bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Improvement of Temperature Resistance of Starch Drilling Fluid Treatment Agent by Composite Plant Phenols
Authors: Liu Huaizhu , Zhao Kangning , Wang Qingchen , Ni Huafeng , Zhang Fan , Xue Le , Wang Quande , Chen Gang
Published: February 2025

Modified starch and other natural polymer materials have found extensive applications in drilling fluids. However, conventional modification methods offer limited scope for further enhancing their temperature resistance, typically with the applicable temperature being below 140 °C. This paper presents the preparation of composite plant phenols using walnut shells, peanut shells, straw, and lignin, which are rich in the fundamental “three elements” of plants. To explore the improvement of the temperature resistance of cellulose-based drilling fluid additives, this study investigated the apparent viscosity, dynamic shear force, filtration performance, and adhesion coefficient of water-based drilling fluids supplemented with composite plant phenols. Additionally, the mechanism of action of the composite in drilling fluids was analyzed via infrared spectroscopy. The results revealed that the combined use of starch and composite plant phenols elevated the temperature resistance limit of starch from 160 °C to 180 °C. After aging at 180 °C, the filtration loss of the drilling fluid formulation containing composite plant phenols dropped to 3.6 mL, while the apparent viscosity climbed from 3.1 mPa·s to 13.6 mPa·s. This clearly demonstrates the excellent high-temperature resistance and filtration-reducing capabilities of composite plant phenols. When the addition of cassava starch was 2%, the filtration loss of the drilling fluid system reached a minimum of 6.2 mL. A positively charged gel was identified as the optimal high-temperature-resistant cutting agent. At a dosage of 1%, the dynamic plastic ratio of the formulation increased from 0.51 to 2.11. Tannin extract emerged as the ideal high-temperature-resistant and environmentally friendly drilling fluid treatment agent. After its addition, the apparent viscosity of the drilling fluid system increased from 2.4 mPa·s to 7.3 mPa·s, and the filtration loss decreased from 140 mL to 14.6 mL.
Positive Mental Health: Psychometric Evaluation of the PMHI-19 in a Sample of University Student-Athletes and Dancers
Authors: Hansen-Oja Morgan , Dluzniewski Alexandra , Baker Russell T. , Casanova Madeline P.
Published: February 2025

Background: Student-athletes and competitive dancers experience significant physical, psychological, and emotional stress, often coupled with academic and social pressures. These stressors may lead to unhealthy coping mechanisms, negatively impacting mental health, quality of life, and athletic performance. While assessing mental illness is important, measuring positive mental health (PMH) can offer valuable insights into overall well-being and resilience. The positive mental health Instrument (PMHI) was developed to assess PMH, but its psychometric properties in student-athletes and competitive athletes have not been explored. Methods: Collegiate student-athletes and competitive dancers completed a survey, including the PMHI-19 and a demographic questionnaire. Confirmatory factor analysis (CFA) was conducted to assess the factor structure of the PMHI-19. An exploratory factor analysis (EFA) was then conducted to identify a more parsimonious structure. Results: The CFA of the PMHI-19 did not meet recommended model fit indices. EFA resulted in two different parsimonious models: a 4-factor, 11-item model (PMHI-11) and a 3-factor, 9-item model (PMHI-9), both meeting recommended fit indices. Conclusions: The condensed PMHI-11 and PMHI-9 models may be more suitable for use in collegiate athletic populations. Further research is needed to refine these instruments and explore their applicability across diverse athletic groups.
How Do Religions and Religiosity Matter for Climate Anxiety?
Authors: Toros Emre
Published: February 2025

During the last decade, the discussions about climate change have evolved beyond technical and technological debates, and politicians and religious leaders have started to acknowledge the complexity of the matter more frequently than ever. These discussions aptly underlined the inadequacy of the offered technical solutions to problems due to climate change, which are bound to social and psychological barriers and evaluated through the lenses of existing culture, values, and beliefs, which can hinder appropriate responses. The current study problematises this puzzle by exploring variations in ideological positions, religious affiliations, and religiosity levels and their interactions with climate anxiety. The findings indicate that differing religious affiliations and religiosity levels are robust predictors of variations in climate anxiety. Moreover, their interactions and ideological positions also effectively explain the variations in perceptions related to climate anxiety. These findings provide critical insights into how religious and ideological dimensions influence responses to climate issues, offering practical implications for policymakers.
Anti-Interference Spectral Confocal Sensors Based on Line Spot
Authors: Wang Bo , Li Jiafu , Luo Mingzhe , Liang Fengshuang , Hu Jiacheng
Published: February 2025

