bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Endocrine and Inflammatory Markers in Preserved Ratio Impaired Spirometry
Authors: Jesus Fabíola Ramos , Moraes Anna Clara Santiago , da Silva Ingrid Lorena Neves , Passos Fabine Correia , Salles Cristina , Neves Margarida Célia Lima Costa , Baccan Gyselle Chrystina
Published: March 2024

Chronic Obstructive Pulmonary Disease (COPD) is a disease of the lungs characterized by chronic airflow obstruction. Individuals with preserved ratio impaired spirometry (PRISm) may be at risk for developing COPD. This study aimed to characterize PRISm and COPD patients in terms of their immune response and endocrine profile to identify differences extending beyond lung function. The participants performed the clinical assessment, pulmonary function test, and blood collection to determine serum hormone levels and concentrations of cytokine. Differences were observed in the nutritional status, lung function, and comorbidity. There were no differences in IL-6, IL-8, IL-10, IL-12, and TNF levels between PRISm and COPD groups. Both PRISm and COPD patients have lower dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEA-S) levels than controls. Correlation analysis of PRISm and COPD patients revealed positive correlations between serum levels of DHEA-S and DHEA, with forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC), which negatively correlated with IL-8 levels. The results indicated that despite differences in lung function parameters, the PRISm and COPD groups exhibited similarities in endocrine profile alterations. This study represents the first attempt to link endocrine with immune markers and lung function in individuals with PRISm.
Accurate Determination of the Low-Light-Level Absorption of DUV-Fused Silica at 193 nm with Laser Calorimetry
Authors: Li Fengting , Sun Haojie , Liu Weijing , Hong Ruijin , Tao Chunxian
Published: March 2024

The low-light-level absorption coefficient of OH-contained and H2-impregnated synthetic fused silica material in 193 nm optical lithography application is determined via a laser calorimetry measurement. The fluence and repetition rate dependences of the absorptances of the deep ultraviolet (DUV)-fused silica samples with different thickness are measured. The measured dependences are fitted to a theoretical model, taking into consideration the generation and annealing of laser irradiation induced defects. The surface absorption, the low-light-level linear absorption coefficient, as well as the nonlinear absorption coefficient of the fused silica material are accurately determined via the fitting. The low-light-level linear absorption coefficients determined via the fluence dependence and the repetition rate dependence are in good agreement, demonstrating the reliability of the measured low-light-level absorption coefficient, which is the key parameter to the determination of the internal transmission of the DUV-fused silica material used in the 193 nm optical lithography.
Performance Enhancement in a Few-Mode Rayleigh-Brillouin Optical Time Domain Analysis System Using Pulse Coding and LMD Algorithm
Authors: Zhang Lixin , Li Xuan , Wang Jianjian , Zhang Lei , Li Yongqian
Published: March 2024

Rayleigh Brillouin optical time domain analysis (BOTDA) uses the backscattered Rayleigh light generated in the fiber as the probe light, which has a lower detection light intensity compared to the BOTDA technique. As a result, its temperature-sensing technology suffers from a low signal-to-noise ratio (SNR) and severe sensing unreliability due to the influence of the low probe signal and high noise level. The pulse coding and LMD denoising method are applied to enhance the performance of the Brillouin frequency shift detection and temperature measurement. In this study, the mechanism of Rayleigh BOTDA based on a few-mode fiber (FMF) is investigated, the principles of the Golay code and local mean decomposition (LMD) algorithm are analyzed, and the experimental setup of the Rayleigh BOTDA system using an FMF is constructed to analyze the performance of the sensing system. Compared with a single pulse of 50 ns, the 32-bit Golay coding with a pulse width of 10 ns improves the spatial resolution to 1 m. Further enhanced by the LMD algorithm, the SNR and temperature measurement accuracy are increased by 5.5 dB and 1.05 °C, respectively. Finally, a spatial resolution of 1.12 m and a temperature measurement accuracy of 2.85 °C are achieved using a two-mode fiber with a length of 1 km.
Unveiling the Biological Function of Phyllostachys edulis FBA6 (PeFBA6) through the Identification of the Fructose-1,6-Bisphosphate Aldolase Gene
Authors: Li Tiankuo , Li Hui , Zhu Chenglei , Yang Kebin , Lin Zeming , Wang Jiangfei , Gao Zhimin
Published: March 2024

