEU-4 score from 42.5 to 43.9 on the OPT-6.7B model, corresponding to relative gains of 1.52% and 3.29%, respectively, while also achieving competitive METEOR (Metric for Evaluation of Translation with Explicit Ordering) scores. Further evaluations on social media datasets show that ECMA generates more coherent, context-aware, and socially informative captions, particularly for images involving complex interactions and socially meaningful scenes. Full article
(This article belongs to the Special Issue Advances and Innovations in Deep Learning: Unveiling Multidisciplinary Applications and Challenges)
►▼ Show Figures

Figure 1

25 pages, 2201 KB  
Article
Design and Research of a Dual-Target Drug Molecular Generation Model Based on Reinforcement Learning
by Peilin Li, Ziyan Yan, Yuchen Zhou, Hongyun Li, Wei Gao and Dazhou Li
Inventions 2026, 11(1), 12; https://doi.org/10.3390/inventions11010012 - 26 Jan 2026
Viewed by 1118
Abstract
Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and [...] Read more.
Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article
(This article belongs to the Special Issue Advances and Innovations in Deep Learning: Unveiling Multidisciplinary Applications and Challenges)
►▼ Show Figures

Figure 1

21 pages, 651 KB  
Article
Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging
by Sajad Amiri, Shahram Taeb, Sara Gharibi, Setareh Dehghanfard, Somayeh Sadat Mehrnia, Mehrdad Oveisi, Ilker Hacihaliloglu, Arman Rahmim and Mohammad R. Salmanpour
Inventions 2026, 11(1), 11; https://doi.org/10.3390/inventions11010011 - 26 Jan 2026
Viewed by 870
Abstract
Gadolinium-based contrast agents (GBCAs) are vital for glioma imaging yet pose safety, cost, and accessibility issues; predicting contrast enhancement from non-contrast MRI via machine learning (ML) provides a safer, economical alternative, as enhancement indicates tumor aggressiveness and informs treatment planning. However, scanner and [...] Read more.
Gadolinium-based contrast agents (GBCAs) are vital for glioma imaging yet pose safety, cost, and accessibility issues; predicting contrast enhancement from non-contrast MRI via machine learning (ML) provides a safer, economical alternative, as enhancement indicates tumor aggressiveness and informs treatment planning. However, scanner and population variability hinder robust model selection. To overcome this, a stability-aware framework was developed to identify reproducible ML pipelines for predicting glioma contrast enhancement across multicenter cohorts. A total of 1367 glioma cases from four TCIA datasets (UCSF-PDGM, UPENN-GB, BRATS-Africa, BRATS-TCGA-LGG) were analyzed, using non-contrast T1-weighted images as input and deriving enhancement status from paired post-contrast T1-weighted images; 108 IBSI-standardized radiomics features were extracted via PyRadiomics 3.1, then systematically combined with 48 dimensionality reduction algorithms and 25 classifiers into 1200 pipelines, evaluated through rotational validation (training on three datasets, external testing on the fourth, repeated across rotations) incorporating five-fold cross-validation and a composite score penalizing instability via standard deviation. Cross-validation accuracies spanned 0.91–0.96, with external testing yielding 0.87 (UCSF-PDGM), 0.98 (UPENN-GB), and 0.95 (BRATS-Africa), averaging ~0.93; F1, precision, and recall remained stable (0.87–0.96), while ROC-AUC varied (0.50–0.82) due to cohort heterogeneity, with the MI + ETr pipeline ranking highest for balanced accuracy and stability. This framework enables reliable, generalizable prediction of contrast enhancement from non-contrast glioma MRI, minimizing GBCA dependence and offering a scalable template for reproducible ML in neuro-oncology. Full article
(This article belongs to the Special Issue Machine Learning Applications in Healthcare and Disease Prediction)
►▼ Show Figures

Figure 1

22 pages, 8822 KB  
Article
Potential Recovery and Recycling of Condensate Water from Atlas Copco ZR315 FF Industrial Air Compressors
by Ali Benmoussa, Zakaria Chalhe, Benaissa Elfahime and Mohammed Radouani
Inventions 2026, 11(1), 10; https://doi.org/10.3390/inventions11010010 - 14 Jan 2026
Viewed by 1037
Abstract
This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources [...] Read more.
This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article
(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)
►▼ Show Figures