Spectral confocal displacement sensors are non-contact optoelectronic sensors widely utilized for their high accuracy, speed, and ability to measure diverse surfaces. However, challenges including vibration, angular deflection, and surface quality variations can reduce sensor stability and accuracy when performing measurements such as lithium battery wafer thickness, wafer warpage, and optical component surface topography. This study proposes a line-spot-based measurement method using a binary diffractive lens and cylindrical lens with a 20× objective, and then the overall structure is simulated and optimized by using ZEMAX, which realizes a confocal measurement system with a measurement range of 800 μm, line spot length of 3.8 mm, and width of 0.2 mm. The system, calibrated with a nanometer displacement stage, achieved 30 nm resolution and significantly improved dynamic stability (standard deviation (SD) of 0.013 μm) compared to a point spectral confocal sensor (SD of 0.064 μm). The results indicate the proposed sensor exhibits improved stability during scanning measurements.
A High-Sensitivity Inkjet-Printed Flexible Resonator for Monitoring Dielectric Changes in Meat
Authors: Abounasr Jamal , Gharbi Mariam El , García Raúl Fernández , Gil Ignacio
Published: February 2025

This paper introduces a flexible loop antenna-based sensor optimized for real-time monitoring of meat quality by detecting changes in dielectric properties over a six-day storage period. Operating within the 2.4 GHz ISM band, the sensor is designed using CST Microwave Studio 2024 to deliver high sensitivity and accuracy. The sensing mechanism leverages resonance frequency shifts caused by variations in permittivity as the meat degrades. Experimental validation across five samples showed a consistent frequency shift from 2.14 GHz (Day 0) to 1.29 GHz (Day 5), with an average sensitivity of 0.173GHz/day. A strong correlation was observed between measured and simulated results, as evidenced by linear regression (R2=0.984 and R2=0.974 for measured and simulated data, respectively). The sensor demonstrated high precision and repeatability, validated by low standard deviations and minimal frequency deviations. Compact, printable, and cost-effective, the proposed sensor offers a scalable solution for food quality monitoring. Its robust performance highlights its potential for integration into IoT platforms and extension to other perishable food products, advancing real-time, non-invasive, RF-based food safety technologies.
The Design of a Low-Power Pipelined ADC for IoT Applications
Authors: Zhang Junkai , Sun Tao , Huang Zunkai , Tao Wei , Wang Ning , Tian Li , Zhu Yongxin , Wang Hui
Published: February 2025

This paper proposes a low-power 10-bit 20 MS/s pipelined analog-to-digital converter (ADC) designed for the burgeoning needs of low-data-rate communication systems, particularly within the Internet of Things (IoT) domain. To reduce power usage, multiple power-saving techniques are combined, such as sample-and-hold amplifier-less (SHA-less) architecture, capacitor scaling, and dynamic comparators. In addition, this paper presents a novel operational amplifier (op-amp) with gain boosting, featuring a dual-input differential pair that enables internal pipeline stage switching, effectively alleviating the crosstalk and memory effects inherent in conventional shared op-amp configurations, thereby further reducing power consumption. A prototype ADC was fabricated in a 180 nm CMOS process and the core size was 0.333 mm2. The ADC implemented operated at a 20 MHz sampling rate under a 1.8 V supply voltage. It achieved a spurious-free dynamic range (SFDR) of 61.83 dB and a signal-to-noise-and-distortion ratio (SNDR) of 54.15 dB while demonstrating a maximum differential non-linearity (DNL) of 0.36 least significant bit (LSB) and a maximum integral non-linearity (INL) of 0.67 LSB. Notably, the ADC consumed less than 5 mW of power at the mentioned sampling frequency, showcasing excellent power efficiency.
Preparation of Ion Composite Photosensitive Resin and Its Application in 3D-Printing Highly Sensitive Pressure Sensor
Authors: Guan Tong , Li Huayang , Liu Jinyun , Zhang Wuxu , Wang Siying , Ye Wentao , Bian Baoru , Yi Xiaohui , Wu Yuanzhao , Liu Yiwei , Du Juan , Shang Jie , Li Run-Wei
Published: February 2025

Flexible pressure sensors play an extremely important role in the fields of intelligent medical treatment, humanoid robots, and so on. However, the low sensitivity and the small initial capacitance still limit its application and development. At present, the method of constructing the microstructure of the dielectric layer is commonly used to improve the sensitivity of the sensor, but there are some problems, such as the complex process and inaccurate control of the microstructure. In this work, an ion composite photosensitive resin based on polyurethane acrylate and ionic liquids (ILs) was prepared. The high compatibility of the photosensitive resin and ILs was achieved by adding a chitooligosaccharide (COS) chain extender. The microstructure of the dielectric layer was optimized by digital light processing (DLP) 3D-printing. Due to the introduction of ILs to construct an electric double layer (EDL), the flexible pressure sensor exhibits a high sensitivity of 32.62 kPa−1, which is 12.2 times higher than that without ILs. It also has a wide range of 100 kPa and a fast response time of 51 ms. It has a good pressure response under different pressures and can realize the demonstration application of human health.
Content-Based Histopathological Image Retrieval
Authors: Nuñez-Fernández  Camilo , Farias  Humberto , Solar  Mauricio
Published: February 2025

Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and Vision Transformers improve the extraction of these feature descriptors. These models typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, these embeddings, by focusing on local spatial details at a single scale, miss out on the richer spatial context from earlier layers. This gap suggests the development of methods that incorporate multi-scale information to enhance the depth and utility of feature descriptors in histopathological image analysis. In this work, we propose the Local–Global Feature Fusion Embedding Model. This proposal is composed of three elements: (1) a pre-trained backbone for feature extraction from multi-scales, (2) a neck branch for local–global feature fusion, and (3) a Generalized Mean (GeM)-based pooling head for feature descriptors. Based on our experiments, the model’s neck and head were trained on ImageNet-1k and PanNuke datasets employing the Sub-center ArcFace loss and compared with the state-of-the-art Kimia Path24C dataset for histopathological image retrieval, achieving a Recall@1 of 99.40% for test patches.
Posture Control of Hydraulic Flexible Second-Order Manipulators Based on Adaptive Integral Terminal Variable-Structure Predictive Method
Authors: Xu Jianliang , Sui Zhen , Xu Feng
Published: February 2025

As operational scenarios become more complex and task demands intensify, the requirements for the intelligence and automation of manipulators in industry are increasing. This work investigates the challenge of posture tracking control for hydraulic flexible manipulators by proposing a discrete-time integral terminal sliding mode predictive control (DITSMPC) method. First, the proposed method develops a second-order dynamic model of the manipulator using the Lagrangian dynamic strategy. Second, a discrete-time sliding mode control (SMC) law based on an adaptive switching term is designed to achieve high-precision tracking control of the system. Finally, to weaken the influence of SMC buffeting on the manipulator system, the predictive time domain function is integrated into the proposed SMC law, and the delay estimation of the unknown term in the manipulator system is carried out. The DITSMPC scheme is derived and its convergence is proven. Simulation experiments comparing the DITSMPC scheme with the classical discrete-time SMC method demonstrate that the proposed scheme results in smooth torque changes in each joint of the manipulator, with the integral of torque variations being 5.22×103. The trajectory tracking errors for each joint remain within ±0.0025 rad, all of which are smaller than those of the classical scheme.
PAPRec: 3D Point Cloud Reconstruction Based on Prior-Guided Adaptive Probabilistic Network
Authors: Liu Caixia , Zhu Minhong , Chen Yali , Wei Xiulan , Li Haisheng
Published: February 2025

Inferring a complete 3D shape from a single-view image is an ill-posed problem. The proposed methods often have problems such as insufficient feature expression, unstable training and limited constraints, resulting in a low accuracy and ambiguity reconstruction. To address these problems, we propose a prior-guided adaptive probabilistic network for single-view 3D reconstruction, called PAPRec. In the training stage, PAPRec encodes a single-view image and its corresponding 3D prior into image feature distribution and point cloud feature distribution, respectively. PAPRec then utilizes a latent normalizing flow to fit the two distributions and obtains a latent vector with rich cues. PAPRec finally introduces an adaptive probabilistic network consisting of a shape normalizing flow and a diffusion model in order to decode the latent vector as a complete 3D point cloud. Unlike the proposed methods, PAPRec fully learns the global and local features of objects by innovatively integrating 3D prior guidance and the adaptive probability network under the optimization of a loss function combining prior, flow and diffusion losses. The experimental results on the public ShapeNet dataset show that PAPRec, on average, improves CD by 2.62%, EMD by 5.99% and F1 by 4.41%, in comparison to several state-of-the-art methods.
Analysis, Design, and Implementation of a User-Friendly Differential Privacy Application
Authors: Tjhin Reynardo , Akbar Muhammad Sajjad , Canonne Clement , Bashir Rabia
Published: February 2025

In the era of artificial intelligence, ensuring privacy in publicly released data is critical to prevent linkage attacks that can reveal sensitive information about individuals. Differential privacy (DP) has emerged as a robust approach for safeguarding privacy, but its mathematical complexity often limits its accessibility to non-experts. This paper introduces a novel, user-friendly web application that bridges the gap between theoretical DP concepts and their practical application. The application includes two main features: a query version, which demonstrates DP mechanisms for statistical queries; and a privatize version, which applies DP techniques to entire datasets. A key contribution of this work is the identification of discrepancies in the implementation of maximum and minimum queries within the OpenDP library, revealing gaps between theory and practice. Additionally, this paper introduces a foundational framework for dataset privatization using OpenDP’s built-in methods. By providing an interactive platform, this work advances the public understanding of DP mechanisms and highlights areas for improvement in existing libraries. The application serves as both an educational tool and a step toward addressing practical challenges in the implementation of DP.
Comparative Analysis of the Selected Photoreceiver Input Stages in Terms of Noise
Authors: Achtenberg Krzysztof , Bielecki Zbigniew
Published: February 2025