Fructose-1,6-bisphosphate aldolase (FBA) is a pivotal enzyme in various metabolic pathways, including glycolysis, gluconeogenesis, and the Calvin cycle. It plays a critical role in CO2 fixation. Building on previous studies on the FBA gene family in Moso bamboo, our study revealed the biological function of PeFBA6. To identify CSN5 candidate genes, this study conducted a yeast two-hybrid library screening experiment. Subsequently, the interaction between CSN5 and PeFBA6 was verified using yeast two-hybrid and LCI experiments. This investigation uncovered evidence that FBA may undergo deubiquitination to maintain glycolytic stability. To further assess the function of PeFBA6, it was overexpressed in rice. Various parameters were determined, including the light response curve, CO2 response curve, and the levels of glucose, fructose, sucrose, and starch in the leaves of overexpressing rice. The results demonstrated that overexpressed rice exhibited a higher saturation light intensity, net photosynthetic rate, maximum carboxylation rate, respiration rate, and increased levels of glucose, fructose, and starch than wild-type rice. These findings indicated that PeFBA6 not only enhanced the photoprotection ability of rice but also improved the photosynthetic carbon metabolism. Overall, this study enhanced our understanding of the function of FBA and revealed the biological function of PeFBA6, thereby providing a foundation for the development of excellent carbon fixation bamboo varieties through breeding.
Reuse of Smoulder in Laser Powder-Bed Fusion of AlSi10Mg—Powder Characterization and Sample Analysis
Authors: Maurer Oliver , Jacob Heiko , Bähre Dirk
Published: March 2024

Metal additive manufacturing technologies, such as Laser Powder-Bed Fusion, often rate as sustainable due to their high material efficiency. However, there are several drawbacks that reduce the overall sustainability and offer potential for improvement. One such drawback is waste emerging from the process. These smoulder particles form when the laser hits the powder-bed surface, are blown away from the part by the shielding gas stream and accumulate on the edge of the build chamber. Usually, smoulder does not contribute to the circular reuse of powder that was part of the powder-bed but was not integrated into a part. Instead, it marks an end-of-life state of powder. Significant amounts of smoulder accumulate depending on the irradiated area or the build volume in one job, respectively. This results in the waste of powder that was produced with low energy efficiency. This study investigates the question of whether smoulder can transform from waste to resource via common powder characterization methods and first build jobs using processed smoulder. The investigation of process-relevant powder properties like apparent density and flowability showed no significant difference between virgin powder and smoulder. Sample characterization indicated that neither porosity, surface quality nor mechanical properties deteriorate when samples contain about 50% smoulder. This allows for the reuse of smoulder in terms of powder characterization and part quality.
Decision Making for Control of the Gasoline Fraction Hydrotreating Process in a Fuzzy Environment
Authors: Orazbayev Batyr , Tanirbergenova Alua , Orazbayeva Kulman , Berikbaeva Meruert , Kaliyeva Samal , Kurmangaziyeva Lyailya , Makhatova Valentina
Published: March 2024