Figure 1

15 pages, 2558 KB  
Article
Optimization of Electric Bus Charging and Fleet Sizing Incorporating Traffic Congestion Based on Deep Reinforcement Learning
by Hai Yan, Xinyu Sui, Ning Chen and Shuo Pan
Inventions 2026, 11(1), 9; https://doi.org/10.3390/inventions11010009 - 13 Jan 2026
Viewed by 758
Abstract
Amid the increasing demand to reduce carbon emissions, replacing diesel buses with electric buses has become a key development direction in public transportation. However, a significant challenge in this transition lies in developing efficient charging strategies and accurately determining the required fleet size, [...] Read more.
Amid the increasing demand to reduce carbon emissions, replacing diesel buses with electric buses has become a key development direction in public transportation. However, a significant challenge in this transition lies in developing efficient charging strategies and accurately determining the required fleet size, as existing research often fails to adequately account for the impact of real-time traffic congestion on energy consumption. To address this gap, in this study, an optimized charging strategy is proposed, and the necessary fleet size is calculated using a deep reinforcement learning (DRL) approach, which integrates actual route characteristics and dynamic traffic congestion patterns into an electric bus operation model. Modeling is conducted based on Beijing Bus Route 400 to ensure the practical applicability of the proposed method. The results demonstrate that the proposed DRL method ensures operational completion while minimizing charging time, with the algorithm showing rapid and stable convergence. In the multi-route scenarios investigated in this study, the DRL-based charging strategy requires 40% more electric buses, with this figure decreasing to 24% when fast-charging technology is adopted. This study provides bus companies with valuable electric bus procurement and route operation references. Full article
(This article belongs to the Special Issue Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems: Second Edition)
►▼ Show Figures

Figure 1

17 pages, 3223 KB  
Article
Reinforcement Learning-Based Handover Algorithm for 5G/6G AI-RAN
by Ildar A. Safiullin, Ivan P. Ashaev, Alexey A. Korobkov, Artur K. Gaysin and Adel F. Nadeev
Inventions 2026, 11(1), 8; https://doi.org/10.3390/inventions11010008 - 10 Jan 2026
Viewed by 1324
Abstract
The increasing number of Base Stations (BSs) and connected devices, coupled with their mobility, poses significant challenges and makes mobility management even more pressing. Therefore, advanced handover (HO) management technologies are required to address this issue. This paper focuses on the ping-pong HO [...] Read more.
The increasing number of Base Stations (BSs) and connected devices, coupled with their mobility, poses significant challenges and makes mobility management even more pressing. Therefore, advanced handover (HO) management technologies are required to address this issue. This paper focuses on the ping-pong HO problem. To address this issue, we propose an algorithm using Reinforcement Learning (RL) based on the Double Deep Q-Network (DDQN). The novelty of our approach is to assign specialized RL agents to users based on their mobility patterns. The use of specialized RL agents simplifies the learning process. The effectiveness of the proposed algorithm is demonstrated in tests on the ns-3 platform due to its ability to replicate real-world scenarios. To compare the results of the proposed approach, the baseline handover algorithm based on Events A2 and A4 is used. The results show that the proposed approach reduces the number of HO by more than four times on average, resulting in a more stable data rate and increasing it up to two times in the best case. Full article
(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)
►▼ Show Figures