Semiconductor radiation detectors usually use a specific signal conditioning circuit, ensuring the required detection system parameters. This paper details the noise properties of specific input stages in photoreceivers that detect various types of radiation. For this purpose, the popular silicon PIN photodiode (BPW34) and two different types of low-noise operational amplifiers (AD797A and ADA4625-1) were used. In the presented experiments, noise measurements were provided for voltage and transimpedance amplifiers operating in input stages, comparing their noise and bandwidths. This made it possible to obtain results for bipolar junction transistor (BJT)- and field-effect transistor (FET)-based input stages of circuity, cooperating directly with a photodiode. Analyzing the obtained characteristics and considering the photodiode operation mode, it is evident that the transimpedance amplifier and photoconductive mode should be considered a typical first-choice solution. In some cases, the performances, such as bandwidth and noise, may be similar to those of voltage. Nevertheless, the bias method used in TIA and feedback compensation can also affect the resulting output noise spectral characteristics due to the photodiode and other capacitances existing in the circuit. In the case of a high transimpedance, the FET-based op-amps ensure lower output noise than the BJT-based ones due to the significantly lower current noise. The simple radiation detector with two-channel differential TIA was also proposed and tested based on the results obtained.
Novel Robotic Balloon-Based Device for Wrist-Extension Therapy of Hemiparesis Stroke Patients
Authors: Marek Klaudia , Olejniczak Aleksandra , Miller Elżbieta , Zubrycki Igor
Published: February 2025

Upper-limb paresis is one of the main complications after stroke. It is commonly associated with impaired wrist-extension function. Upper-limb paresis can place a tremendous burden on stroke survivors and their families. A novel soft-actuator device, the Balonikotron, was designed to assist in rehabilitation by utilizing a balloon mechanism to facilitate wrist-extension exercises. This pilot study aimed to observe the functional changes in the paralyzed upper limb and improvements in independent and cognitive functions following a 4-week regimen using the device, which incorporates a multimedia tablet application providing audiovisual feedback. The device features a cardboard construction with a hinge at wrist level and rails that guide hand movement as the balloon inflates, controlled by a microcontroller and a tablet-based application. It operates on the principle of moving the hand at the wrist by pushing the palm upwards through a surface actuated by a balloon. A model was developed to describe the relationship between the force exerted on the hand, the angle on hinge, the pressure within the balloon, and its volume. Experimental validation demonstrated a Pearson correlation of 0.936 between the model’s force predictions and measured forces, supporting its potential for real-time safety monitoring by automatically shutting down when force thresholds are exceeded. A pilot study was conducted with 12 post-stroke patients (six experimental, six control), who participated in a four-week wrist-extension training program. Clinical outcomes were assessed using the Fugl–Meyer Assessment for the Upper Extremity (FMA-UE), Modified Rankin Scale (mRS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), wrist Range of Motion (ROM), and Barthel Index (BI). Statistically significant results were obtained for the Barthel index (p < 0.05) and FMA-UE, indicating that the experimental use of the device significantly improved functional independence and self-care abilities. The results of our pilot study suggest that the Balonikotron device, which uses the principles of mirror therapy, may serve as a valuable adjunct to conventional rehabilitation for post-stroke patients with hemiparetic hands (BI p = 0.009, MMSE p = 0.151, mRS p = 0.640, FMA-UE p = 0.045, MOCA p = 0.187, ROM p = 0.109).
Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors
Authors: Yongchareon Sira , Yu Jian , Ma Jing
Published: February 2025

Internet of Things (IoT) technology has continuously advanced over the past decade. As a result, device-free indoor localization functions have become a crucial part of application areas such as healthcare, safety, and energy management. Passive infrared (PIR) sensors detecting changes in temperature in an environment are one of the suitable options for human localization due to their lower cost, low energy consumption, electromagnetic tolerance, and enhanced private awareness. Although existing localization methods, including machine/deep learning, have been proposed to detect multiple persons based on signal phase and amplitude, they still face challenges regarding signal quality, ambiguity, and interference caused by the complex, interleaving movements of multiple persons. This paper proposes a novel deep learning method for multi-person localization using channel separation and template-matching techniques. The approach is based on a deep CNN-LSTM architecture with ensemble models using a mean bagging technique for achieving higher localization accuracy. Our results show that the proposed method can estimate the locations of two participants simultaneously with a mean distance error of 0.55 m, and 80% of the distance errors are within 0.8 m.
Sensitivity Analysis of Long Short-Term Memory-Based Neural Network Model for Vehicle Yaw Rate Prediction
Authors: Kontos János , Bódis László , Vathy-Fogarassy Ágnes
Published: February 2025