This article is devoted to the study of decision-making problems of hydrotreating process control in the production of high-quality gasoline under conditions of scarcity and fuzziness of the initial information, ultimately developing an approach to solve them. A systematic method is proposed that makes it possible to develop a package of mathematical models of a complex of interconnected units of chemical-technological systems based on available information of various types. Using the proposed system method, a package of models of the main interconnected units in which the hydrotreating process took place was developed. A decision-making problem was formulated to control the hydrotreating process in a fuzzy environment based on the developed system of models. By modifying the Pareto principle of optimality for fuzzy conditions, a heuristic method for solving the given decision-making problem was developed to control the hydrotreating process in a fuzzy environment. The novelty of the proposed heuristic method lies in the full use of the collected fuzzy information, which represents the knowledge, intuition and experience of the decision makers and experts. Accordingly, the proposed heuristic decision-making method makes it possible to achieve a high adequacy and efficiency of decisions made when solving production problems in a fuzzy environment. The results obtained were applied in practice to solve decision-making problems for hydrotreating process control at the Atyrau refinery. The results obtained show the advantages of the proposed heuristic method for solving decision-making problems of hydrotreating process control over known methods.
A Forgotten Eminent Buddhist Monk and His Social Network for Constructing Buddhist Statues in Qionglai 邛崍: A Study Based on the Statue Construction Account in 798
Authors: Sun Mingli
Published: March 2024

By transcribing, punctuating, and analyzing the Statue Construction Account undertaken in 798, this article attempts a refreshed study of the construction background of the Buddhist statues and niches at Huazhi Temple 花置寺 in Qionglai. The aim of this article is twofold. Firstly, it brings to light an eminent monk named Sengcai, who has been forgotten in both secular and monastic histories. Secondly, it tries to clarify the social network formed by various figures recorded in the Statue Construction Account by tracing their roles and relationships in the course of constructing the Buddhist niches. The analysis of this article expounds that in the process of the statue construction project, Sengcai made full use of his social network to support this project and to seek protection for Huazhi Temple. The construction activities of the Buddhist niches at Huazhi Temple not only brought people of different identities together through politics, Buddhism, economics or kinship, but also connected Qiongzhou (in Sichuan) and the capital of Chang’an to the formation of a multi-identity and cross-regional network of power in which emperor, officials, monks, military generals, craftsmen, literati, and so on, participated and interacted with each other. The whole social network can be divided into two sub-networks in Chang’an and Qiongzhou, with Sengcai as the central figure connecting these two sub-networks. Although the Buddhist niches of Huazhi Temple were carved in Qiongzhou, both the decisive preparatory work and the composition of the Statue Construction Account took place in Chang’an. Hence, the power of the Chang’an sub-network was greater than that of the one based in Qiongzhou. This means that the Buddhist niches at Huazhi Temple from Sengcai’s project were not merely a local project, but one that was strongly connected with the capital Chang’an in 798. Lastly, the Statue Construction Account in 798 at Huazhi Temple indicated mutual aid and support between Sichuan Buddhism and Chang’an Buddhism.
Two-Population Mortality Forecasting: An Approach Based on Model Averaging
Authors: De Mori Luca , Millossovich Pietro , Zhu Rui , Haberman Steven
Published: March 2024

The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict the mortality of two or more related populations simultaneously are particularly important. Indeed, these models, in addition to improving the forecasting accuracy overall, enable evaluation of the basis risk in index-based longevity risk transfer deals. This paper implements and compares several model-averaging approaches in a two-population context. These approaches generate predictions for life expectancy and the Gini index by averaging the forecasts obtained using a set of two-population models. In order to evaluate the eventual gain of model-averaging approaches for mortality forecasting, we quantitatively compare their performance to that of the individual two-population models using a large sample of different countries and periods. The results show that, overall, model-averaging approaches are superior both in terms of mean absolute forecasting error and interval forecast accuracy.
Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System
Authors: Li Jian , Ma Chi , Jiang Yuqiang
Published: March 2024

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of −2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery.
Research on Visual Perception of Speed Bumps for Intelligent Connected Vehicles Based on Lightweight FPNet
Authors: Wang Ruochen , Luo Xiaoguo , Ye Qing , Jiang Yu , Liu Wei
Published: March 2024

