bluuebunny/crossref_metadata_2025 huggingface dataset:
Malnutrition and Its Determinants among Older Adults Living in French Caribbean Nursing Homes: A Cross-Sectional Study
Authors: Tabue Teguo Maturin , Letchimy Laurys , Rinaldo Leila , Bonnet Michel , Tchero Huidi , Simo-Tabue Nadine , Boucaud-Maitre Denis
Published: July 2024

Background: This study aimed to assess the prevalence of malnutrition and its determinants in older adults living in French Caribbean nursing homes. Methods: This cross-sectional study was taken from the KASEHAD (Karukera Study of Ageing in EHPAD) study. Nutritional status was assessed with the Mini Nutritional Assessment Short-Form (MNA-SF). Clinical characteristics and scores on geriatric scales (Mini-Mental State Examination (MMSE), Activities of Daily Living (ADL), Short Physical Performance Battery (SPPB), Center for Epidemiologic Studies-Depression (CESD) and Questionnaire Quality of Life Alzheimer’s Disease (QoL-AD)) were extracted. Bivariate analysis and logistic models adjusted were performed to test the association between nutritional status and both socio-demographic variables and geriatric scales. Results: A total of 332 older adults from six nursing homes were included in the KASEHPAD study. Among the participants, 319 had an MNA-SF score. The mean age was 81.3 ± 10.6 years, and half of the participants were men. The frequency of malnutrition (MNA-SF ≤ 7) was 27.6% (95% confidence interval (CI): 22.0–32.5) (n = 88). Based on the multivariable analysis, a low MMSE was associated with malnutrition (OR: 0.81 (0.68–0.92); p = 0.015) and there was a borderline significant link between a higher CESD score and malnutrition (OR: 1.05 (1.00–1.12); p = 0.07). Conclusions: Cognitive decline and a tendency toward depression were associated with malnutrition in nursing homes in the French West Indies. Although this study cannot establish causal relationships, the identification of these three geriatric syndromes in nursing homes is crucial for preventing adverse health events.
Investigating Safety and Technological Traits of a Leading Probiotic Species: Lacticaseibacillus paracasei
Authors: Colautti Andrea , Ginaldi Federica , Camprini Lucia , Comi Giuseppe , Reale Anna , Iacumin Lucilla
Published: July 2024

Lacticaseibacillus spp. are genetically close lactic acid bacteria species widely used in fermented products for their technological properties as well as their proven beneficial effects on human and animal health. This study, the first to include such a large collection of heterogeneous isolates (121) obtained from international collections belonging to Lacticaseibacillus paracasei, aimed to characterize the safety traits and technological properties of this important probiotic species, also making comparisons with other genetically related species, such as Lacticaseibacillus casei and Lacticaseibacillus zeae. These strains were isolated from a variety of heterogeneous sources, including dairy products, sourdoughs, wine, must, and human body excreta. After a preliminary molecular characterization using repetitive element palindromic PCR (Rep-PCR), Random Amplification of Polymorphic DNA (RAPD), and Sau-PCR, particular attention was paid to safety traits, evaluating antibiotic resistance profiles, biogenic amine (BA) production, the presence of genes related to the production of ethyl carbamate and diaminobenzidine (DAB), and multicopper oxidase activity (MCO). The technological characteristics of the strains, such as the capability to grow at different NaCl and ethanol concentrations and different pH values, were also investigated, as well as the production of bacteriocins. From the obtained results, it was observed that strains isolated from the same type of matrix often shared similar genetic characteristics. However, phenotypic traits were strain-specific. This underscored the vast potential of the different strains to be used for various purposes, from probiotics to bioprotective and starter cultures for food and feed production, highlighting the importance of conducting comprehensive evaluations to identify the most suitable strain for each purpose with the final aim of promoting human health.
Impact of Microbiota and Metabolites on Intestinal Integrity and Inflammation in Severe Obesity
Authors: Custers Emma , Vreeken Debby , Schuren Frank , van den Broek Tim J. , van Dongen Lieke , Geenen Bram , de Blaauw Ivo , Wiesmann Maximilian , Hazebroek Eric J. , Kleemann Robert , Kiliaan Amanda J.
Published: July 2024