In recent years, the application of artificial neural network models has become increasingly widespread in the automotive industry; however, the sensitivity analysis of these models is often neglected. This shortfall poses significant risks in safety-critical applications, where the reliability of models under varying conditions is of critical importance. This study focuses on the sensitivity analysis of a long short-term memory neural network model, previously published by us, designed to predict the future yaw rates of vehicles. Our research aimed to determine the minimum amount of data required for effective model training and to conduct a comprehensive sensitivity analysis, examining the performance and applicability of the trained model under varying tire pressures, different passenger loads, and different passenger configurations. Additionally, we investigated whether the trained model could be applied to other vehicle types. Our results indicated that the vehicle weight distribution was the most influential factor affecting the accuracy of the model. Nonetheless, the model’s predictive error remained consistently within the safety thresholds defined by the standards under all tested conditions. Our experiments and analyses were performed using over 7.5 h of data collected under real-world conditions, which will be freely available to the research community.
3D Deployment Optimization of Wireless Sensor Networks for Heterogeneous Functional Nodes
Authors: Lu Zean , Wang Chengqun , Wang Peng , Xu Weiqiang
Published: February 2025

The optimization of wireless sensor network (WSN) deployment is a current research hotspot, particularly significant in industrial applications. While some existing optimization methods focus more on balancing network coverage, connectivity, and deployment costs, aligning them with practical needs compared to single-performance optimization schemes, they still tend to be overly idealized. In practical applications, networks often face monitoring requirements for different data types, and some single-function sensors can be integrated into multifunctional sensors capable of monitoring multiple types of data. When encountering diverse data detection needs in a target area, this integration can be further considered to reduce deployment costs. Therefore, this paper designs a new multi-objective optimization problem aimed at optimizing heterogeneous-function wireless sensor networks, balancing coverage, connectivity, and cost, while introducing an additional cost dimension to meet the monitoring needs of different functional sensors in specific areas. This problem is a typical non-convex, multimodal, NP-hard problem. To address this, an improved Secretary Bird Optimization Algorithm (ISBOA) is proposed, incorporating Gaussian Cuckoo Mutation and a smooth exploitation mechanism. The algorithm is compared with the original SBOA, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Northern Goshawk Optimization (NGO). Simulation results demonstrate that ISBOA exhibits a faster convergence speed and higher accuracy in both the 23 benchmark functions and the newly designed multi-objective optimization problem, significantly overcoming the shortcomings of the compared algorithms. Finally, for large-scale optimization problems, a minimum spanning tree domain reduction strategy is proposed, which significantly improves solving efficiency with a moderate sacrifice in accuracy.
A MID-1DC+LRT Multi-Task Model for SOH Assessment and RUL Prediction of Mechanical Systems
Authors: Yang Hai , Yang Xudong , Sun Dong , Hu Yunjin
Published: February 2025

Predictive health management (PHM) plays a pivotal role in the maintenance of contemporary industrial systems, with the evaluation of the state of health (SOH) and the prediction of remaining useful life (RUL) constituting its central objectives. Nevertheless, existing studies frequently approach these tasks in isolation, overlooking their interdependence, and predominantly concentrate on single-condition settings. While Transformers have demonstrated exceptional performance in RUL prediction, their substantial parameter requirements pose challenges to computational efficiency and practical implementation. Further, multi-task learning (MTL) models often experience performance deterioration as a result of imbalanced weighting in their loss functions. To address these challenges, the MID-1DC+LRT model was proposed in the present study. The proposed model integrates a multi-input data 1D convolutional neural network (1D-CNN) and low-rank transformer (LRT) within an MTL framework. This model processes high-dimensional sensor data, multi-condition data, and health indicator data, optimizing the Transformer structure to reduce computational complexity. A homoscedastic uncertainty-based method dynamically adjusts multi-task loss function weights, improving task collaboration and model generalization. The results demonstrate that the proposed model significantly outperformed existing methods in SOH assessment and RUL prediction under multi-condition scenarios, demonstrating superior prediction accuracy and computational efficiency, especially in complex and dynamic environments.
Recent Advances in Surface-Enhanced Raman Scattering for Pathogenic Bacteria Detection: A Review
Authors: Wang Yimai , Zhang Zhiqiang , Sun Yixiang , Wu Huimin , Luo Liqiang , Song Yizhi
Published: February 2025

Bacterial infection is one of the common infectious diseases in clinical practice, and the research on efficient detection of bacteria has attracted much attention in recent years. Currently, the traditional detection methods of bacteria are mainly based on cell culturing, microscopic examination, and molecular biology techniques, all of which have the disadvantages of complex operation and time-consuming. Surface-enhanced Raman spectroscopy (SERS) technology has shown prominent advantages in bacterial detection and identification because of the merit of high-sensitivity, fast detection and unique molecular fingerprint spectrum. This paper mainly investigates and discusses the application of SERS in bacterial detection, and systematically reviews the progress of SERS applications, including nano-enhanced dielectric materials of SERS, signal amplification of SERS labeled molecules, and the integration of SERS with microfluidic technology. Finally, the paper analyzes the challenges associated with the application of SERS in bacterial detection and offers insights into future development trends.
Sea Surface Temperature Prediction Enhanced by Exploring Spatiotemporal Correlation Based on LSTM and Gaussian Process
Authors: Li Zhenglin , Zhu Qingxiong , Zhang Dan , Wu Hao , Peng Yan
Published: February 2025

