bluuebunny/crossref_metadata_2025 huggingface dataset:
Dual-Dependency Attention Transformer for Fine-Grained Visual Classification
Authors: Cui Shiyan , Hui Bin
Published: April 2024

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model. It decouples global token interactions into two paths. The first is a position-dependency attention pathway based on the intersection of two types of grouped attention. The second is a semantic dependency attention pathway based on dynamic central aggregation. This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity. In addition, we develop discriminative enhancement strategies. These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach. Experiments on three datasets, NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification. It finds a balance between computational cost and performance.
Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits
Authors: Thieme Alison , Prabhakara Kusuma , Jennewein Jyoti , Lamb Brian T. , McCarty Greg W. , Hively Wells Dean
Published: April 2024

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas
Authors: Wang Yonggang , Li Zhida , Zhang Nannan
Published: April 2024

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm–long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm–long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization–least squares support vector machine (PSO-LSSVM), and particle swarm optimization–long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.
Temperature Uncertainty Reduction Algorithm Based on Temperature Distribution Prior for Optical Sensors in Oil Tank Ground Settlement Monitoring
Authors: Liu Tao , Jiang Tao , Liu Gang , Sun Changsen
Published: April 2024

Ground settlement (GS) in an oil tank determines its structural integrity and commercial service. However, GS monitoring faces challenges, particularly due to the significant temperature differences induced by solar radiation around the tank in daytime. To address this problem, this paper digs out a prior and proposes a temperature uncertainty reduction algorithm based on that. This prior has a spatial Gaussian distribution of temperature around the tank, and numerical simulation and practical tests are conducted to demonstrate it. In addition, combining uniformly packaged sensor probes and the spatial prior of temperature, the temperature uncertainty is verified to be Gaussian-distributed too. Then, the overall temperature uncertainty can be captured by Gaussian fitting and then removed. The practical test verified a 91% reduction rate in temperature uncertainty, and this approach enables GS sensors to effectively perform daytime monitoring by mitigating temperature-related uncertainties.
Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method
Authors: Du Libin , Chen Hao , Yuan Yibo , Song Longjiang , Meng Xiangqian
Published: April 2024

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.
Colorectal Cancer Diagnosis through Breath Test Using a Portable Breath Analyzer—Preliminary Data
Authors: Picciariello Arcangelo , Dezi Agnese , Vincenti Leonardo , Spampinato Marcello Giuseppe , Zang Wenzhe , Riahi Pamela , Scott Jared , Sharma Ruchi , Fan Xudong , Altomare Donato F.
Published: April 2024

Screening methods available for colorectal cancer (CRC) to date are burdened by poor reliability and low patient adherence and compliance. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a non-invasive potential diagnostic tool for distinguishing CRC patients from healthy controls (HC). The aim of this study was to evaluate the reliability of an innovative portable device containing a micro-gas chromatograph in enabling rapid, on-site CRC diagnosis through analysis of patients’ exhaled breath. In this prospective trial, breath samples were collected in a tertiary referral center of colorectal surgery, and analysis of the chromatograms was performed by the Biomedical Engineering Department. The breath of patients with CRC and HC was collected into Tedlar bags through a Nafion filter and mouthpiece with a one-way valve. The breath samples were analyzed by an automated portable gas chromatography device. Relevant volatile biomarkers and discriminant chromatographic peaks were identified through machine learning, linear discriminant analysis and principal component analysis. A total of 68 subjects, 36 patients affected by histologically proven CRC with no evidence of metastases and 32 HC with negative colonoscopies, were enrolled. After testing a training set (18 CRC and 18 HC) and a testing set (18 CRC and 14 HC), an overall specificity of 87.5%, sensitivity of 94.4% and accuracy of 91.2% in identifying CRC patients was found based on three VOCs. Breath biopsy may represent a promising non-invasive method of discriminating CRC patients from HC.
A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum
Authors: Fernandes Rigel Procópio , Apolinário José Antonio , de Seixas José Manoel
Published: April 2024

