bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry
Authors: Saldaña Manuel , Gálvez Edelmira , Navarra Alessandro , Toro Norman , Cisternas Luis A.
Published: April 2023

Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process’s productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.
Reductions in the Laser Welding Deformation of STS304 Cylindrical Structure Using the Pre-Stress Method
Authors: Lee Se-Hwan , Jeon Ho-Chan , Park Jeong-Ung
Published: April 2023

Welding deformation occurs due to non-uniform thermal expansion, thermal contraction, restraint, and phase transformation in a metal by a local welding heat source. This causes problems such as low buckling strength and the reduced workability of the production process. Correcting welding deformations in stainless steel using heat—such as by linear heating—causes metal sensitization, which should be avoided. Herein, welding deformation was reduced by applying tension stress instead of correcting the deformation by heating. A deformation-prevention jig was used to reduce welding deformation during the manufacturing of a cylinder made of STS304 by laser welding. The tensile stress was induced by pushing the cylinder shell outward using the deformation-prevention jig. A thermo-elastoplastic analysis was performed to investigate the effects of the magnitude of the tensile stress on welding deformation. Furthermore, the parametric results—which indicated a reduction in welding deformation—were verified through experiments. The thermo-elastoplastic analysis suggested that deformation did not occur when the magnitude of tensile stress was approximately 50% of the yield stress of the base metal. Moreover, the deformation was experimentally reduced by 11–20% when a tensile stress of 30 MPa was applied to the cylinder, compared with that in the absence of tensile stress.
Boosting Electrocatalytic Reduction of Nitrate to Ammonia over Co3O4 Nanosheets with Oxygen Vacancies
Authors: Wu Xing , Liu Zhigong , Gao Tianyu , Li Zhizhuo , Song Zhenhui , Tang Jia , Feng Fan , Qu Caiyan , Yao Fubing , Tang Chongjian
Published: April 2023

Electrocatalytic nitrate reduction into ammonia is promising for its restricted activity and selectivity in wastewater treatment, however, it remains challenging. In this work, Co3O4 nanosheet electrodes with rich oxygen vacancies (OVs) (Co3O4−x/NF) are prepared and then applied as efficient catalysts for selective electrocatalytic reduction of nitrate to ammonia. The resulting Co3O4−x/NF electrodes exhibit high NO3−-N removal efficiency and NH4+-N selectivity, at 93.7% and 85.4%, respectively. X-Ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance spectra (EPR) results clearly reveal the formation of OVs in Co3O4−x/NF. The electrochemical characterization results confirm that OVs can effectively improve electron transfer as well as the electrochemically active area. The Co2+/Co3+ ratio of Co3O4−x/NF increases after the electrocatalytic reduction of nitrate, highlighting the crucial role played by Co2+ in mediating ammonia production via the Co2+/Co3+ cycle. These findings offer valuable guidelines for the development of more efficient and sustainable approaches for nitrate-contaminated wastewater treatment and ammonia synthesis.
Challenges for Plant Growth Promoting Microorganism Transfer from Science to Industry: A Case Study from Chile
Authors: Muñoz-Carvajal Eduardo , Araya-Angel Juan Pablo , Garrido-Sáez Nicolás , González Máximo , Stoll Alexandra
Published: April 2023

Research on the plant growth promoting microorganisms (PGPM) is increasing strongly due to the biotechnological potential for the agricultural, forestry, and food industry. The benefits of using PGPM in crop production are well proven; however, their incorporation in agricultural management is still limited. Therefore, we wanted to explore the gaps and challenges for the transfer of biotechnological innovations based on PGPM to the agricultural sector. Our systematic review of the state of the art of PGPM research and knowledge transfer takes Chile as an example. Several transfer limiting aspects are identified and discussed. Our two main conclusions are: neither academia nor industry can meet unfounded expectations during technology transfer, but mutually clarifying their needs, capabilities, and limitations is the starting point for successful collaborations; the generation of a collaborative innovation environment, where academia as well as public and private stakeholders (including the local community) take part, is crucial to enhance the acceptance and integration of PGPM on the way to sustainable agriculture.
Bidimensional SnSe2—Mesoporous Ordered Titania Heterostructures for Photocatalytically Activated Anti-Fingerprint Optically Transparent Layers
Authors: De Santis Jessica , Paolucci Valentina , Stagi Luigi , Carboni Davide , Malfatti Luca , Cantalini Carlo , Innocenzi Plinio
Published: April 2023