Obesity is a multifactorial disease associated with low-grade inflammation. The gut is thought to be involved in obesity-related inflammation, as it is continuously exposed to antigens from food, microbiota and metabolites. However, the exact underlying mechanisms are still unknown. Therefore, we examined the relation between gut pathology, microbiota, its metabolites and cytokines in adults with severe obesity. Individuals eligible for bariatric surgery were included. Fecal and plasma samples were collected at surgery timepoint, to assess microbiota and metabolite composition. Jejunal biopsies were collected during surgery and stained for cytotoxic T cells, macrophages, mast cells and tight junction component zonula occludens-1. Based on these stainings, the cohort was divided into four groups: high versus low intestinal inflammation and high versus low intestinal integrity. We found no significant differences in microbiota diversity between groups, nor for individual bacterial species. No significant differences in metabolites were observed between the intestinal inflammatory groups. However, some metabolites and cytokines differed between the intestinal integrity groups. Higher plasma levels of interleukin-8 and tauro-chenodeoxycholic acid were found, whereas isovaleric acid and acetic acid were lower in the high intestinal integrity group. As the results were very subtle, we suggest that our cohort shows very early and minor intestinal pathology.
Oral Nanoformulations in Cardiovascular Medicine: Advances in Atherosclerosis Treatment
Authors: Sun Xu , Jia Xushuang , Tan Zhaolin , Fan Dongmei , Chen Meiqi , Cui Ning , Liu Aidong , Liu Da
Published: July 2024

Atherosclerosis (AS) is the formation of atherosclerotic plaques on the walls of the arteries, causing them to narrow. If this occurs in the coronary arteries, the blood vessels may be completely blocked, resulting in myocardial infarction; if it occurs in the blood vessels of the brain, the blood vessels may be blocked, resulting in cerebral infarction, i.e., stroke. Studies have shown that the pathogenesis of atherosclerosis involves the processes of inflammation, lipid infiltration, oxidative stress, and endothelial damage, etc. SIRT, as a key factor regulating the molecular mechanisms of oxidative stress, inflammation, and aging, has an important impact on the pathogenesis of plaque formation, progression, and vulnerability. Statistics show that AS accounts for about 50 per cent of deaths in Western countries. Currently, oral medication is the mainstay of AS treatment, but its development is limited by side effects, low bioavailability and other unfavourable factors. In recent years, with the rapid development of nano-preparations, researchers have combined statins and natural product drugs within nanopreparations to improve their bioavailability. Based on this, this paper summarises the main pathogenesis of AS and also proposes new oral nanoformulations such as liposomes, nanoparticles, nanoemulsions, and nanocapsules to improve their application in the treatment of AS.
On Splits, Big and Little: Towards an Intensive Model of Media and Mediation
Authors: Jenkins Eric S.
Published: July 2024

This essay forwards an intensive model of mediation contrasted with the extensive model implicit in much of media theory, which conceives of communication media as an extension of human faculties. An intensive model, instead, conceives of mediation as a phenomenological process of splitting or folding affective capacities. An extensive model results in a dualistic, essentialist theory of communication media and unresolvable normative debates about the connecting or disconnecting consequences of media. An intensive model avoids these limitations by diagramming various modes of mediation and illustrating how their consequences stem from alterations to intensive properties, thereby helping constitute subjects and media objects alike rather than presuming a media bridge between pre-existing subjects and objects. The essay employs a number of examples to illustrate the extensive model, including telephone conversations, cinema, animation, and social media. The essay concludes with the division of families over QAnon conspiracies to illustrate the analytic gain from an intensive model.
Insights into Plant Sensory Mechanisms under Abiotic Stresses
Authors: Jin Songsong , Wei Mengting , Wei Yunmin , Jiang Zhonghao
Published: July 2024