Accurate direction of arrival (DoA) estimation is paramount in various fields, from surveillance and security to spatial audio processing. This work introduces an innovative approach that refines the DoA estimation process and demonstrates its applicability in diverse and critical domains. We propose a two-stage method that capitalizes on the often-overlooked secondary peaks of the cross-correlation function by introducing a reduced complexity DoA estimation method. In the first stage, a low complexity cost function based on the zero cyclic sum (ZCS) condition is used to allow for an exhaustive search of all combinations of time delays between pairs of microphones, including primary peak and secondary peaks of each cross-correlation. For the second stage, only a subset of the time delay combinations with the lowest ZCS cost function need to be tested using a least-squares (LS) solution, which requires more computational effort. To showcase the versatility and effectiveness of our method, we apply it to the challenging acoustic-based drone DoA estimation scenario using an array of four microphones. Through rigorous experimentation with simulated and actual data, our research underscores the potential of our proposed DoA estimation method as an alternative for handling complex acoustic scenarios. The ZCS method demonstrates an accuracy of 89.4%±2.7%, whereas the ZCS with the LS method exhibits a notably higher accuracy of 94.0%±3.1%, showcasing the superior performance of the latter.
Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging
Authors: Behnamfar Milad , Stevenson Alexander , Tariq Mohd , Sarwat Arif
Published: April 2024

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.
Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems
Authors: Song Jiho , Hyun Seong-Hwan
Published: April 2024

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.
Analysis of Heart Rate Variability in Individuals Affected by Amyotrophic Lateral Sclerosis
Authors: Maset-Roig Rosa , Caplliure-Llopis Jordi , de Bernardo Nieves , Privado Jesús , Alarcón-Jiménez Jorge , Martín-Ruiz Julio , Botella-Navas Marta , Villarón-Casales Carlos , Sancho-Cantus David , de la Rubia Ortí José Enrique
Published: April 2024

Introduction: Amyotrophic lateral sclerosis (ALS) produces alterations in the autonomic nervous system (ANS), which explains the cardiac manifestations observed in patients. The assessment of heart rate variability (HRV) is what best reflects the activity of the ANS on heart rate. The Polar H7 Bluetooth® device proves to be a non-invasive and much faster technology than existing alternatives for this purpose. Objective: The goal of this study is to determine HRV using Polar H7 Bluetooth technology in ALS patients, comparing the obtained measurements with values from healthy individuals. Method: The sample consisted of 124 participants: 68 diagnosed with ALS and 56 healthy individuals. Using Polar H7 Bluetooth technology and the ELITE HRV application, various HRV measurements were determined for all participants, specifically the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, LF, HF, LF/HF ratio, HR average, and HF peak frequency. Results: Statistically significant differences were observed between ALS patients and healthy individuals in the HRV index, RMSSD, RMSSD LN, SDNN index, PNN50, HF, and LF, where healthy individuals exhibited higher scores. For the HR average, the ALS group showed a higher value. Values were similar when comparing men and women with ALS, with only a higher HF peak frequency observed in women. Conclusion: The Polar H7 Bluetooth® device is effective in determining heart rate variability alterations in ALS, being a promising prognostic tool for the disease.
Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction
Authors: Uddin Md Jasim , Sherrell Jordan , Emami Anahita , Khaleghian Meysam
Published: April 2024

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.
Thermalization of Mesh Reinforced Ultra-Thin Al-Coated Plastic Films: A Parametric Study Applied to the Athena X-IFU Instrument
Authors: Montinaro Nicola , Sciortino Luisa , D’Anca Fabio , Lo Cicero Ugo , Bozzo Enrico , Paltani Stéphane , Todaro Michela , Barbera Marco
Published: April 2024

The X-ray Integral Field Unit (X-IFU) is one of the two focal plane detectors of Athena, a large-class high energy astrophysics space mission approved by ESA in the Cosmic Vision 2015–2025 Science Program. The X-IFU consists of a large array of transition edge sensor micro-calorimeters that operate at ~100 mK inside a sophisticated cryostat. To prevent molecular contamination and to minimize photon shot noise on the sensitive X-IFU cryogenic detector array, a set of thermal filters (THFs) operating at different temperatures are needed. Since contamination already occurs below 300 K, the outer and more exposed THF must be kept at a higher temperature. To meet the low energy effective area requirements, the THFs are to be made of a thin polyimide film (45 nm) coated in aluminum (30 nm) and supported by a metallic mesh. Due to the small thickness and the low thermal conductance of the material, the membranes are prone to developing a radial temperature gradient due to radiative coupling with the environment. Considering the fragility of the membrane and the high reflectivity in IR energy domain, temperature measurements are difficult. In this work, a parametric numerical study is performed to retrieve the radial temperature profile of the larger and outer THF of the Athena X-IFU using a Finite Element Model approach. The effects on the radial temperature profile of different design parameters and boundary conditions are considered: (i) the mesh design and material, (ii) the plating material, (iii) the addition of a thick Y-cross applied over the mesh, (iv) an active heating heat flux injected on the center and (v) a Joule heating of the mesh. The outcomes of this study have guided the choice of the baseline strategy for the heating of the Athena X-IFU THFs, fulfilling the stringent thermal specifications of the instrument.
Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data
Authors: Hao Zhanjun , Han Kaikai , Zhang Zinan , Dang Xiaochao
Published: April 2024

