Engineering reports : open access最新文献

{"title":"Scalable Gecko-Inspired Adhesives via Diffraction-Grated Molds: A Low-Cost, Directional PDMS System","authors":"Motaz Hassan,&nbsp;Ajay Mahajan,&nbsp;Xiaosheng Gao,&nbsp;D. Dane Quinn,&nbsp;Siamak Farhad","doi":"10.1002/eng2.70352","DOIUrl":"https://doi.org/10.1002/eng2.70352","url":null,"abstract":"<p>Geckos achieve exceptional adhesion through hierarchical micro/nanoscale setae exploiting van der Waals forces, a mechanism challenging to replicate synthetically due to fabrication complexity. This study presents a cost-effective, lithography-free method for gecko-inspired adhesives by casting PDMS onto commercial diffraction-grated sheets. The resulting microstructure exhibits directional adhesion, passive detachment, and a non-adhesive default state. Shear and peel tests across 8.06–103.23 cm² contact areas demonstrated a maximum shear stress of 19.10 kPa (supporting up to 7.105 kg) and peel forces below 1 N at 90°, confirming controlled release. Durability testing showed performance recovery after contamination and cleaning, ensuring reusability. The fabrication method eliminates cleanroom requirements, using RTV silicone, 3D-printed fixtures for rapid, scalable prototyping, and diffraction-grated molds. Current limitations include single-level microstructures and absent nanoscale features, reducing efficacy on varying surface structures. Future work will integrate resin-printed molds to introduce wedge-shaped/angled structures and microporous filters for nanoscale fidelity, aiming to develop hierarchical adhesives that rival state-of-the-art systems. These advancements target high performance while maintaining affordability and scalability for diverse applications, from robotics to industrial automation. By bridging the gap between biological inspiration and manufacturable design, this approach offers a practical pathway toward reusable, high-capacity adhesives with broad real-world utility.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Neuro-Fuzzy Inference System-Based Sliding Mode Control in the Presence of External Disturbances and Parameter Variation for Quadcopter UAV","authors":"Daniel Fikadu Assefa,&nbsp;Elisabeth Andarge Gedefaw,&nbsp;Chala Merga Abdissa,&nbsp;Lebsework Negash Lemma","doi":"10.1002/eng2.70417","DOIUrl":"https://doi.org/10.1002/eng2.70417","url":null,"abstract":"<p>Quadrotor unmanned aerial vehicles (UAVs) are increasingly becoming essential tools in applications such as surveillance, military operations, crop monitoring, search and rescue, and inspection of hazardous terrain. Their control is not an easy endeavor due to the underactuated and highly coupled dynamics. Among many control methodologies, sliding mode control (SMC) has long been recognized as one that is insensitive to system nonlinearities and external disturbances. Yet, the inherent chattering effect of SMC will lead to system degradation and actuator damage. To mitigate this limitation, this study proposes an adaptive neuro-fuzzy inference system-based sliding mode control (ANFIS-SMC) method that incorporates the strength of ANFIS and the robustness of SMC to enhance quadrotor trajectory tracking with reduced chattering effects. The control system comprises position, altitude, and attitude controllers that online learn from system errors and control signals and ensure stable and precise flight under dynamic flight conditions. The performance of the ANFIS-SMC controller developed in the current study is validated using MATLAB/SIMULINK simulations and compared with a classical SMC scheme. Results confirm that a Comparison between SMC and the proposed ANFIS-SMC controller is conducted in terms of both disturbance and parameter variation, and the proposed ANFIS-SMC controller has shown better performance improvement of 58.1%. Reduces chattering and achieves improved tracking accuracy, confirming its worthiness for robust quadrotor control tasks.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hardware Design for Secure Telemedicine Using A Novel Framework, A New 4D Memristive Chaotic Oscillator, and Dispatched Gray Code Scrambler","authors":"Fritz Nguemo Kemdoum,&nbsp;Serge Raoul Dzonde Naoussi,&nbsp;Gideon Pagnol Ayemtsa Kuete,&nbsp;Justin Roger Mboupda Pone","doi":"10.1002/eng2.70383","DOIUrl":"https://doi.org/10.1002/eng2.70383","url":null,"abstract":"<p>This study introduces an energy-efficient FPGA-based image encryption mechanism utilizing a 4D memristive chaotic oscillator and a Dispatched Gray Code Scrambler (DGCS) within a MATLAB/Simulink FPGA-in-the-loop framework. Tailored for secure telemedicine, the system improves