The design of functional coatings for touchscreens and haptic interfaces is of paramount importance for smartphones, tablets, and computers. Among the functional properties, the ability to suppress or eliminate fingerprints from specific surfaces is one of the most critical. We produced photoactivated anti-fingerprint coatings by embedding 2D-SnSe2 nanoflakes in ordered mesoporous titania thin films. The SnSe2 nanostructures were produced by solvent-assisted sonication employing 1-Methyl-2-pyrrolidinone. The combination of SnSe2 and nanocrystalline anatase titania enables the formation of photoactivated heterostructures with an enhanced ability to remove fingerprints from their surface. These results were achieved through careful design of the heterostructure and controlled processing of the films by liquid phase deposition. The self-assembly process is unaffected by the addition of SnSe2, and the titania mesoporous films keep their three-dimensional pore organization. The coating layers show high optical transparency and a homogeneous distribution of SnSe2 within the matrix. An evaluation of photocatalytic activity was performed by observing the degradation of stearic acid and Rhodamine B layers deposited on the photoactive films as a function of radiation exposure time. FTIR and UV-Vis spectroscopies were used for the photodegradation tests. Additionally, infrared imaging was employed to assess the anti-fingerprinting property. The photodegradation process, following pseudo-first-order kinetics, shows a tremendous improvement over bare mesoporous titania films. Furthermore, exposure of the films to sunlight and UV light completely removes the fingerprints, opening the route to several self-cleaning applications.
Exact Solutions for Non-Isothermal Flows of Second Grade Fluid between Parallel Plates
Authors: Baranovskii Evgenii S.
Published: April 2023

In this paper, we obtain new exact solutions for the unidirectional non-isothermal flow of a second grade fluid in a plane channel with impermeable solid walls, taking into account the fluid energy dissipation (mechanical-to-thermal energy conversion) in the heat transfer equation. It is assumed that the flow is time-independent and driven by the pressure gradient. On the channel walls, various boundary conditions are stated. Namely, we consider the no-slip conditions, the threshold slip conditions, which include Navier’s slip condition (free slip) as a limit case, as well as mixed boundary conditions, assuming that the upper and lower walls of the channel differ in their physical properties. The dependence of solutions on the boundary conditions is discussed in some detail. Moreover, we establish explicit relationships for the model parameters that guarantee the slip (or no-slip) regime on the boundaries.
Dietary Patterns and Associations with Myopia in Chinese Children
Authors: Yin Chunjie , Gan Qian , Xu Peipei , Yang Titi , Xu Juan , Cao Wei , Wang Hongliang , Pan Hui , Ren Zhibin , Xiao Hui , Wang Kai , Xu Ying , Zhang Qian
Published: April 2023

Dietary shifts in populations undergoing rapid economic transitions have been proposed as partly contributing toward the rapid intergenerational rise in myopia prevalence; however, empirical evidence of the effect of dietary factors on myopia is limited. This study investigated the association between dietary factors and incident myopia in Chinese children aged 10–11 years. We evaluated dietary habits using a 72-item food frequency questionnaire (FFQ) among 7423 children. Myopic status was assessed using the “General Personal Information Questionnaire”. Principal component analysis was used to extract dietary patterns and investigate their association with myopia. After adjusting for potential confounders, participants with the highest adherence to dietary pattern A (95% confidence interval (CI): 0.66–0.92, p for trend = 0.007) and dietary pattern C (95% CI: 0.58–0.80, p for trend < 0.001) had a lower risk of myopia than participants with the least adherence. Both of these dietary patterns are characterized by high consumption of meats, aquatic product, dairy and its products, eggs, legumes, vegetables, fruits, grains, and potatoes. Our findings suggest that other environmental factors, such as those related to the dietary environment, may contribute to the development of myopia. These findings can serve as a reference for diet-related primary prevention of myopia.
A Tailored and Engaging mHealth Gamified Framework for Nutritional Behaviour Change
Authors: Orte Silvia , Migliorelli Carolina , Sistach-Bosch Laura , Gómez-Martínez Meritxell , Boqué Noemi
Published: April 2023