In the field of intelligent connected vehicles, the precise and real-time identification of speed bumps is critically important for the safety of autonomous driving. To address the issue that existing visual perception algorithms struggle to simultaneously maintain identification accuracy and real-time performance amidst image distortion and complex environmental conditions, this study proposes an enhanced lightweight neural network framework, YOLOv5-FPNet. This framework strengthens perception capabilities in two key phases: feature extraction and loss constraint. Firstly, FPNet, based on FasterNet and Dynamic Snake Convolution, is developed to adaptively extract structural features of distorted speed bumps with accuracy. Subsequently, the C3-SFC module is proposed to augment the adaptability of the neck and head components to distorted features. Furthermore, the SimAM attention mechanism is embedded within the backbone to enhance the ability of key feature extraction. Finally, an adaptive loss function, Inner–WiseIoU, based on a dynamic non-monotonic focusing mechanism, is designed to improve the generalization and fitting ability of bounding boxes. Experimental evaluations on a custom speed bumps dataset demonstrate the superior performance of FPNet, with significant improvements in key metrics such as the mAP, mAP50_95, and FPS by 38.76%, 143.15%, and 51.23%, respectively, compared to conventional lightweight neural networks. Ablation studies confirm the effectiveness of the proposed improvements. This research provides a fast and accurate speed bump detection solution for autonomous vehicles, offering theoretical insights for obstacle recognition in intelligent vehicle systems.
Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection
Authors: Gruber Hannes , Fuchs Anna , Bader Michael
Published: March 2024

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI—Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery’s health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm’s responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm’s accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm’s effectiveness.
Smartphone-Derived Seismocardiography: Robust Approach for Accurate Cardiac Energy Assessment in Patients with Various Cardiovascular Conditions
Authors: Hossein Amin , Abdessater Elza , Balali Paniz , Cosneau Elliot , Gorlier Damien , Rabineau Jérémy , Almorad Alexandre , Faoro Vitalie , van de Borne Philippe
Published: March 2024

Seismocardiography (SCG), a method for measuring heart-induced chest vibrations, is gaining attention as a non-invasive, accessible, and cost-effective approach for cardiac pathologies, diagnosis, and monitoring. This study explores the integration of SCG acquired through smartphone technology by assessing the accuracy of metrics derived from smartphone recordings and their consistency when performed by patients. Therefore, we assessed smartphone-derived SCG’s reliability in computing median kinetic energy parameters per record in 220 patients with various cardiovascular conditions. The study involved three key procedures: (1) simultaneous measurements of a validated hardware device and a commercial smartphone; (2) consecutive smartphone recordings performed by both clinicians and patients; (3) patients’ self-conducted home recordings over three months. Our findings indicate a moderate-to-high reliability of smartphone-acquired SCG metrics compared to those obtained from a validated device, with intraclass correlation (ICC) > 0.77. The reliability of patient-acquired SCG metrics was high (ICC > 0.83). Within the cohort, 138 patients had smartphones that met the compatibility criteria for the study, with an observed at-home compliance rate of 41.4%. This research validates the potential of smartphone-derived SCG acquisition in providing repeatable SCG metrics in telemedicine, thus laying a foundation for future studies to enhance the precision of at-home cardiac data acquisition.
Design and Realization of Ultra-Wideband Differential Amplifiers for M-Sequence Radar Applications
Authors: Sokol Miroslav , Galajda Pavol , Jurik Patrik
Published: March 2024