confusion and diffusion via structured pixel scrambling and chaos-driven key generation. Security assessments indicate substantial robustness, with global entropy of 7.9973, local entropy of 7.9040, near-zero correlation coefficients, NPCR of 99.6170%, and UACI of 33.3172%. The system records a PSNR of 29.72 dB under 1% salt-and-pepper noise, and 19.76 dB under Gaussian noise with variance 0.001, showcasing considerable resilience to both impulsive and distributed distortions. This robustness against Gaussian noise is particularly vital in telemedicine, where image integrity is essential amidst transmission challenges. The keystream successfully passes NIST SP 800-22 and TestU01 statistical evaluations. Designed on an Artix-7 FPGA, the system's power consumption stands at 105 mW, utilizing 11.38% of LUTs, 6.25% of DSPs, and 10.48% of I/Os, achieving a performance frequency of 7.24 MHz. These findings underscore its appropriateness for embedded, low-latency, and noise-resistant image safeguarding in resource-limited medical settings.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of Deep Learning Techniques for Anomaly Detection in IoT Networks: Methods, Challenges, and Datasets","authors":"Roya Morshedi,&nbsp;S. Mojtaba Matinkhah","doi":"10.1002/eng2.70415","DOIUrl":"https://doi.org/10.1002/eng2.70415","url":null,"abstract":"<p>With the rapid growth of the Internet of Things (IoT) and the widespread deployment of smart connected devices, ensuring the security of these networks has become a critical challenge. Anomaly detection is considered one of the most effective techniques for identifying abnormal behaviors and cyber-attacks in IoT networks. In recent years, deep learning techniques have gained significant attention in this domain due to their powerful capabilities in automatic feature extraction and modeling complex patterns. This review article provides a comprehensive overview of deep learning methods applied to anomaly detection in IoT networks. Various deep architectures including CNNs, LSTMs, autoencoders, GANs, and hybrid models are analyzed and compared. In addition, commonly used datasets such as CICIDS2017, BoT-IoT, NSL-KDD, and TON_IoT are introduced and evaluated in terms of their quality and suitability for deep learning-based models. Key challenges including the lack of real-world data, high resource consumption, vulnerability to adversarial attacks, and lack of interpretability are also discussed. Finally, potential future research directions are suggested to enhance the performance and real-world applicability of deep learning-based anomaly detection systems in IoT environments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on the Properties of Controlled Low-Strength Materials Prepared by Pipe Jacking Slag","authors":"Jianchen Song,&nbsp;Xuebo Song,&nbsp;Feilun Luo,&nbsp;Liang Xiong","doi":"10.1002/eng2.70374","DOIUrl":"https://doi.org/10.1002/eng2.70374","url":null,"abstract":"<p>As a byproduct of urban pipeline network construction, pipe jacking waste soil exhibits characteristics of massive volume, regional specificity, and complex composition. The presence of surface-active components impedes drainage consolidation, thereby restricting its resource utilization. This study developed self-compacting Controlled Low Strength Materials (CLSM) using waste soil from utility tunnel pipe jacking operations, investigating the effects of water-solid ratio, lime-soil ratio, and fly ash-to-cement ratio (F/C) on flowability, water secretion rate, and compressive strength. Experimental results demonstrate that: (1) Water–solid ratio predominantly governs CLSM flowability; (2) Compressive strength decreases with increasing F/C but enhances with higher lime-soil ratios; (3) Optimized mixtures achieved flowability (100–200 mm), water secretion rate (&lt; 3%), and compressive strength (0.35–0.7 MPa) meeting trench backfill specifications. The developed CLSM exhibits self-compacting properties and high flowability, satisfying both operational performance and mechanical requirements for engineering applications. This research provides technical parameters for sustainable recycling of pipe jacking waste in urban underground engineering projects.