Mobile health applications (apps) have been shown to be effective for improving eating habits. However, most of the existing apps rely on calorie and nutrient counting which have several limitations including the difficulty in sustaining long-term use, inaccuracy, and the risk of developing eating disorders. We designed and developed a mHealth framework for nutritional behaviour change, integrated into the CarpeDiem app, that focuses on the intake of key food groups which are known to have a higher impact on health indicators instead of the intake of nutrients. This framework is mainly based on a gamified system that delivers personalized dietary missions to the user and provides motivational recommendations that help the user to achieve these missions. Its design was guided by an evidenced-based theory of behavioural change, the HAPA model, and it is also characterized by the personalization of the system and the use of a recommender system based on advanced artificial intelligence techniques. Overall, the approach used in the present app could foster a sustained improvement of eating habits among the general population, which is the main challenge of dietary interventions, decreasing the risk of developing the chronic diseases associated with unhealthy dietary habits.
Serum Interleukin 6, Controlling Nutritional Status (CONUT) Score and Phase Angle in Patients with Crohn’s Disease
Authors: Cioffi Iolanda , Scialò Filippo , Di Vincenzo Olivia , Gelzo Monica , Marra Maurizio , Testa Anna , Castiglione Fabiana , Vitale Maria , Pasanisi Fabrizio , Santarpia Lidia
Published: April 2023

Crohn’s disease (CD) is a chronic inflammatory disorder that may occur in any segment of the gastrointestinal tract. Asymptomatic or untreated inflammation along with malnutrition can often coexist in patients with CD, impairing clinical outcomes, therefore the aim of this study was to assess the relationship between inflammation, malnutrition risk and nutritional status in CD patients. Consecutive adult CD outpatients aged 18-65 years were recruited. Disease activity was clinically defined by the Crohn’s Disease Activity Index (CDAI), whilst anthropometry and phase angle (PhA) were measured. The Controlling Nutritional Status (CONUT) score was retrospectively calculated for screening malnutrition risk and blood samples were taken. A total of 140 CD patients with a mean age of 38.8 ± 13.9 years and an average weight of 64.9 ± 12.0 kg were included. Serum interleukin (IL)-6 concentration was increased in active-CD patients, unrelated to medical treatment, which was associated with CDAI and PhA. Based on the CONUT score, the prevalence of patients with moderate/severe malnutrition risk (score ≥ 5) was 10%, showing lower age, body mass index and fat mass, but higher IL-6 and IL-1β levels than subjects classified as not at risk (score 0–1). Finally, increased IL-6 levels and reduced PhA values were identified as independent predictors of moderate/severe malnutrition risk (p < 0.05). In conclusion, IL-6 increased in active-CD patients, which was inversely correlated with PhA. Although the CONUT score might be helpful for identifying CD patients at moderate/severe risk of malnutrition, larger studies are needed to verify these results in different settings.
Soy and Gastrointestinal Health: A Review
Authors: Belobrajdic Damien P. , James-Martin Genevieve , Jones Darren , Tran Cuong D.
Published: April 2023

Soybean is the most economically important legume globally, providing a major source of plant protein for millions of people; it offers a high-quality, cost-competitive and versatile base-protein ingredient for plant-based meat alternatives. The health benefits of soybean and its constituents have largely been attributed to the actions of phytoestrogens, which are present at high levels. Additionally, consumption of soy-based foods may also modulate gastrointestinal (GI) health, in particular colorectal cancer risk, via effects on the composition and metabolic activity of the GI microbiome. The aim of this narrative review was to critically evaluate the emerging evidence from clinical trials, observational studies and animal trials relating to the effects of consuming soybeans, soy-based products and the key constituents of soybeans (isoflavones, soy proteins and oligosaccharides) on measures of GI health. Our review suggests that there are consistent favourable changes in measures of GI health for some soy foods, such as fermented rather than unfermented soy milk, and for those individuals with a microbiome that can metabolise equol. However, as consumption of foods containing soy protein isolates and textured soy proteins increases, further clinical evidence is needed to understand whether these foods elicit similar or additional functional effects on GI health.
Antifungal Compounds from Microbial Symbionts Associated with Aquatic Animals and Cellular Targets: A Review
Authors: Ngo-Mback Madeleine Nina Love , Zeuko’o Menkem Elisabeth , Marco Heather G.
Published: April 2023