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR’s robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.
Next Generation Computing and Communication Hub for First Responders in Smart Cities
Authors: Shaposhnyk Olha , Lai Kenneth , Wolbring Gregor , Shmerko Vlad , Yanushkevich Svetlana
Published: April 2024

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.
Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions
Authors: Karimov Timur , Ostrovskii Valerii , Rybin Vyacheslav , Druzhina Olga , Kolev Georgii , Butusov Denis
Published: April 2024

Josephson junctions (JJs) are superconductor-based devices used to build highly sensitive magnetic flux sensors called superconducting quantum interference devices (SQUIDs). These sensors may vary in design, being the radio frequency (RF) SQUID, direct current (DC) SQUID, and hybrid, such as D-SQUID. In addition, recently many of JJ’s applications were found in spiking models of neurons exhibiting nearly biological behavior. In this study, we propose and investigate a new circuit model of a sensory neuron based on DC SQUID as part of the circuit. The dependence of the dynamics of the designed model on the external magnetic flux is demonstrated. The design of the circuit and derivation of the corresponding differential equations that describe the dynamics of the system are given. Numerical simulation is used for experimental evaluation. The experimental results confirm the applicability and good performance of the proposed magnetic-flux-sensitive neuron concept: the considered device can encode the magnetic flux in the form of neuronal dynamics with the linear section. Furthermore, some complex behavior was discovered in the model, namely the intermittent chaotic spiking and plateau bursting. The proposed design can be efficiently applied to developing the interfaces between circuitry and spiking neural networks. However, it should be noted that the proposed neuron design shares the main limitation of all the superconductor-based technologies, i.e., the need for a cryogenic and shielding system.
MSK-TIM: A Telerobotic Ultrasound System for Assessing the Musculoskeletal System
Authors: Ochitwa Zachary , Fotouhi Reza , Adams Scott J. , Noguera Cundar Adriana Paola , Obaid Haron
Published: April 2024

The aim of this paper is to investigate technological advancements made to a robotic tele-ultrasound system for musculoskeletal imaging, the MSK-TIM (Musculoskeletal Telerobotic Imaging Machine). The hardware was enhanced with a force feedback sensor and a new controller was introduced. Software improvements were developed which allowed the operator to access ultrasound functions such as focus, depth, gain, zoom, color, and power Doppler controls. The device was equipped with Wi-Fi network capability which allowed the master and slave stations to be positioned in different locations. A trial assessing the system to scan the wrist was conducted with twelve participants, for a total of twenty-four arms. Both the participants and radiologist reported their experience. The images obtained were determined to be of satisfactory quality for diagnosis. The system improvements resulted in a better user and patient experience for the radiologist and participants. Latency with the VPN configuration was similar to the WLAN in our experiments. This research explores several technologies in medical telerobotics and provides insight into how they should be used in future. This study provides evidence to support larger-scale trials of the MSK-TIM for musculoskeletal imaging.
A High-Finesse Suspended Interferometric Sensor for Macroscopic Quantum Mechanics with Femtometre Sensitivity
Authors: Smetana Jiri , Yan Tianliang , Boyer Vincent , Martynov Denis
Published: April 2024

We present an interferometric sensor for investigating macroscopic quantum mechanics on a table-top scale. The sensor consists of a pair of suspended optical cavities with finesse over 350,000 comprising 10 g fused silica mirrors. The interferometer is suspended by a four-stage, light, in-vacuum suspension with three common stages, which allows for us to suppress common-mode motion at low frequency. The seismic noise is further suppressed by an active isolation scheme, which reduces the input motion to the suspension point by up to an order of magnitude starting from 0.7 Hz. In the current room-temperature operation, we achieve a peak sensitivity of 0.5 fm/Hz in the acoustic frequency band, limited by a combination of readout noise and suspension thermal noise. Additional improvements of the readout electronics and suspension parameters will enable us to reach the quantum radiation pressure noise. Such a sensor can eventually be utilized for demonstrating macroscopic entanglement and for testing semi-classical and quantum gravity models.
A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools
Authors: Egaña Fernando , Mutilba Unai , Yagüe-Fabra José A. , Gomez-Acedo Eneko
Published: April 2024