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Experimental and Machine Learning Approach for Machinability Assessment and Tool Life Prediction in Drilling of 14NiCr10 Alloy Using AlTiN-Coated Carbide Tools","authors":"Adik M. Takale,&nbsp;Uday A. Dabade,&nbsp;Manjunath G. Avalappa,&nbsp;Uttam U. Deshpande,&nbsp;Mukesh Kumar","doi":"10.1002/eng2.70397","DOIUrl":"https://doi.org/10.1002/eng2.70397","url":null,"abstract":"<p>To maximize tool life and process efficiency in high-performance drilling applications, it is necessary to examine the machinability of 14NiCr10 alloy using AlTiN-coated carbide tools with varying cobalt compositions. We propose a method to evaluate tool wear, cutting force, temperature, and the number of holes drilled before resharpening during experimental trials in dry cutting settings. In comparison to normal tools, carbide tools with a higher cobalt content demonstrated better wear resistance, lower thermal load, and a 51% increase in tool life during experiments. To validate the measured mechanical and thermal loads, we carried out simulation tests using Finite Element Analysis (FEA) on temperature distribution, torque, and stress behavior loads. For normal and increased cobalt content tool versions, the values stayed within safe operating limits. We trained the sensor-acquired force and temperature data using the Decision Tree Regressor to create a machine learning-based predictive model, further improving process reliability. With more than 90% of tool life estimates falling within an acceptable error range of ±10%, the model exhibited excellent predictive accuracy. Our method provides a comprehensive hybrid framework for machining high-strength alloys by utilizing a combination of simulation, machine learning, experimental analysis, and enhancing tool performance. Thus, it facilitates predictive maintenance and supports the development of smart manufacturing processes.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving Changes: Analyzing the Factors Influencing Lean Manufacturing Adoption in Tanzania Through Structural Equation Modeling (SEM)","authors":"Juma M. Matindana,&nbsp;Francis D. Sinkamba,&nbsp;Mussa I. Mgwatu","doi":"10.1002/eng2.70414","DOIUrl":"https://doi.org/10.1002/eng2.70414","url":null,"abstract":"<p>With growing competition among manufacturing industries in Tanzania, there is a need to adopt lean manufacturing (LM). The adoption of LM in Tanzania and other developing countries is low. This study identifies drivers for LM implementation in the country. Survey and purposive sampling were used to collect responses from 243 manufacturing industries in Tanzania. Partial least squares—structural equation modeling (PLS-SEM) and relative importance index (RII) were used to determine and rank the drivers for LM. PLS-SEM involved the development of a measurement and structural model for drivers of LM adoption using Smart PLS 4. Model fit indices on the effects of drivers on the adoption of LM, such as the normed fit index (NFI), were ≥ 0.7, demonstrating the model was good. External and policy drivers positively impact the adoption of LM in Tanzania. The drivers are to increase capacity to fulfill demands, establish standard operating procedures, balance workload on different workstations, reduce lead time, and improve process control. Identifying the drivers enhances competition among local industries, which, in turn, improves the sector's contribution to the country's gross domestic product. Furthermore, it assists policymakers in setting appropriate policies and strategies for promoting industrial growth in Tanzania.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Modulation Recognition Based on a Lightweight Transformer Network Under Strong Interference Conditions","authors":"Dingrui Liu,&nbsp;Pengli Liu,&nbsp;Jialin Chen,&nbsp;Ke Xiong,&nbsp;Bo Jing,&nbsp;Dongsheng Liao","doi":"10.1002/eng2.70405","DOIUrl":"https://doi.org/10.1002/eng2.70405","url":null,"abstract":"<p>In complex environments with strong electromagnetic interference, which are characterized by high noise levels and low signal-to-noise ratios (SNRs), deep learning improves the efficiency and accuracy of automatic modulation recognition (AMR) in electronic reconnaissance operations. The deep-learning architecture Transformer, a prominent neural network model, captures global feature dependencies in parallel through a multi-head attention mechanism. This improves both the receptive field and the flexibility of the network. However, Transformer fails to effectively model local, subtle features, and its high computational complexity creates challenges in mobile deployment. To address these limitations under conditions of heavy interference, this paper proposes a mobile convolution self-attention network (MCSAN), which utilizes multiple inverted residual blocks to extract local signal features, reducing the spatial dimensions while increasing the channel dimensions of the feature map. Additionally, a novel global window self-attention (GWSA) block is inserted after