Fungal infections continue to be a serious public health problem, leading to an estimated 1.6 million deaths annually. It remains a major cause of mortality for people with a weak or affected immune system, such as those suffering from cancer under aggressive chemotherapies. On the other hand, pathogenic fungi are counted among the most destructive factors affecting crops, causing a third of all food crop losses annually and critically affecting the worldwide economy and food security. However, the limited number currently available and the cytotoxicity of the conventional antifungal drugs, which are not yet properly diversified in terms of mode of action, in addition to resistance phenomena, make the search for new antifungals imperative to improve both human health and food protection. Symbiosis has been a crucial alternative for drug discovery, through which many antimicrobials have been discovered. This review highlights some antifungal models of a defensive symbiosis of microbial symbiont natural products derived from interacting with aquatic animals as one of the best opportunities. Some recorded compounds with supposed novel cell targets such as apoptosis could lead to the development of a multitherapy involving the mutual treatment of fungal infections and other metabolic diseases involving apoptosis in their pathogenesis pathways.
Human Salivary Histatin-1 Attenuates Osteoarthritis through Promoting M1/M2 Macrophage Transition
Authors: Wu Antong , Pathak Janak Lal. , Li Xingyang , Cao Wei , Zhong Wenchao , Zhu Mingjing , Wu Qiuyu , Chen Wanyi , Han Qiao , Jiang Siqing , Hei Yuzhuo , Zhang Ziyi , Wu Gang , Zhang Qingbin
Published: April 2023

Osteoarthritis (OA) is an inflammation-driven degenerative joint disease. Human salivary peptide histatin-1 (Hst1) shows pro-healing and immunomodulatory properties. but its role in OA treatment is not fully understood. In this study, we investigated the efficacy of Hst1 in the inflammation modulation-mediated attenuation of bone and cartilage damage in OA. Hst1 was intra-articularly injected into a rat knee joint in a monosodium iodoacetate (MIA)-induced OA model. Micro-CT, histological, and immunohistochemical analyses showed that Hst1 significantly attenuates cartilage and bone deconstruction as well as macrophage infiltration. In the lipopolysaccharide-induced air pouch model, Hst1 significantly reduced inflammatory cell infiltration and inflammation. Enzyme-linked immunosorbent assay (ELISA), RT-qPCR, Western blot, immunofluorescence staining, flow cytometry (FCM), metabolic energy analysis, and high-throughput gene sequencing showed that Hst1 significantly triggers M1-to-M2 macrophage phenotype switching, during which it significantly downregulated nuclear factor kappa-B (NF-κB) and mitogen-activated protein kinases (MAPK) signaling pathways. Furthermore, cell migration assay, Alcian blue, Safranin O staining, RT-qPCR, Western blot, and FCM showed that Hst1 not only attenuates M1-macrophage-CM-induced apoptosis and matrix metalloproteinase expression in chondrogenic cells, but it also restores their metabolic activity, migration, and chondrogenic differentiation. These findings show the promising potential of Hst1 in treating OA.
Electrosprayed Mesenchymal Stromal Cell Extracellular Matrix Nanoparticles Accelerate Cellular Wound Healing and Reduce Gram-Negative Bacterial Growth
Authors: Wandling Emily N. , Rhoads Keera , Ohman Dennis E. , Heise Rebecca L.
Published: April 2023

Treatments for acute respiratory distress syndrome are still unavailable, and the prevalence of the disease has only increased due to the COVID-19 pandemic. Mechanical ventilation regimens are still utilized to support declining lung function but also contribute to lung damage and increase the risk for bacterial infection. The anti-inflammatory and pro-regenerative abilities of mesenchymal stromal cells (MSCs) have shown to be a promising therapy for ARDS. We propose to utilize the regenerative effects of MSCs and the extracellular matrix (ECM) in a nanoparticle. Our mouse MSC (MMSC) ECM nanoparticles were characterized using size, zeta potential, and mass spectrometry to evaluate their potential as pro-regenerative and antimicrobial treatments. The nanoparticles had an average size of 273.4 nm (±25.6) and possessed a negative zeta potential, allowing them to surpass defenses and reach the distal regions of the lung. It was found that the MMSC ECM nanoparticles are biocompatible with mouse lung epithelial cells and MMSCs, increasing the wound healing rate of human lung fibroblasts while also inhibiting the growth of Pseudomonas aeruginosa, a common lung pathogen. Our MMSC ECM nanoparticles display characteristics of healing injured lungs while preventing bacterial infection, which can increase recovery time.
Doxorubicin-Loaded Fungal-Carboxymethyl Chitosan Functionalized Polydopamine Nanoparticles for Photothermal Cancer Therapy
Authors: Suneetha Maduru , Kim Hyeonjin , Han Sung Soo
Published: April 2023