As sessile organisms, plants cannot survive in harmful environments, such as those characterized by drought, flood, heat, cold, nutrient deficiency, and salt or toxic metal stress. These stressors impair plant growth and development, leading to decreased crop productivity. To induce an appropriate response to abiotic stresses, plants must sense the pertinent stressor at an early stage to initiate precise signal transduction. Here, we provide an overview of recent progress in our understanding of the molecular mechanisms underlying plant abiotic stress sensing. Numerous biomolecules have been found to participate in the process of abiotic stress sensing and function as abiotic stress sensors in plants. Based on their molecular structure, these biomolecules can be divided into four groups: Ca2+-permeable channels, receptor-like kinases (RLKs), sphingolipids, and other proteins. This improved knowledge can be used to identify key molecular targets for engineering stress-resilient crops in the field.
Poly(ester imide)s with Low Linear Coefficients of Thermal Expansion and Low Water Uptake (VIII): Structure–Flame Retardancy Relationship
Authors: Hasegawa Masatoshi , Takeuchi Yuta , Saito Takayuki
Published: July 2024

A series of ester-linked tetracarboxylic dianhydrides containing multiple para-phenylene units (TA-pPhs) was synthesized to obtain novel modified polyimides, namely poly(ester imide)s (PEsIs). The flame retardancy and film toughness of PEsIs tended to deteriorate with the structural extension of the repeating units (or monomers) via ester groups. To identify the structural factors necessary for achieving the highest flame retardancy rank (UL-94, V-0), we systematically investigated the structure–property relationships of a series of TA-pPh-based PEsIs. Among them, a PEsI derived from para-quaterphenylene-containing TA-pPh (TA-DPQP) and p-phenylenediamine (p-PDA) exhibited the best property combination, featuring an extremely high glass transition temperature (Tg), very low linear coefficient of thermal expansion (CTE), low water uptake (WA), ultralow linear coefficient of humidity (hygroscopic) expansion (CHE), unexpectedly high film toughness, and excellent flame retardancy (V-0 rank). Moreover, we examined the effects of substituents of TA-pPh and discussed the mode of action for the increased film toughness. This study also investigated the structure–property relationship for a series of PEsIs derived from isomeric naphthalene-containing tetracarboxylic dianhydrides. Some of the PEsIs obtained in this study, such as the TA-DPQP/p-PDA system, hold promise as novel high-temperature dielectric substrates for use in flexible printed circuits.
Fabrication and Characterization of PLA/PBAT Blends, Blend-Based Nanocomposites, and Their Supercritical Carbon Dioxide-Induced Foams
Authors: Behera Kartik , Tsai Chien-Hsing , Liao Xiang-Bo , Chiu Fang-Chyou
Published: July 2024

In this study, a twin-screw extruder was used to fabricate poly(lactic acid) (PLA)/poly(butylene adipate-co-terephthalate) (PBAT) blends and blend-based nanocomposites with carbon nanotube (CNT) or nanocarbon black (CB) as nanofillers. The fabricated samples were subsequently treated with supercritical carbon dioxide (scCO2) to fabricate the corresponding foams. Bi-phasic morphology and selective distribution of CNTs or CBs in the PBAT phase were observed in the blends/composites through scanning electron microscopy. After the scCO2 treatment, the selective foaming of the PBAT phase in the prepared blends/composites was confirmed. The cellular structure of PBAT phase in scCO2-treated blends is similar to the size/shape of PBAT domains in untreated blends or treated neat PBAT foam. The addition of CNTs or CBs in the blends led to a slight reduction in cell size of the foamed PBAT phase, demonstrating CNT/CB-induced cell nucleation. Differential scanning calorimetry (DSC) results showed that CNTs and CBs played as nucleating agents and increased the initial crystallization temperature up to 14 °C compared with neat PBAT for PBAT in different composites during cooling. The scCO2 treatment induced the bimodal stability of PBAT crystals in different samples, which melted mainly in two temperature regions in DSC studies. Thermogravimetric analyses revealed that compared with parent blends, the addition of CNTs or CBs increased the temperature at 80 wt.% loss (degradation of PBAT portion) up to 6 °C. The electrical resistivity decreased by more than six orders of magnitude for certain CNT- or CB-added composites compared with the parent blends. The hardness of the blends slightly increased after forming the corresponding composites and then declined after the scCO2 treatment.
Evaluation of the Hydraulic Fracturing Tendencies of Consolidated Sandstone Reservoirs Based on the Catastrophe Theory
Authors: Feng Haowei , Wang Ping , Qu Zhan , Huang Hai , Wang Liang , Wei Yongsheng , He Yawen
Published: July 2024