The accurate prediction of sea surface temperature (SST) is essential for studying marine phenomena, understanding climate dynamics, and forecasting environmental changes. However, developing a general SST prediction model is challenging due to significant regional variations and the impacts of diverse climate phenomena. To improve the performance of SST predictions, we propose a hybrid framework that effectively models the spatial and temporal dependencies of SST data with a Gaussian process-enhanced Long Short-Term Memory network. The LSTM module adaptively captures both long and short-term temporal trends in SST variation, while the Gaussian process incorporates the spatial dependency of neighboring data to further refine the predictions. Furthermore, our proposed framework estimates the uncertainty associated with SST predictions, providing crucial information for practical applications. Comprehensive experiments are conducted on the OISST dataset, with a focus on the Bohai Sea and the South China Sea. The results of our framework outperform state-of-the-art methods, validating its superiority in SST prediction.
4D+ City Sidewalk: Integrating Pedestrian View into Sidewalk Spaces to Support User-Centric Urban Spatial Perception
Authors: Zhao Jinjing , Chen Yunfan , Li Yancheng , Xu Haotian , Xu Jingjing , Li Xuliang , Zhang Hong , Jin Lei , Xu Shengyong
Published: February 2025

As urban environments become increasingly interconnected, the demand for precise and efficient pedestrian solutions in digitalized smart cities has grown significantly. This study introduces a scalable spatial visualization system designed to enhance interactions between individuals and the street in outdoor sidewalk environments. The system operates in two main phases: the spatial prior phase and the target localization phase. In the spatial prior phase, the system captures the user’s perspective using first-person visual data and leverages landmark elements within the sidewalk environment to localize the user’s camera. In the target localization phase, the system detects surrounding objects, such as pedestrians or cyclists, using high-angle closed-circuit television (CCTV) cameras. The system was deployed in a real-world sidewalk environment at an intersection on a university campus. By combining user location data with CCTV observations, a 4D+ virtual monitoring system was developed to present a spatiotemporal visualization of the mobile participants within the user’s surrounding sidewalk space. Experimental results show that the landmark-based localization method achieves a planar positioning error of 0.468 m and a height error of 0.120 m on average. With the assistance of CCTV cameras, the localization of other targets maintains an overall error of 0.24 m. This system establishes the spatial relationship between pedestrians and the street by integrating detailed sidewalk views, with promising applications for pedestrian navigation and the potential to enhance pedestrian-friendly urban ecosystems.
ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection
Authors: Li Bin , Li Jie , Jia Mingyu
Published: February 2025

Network intrusion detection systems can identify intrusion behavior in a network by analyzing network traffic data. It is challenging to detect a very small proportion of intrusion data from massive network traffic and identify the attack class in intrusion detection tasks. Many existing intrusion detection studies often fail to fully extract the spatial features of network traffic and make reasonable use of temporal features. In this paper, we propose ADFCNN-BiLSTM, a novel deep neural network for network intrusion detection. ADFCNN-BiLSTM uses deformable convolution and an attention mechanism to adaptively extract the spatial features of network traffic data, and it pays attention to the important features from both channel and spatial perspectives. It uses BiLSTM to mine the temporal features from the traffic data and employs the multi-head attention mechanism to allow the network to focus on the time-series information related to suspicious traffic. In addition, ADFCNN-BiLSTM addresses the issue of class imbalance during the training process at both the data level and algorithm level. We evaluated the proposed ADFCNN-BiLSTM on three standard datasets, i.e., NSL-KDD, UNSW-NB15, and CICDDoS2019. The experimental results show that ADFCNN-BiLSTM outperforms the state-of-the-art model in terms of accuracy, detection rate, and false-positive rate.
U-Net-Based Fingerprint Enhancement for 3D Fingerprint Recognition
Authors: Mogharen Askarin Mohammad , Wang Min , Yin Xuefei , Jia Xiuping , Hu Jiankun
Published: February 2025

Biometrics-based authentication mechanisms can address the built-in weakness of conventional password or token-based authentication in identifying genuine users. However, 2D-based fingerprint biometrics authentication faces the problem of sensor spoofing attacks. In addition, most 2D fingerprint sensors are contact-based, which can boost the spread of deadly diseases such as the COVID-19 virus. Three-dimensional fingerprint-based recognition is the emerging technology that can effectively address the above issues. A 3D fingerprint is captured contactlessly and can be represented by a 3D point cloud, which is strong against sensor spoofing attacks. To apply conventional 2D fingerprint recognition methods to 3D fingerprints, the 3D point cloud needs to be converted into a 2D gray-scale image. However, the contrast of the generated image is often not of good quality for direct matching. In this work, we propose an image segmentation approach using the deep learning U-Net to enhance the fingerprint contrast. The enhanced fingerprint images are then used for conventional fingerprint recognition. By applying the proposed method, the fingerprint recognition Equal Error Rate (EER) in experiment A and B improved from 41.32% and 41.97% to 13.96 and 12.49%, respectively, over the public dataset.
A Combined Raman Spectroscopy and Chemometrics Study of the Interaction of Water-Soluble Polymers with Microorganisms
Authors: Tewes Thomas J. , Kaba Arjana , Schacher Felix H. , Bockmühl Dirk P.
Published: February 2025