Figure 1

26 pages, 4558 KB  
Review
Integrating Additive Manufacturing into Dental Production: Innovations, Applications and Challenges
by Maryna Yeromina, Jan Duplak, Jozef Torok, Darina Duplakova and Monika Torokova
Inventions 2026, 11(1), 7; https://doi.org/10.3390/inventions11010007 - 7 Jan 2026
Cited by 1 | Viewed by 1141
Abstract
Additive manufacturing (AM) has emerged as a key enabling technology in contemporary dental manufacturing, driven by its capacity for customization, geometric complexity, and seamless integration with digital design workflows. This article presents a technology-oriented narrative review of additive manufacturing in dental implant production, [...] Read more.
Additive manufacturing (AM) has emerged as a key enabling technology in contemporary dental manufacturing, driven by its capacity for customization, geometric complexity, and seamless integration with digital design workflows. This article presents a technology-oriented narrative review of additive manufacturing in dental implant production, focusing on dominant processing routes, material systems, and emerging research trends rather than a systematic or critical appraisal of the literature. An indicative descriptive analysis of publications indexed in the Web of Science and Scopus databases between 2014 and 2024 was used to contextualize the technological development of the field and identify major research directions. Emphasis was placed on metal powder bed fusion technologies, specifically Selective Laser Melting (SLM) and Direct Metal Laser Sintering (DMLS), which enable the fabrication of titanium implants with controlled porosity and enhanced osseointegration. Ceramic AM approaches, including SLA, DLP, and PBF, are discussed in relation to their potential for aesthetic dental restorations and customized prosthetic components. The publication trend overview indicates a growing interest in ceramic AM after 2020, an increasing focus on hybrid and functionally graded materials, and persistent challenges related to standardization and the availability of long-term clinical evidence. Key technological limitations—including manufacturing accuracy, material stability, validated metrology, and process reproducibility—are highlighted alongside emerging directions such as artificial intelligence-assisted workflows, nanostructured surface modifications, and concepts enabling accelerated or immediate clinical use of additively manufactured dental restorations. Full article
(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)
►▼ Show Figures

Figure 1

14 pages, 1025 KB  
Article
visionMC: A Low-Cost AI System Using Facial Recognition and Voice Interaction to Optimize Primary Care Workflows
by Marius Cioca and Adriana Lavinia Cioca
Inventions 2026, 11(1), 6; https://doi.org/10.3390/inventions11010006 - 6 Jan 2026
Cited by 1 | Viewed by 834
Abstract
This pilot study evaluated the visionMC system, a low-cost artificial intelligence system integrating HOG-based facial recognition and voice notifications, for workflow optimization in a family medicine practice. Implemented on a Raspberry Pi 4, the system was tested over two weeks with 50 patients. [...] Read more.
This pilot study evaluated the visionMC system, a low-cost artificial intelligence system integrating HOG-based facial recognition and voice notifications, for workflow optimization in a family medicine practice. Implemented on a Raspberry Pi 4, the system was tested over two weeks with 50 patients. It achieved 85% recognition accuracy and an average detection time of 3.4 s. Compared with baseline, patient waiting times showed a substantial reduction in waiting time and administrative workload, and the administrative workload decreased by 5–7 min per patient. A satisfaction survey (N = 35) indicated high acceptance, with all scores above 4.5/5, particularly for usefulness and waiting time reduction. These results suggest that visionMC can improve efficiency and enhance patient experience with minimal financial and technical requirements. Larger multicenter studies are warranted to confirm scalability and generalizability. visionMC demonstrates that effective AI integration in small practices is feasible with minimal resources, supporting scalable digital health transformation. Beyond biometric identification, the system’s primary contribution is streamlining practice management by instantly displaying the arriving patient and enabling rapid chart preparation. Personalized greetings enhance patient experience, while email alerts on motion events provide a secondary security benefit. These combined effects drove the observed reductions in waiting and administrative times. Full article
(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)
►▼ Show Figures

Figure 1

20 pages, 9150 KB  
Article
Heat Transfer Enhancement and Flow Resistance Characteristics in a Tube with Alternating Corrugated-Smooth Segments
by Junwen Cheng, Jiahao Zhu, Xin Wen, Haodong Yu, Wei Lin, Zuqiang Xin and Jiuyang Yu
Inventions 2026, 11(1), 5; https://doi.org/10.3390/inventions11010005 - 5 Jan 2026
Viewed by 864
Abstract
To mitigate the inherent high flow resistance of conventional corrugated tubes, a novel design with alternating clockwise/counterclockwise corrugated segments separated by smooth sections is proposed. A 3D numerical model was developed to systematically evaluate the thermal-hydraulic performance of the novel tube against smooth [...] Read more.
To mitigate the inherent high flow resistance of conventional corrugated tubes, a novel design with alternating clockwise/counterclockwise corrugated segments separated by smooth sections is proposed. A 3D numerical model was developed to systematically evaluate the thermal-hydraulic performance of the novel tube against smooth and conventional corrugated tubes, with simulations conducted at Reynolds number (Re) = 9952–35,827. Results show both corrugated configurations enhanced heat transfer significantly relative to the smooth tube: the conventional tube had the highest Nusselt number (Nu) (1.76–1.79 times that of the smooth tube), while the novel tube achieved Nu = 1.61–1.65 times that of the smooth tube. Notably, the novel tube reduced flow resistance substantially—at Re = 35,827, its friction factor (f) was only 65.2% of the conventional tube’s. Parametric studies revealed that more corrugated segments improved heat transfer but increased pressure drop: the 72-12 configuration exhibited the best heat transfer, while the 72-2 configuration reduced f by 40.7%. The novel tube showed superior overall performance (Performance Evaluation Criterion (PEC) > 1.24 for all Re), as corrugated segments generated periodic vortices to disrupt the thermal boundary layer, while smooth segments enabled flow redevelopment and pressure recovery. This study provides valuable guidance for designing high-efficiency, low-resistance heat exchange elements. Full article
(This article belongs to the Special Issue Innovations and Inventions in Two-Phase Flow and Heat Transfer)
►▼ Show Figures