The evaluation of rock hydraulic fracturing tendency plays a crucial role in the selection of fracturing layers within reservoirs and the evaluation of post-compression capacity. The sandstone reservoirs in the Yihuang New Area have poor physical properties and are deeply buried. It is necessary to increase the production of oil and gas by hydraulic fracturing. Regarding the sandstones in the region, the following parameters were considered: combined compressive strength, bulk modulus, shear modulus, fracture index, horizontal-stress difference coefficient, and fracture toughness. In accordance with the catastrophe theory, a multi-level structure was established for the hydraulic fracturing-tendency evaluation of sandstone reservoirs, consisting of a target layer, a guide layer, and an indicator layer. A catastrophic model for evaluating the hydraulic fracturing tendency of sandstone reservoirs was established. The results are consistent with those obtained from the Analytic Hierarchy Process. However, the catastrophe theory significantly reduces subjective interference. The results indicate that when the hydraulic fracturing-tendency evaluation value is greater than 0.8, the reservoir can be fractured well; when the hydraulic fracturing-tendency evaluation value is between 0.7 and 0.8, the fracture reservoir is moderate; and when the hydraulic fracturing-tendency evaluation value is less than 0.7, the fractured reservoir is poor. The optimal fracture intervals for the Yi 70 well are 1320–1323 m, 1350–1355 m, and 1355–1360 m. The optimal fracture planes for the Yi 76 well are 1921–1925 m and 1925–1930 m. The optimal fracture planes for the Yi 10-1-26 well are 2487–2495 m, 2585–2587 m, and 2589–2591 m. The hydraulic fracturing-tendency model developed in this study has been applied to several well sections of sandstone reservoirs in the Yihuang New Area. Additionally, the model was compared with existing hydraulic fracturing-tendency evaluation models. The evaluation results are in agreement with the post-pressure capacity-monitoring data. The accuracy of the model presented in this study has been verified, as has its applicability to other sandstone reservoirs.
A Study on Effect Analysis and Process Parameter Optimization of Viscous Acid Acidification in a Porous Heterogeneous Carbonate Reservoir
Authors: Zhang Liangjun , Xu Hualei , Zhao Kangjia , Wang Jie , Jiang Houshun
Published: July 2024

The homogeneous acid etching of conventional acid in porous heterogeneous carbonate reservoirs leads to a large amount of consumption in the near-wellbore area, which makes the acidification effect often not ideal. In order to improve the acidizing effect of porous heterogeneous carbonate reservoirs, viscous acid is used to increase the stimulation of the target block in this paper. Through systematic experiments, the adaptability of the viscous acid in the four layers of the M reservoir in the target block was evaluated, and the MD and ME layers suitable for acidizing stimulation were determined in combination with physical property analysis. Finally, based on the geological characteristics and experimental data of the preferred layers, a two-scale acid wormhole growth radial model was established, and the construction parameters of acidizing stimulation were optimized. The results show that (1) The preferred viscous acid system has a dissolution rate of more than 95% for the rock powder in the four layers. When the matrix permeability is high, the effect of the acid wormhole is obvious and the permeability increase is higher. (2) The steel sheet corrosion and residual acid damage experiments showed that the acid system was not corrosive to the wellbore, and the reservoir damage rate of the residual acid after the reaction was low. (3) Based on the relationship between reservoir porosity and permeability and the position of edge and bottom water, the MD and ME layers with more potential for acidizing stimulation are selected. (4) The results of the numerical simulation show that the optimal acid pump rate of the MD and ME layers is 1.4 bpm and 1.0 bpm, and the acidizing fluid volume is 255 bbl, which can form effective acid wormholes, and the range of reservoir permeability transformation is the largest. The field application results show that the optimization scheme effectively improves the production of oil wells, verifies the practicability of the scheme, and provides a reference for the process optimization of viscous acid in the same type of porous heterogeneous carbonate reservoir stimulation.
Hydrographic Inspection Using a USV of a Harbour Bottom Deepened by the Periodic Actuation of SAR Vessel Propellers
Authors: Specht Cezary , Śliwińska Dominika
Published: July 2024