different inverted residual blocks to extract global signal features. GWSA reduces computational complexity and achieves higher accuracy than conventional multi-head attention mechanisms. In this paper, we evaluate MCSAN under conditions of severe interference using the RML2016.10a dataset at SNRs as low as −20 dB. Additionally, we analyze the model's architecture, hyperparameters, and confusion matrices. Finally, we compare this model to existing deep learning-based AMR models. Our experimental results demonstrate that MCSAN effectively improves recognition accuracy while requiring considerably fewer computational resources and parameters than current Transformer-based AMR approaches. Notably, MCSAN achieves a recognition accuracy of 53.21% even at an SNR of −20 dB.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Some Vegetable Oil-Based Additives for Petroleum Industry: A Review of Sunflower, Rapeseed and Used Cooking Oil-Derived Surfactants","authors":"Roland Nagy,&nbsp;Gábor Zoltán Nagy,&nbsp;Ditta Adrienn Gerbovits","doi":"10.1002/eng2.70345","DOIUrl":"https://doi.org/10.1002/eng2.70345","url":null,"abstract":"<p>The increasing environmental concerns associated with conventional petroleum-based additives—such as high toxicity, poor biodegradability, and long-term ecological persistence—have driven the development of more sustainable alternatives. This review focuses on the synthesis and industrial relevance of bio-based green gemini surfactants and sulphurized vegetable oil-derived extreme pressure (EP) additives, especially those prepared from sunflower oil, rapeseed oil, and used cooking oil. These compounds offer advantages including lower environmental impact, high thermal stability, and effective lubrication or interfacial properties in petroleum-related applications. The paper summarizes recent advances in the field, outlines key mechanisms, and explores their potential in enhanced oil recovery, metalworking, and lubrication. By compiling and evaluating current literature, the work contributes to identifying environmentally friendly and industrially viable bio-additive candidates.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Mosquito-Nets: A Natural Approach to Controlling Malaria Using Larvicidal Plant Extracts and Internet of Things","authors":"Juliet Onyinye Nwigwe,&nbsp;Kennedy Chinedu Okafor,&nbsp;Ogonna Christiana Ani,&nbsp;Titus Ifeanyi Chinebu,&nbsp;Okafor Ijeoma Peace,&nbsp;Omowunmi Mary Longe,&nbsp;Kelvin Anoh","doi":"10.1002/eng2.70407","DOIUrl":"https://doi.org/10.1002/eng2.70407","url":null,"abstract":"<p>Malaria mosquitoes, Anopheles, are well-known for carrying and spreading the malaria pathogens, known as Plasmodium. The public health challenge it brings has remained a global health challenge, of which the most robust control measures include mosquito-treated nets and electronic mosquito killer lamps. Due to health and cost problems, for example, in developing countries, these methods are not suitable for controlling mosquitoes and their plasmodiumic pathogens. In this study, we propose the use of two natural plant (e.g., <i>Petiveria alliacea</i> and <i>Hyptis suavolens</i> leaf) extracts that are cheap, ubiquitous, and effective for the control of mosquitoes, especially in temperate regions such as sub-Saharan Africa. On top of that, the study uses memory, non-locality, and fractal properties of fractal-fractional derivatives from compartmental modeling to capture susceptibility of infected persons, wider coverage, and heterogeneous breeding of mosquitoes, respectively, to evaluate the effectiveness of the two leaf extracts as natural larvicides against <i>Anopheles</i> mosquitoes. To measure the effectiveness of the two plant extracts in controlling malaria, this study develops a basic reproduction number model of Anopheles mosquitoes and evaluates the endemic points of the model. Comparing the results of larvicidal control with those of mosquito-treated nets, the proposed larvicidal control achieved 94.86% efficacy when applied alone and 96.83% efficacy when combined with mosquito nets, each outperforming mosquito nets (83.33%). These findings position compartmental fractal fractional-order modeling as an innovative tool for bioinformatic disease vector control. The study also presents a smart mosquito-net model where data collected from the host nodes on the performance of larvicides in mosquito and malaria control are transmitted via the Internet of Things infrastructure to the edge and cloud servers for computation, processing, artificial intelligence analytics, and policy-making.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}