Large machine tools are critically affected by ambient temperature fluctuations, impacting their performance and the quality of machined products. Addressing the challenge of accurately measuring thermal effects on machine structures, this study introduces the Machine Tool Integrated Inverse Multilateration method. This method offers a precise approach for assessing geometric error parameters throughout a machine’s working volume, featuring a low level of uncertainty and high speed suitable for effective temperature change monitoring. A significant innovation is found in the capability to automatically realise the volumetric error characterisation of medium- to large-sized machine tools at intervals of 40–60 min with a measurement uncertainty of 10 µm. This enables the detailed study of thermal errors which are generated due to variations in ambient temperature over extended periods. To validate the method, an extensive experimental campaign was conducted on a ZAYER Arion G™ large machine tool using a LEICA AT960™ laser tracker with four wide-angle retro-reflectors under natural workshop conditions. This research identified two key thermal scenarios, quasi-stationary and changing environments, providing valuable insights into how temperature variations influence machine behaviour. This novel method facilitates the optimization of machine tool operations and the improvement of product quality in industrial environments, marking a significant advancement in manufacturing metrology.
Capacity Allocation in Cancer Centers Considering Demand Uncertainty
Authors: Keshtzari Maryam , Norman Bryan A.
Published: April 2024

This paper introduces a model to aid decision-makers in answering many of the important questions regarding how best to operate a cancer center. This study aims to allocate the available cancer center capacity to different cancer types to minimize the deviation in patient demand satisfied from desired supply targets across multiple cancer types. A stochastic chance-constrained model is proposed to consider uncertainties in new and returning patient demand. The proposed model determines the optimal specialization mix for oncologists based on the distribution of demand by cancer type, preventing potential mismatches. Additionally, it aims to balance workloads among oncologists and individual clinics and indirectly reduce support service costs by limiting their clinic days. Numerical results are presented using historical data collected from our collaborating cancer center to demonstrate the usefulness of the model. The results confirm that the ability to satisfy patient demand increases as oncologists become more flexible. In addition, the results show that even having a small number of highly flexible oncologists is sufficient to achieve strong patient demand satisfaction. Moreover, restricting the allowable workload difference among oncologists achieves an acceptable trade-off between workload balance and satisfying patient demand.
An Evaluation of Microfiltration and Ultrafiltration Pretreatment on the Performance of Reverse Osmosis for Recycling Poultry Slaughterhouse Wastewater
Authors: Fatima Faryal , Fatima Sana , Du Hongbo , Kommalapati Raghava Rao
Published: April 2024

To implement sustainable water resource management, the industries that produce a huge amount of wastewater are aiming to recycle wastewater. Reverse osmosis (RO) is an advanced membrane process that can produce potable water from wastewater. However, the presence of diverse pollutants in the wastewater necessitates effective pretreatment to ensure successful RO implementation. This study evaluated the efficiency of microfiltration (MF) and ultrafiltration (UF) as two pretreatment methods prior to RO, i.e., MF-RO and UF-RO, for recycling poultry slaughterhouse wastewater (PSWW). The direct treatment of PSWW with RO (direct RO) was also considered for comparison. In this study, membrane technology serves as a post treatment for PSWW, which was conventionally treated at Sanderson Farm. The results demonstrated that all of the processes, including MF-RO, UF-RO, and direct RO treatment of PSWW, rejected 100% of total phosphorus (TP), over 91.2% of chemical oxygen demand (COD), and 87% of total solids (TSs). Total nitrogen (TN) levels were reduced to 5 mg/L for MF-RO, 4 mg/L for UF-RO, and 9 mg/L for direct RO. In addition, the pretreatment of PSWW with MF and UF increased RO flux from 46.8 L/m2 h to 51 L/m2 h, an increase of approximately 9%. The product water obtained after MF-RO, UF-RO, and direct RO meets the required potable water quality standards for recycling PSWW in the poultry industry. A cost analysis demonstrated that MF-RO was the most economical option among membrane processes, primarily due to MF operating at a lower pressure and having a high water recovery ratio. In contrast, the cost of using RO without MF and UF pretreatments was approximately 2.6 times higher because of cleaning and maintenance expenses related to fouling. This study concluded that MF-RO is a preferable option for recycling PSWW. This pretreatment method would significantly contribute to environmental sustainability by reusing well-treated PSWW for industrial poultry purposes while maintaining cost efficiency.
University Rankings: Proposal for a Future Research Agenda through a Systematic Literature Review
Authors: Teixeira Tatiane , Picinin Claudia Tania
Published: April 2024