Optical spectroscopic methods such as Raman spectroscopy offer several advantages for the analysis of water-soluble polymers (WSPs). There is often no need for complex sample preparation, and measurements are usually rapid, mostly non-destructive and no harmful chemicals are required. In this work, we investigated WSPs and their interaction with bacteria using Raman spectroscopic methods. We analyzed four different WSPs, each with three different molar masses, in solid form using Raman microscopy, and in aqueous solutions using another Raman system designed for measurements in cuvettes, to train predictive models for concentration determination. Thus, we were able to show both the high potential of these approaches, especially for fast and easy investigations both qualitatively and quantitatively, as well as their limitations. Furthermore, we chose one of the molar masses of each tested polymer to carry out extensive Raman spectroscopic investigations with Escherichia coli and Enterococcus faecium, and revealed that bacterial cells exposed to polymers exhibited distinguishable spectral characteristics compared to those not in contact with polymers. Using Raman microscopy combined with partial least squares discriminant analysis (PLS-DA), we effectively distinguished between these groups. Further chemometric analysis indicated potential polymer-induced modifications to the bacterial cell membranes. While this differentiation may partly reflect polymer interactions at the membrane level, it could also correspond to shifts in bacterial growth phases. Together, these findings suggest a complex interplay between polymer exposure and bacterial physiological states.
A Fuzzy LARG Index for Assessing the Lean, Agile, Resilience, and Green Paradigms in Industrial Companies
Authors: Al-Refaie Abbas , Lepkova Natalija
Published: February 2025

Today’s sharp competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, and resilience (LARG). However, an assessment tool is necessary to monitor the progress of LARG adoption and evaluate its effectiveness. This research, therefore, developed an index for assessing the effectiveness of LARG paradigms by evaluating their principles and practices with experts’ fuzzy evaluations. Initially, thorough research on LARG paradigms was conducted to determine the paradigm principles and their measures and prepare a comprehensive LARG survey. The Delphi method with four experts was used to rate item measures of LARG based on a five-point Likert scale. The principles and measures of each paradigm were represented by triangular fuzzy membership functions. Then, defuzzified values were obtained for each principle and set as inputs in the fuzzy inference system (FIS) to obtain a crisp value for each paradigm. Next, the defuzzified values of lean, agile, and green (LAG) were input in the FIS to obtain a crisp LAG index. Finally, the defuzzified values of the LAG and resilience (R) were measured and then inserted as inputs in the FIS to obtain a comprehensive defuzzified LARG value. The effectiveness of the proposed LARG framework was validated in pharmaceutical and chemical organizations. The results revealed that the LARG index is an effective tool for evaluating lean, agile, green, and resilience paradigms for both organizations. In conclusion, the LARG index provides valuable support to decision-makers in determining a business’s weaknesses and strengths and guides technical managers to possible improvement actions to enhance competitiveness and sustainability.
Assessment, Evolutionary Trends, and Convergence of Land Green Utilization Efficiency in Three Northeastern Provinces of China Undergoing Population Shrinkage
Authors: Ning Jing , Wang Mengqiu , Wang Ning , Sun Yu , Ma Haozhi
Published: February 2025

To achieve the green and efficient utilization of land in China’s three northeastern provinces and to harmonize the human–land relationship, this paper examines the spatiotemporal evolution and convergence of land green utilization efficiency (LGUE) in these provinces against the backdrop of population contraction. Utilizing data from 2000 to 2020, we employ the geometric mean method to characterize the features of population contraction and construct a global reference non-desired super-efficiency SBM-DEA model to measure LGUE. The dynamic evolution of LGUE is analyzed using kernel density estimation and spatial visualization techniques, and its convergence is tested through σ-convergence and absolute β-convergence methods. The results indicate that during the study period, the population in the three northeastern provinces has experienced overall contraction, predominantly characterized by a “growth-to-contraction” pattern, which is more pronounced in resource-based cities. Land green utilization efficiency exhibits a fluctuating upward trend, evolving spatially into a distribution pattern of “high in the middle and low on both sides along the northeast–southwest axis”. The degree of dispersion among cities also shows an upward fluctuation, with evidence of σ-convergence and absolute β-convergence. Regional balance has improved. This study concludes that there is significant potential for enhancing land green utilization efficiency in the three northeastern provinces. We recommend adopting the “smart contraction” model to promote coordinated development between population and land, capital and land, as well as ecology and land, thereby fostering sustainable development in these regions.
Pathways to Social and Business Sustainability: Place Attachment, Trust in Government, and Quality of Life
Authors: Ramkissoon Haywantee , Nekmahmud Md. , Mavondo Felix T.
Published: February 2025