In contrast to classic hydrographic cutters, unmanned surface vehicles, due to their size, ease of transport and the equipment installed, enable the performance of quick and cost-effective bottom inspections in various water areas. Thanks to their shallow draught and high manoeuvrability, hydrographic drones are capable of the bathymetric exploration of shallow waters such as harbours, hydrotechnical structures and the areas where classic naval vessels could encounter implementation difficulties. The aim of this paper is to demonstrate, using a selected practical example, the specific ability of an unmanned surface vehicle (USV) to carry out the urgent and immediate inspection of the bottom of a specific water area. The freedom to move between restricted areas, the ease of transport and the satisfactory quality of the surveys make hydrographic drones ideal tools for projects of this type. The referenced study produced a bathymetric map of a section of the seabed adjacent to the quay at which a Search and Rescue (SAR) vessel is moored and regularly, at its permanent fixed location, actuates its propellers. The effect of its propellers is the local deepening of the bottom in two places. The research showed a local decrease in the depth from 5.5 m to less than 7 m, which may threaten the stability of the quay structure. In addition, it was noted that the washed bottom material had been moved approximately 10 m from the quay, causing shallowing in two places and reducing the depth from 5.5 m to 4.7 m. This study demonstrated that the use of USVs for applications of this type is very effective in terms of the implementation time and is economically justified.
Enhanced Strapdown Inertial Navigation System (SINS)/LiDAR Tightly Integrated Simultaneous Localization and Mapping (SLAM) for Urban Structural Feature Weaken Occasions in Vehicular Platform
Authors: Xu Xu , Guan Lianwu , Gao Yanbin , Chen Yufei , Liu Zhejun
Published: July 2024

LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods.
Early Radiometric Assessment of NOAA-21 Visible Infrared Imaging Radiometer Suite Reflective Solar Bands Using Vicarious Techniques
Authors: Wu Aisheng , Xiong Xiaoxiong , Mu Qiaozhen , Angal Amit , Bhatt Rajendra , Shea Yolanda
Published: July 2024

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS.
Watermarking Algorithm for Remote Sensing Images Based on Ring-Shaped Template Watermark and Multiscale LCM
Authors: Zhou Qifei , Sun Hua , Pang Xinyan , Ai Chi , Zhu Xiaoye , Zhu Changqing , Ren Na
Published: July 2024

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility.
Synthetic-Aperture Radar Radio-Frequency Interference Suppression Based on Regularized Optimization Feature Decomposition Network
Authors: Fang Fuping , Li Haoliang , Meng Weize , Dai Dahai , Xing Shiqi
Published: July 2024

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset.
Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging
Authors: Wang Jihan , Zhang Nan , Zhang Laifu , Jing Haoyu , Yan Yiming , Wu Sensen , Liu Renyi
Published: July 2024

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale.
Sensor Network Attack Synthesis against Fault Diagnosis of Discrete Event Systems
Authors: Kang Tenglong , Hou Yifan , Liu Ding
Published: July 2024

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.
A Detection Algorithm for Citrus Huanglongbing Disease Based on an Improved YOLOv8n
Authors: Xie Wu , Feng Feihong , Zhang Huimin
Published: July 2024

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once–Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal–EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards.
Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions
Authors: Zhang Yujia , Tang Xingwang , Xu Sichuan , Sun Chuanyu
Published: July 2024

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications.
PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics
Authors: Gao Feng , Li Sen , Ye Yuankang , Liu Chang
Published: July 2024

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network’s advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.
Relationship of Cycling Power and Non-Linear Heart Rate Variability from Everyday Workout Data: Potential for Intensity Zone Estimation and Monitoring
Authors: Andriolo Stefano , Rummel Markus , Gronwald Thomas
Published: July 2024