The purpose of this study is to develop a proposal for a future research agenda on university rankings, based on a systematic review of the existing literature, ith emphasis on the following criteria: objectives, countries, types, variables, methodologies, and future lines of research. The analysis of university rankings revealed a need to review evaluation methodologies and reflect on their consequences for educational policies and the strategic management of higher education institutions. The PRISMA method was used to map the literature available on the Scopus database. The findings were systematized to promote a broad understanding by the reader: (i) drawing up a table that condenses the results of this review; (ii) categorizing the objectives identified in the articles; (iii) surveying the number of studies by country; (iv) categorizing the methodologies employed; (v) developing a figure of the categories of variables associated with rankings; and (vi) compiling a table that brings together the recommendations for future research suggested by scholars in the field. As a result, this research not only goes into great detail on issues related to university rankings, but also establishes a path for future research, constituting a robust foundation for educational managers, policymakers, and academics interested in the subject.
Assessment of the Sustainability of Productive Systems in Nariño, Colombia
Authors: Velasquez-Vasconez Pedro Alexander , Andrade-Diaz Danita , Zambrano-Díaz Maria Iveth , Chiriví-Salomon Juan S.
Published: April 2024

Human development must ensure the welfare and survival of an ever-growing population. To assess the impact of agroecosystems on sustainability in Nariño (Colombia), variables related to sustainable development were evaluated through a semi-structured survey conducted with 122 producers from 6 municipalities in the Nariño department. The association between the categorical variables associated with the pillars of sustainability was analyzed using multiple correspondence analysis (MCA). From the MCA, 23 indicators were selected, representing quantitative information on the economic, social, and environmental characteristics of the participants. The municipalities were characterized according to the needs and strengths of the producers. Five groups encompass most characteristics of the producers, which are largely independent of geopolitical conditions. On the other hand, the population characterization indicates that the economic well-being of fruit growers can promote the conservation of natural resources. Finally, a principal component factor analysis (PCFA) was conducted to construct the General Sustainability Index (GSI). A network graph for each municipality revealed that the indicators of the production systems are in an unfavorable environment in almost all aspects related to sustainability. The GSI values reveal unstable agroecological conditions that are causing environmental deterioration, with critical levels in the municipalities of Arboleda, Sandoná, and Providencia. Social investment and public policies must be implemented to improve the economic conditions of the producers, accompanied by legislation on the proper management of natural resources.
How Does Digital Transformation Improve Supply Chain Performance: A Manufacturer’s Perspective
Authors: Kim Jae Wook , Rhee Jin Hwa , Park Chul Hung
Published: April 2024

A prominent research area pertains to the integration of digital technologies in corporate frameworks and their strategic utilization. In particular, as both intercompany dependencies in business activities and environmental uncertainty increase, digital transformation has become an important means of managing transaction relationships not only within but also between companies. The purpose of this study is to explicate the process of how digital transformation technology used among supply chain members can improve corporate performance and to identify the influencing variables for making good use of it. The findings have implications that can help companies invest time and money in digital innovation to achieve effective corporate performance. This research model analyzed data from 222 domestic manufacturing companies through structural equation model analysis. We found that the more developed the corporate culture and the higher the trust with partner companies, the more active the companies are in utilizing digital transformation. In addition, while digital transformation has a direct impact on corporate performance, we also confirmed the mediating effect of information sharing between companies, which can have a greater positive impact on corporate performance as its level increases. A notable result is that digital transformation significantly improves information sharing in low-trust corporate relationships. These results suggest that digital, non-face-to-face technologies can complement and strengthen relationships that have traditionally been formed through interpersonal relationships. This study compensates for the shortcomings of previous studies that verify the fragmentary achievements of digital transformation. It also has theoretical significance in that it hypothesizes and demonstrates the entire process of how digital transformation is activated in what type of environment and leads to corporate performance. In addition, although companies with a strong relationship of trust may find it easy to invest in innovation, there are practical implications that even new companies that do not have a relationship of trust should consider active use of digital transformation when conducting important transactions.
An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances
Authors: Kasimalla Swetha Rani , Park Kuchan , Zaboli Aydin , Hong Younggi , Choi Seong Lok , Hong Junho
Published: April 2024

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.
Education and Digital Societies for a Sustainable World
Authors: Serpa Sandro , Sá Maria José
Published: April 2024