Figure 1

16 pages, 1904 KB  
Patent Summary
Screw-Type Shredder for Solid Photopolymer Resin in Microgravity Environments
by Iulian Vlăducă and Emilia Georgiana Prisăcariu
Inventions 2026, 11(1), 4; https://doi.org/10.3390/inventions11010004 - 2 Jan 2026
Cited by 1 | Viewed by 751
Abstract
The invention concerns a screw-driven shredder for solid photopolymer resin, designed for both terrestrial use and prospective deployment in microgravity environments. The system addresses the need for efficient recycling of cured photopolymer waste generated by stereolithography (SLA) 3D printing—a process not yet implemented [...] Read more.
The invention concerns a screw-driven shredder for solid photopolymer resin, designed for both terrestrial use and prospective deployment in microgravity environments. The system addresses the need for efficient recycling of cured photopolymer waste generated by stereolithography (SLA) 3D printing—a process not yet implemented in orbit, but envisioned as part of future closed-loop additive manufacturing systems aboard space stations or lunar habitats. The proposed device is a compact, hermetically sealed mechanical unit composed of ten subassemblies, featuring two counter-rotating screw shafts equipped with carbide milling inserts arranged helically to achieve uniform and controlled fragmentation of solid SLA residues. The shredding process is supported by a pressurized inert fluid circuit, utilizing carbon dioxide (CO2) as a cryogenic working medium to enhance cutting efficiency, reduce heat accumulation, and ensure particle evacuation under microgravity conditions. Studies indicate that CO2-assisted cooling can reduce tool-tip temperature by 10–30 °C, cutting forces by 5–15%, and electrical power consumption by 5–12% while extending tool life by up to 50%. This invention thus provides a key component for a future in situ photopolymer recycling loop in space while also offering a high-efficiency shredding solution for Earth-based photopolymer waste management in additive manufacturing. Full article
(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)
►▼ Show Figures

Figure 1

19 pages, 4902 KB  
Article
A Distributed, Energy-Autonomous Multi-Sensor IoT System for Monitoring and Reducing Water Losses in Distribution Networks
by Juan Arquero-Gallego, Carlos Gilarranz-Casado, Vicente Garcia-Alcántara and José Álvarez
Inventions 2026, 11(1), 3; https://doi.org/10.3390/inventions11010003 - 31 Dec 2025
Viewed by 935
Abstract
Water resources are fundamental for human development in every possible sense; from natural development, since they are the main biological factor necessary for the development of life, to economic development, since they are essential for a large number of productive systems, especially in [...] Read more.
Water resources are fundamental for human development in every possible sense; from natural development, since they are the main biological factor necessary for the development of life, to economic development, since they are essential for a large number of productive systems, especially in the primary and secondary sectors. This makes them a resource which, although at first glance may seem unlimited, is critical since their scarcity and unavailability compromise the whole of human development, greatly limiting productive and economic activity and, ultimately, social welfare. The current development of IoT technology, on the other hand, provides tools to face this problem in a technical way, allowing the adoption of distributed and automated solutions that, together with the knowledge provided by disciplines such as agricultural and alimentary engineering, make viable the development of a system that allows us to monitor and control water distribution networks (WDNs). Next, the situations that involve the mentioned problem will be detailed and different aspects will be proposed in which the implementation of the presented system is intended to have a direct impact. Full article
(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)
►▼ Show Figures