Amplification of wideband high-frequency and microwave signals is a fundamental element within every high-frequency circuit and device. Ultra-wideband (UWB) sensor applications use circuits designed for their specific application. The article presents the analysis, design, and implementation of ultra-wideband differential amplifiers for M-sequence-based UWB applications. The designed differential amplifiers are based on the Cherry–Hooper structure and are implemented in a low-cost 0.35 µm SiGe BiCMOS semiconductor process. The article presents an analysis and realization of several designs focused on different modifications of the Cherry–Hooper amplifier structure. The proposed amplifier modifications are focused on achieving the best result in one main parameter’s performance. Amplifier designs modified by capacitive peaking to achieve the largest bandwidth, amplifiers with the lowest possible noise figure, and designs focused on achieving the highest common mode rejection ratio (CMRR) are described. The layout of the differential amplifiers was created and the chip was manufactured and wire-bonded to the QFN package. For evaluation purposes, a high-frequency PCB board was designed. Schematic simulations, post-layout simulations, and measurements of the individual parameters of the designed amplifiers were performed. The designed and fabricated ultra-wideband differential amplifiers have the following parameters: a supply current of 100–160 mA at −3.3 V or 3.3 V, bandwidth from 6 to 12 GHz, gain (at 1 GHz) from 12 to 16 dB, noise figure from 7 to 13 dB, and a common mode rejection ratio of up to 70 dB.
0.5 V Multiple-Input Fully Differential Operational Transconductance Amplifier and Its Application to a Fifth-Order Chebyshev Low-Pass Filter for Bio-Signal Processing
Authors: Kulej Tomasz , Khateb Fabian , Kumngern Montree
Published: March 2024

This paper presents a multiple-input fully differential operational transconductance amplifier (MI-FD OTA) with very low power consumption. To obtain a differential MOS pair with minimum supply voltage and minimum power consumption, the multiple-input bulk-driven MOS transistor operating in the subthreshold region is used. To show the advantage of the MI-FD OTA, a fifth-order Chebyshev filter was used to realize a low-pass filter capable of operating with a supply voltage of 0.5 V and consuming 60 nW at a nominal setup current of 3 nA. The proposed filter uses five MI-FD OTAs and five capacitors. The total harmonic distortion (THD) was 0.97% for a rail-to-rail sinusoidal input signal. The MI-FD OTA and the filter application were designed and simulated in the Cadence environment using a 0.18 µm CMOS process from TSMC. The robustness of the design was confirmed by Monte Carlo analysis and process, voltage, and temperature corner analysis.
Advances in Aeroengine Cooling Hole Measurement: A Comprehensive Review
Authors: Yan Shuyan , Shi Junkai , Li Guannan , Hao Can , Wang Ying , Yu Hao , Zhou Weihu
Published: March 2024

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology and reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography (CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches.
Online Calibration of Extrinsic Parameters for Solid-State LIDAR Systems
Authors: Mints Mark O. , Abayev Roman , Theisen Nick , Paulus Dietrich , von Gladiss Anselm
Published: March 2024

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.
Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network
Authors: Tong Anshi , Zhang Jun , Xie Liyang
Published: March 2024

With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively.
Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework
Authors: Bazarov Ravshan Ugli Dilshod , Mohammed Alaelddin F. Y. , Na Taeheum , Lee Joohyung
Published: March 2024

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.
Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
Authors: de Curtò J. , de Zarzà I. , Roig Gemma , Calafate Carlos T.
Published: March 2024

X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.
New Insights into Aromaticity through Novel Delta Polynomials and Delta Aromatic Indices
Authors: Balasubramanian Krishnan
Published: March 2024

We have developed novel polynomials called delta polynomials, which are, in turn, derived from the characteristic and matching polynomials of graphs associated with polycyclic aromatic compounds. Natural logarithmic aromatic indices are derived from these delta polynomials, which are shown to provide new insights into the aromaticity of polycyclic aromatic compounds, including the highly symmetric C60 buckminsterfullerene, several other fullerenes, graphene, kekulene series and other cycloarenes, such as polycyclic circumcoronaphenes and coronoids. The newly developed aromatic index yields a value of 6.77 for graphene, 6.516865 for buckminsterfullerene C60(Ih), 5.914023 for kekulene (D6h symmetry), 6.064420 for coronene (D6h), 6.137828 for circumcoronene (D6h), 6.069668 for dicronylene and so forth. Hence, the novel scaled logarithmic aromatic delta indices developed here appear to provide good quantitative measures of aromaticity, especially when they are used in conjunction with other aromatic indicators.
Integration of Efficient Techniques Based on Endpoints in Solution Method for Lifelong Multiagent Pickup and Delivery Problem
Authors: Matsui Toshihiro
Published: March 2024