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has been shown to be a sensitive marker for assessing global organismic demands. The wide dynamic range within the exercise intensity spectrum and the relationship to established physiologic threshold boundaries potentially allow in-field use and also open opportunities to provide real-time feedback. The present study expands the idea of using everyday workout data from the AI Endurance app to obtain the relationship between cycling power and DFA-a1. Collected data were imported between September 2021 and August 2023 with an initial pool of 3123 workouts across 21 male users. The aim of this analysis was to further apply a new method of implementing workout group data considering representative values of DFA-a1 segmentation compared to single workout data and including all data points to enhance the validity of the internal-to-external load relationship. The present data demonstrate a universal relationship between cycling power and DFA-a1 from everyday workout data that potentially allows accessible and regular tracking of intensity zone demarcation information. The analysis highlights the superior efficacy of the representative-based approach of included data in most cases. Validation data of the performance level and the up-to-date relationship are still pending.
The Key Role of Floors for the Sustainability of Retrofit Interventions in Older Existing Reinforced Concrete Buildings
Authors: Vona Marco , Anelli Angelo , Del Giudice Francesco Paolo , Manganelli Benedetto
Published: July 2024

In recent decades, the seismic performance of existing reinforced concrete (RC) buildings has played a key role. Nevertheless, the performance and reliability verification of important structural elements such as floors has often been neglected. Floors are primary structural elements that can affect the life cycle life of a building. However, the widespread lack of maintenance planning over time and the original construction practice (which was not always correct) are frequently the cause of unpredictable local or global collapse. In addition, although recent standards and codes recognize the importance of floors by prioritizing their verification with respect to gravitational load conditions, the verification of floor reliability with respect to the load combinations required by modern standards and codes is often not satisfied. Consequently, the intervention costs could be significantly affected by the floor conditions, and their overall amount might even discourage the implementation of interventions. The main purpose of this study is to evaluate the effects (in terms of sustainability) of interventions on residential RC buildings, considering the need to retrofit their existing floors. To this aim, the most vulnerable and potentially most degraded floor types are identified, and their capacity–demand relationships are evaluated. In the case of unverified floors, the main and most popular intervention methods are evaluated and related to the overall intervention costs, taking into account the main uncertainties in performance and cost predictions. The problems and critical issues of floors are key in determining the safety of the building and the cost-effectiveness (i.e., sustainability) of the retrofit intervention. Professionals and decision makers could benefit from the proposed study cost model to define intervention strategies on a regional or national scale.
Measuring the Sustainability of Tourism (SF-MST): New Wine in an Old Bottle?
Authors: Dwyer Larry
Published: July 2024

In March 2024, UN Tourism (formerly UNWTO) released for publication the Statistical Framework for Measuring Sustainable Tourism (SF-MST). The SF-MST is claimed to give a full account of tourism’s current and future economic, social and environmental impacts, addressing the needs of visitors, the industry, the environment and host communities. An important motivation for the development of the SF-MST is recognition that standard economic measures such as GDP do not capture important dimensions of people’s well-being. While the SF-MST contributes to the development of an international statistical framework for tourism, this paper argues that failure to adequately incorporate well-being outcomes within its recommended range of ‘sustainability’ indicators greatly restricts the scope and policy significance of the SF-MST. The paper concludes with suggestions for further extensions of the SF-MST framework to strengthen its policy relevance.
Assessment of Management Practices to Prevent Soil Degradation Threats on Lithuanian Acid Soils
Authors: Mockeviciene Ieva , Karcauskiene Danute , Vilkiene Monika , Repsiene Regina , Feiza Virginijus , Budryte Otilija
Published: July 2024