In this work, we synthesized doxorubicin-loaded fungal-carboxymethyl chitosan (FC) functionalized polydopamine (Dox@FCPDA) nanoparticles for improved anticancer activity via photothermal drug release. The photothermal properties revealed that the FCPDA nanoparticles with a concentration of 400 µg/mL produced a temperature of about 61.1 °C at 2 W/cm2 laser illumination, which is more beneficial for cancer cells. Due to the hydrophilic FC biopolymer, the Dox was successfully encapsulated into FCPDA nanoparticles via electrostatic interactions and pi-pi stacking. The maximum drug loading and encapsulation efficiency were calculated to be 19.3% and 80.2%, respectively. The Dox@FCPDA nanoparticles exhibited improved anticancer activity on HePG2 cancer cells when exposed to an NIR laser (800 nm, 2 W/cm2). Furthermore, the Dox@FCPDA nanoparticles also improved cellular uptake with HepG2 cells. Therefore, functionalizing FC biopolymer with PDA nanoparticles is more beneficial for drug and photothermal dual therapeutic properties for cancer therapy.
Research on Very-Low-Frequency Hydroacoustic Acoustic Velocity Sensor Based on DFB Fiber Laser
Authors: Ruan Chenxia , Chen Mo , Yu Yang , Zhang Yichi , Wang Jianfei , Zhang Zhenrong , Yang Junbo , Zhu Shuolong , Qu Boyuan
Published: April 2023

We have conducted a study on a very-low-frequency acoustic-velocity sensor which is based on a cantilever of distributed-feedback (DFB) fiber laser immersed in castor oil. A mathematical model of the frequency dependent response of the proposed sensor to the acoustic pressure signal influenced by the fluid viscosity is established. We have fabricated the proposed sensor and conducted experimental measurements in the standing wave tube. The results show that the sensor has an average phase sensitivity of −179.5 dB (0 dB = 1 rad/μPa) with ±1.45 dB fluctuation over the frequency range of 20–38 Hz. It has good cosine directivity with a directivity index of 32.5 dB and axial maximum asymmetry of 0.4 dB. The sensor presents promising applications for detecting very-low-frequency underwater acoustic signals.
Low-Arsenic Accumulating Cabbage Possesses Higher Root Activities against Oxidative Stress of Arsenic
Authors: Li Hanhao , Li Yongtao , Li Xing , Chen Xun Wen , Chen Aoyu , Wu Li , Wong Ming Hung , Li Hui
Published: April 2023

Cabbage grown in contaminated soils can accumulate high levels of arsenic (As) in the edible parts, posing serious health risks. The efficiency of As uptake varies drastically among cabbage cultivars, but the underlying mechanisms are not clear. We screened out low (HY, Hangyun 49) and high As accumulating cultivars (GD, Guangdongyizhihua) to comparatively study whether the As accumulation is associated with variations in root physiological properties. Root biomass and length, reactive oxygen species (ROS), protein content, root activity, and ultrastructure of root cells of cabbage under different levels of As stress (0 (control), 1, 5, or 15 mg L−1) were measured As results, at low concentration (1 mg L−1), compared to GD, HY reduced As uptake and ROS content, and increased shoot biomass. At a high concentration (15 mg L−1), the thickened root cell wall and higher protein content in HY reduced arsenic damage to root cell structure and increased shoot biomass compared to GD. In conclusion, our results highlight that higher protein content, higher root activity, and thickened root cell walls result in lower As accumulation properties of HY compared to GD.
Yield and Nutraceutical Value of Lettuce and Basil Improved by a Microbial Inoculum in Greenhouse Experiments
Authors: Pagliarini Elia , Gaggìa Francesca , Quartieri Maurizio , Toselli Moreno , Di Gioia Diana
Published: April 2023