We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an extended class of iterative multiagent pathfinding problems where a set of shortest collision-free travel paths of multiple agents is iteratively planned. This problem models a system in automated warehouses with robot-carrier agents that are allocated to pickup-and-delivery tasks generated on demand. In the task allocation to agents, several solution methods for lifelong MAPD problems consider the endpoints of the agents’ travel paths to avoid the deadlock situations among the paths due to the conflict of the endpoints. Since redundancies are found in the problem settings themselves and the concurrency of allocated tasks, several additional techniques have been proposed to reduce them in solution methods. However, there should be opportunities to investigate the integration of additional techniques with improvements for more practical solution methods. As analysis and an improved understanding of the additional solution techniques based on endpoints, we incrementally integrate the techniques and experimentally investigate their contributions to the quality of task allocation and the paths of the agents. Our result reveals significant complementary effects of the additionally integrated techniques and trade-offs among them in several different problem settings.
Towards Sustainable Transport in the Moroccan Context: The Key Determinants of Electric Cars Adoption Intention
Authors: Boubker Omar , Lakhal Marwan , Ait Yassine Youssef , Lotfi Hicham
Published: March 2024

In recent years, many countries have actively promoted sustainable mobility as part of their efforts to decarbonize transportation through automotive electrification. Therefore, identifying the factors that influence individuals’ interest in using electric cars (ECs) is crucial for guiding public opinion toward choosing this sustainable mode of transportation. Consequently, the present study mobilized the theory of planned behavior and the technology acceptance model to interpret the various factors influencing the intention to adopt ECs in a developing country. Following the developed model, data were collected from individuals using cars in Morocco through an online questionnaire. Data analysis using structural equation modeling revealed a positive influence of relative advantage on both the perceived ease of use and green perceived usefulness. Furthermore, the perceived ease of use, green perceived usefulness, environmental concern, and social influence positively affected attitudes toward using ECs. Similarly, these results confirmed that green perceived usefulness and individual attitudes positively enhance ECs adoption intention. These findings contribute to the literature related to ECs adoption and offer guidance to policymakers on promoting ECs adoption in developing countries.
ASUHAN KEBIDANAN KOMPREHENSIF PADA NY.E DI PUSKESMAS MALANGBONG KABUPATEN GARUT
Authors: Regina Putri Natasha , Noviani Fadilah Lola
Published: March 2024

Proses kehamilan, persalinan, nifas, dan kelahiran BBL merupakan siklus fisiologis yang dialami oleh seorang perempuan. Meskipun hal tersebut adalah hal yang fisiologis, tetap akan mengalami perubahan fisik maupun psikologis yang akan menimbulkan ketidaknyamanan utamanya pada ibu hamil trimester 3. Peran bidan sangatlah dibutuhkan untuk mendampingi perempuan dalam mengatasi ketidaknyamanan tersebut. Penerapan asuhan secara berkesinambungan (continuity of care) penting dilakukan oleh bidan karena memberikan dampak positif bagi perempuan dan memberikan outcome yang baik, sebab continuity of care dapat memudahkan bidan dalam melakukan deteksi dini komplikasi.

            Penelitian ini menggunakan metode studi kasus dengan teknik pengumpulan data primer dan sekunder yang dilakukan sejak Bulan Januari - Mei 2023 di Puskesmas Malangbong Kabupaten Garut. Asuhan diberikan sejak usia kehamilan ibu 39 Minggu hingga masa nifas KF4, serta asuhan pada neonatus hingga KN3.