The Topic “Education and Digital Societies for a Sustainable World” falls within the Digital Society Topic, which materializes in various dimensions, considering distinct social contexts [...]
Destination Management Organizations’ Roles in Sustainable Tourism in the Face of Climate Change: An Overview of Prince Edward Island
Authors: MacEachern Joe , MacInnis Brandon , MacLeod David , Munkres Romy , Jaspal Simrat Kaur , Kinay Pelin , Wang Xiuquan
Published: April 2024

Globally, destination management organizations (DMOs) are becoming increasingly known for their sustainable practices. Despite the importance of destination management organizations’ sustainability initiatives in the face of climate change, minimal research has been undertaken on the topic in Canada. DMOs on PEI can inform tourists better if they know what other adaptive strategies are taken into consideration around Canada. Our work included host-community interviews and perceptions on DMOs’ roles in improving tourism in the face of climate change. The interviews concluded that tourism officials in Prince Edward Island are becoming more conscious of climate change, but more has to be done to slow down the effects of the phenomenon. This paper also identified challenges facing DMOs in the area of sustainable tourism in the context of climate change. One of the recommendations was that DMOs should have access to techniques for mitigation and adaptation in addition to incentives that are sensitive to local situations. They may successfully advocate for climate change in this way and inform visitors if they are staying in risky places because of the consequences of climate change. The information on the standard operating procedures that DMOs use was intended to be useful to travelers, DMOs, and enterprises involved in the tourism industry. Future implications should discover new approaches for sustainability projects and to achieve a better understanding of how to enhance processes within the tourism industry, and more research on DMOs’ sustainability practices in the face of climate change could help improve this field.
Fabrication of Functional Gypsum Boards Using Waste Eggshells to Prevent Sick Building Syndrome
Authors: Tsubouchi Kentaro , Tsukaguchi Yuta , Shimizu Takeshi , Yoshikawa Hirofumi , Hino Ei-ichi , Date Yusuke , Aoki Kaoru , Tanifuji Naoki
Published: April 2024

Eggshells can adsorb chemicals, but no studies regarding the adsorption of gaseous chemicals using eggshells or eggshell membranes have been reported. The purpose of this study was to apply chemical adsorption using eggshells to the maintenance of human health. Building materials containing eggshells may reduce the concentrations of toxic substances, such as formaldehyde, via the adsorption functions of eggshells. In the bending study, the strength of board-shaped gypsum-containing eggshells was not compromised when the content of eggshells within the gypsum was ≤10%. Compared to those of gypsum boards containing seashells, which comprise calcium carbonate, similar to eggshells, gypsum containing eggshells displayed a higher strength. In the adsorption study, board-shaped gypsum containing eggshells placed inside a sealed box rapidly decreased the formaldehyde concentration. A gypsum board with an eggshell content of ≥5% could limit the formaldehyde concentration to ≤0.08 ppm. Furthermore, the results were compared with those of adsorption studies using plasterboard mixed with other natural materials. Eggshells displayed excellent functionalities as novel formaldehyde adsorbents.
A Study of Quantum Game for Low-Carbon Transportation with Government Subsidies and Penalties
Authors: Li Yongfei , Wang Jiangtao , Wang Bin , Luo Clark
Published: April 2024

Traditional classical game theory struggles to effectively address the inefficiencies in subsidizing and penalizing the R&D and production of low-carbon transportation vehicles. To avoid the shortcomings of classic game theory, this research integrates quantum game theory with Nash games to explore the characteristics of automakers’ behavior for low-carbon transportation with government subsidies and penalties. We first constructed a low-carbon transportation game model between the government and automakers. Then, the optimal profit strategies for both parties in a quantum entangled state were analyzed. Finally, the impact of quantum superposition states and the initial entangled state on the profit strategies of both parties was simulated and analyzed using Monte Carlo simulations. We find that under the joint effects of government subsidies and penalties, quantum game states and the initial quantum entangled state have a crucial influence on the game’s outcomes. They can encourage the realization of Nash equilibrium and perfect coordination in the quantum game, significantly increasing the profits for both parties. This in turn effectively stimulates automakers to research and produce low-carbon transportation solutions, promoting the rapid development of low-carbon transportation technology. In theory, this research can enrich the Quantum game for improvements in the Nash equilibrium solution for the government to subsidize and penalize the low-carbon transportation problem. Meanwhile, in practice, it can provide guidance and reference in optimal strategy selection conditions for government policymakers and automakers for low-carbon transportation.