An assessment of soil characteristics pertaining to their suitability for agricultural use in general is necessary to reverse the declining trend of soil quality and to ensure sustainable agriculture. The aim of this study was to determine the soil resistance (SRI) and degradation indices (SDI) under different agrotechniques and to find out whether management-induced changes are large enough to reduce soil degradation. The study was based on the comparison of physicochemical data of 3 long-term experiments conducted in the western part of Lithuania. Changes in soil properties over the past 20 years (1999–2019) have been determined. The most commonly used measures in Lithuania, such as soil liming, manuring, residue maintenance, and tillage, were selected for the analysis. The analysis carried out showed that the soil, which was fertilized with manure, had a higher value of soil quality parameters compared to natural Retisol: organic matter (by 0.53 percentage points), total nitrogen (by 0.04 percentage points), and the available amounts of phosphorus (by 69 mg/kg) and potassium (by 6.6 mg/kg). The assessment of the relative annual change in SOC content revealed that long-term soil manuring has significant SOC sequestration capability. Among the soil management techniques examined, it appeared that the greatest relative annual change (0.47 g kg−1yr−1) in SOC content was noted in manured soil. The results indicate that the higher degradation, and resistance values were observed in acid soil (pH 4.2), where liming was applied, indicating greater sensitivity to degradation. Based on analyzed indices, the agricultural practices ranked as: manuring > residue management > reduced tillage > liming. The lowest SRI values were obtained for low level of nutrients (from −0.11 to 0.89), organic carbon (from −0.72 to −0.49), and pH (from −0.25 to –0.1), indicating that these properties are more sensitive to applied agricultural practices compared to others. All these findings provide information for promoting better soil management, soil protection, land use planning, and the planning of remedial measures, especially in the most afflicted areas.
The Prediction of Pectin Viscosity Using Machine Learning Based on Physical Characteristics—Case Study: Aglupectin HS-MR
Authors: Siejak Przemysław , Przybył Krzysztof , Masewicz Łukasz , Walkowiak Katarzyna , Rezler Ryszard , Baranowska Hanna Maria
Published: July 2024

In the era of technology development, the optimization of production processes, quality control and at the same time increasing production efficiency without wasting food, artificial intelligence is becoming an alternative tool supporting many decision-making processes. The work used modern machine learning and physical analysis tools to evaluate food products (pectins). Various predictive models have been presented to estimate the viscosity of pectin. Based on the physical analyses, the characteristics of the food product were isolated, including L*a*b* color, concentration, conductance and pH. Prediction was determined using the determination index and loss function for individual machine learning algorithms. As a result of the work, it turned out that the most effective estimation of pectin viscosity was using Decision Tree (R2 = 0.999) and Random Forest (R2 = 0.998). In the future, the prediction of pectin properties in terms of viscosity recognition may be significantly perceived, especially in the food and pharmaceutical industries. Predicting the natural pectin substrate may contribute to improving quality, increasing efficiency and at the same time reducing losses of the obtained final product.
The Indirect Carbon Cost of E-Mobility for Select Countries Based on Grid Energy Mix Using Real-World Data
Authors: Twum-Duah Nana Kofi , Neves Mosquini Lucas Hajiro , Shahid Muhammad Salman , Osonuga Seun , Wurtz Frédéric , Delinchant Benoit
Published: July 2024

Electric vehicles are considered by many as an emission-free or low-emission solution to meet the challenge of sustainable transportation. However, the operational input, electrical energy, has an associated cost, greenhouse gasses, which results in indirect emissions. Given this knowledge, we pose the following question: “Are zero-emission transportation targets achievable given our current energy mix?” The objective of this article is to assess the impact of a grid’s energy mix on the indirect emissions of an electric vehicle. The study considers real-world data, vehicle usage data from an electric vehicle, and carbon intensity data for India, the USA, France, the Netherlands, Brazil, Germany, and Poland. Linear programming-based optimization is used to compute the best charging scenario for each of the given grids and, consequently, the indirect emissions are compared to those of a high-efficiency 1.5 L diesel internal combustion engine for the vehicle: a 2019 Renault Clio dCi 85. The results indicate that for grids with low renewable energy penetration, such as those of Poland and India (Maharashtra), an electric vehicle, even when optimally charged, can be classified as neither a low- nor zero-emission alternative to normal thermal vehicles. Also, for grids with elevated levels of variation in their carbon intensity, there is significant potential to reduce the carbon footprint related to charging an electric vehicle. This article provides a real-world perspective of how an electric vehicle performs in the face of different energy mixes and serves as a precursor to the development of robust indicators for determining the carbon reductions related to the e-mobility transition.