            Hasil penelitian didapatkan bahwa pada asuhan kehamilan didapatkan ketidaknyamanan berupa sering buang air kecil. Proses persalinan berlangsung secara normal dan ibu datang saat fase persalinan telah memasuki kala II. Tidak terdapat permasalahan selama masa nifas, produksi ASI sangat banyak. Dilakukan perawatan payudara, senam nifas dan konseling KB. Asuhan pada BBL berjalan lancar, tidak terdapat masalah apapun pada bayi. Bayi telah mendapatkan imunisasi HB0. Imunisasi BCG serta polio 1 akan diberikan saat usianya genap satu bulan. Dilakukan pijat bayi saat KN 3.

            Asuhan yang dilakukan telah sesuai dengan manajemen kebidanan. Diharapkan bidan dapat melakukan penerapan continuity of care secara komprehensif sesuai kebutuhan pasien dan sesuai kewenangan profesi bidan yang telah ditetapkan.
PESTLE Analysis and Sustainable Event Marketing - Evidences from Mount Kinabalu International Climbathon Destination, Borneo
Authors: Jamil Jamaliah
Published: March 2024

As an industry practitioner, this research strives to explore and engage in intellectual discourse on the current theoretical paradigm practice of sustainable marketing efficiency strategy design for tourism, hospitality establishments, and event staging from the viewpoints of Destination management organisations (DMOs). Critically, this industry-academic writing is committed to contributing to a better interpretation and, possibly, an assessment of the current marketing principle application by highlighting a potential gap between theory (academic) and practice (industry), accompanied by recommendations on holistic marketing, thereby bridging between conventional and sustainable marketing. A systematic qualitative review using explorative content analysis combined with PESTLE feasibility studies of the destination. Approximately 60 journals, book chapters, and relevant newspaper topics, including pertinent literatures, were screened for analysis. The PESTLE approach to DMOs marketing covers direction, product, service, positioning, branding, and niche segmentation. It also explores Muslim-friendly marketing ideas and their role in compelling destination and event hospitality management. The findings provide analytical perspectives of future hallmarks events for developing countries. Furthermore, it presents a distinct and explicit sustainable marketing policy for event marketers, event organisers, DMOs, and relevant bodies to embed sustainable marketing strategy execution in the event hospitality segment. Additionally, it includes an illustration of a sustainable framework for the future Mount Kinabalu International Climbathon (MKIC) event, recommended to the respective DMOs and event organisers.Keywords: Sustainable Marketing Plan; Marketing Design; Destination Management Organisations DMOs; Mount Kinabalu International Climbathon Event (MKIC); PESTLE analysis.
Influence of Celebrity Endorser and Product Quality on Purchase Decision Through Brand Image Package PT Aminareka Perdana
Authors: Dhamayanty Stanny , Nokialita Fitri
Published: March 2024

This research aims to look at the direct and indirect effects of celebrity endorser, product quality, and brand image on purchasing decisions. The group studied was PT Arminareka Perdana Umrah pilgrims. Samples were taken from 100 respondents randomly. Data collection was carried out by conducting a survey of consumers of Umrah pilgrims. The variables studied were celebrity endorser (X1), product quality (X2), product image (Y), and purchase decision (Z). The data obtained were then analyzed using descriptive methods and path analysis. After the research was carried out, the results obtained were that the variable celebrity endorser (X1) and product quality (X2) had a direct effect on purchasing decisions (Z). Meanwhile, the relationship between celebrity endorser (X1) and product quality (X2) can indirectly affect purchasing decisions. The results of the path analysis test show that the research variable celebrity endorser (X1) influences purchasing decisions through brand image and product quality (X2).
Sinergitas Industri Lokal Penyelenggara Wisata MICE dan Event di Destinasi Pariwisata Super Prioritas (DPSP) Mandalika
Authors: Satiadji Amirosa Ria
Published: March 2024