This research investigates the role residents’ place attachment plays in developing their trust in the government in the city of Budapest. Rooted in different domains such as politics, environmental, and social psychology and aligned with the related SDGs (goals 3, 11 16, 17), an integrative model of residents’ place attachment, trust in the local government, QoL, social impacts, and pro-social engagement is proposed, which the authors argue are important mechanisms to be explored in determining support for sustainable tourism development to promote responsible consumption and production (SDG 12). We applied SEM on a sample of 350 residents. Findings suggest that place attachment significantly influences residents’ trust in the local government, which also has a positive influence on quality of life. Quality of life has a significant influence on perceived social impacts, which strongly leads to pro-social engagement; pro-social engagement underpins support for tourism development. This paper sets a future research agenda for sustainable tourism by indicating its possible antecedents. Sustainable tourism development has important implications for the achievement of other sustainability goals. The study aligns well with the United Nations Tourism’s vision of tourism as an important driver of positive change for responsible consumption and advancing other SDGs.
Real-Time Household Waste Detection and Classification for Sustainable Recycling: A Deep Learning Approach
Authors: Arishi Ali
Published: February 2025

As global waste production continues to rise, improper handling of household waste significantly contributes to environmental pollution and resource depletion. Inefficient sorting at the household level leads to the contamination of recyclables, reducing recycling efficiency and increasing landfill waste. Effective waste sorting is essential for conserving manual labor, protecting the environment, and ensuring sustainable development for human progress. Recently, advancements in deep learning and computer vision have offered a promising pathway to improve the sorting process, though significant developmental steps are still required. Enhancing the efficiency of automated waste detection and classification through computer vision could bring substantial societal and environmental benefits. However, classifying and identifying waste materials presents challenges due to the complex and diverse nature of waste, coupled with the limited availability of data on waste management. This paper presents a real-time waste detection and classification system based on the YOLOv8 deep learning model, designed to enhance waste sorting processes at the household level. The proposed system detects and classifies a diverse range of household waste items. Experiments were conducted on a custom waste dataset comprising 3775 images across 17 types of common household waste. The one-stage YOLOv8 model demonstrated superior performance, outperforming traditional two-stage detectors. To improve the accuracy and robustness of the original YOLOv8, five data augmentation techniques and two attention mechanisms were incorporated. Notably, the enhanced YOLOv8-CBAM model achieved a mean average precision (mAP) of 89.5%, a significant improvement with a 4.2% increase over the baseline model. The methodology and improvements applied provide a more efficient and effective AI framework for real-time applications in smart bins, robotic waste pickers, and large-scale recycling systems.
Utilizing Alkali-Activated Recycled Concrete Aggregates from Demolished Structures to Investigate Concrete Properties in the Jeddah Region of Saudi Arabia
Authors: Saeed Muneer K. , Al Sayed Abd Al-Kader A. , Almutairi Ahmed D. , Dahish Hany A. , Al-Fasih Mohammed Yahya Mohammed
Published: February 2025

The construction sector in the Kingdom of Saudi Arabia has shown notable growth and progress as seen by the transformation of outdated neighborhoods into modern smart infrastructure that aligns with the 2030 vision of the nation. As a result of this development trend, which is led by the Jeddah region, many older buildings are being demolished, and a significant amount of concrete is being produced. Utilizing recycled aggregates from construction and demolition waste has been recognized as an effective approach to addressing the current environmental problems that our world faces. An appropriate process of treatment techniques must be incorporated into the production of recycled aggregate in order to enhance its properties and optimize its use. The purpose of this study is to determine the potential benefits of an alkaline Ca(OH)2 activated by fly ash Class F (FAF) for various concrete mixes composed using recycled concrete aggregate (RCA). Seven mixes were developed to determine the effects of several factors on the properties of recycled concrete aggregate (RCA), including the type of recycled aggregate, the activator used, and the impact of the filler material. The study assessed how different forms of recycled aggregate affected physical characteristics. The compressive strength, flexural strength, indirect tensile strength, and pull-out strength of the concrete mixes was measured. The aggregate water absorption of Jeddah concrete aggregate (JRCA) and variable concrete aggregate (VRCA) was evaluated. Activated JRCA water absorption drops to 1.7% while inactivated JRCA water absorption was 3.95%. The AIV values of JRCA rise to 18.4 while the AIV for NA is 13. The same trend complies with the findings, which demonstrated that the strength performance and other characteristics of the various concrete mixes under study were significantly impacted by the type of recycled aggregate utilized and the mix design. Among these, Jeddah recycled concrete aggregate (JRCA) showed promising results that could lead to a wider use of (JRCA) in civil engineering applications.