Figure 1

14 pages, 3993 KB  
Article
A Three-Dimensional Visualization System for Tea Production Lines Based on Digital Twins
by Honghao Liu, Guoliang Ma and Kaixing Zhang
Inventions 2026, 11(1), 2; https://doi.org/10.3390/inventions11010002 - 31 Dec 2025
Viewed by 762
Abstract
Current traditional tea processing production lines suffer from issues such as fragmented data and low levels of intelligence. This paper proposes a three-dimensional visualization system for tea processing production lines based on digital twins. Firstly, the system’s overall framework and functional architecture were [...] Read more.
Current traditional tea processing production lines suffer from issues such as fragmented data and low levels of intelligence. This paper proposes a three-dimensional visualization system for tea processing production lines based on digital twins. Firstly, the system’s overall framework and functional architecture were established. Secondly, multi-source heterogeneous data from the production line was collected and managed through a driver architecture, enabling the construction and mapping of the digital twin information model. Thirdly, referencing the actual environment of a green tea processing line, scene-specific lighting models and rendering techniques were employed to recreate a virtual green tea processing environment. During this process, lighting optimization enhanced the realism of the system’s scenes. Finally, employing data-driven methodologies, the system dynamically simulates the operational states of various production line equipment and the morphological changes in tea leaves. This achieves comprehensive three-dimensional visualization and all-round monitoring of the tea processing production line. Experimental validation confirms the feasibility of this visualized 3D system, injecting fresh impetus into advancing intelligent tea production. Full article
(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)
►▼ Show Figures

Figure 1

22 pages, 5454 KB  
Article
Extreme Strengthening of Nickel by Ultralow Additions of SiC Nanoparticles: Synergy of Microstructure Control and Interfacial Reactions During Spark Plasma Sintering
by Leonid Agureev, Svetlana Savushkina and Artem Ashmarin
Inventions 2026, 11(1), 1; https://doi.org/10.3390/inventions11010001 - 29 Dec 2025
Viewed by 597
Abstract
Ni–ySiC system (where y = 0.001, 0.005, and 0.015 wt.%) composite materials with enhanced mechanical properties have been fabricated and comprehensively investigated. The composites were synthesized using a combined technology involving preliminary mechanical activation of powder components in a planetary mill followed by [...] Read more.
Ni–ySiC system (where y = 0.001, 0.005, and 0.015 wt.%) composite materials with enhanced mechanical properties have been fabricated and comprehensively investigated. The composites were synthesized using a combined technology involving preliminary mechanical activation of powder components in a planetary mill followed by consolidation via spark plasma sintering (SPS) at 850 °C. The microstructure and phase composition were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The physico-mechanical properties were evaluated by density measurements (hydrostatic weighing), three-point bending tests (25 °C and 400 °C), and Young’s modulus measurement using an ultrasonic method (25–750 °C). It was found that the introduction of ultralow amounts of SiC nanoparticles (0.001 wt.%) leads to an extreme increase in flexural strength: by 115% at 20 °C (up to 1130 MPa) and by 86% at 400 °C (up to 976 MPa) compared to pure nickel. Microstructural analysis revealed the formation of an ultrafine-grained structure (0.15–0.4 µm) with the presence of pyrolytic carbon and probable nickel silicide interlayers at the grain boundaries. Thermodynamic and kinetic modeling, including the calculation of chemical potentials and diffusion coefficients, confirmed the possibility of reactions at the Ni/SiC interface with the formation of nickel silicides (Ni2Si, NiSi) and free carbon. The scientific novelty of the work lies in establishing a synergistic strengthening mechanism combining the Hall–Petch, Orowan (dispersion), and solid solution strengthening effects, and in demonstrating the property extremum at an ultralow content of the dispersed phase (0.001 wt.%), explained from the standpoint of quantum-chemical analysis of phase stability. The obtained results are of practical importance for the development of high-strength and thermally stable nickel composites, promising for application in aerospace engineering. Full article
(This article belongs to the Section Inventions and Innovation in Applied Chemistry and Physics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-19
Previous Issue
Next Issue
Back to TopTop