Event is one of tourist attraction that can invite more people to come in a destination. As Nusa Tenggara Barat (NTB) has Mandalika as one of Super Priority Tourism Development di Indonesia created two big event named World Superbike (WSBK) and Moto GP all tourism stakeholder should collaborate to ensure that the development will positively impactful for the society welfare. The governor of NTB create event as a tool for destination marketing. The aim of this research is to know how to develop an impactful event as attraction and bring positive impact to the society. Using qualitative method by interviewing the key informant and continuing with snowball than data triangulation this research has been given a recommendation to planning a creative event. Sewing Model in Event Creative Planning is a model to make an event became larger, gaining resources and giving more over impact to the stakeholder such as government, media, industry, society and education institution (Penta helix). To build the synergy things that all partners must have are:  1. Creative; 2. Have a good network; 3. Trustable; 4. Having permits for creating event; 5. Have a good communication skill; 6. Willingness for beneficial collaboration; 7. A win-win agreement. The collaborations will be successful if all the stakeholders get a positive impact. The organizer has to be ensured that the event created is a sustainable event that bring welfare to all stakeholder, such as support local industry, creative economy by bringing buyer to the event, involving the culinary industry, arts, and encouraging for the development of a sustainable tourism destination for the next generation of the society.

Keywords:  Creative Event, Stakeholder, Tourism, Marketing, Sustainability.
A case of pediatric enteroviral meningitis with a travel history
Authors: Baddal Buket , Sanlidag Burcin , Aksoy Tutku , Doğramacıoğlu Yaren
Published: March 2024

Enterovirus meningitis represents a common cause of meningitis worldwide. In this case study, a 5-year-old male with a travel history presenting with persistent nausea, vomiting, fever and headache for 3 days is reported. The patient described a fever of 39.5°C on the first day of symptom onset as well as decreased appetite and pain in the front right side of head. In physical examination, initially there was no sign of stiff neck, however he had neck stiffness the following day, Kernig's sign and Brudzinski's signs were positive. On admission, blood tests showed a slightly increased C-reactive protein and a normal white cell count. He was admitted to the pediatrics service and a lumbar puncture was performed. Intravenous ceftriaxone and acyclovir were empirically administered. Cerebrospinal fluid (CSF) analysis revealed lymphocytic pleocytosis, with normal protein and glucose concentration. CSF molecular analysis was positive for enterovirus RNA. Cranial magnetic resonance imaging with contrast was normal. Following confirmed diagnosis, ceftriaxone and acyclovir treatments were discontinued and he was given supportive care. He successfully recovered and was discharged without any complication. This case report highlights that rapid molecular testing favorably impacts patient management by improving antimicrobial stewardship through the reduction of intravenous therapy, side effects and inpatient bed-days.
Self-Assembled DNA Machine and Selective Complexation Recognition Enable Rapid Homogeneous Portable Quantification of Lung Cancer CTCs
Authors: Wang Yue , Shen Congcong , Wu Chengyong , Zhan Zixuan , Qu Runlian , Xie Yi , Chen Piaopiao
Published: March 2024

In this study, we systematically investigated the interactions between Cu
2+
and various biomolecules, including double-stranded DNA, Y-shaped DNA nanospheres, the double strand of the hybridization chain reaction (HCR), the network structure of cross-linked HCR (cHCR), and small molecules (PPi and His), using Cu
2+
as an illustrative example. Our research demonstrated that the coordination between Cu
2+
and these biomolecules not only is suitable for modulating luminescent material signals through complexation reactions with Cu
2+
but also enhances signal intensities in materials based on chemical reactions by increasing spatial site resistance and local concentration. Building upon these findings, we harnessed the potential for signal amplification in self-assembled DNA nanospheres and the selective complexation modulation of calcein in conjunction with the aptamer targeting mucin 1 as a recognition probe. We applied this approach to the analysis of circulating tumor cells, with the lung cancer cell line A549 serving as a representative model. Our assay, utilizing both a fluorometer and a handheld detector, achieved impressive detection limits of ag/ml and single-cell levels for mucin 1 and A549 cells, and this approach was successfully validated using 46 clinical samples, yielding 100% specificity and 86.5% sensitivity. Consequently, our strategy has paved the way for more portable and precise disease diagnosis.