Members of Bacillus spp. have been widely used to enrich the soil/root interface to provide plant growth promoting activities. A new isolate, namely to Bacillus sp. VWC18, has been tested under greenhouse conditions in lettuce (Lactuca sativa L.) pots at different concentrations (103, 105, 107, and 109 CFU·mL−1) and application time (single inoculum at transplant and multiple inoculum every ten days) to evaluate the best application dose and frequency. Analysis of foliar yield, main nutrients, and minerals evidenced a significant response for all applications. The lowest (103 CFU·mL−1) and the highest doses (109 CFU·mL−1), applied every ten days until harvest, had the greatest efficacy; the nutrient yield (N, K, P, Na, Ca, Fe, Mg, Mn, Cu, and B) increased more than twice. A new randomized block design with three replicates was then performed in lettuce and basil (Ocinum basilicum L.), with the two best performing concentrations applied every ten days. In addition to previous analysis, root weight, chlorophyll, and carotenoids were also examined. Both experiments confirmed the previous results: inoculation of the substrate with Bacillus sp. VWC18 promoted plant growth, chlorophyll, and mineral uptake in both crop species. Root weight duplicated or triplicated compared to control plants, and chlorophyll concentration reached even higher values. Both parameters had a dose-dependent increase.
The Additive Manufacturing Approach to Polydimethylsiloxane (PDMS) Microfluidic Devices: Review and Future Directions
Authors: Tony Anthony , Badea Ildiko , Yang Chun , Liu Yuyi , Wells Garth , Wang Kemin , Yin Ruixue , Zhang Hongbo , Zhang Wenjun
Published: April 2023

This paper presents a comprehensive review of the literature for fabricating PDMS microfluidic devices by employing additive manufacturing (AM) processes. AM processes for PDMS microfluidic devices are first classified into (i) the direct printing approach and (ii) the indirect printing approach. The scope of the review covers both approaches, though the focus is on the printed mold approach, which is a kind of the so-called replica mold approach or soft lithography approach. This approach is, in essence, casting PDMS materials with the mold which is printed. The paper also includes our on-going effort on the printed mold approach. The main contribution of this paper is the identification of knowledge gaps and elaboration of future work toward closing the knowledge gaps in fabrication of PDMS microfluidic devices. The second contribution is the development of a novel classification of AM processes from design thinking. There is also a contribution in clarifying confusion in the literature regarding the soft lithography technique; this classification has provided a consistent ontology in the sub-field of the fabrication of microfluidic devices involving AM processes.
The Application of Hyperspectral Images in the Classification of Fresh Leaves’ Maturity for Flue-Curing Tobacco
Authors: Lu Xiaochong , Zhao Chen , Qin Yanqing , Xie Liangwen , Wang Tao , Wu Zhiyong , Xu Zicheng
Published: April 2023

The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, hyperspectral imaging combined with a novel algorithm was used to develop a classification model that could accurately determine the maturity of tobacco leaves. First, tobacco leaves of different maturity levels (unripe, under-ripe, ripe, and over-ripe) were collected. ENVI software was used to remove the hyperspectral imaging (HSI) background, and 11 groups of filtered images were obtained using Python 3.7. Finally, a full-band-based partial least-squares discriminant analysis (PLS-DA) classification model was established to identify the maturity of the tobacco leaves. In the calibration set, the model accuracy of the original spectrum was 88.57%, and the accuracy of the de-trending, multiple scattering correction (MSC), and standard normalization variable (SNV) treatments was 91.89%, 95.27%, and 92.57%, respectively. In the prediction set, the model accuracy of the de-trending, MSC, and SNV treatments was 93.85%, 96.92%, and 93.85%, respectively. The experimental results indicate that a higher model accuracy was obtained with the filtered images than with the original spectrum. Because of the higher accuracy, de-trending, MSC, and SNV treatments were selected as the candidate characteristic spectral bands, and a successive projection algorithm (SPA), competitive adaptive reweighted sampling (CASR), and particle swarm optimization (PSO) were used as the screening methods. Finally, a genetic algorithm (GA), PLS-DA, line support vector machine (LSVM), and back-propagation neural network (BPNN) classification and discrimination models were established. The combination SNV-SPA-PLS-DA model provided the best accuracy in the calibration and prediction sets (99.32% and 98.46%, respectively). Our findings highlight the efficacy of using visible/near-infrared (ViS/NIR) hyperspectral imaging for detecting the maturity of tobacco leaves, providing a theoretical basis for improving tobacco production.
GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks
Authors: Lu Pan , Jing Chuanfang , Zhu Xiaorong
Published: April 2023

Wireless mesh networks (WMN) promise to be an effective way to solve the “last mile” access problem on the Internet of Things (IoT) and the key to next-generation wireless networks. The current routing algorithms of WMN are difficult to adapt to complex environments and guarantee the reliable transmission of services. Therefore, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and a graph neural network, namely GraphSAGE. The algorithm consists of two parts: (1) A multi-path routing algorithm based on the improved breadth-first search. This algorithm can continuously iterate link information based on network topology and output all shortest paths. (2) A GraphSAGE-based performance optimization algorithm. This algorithm creates a method to generate network labels for supervised training of GraphSAGE. Then, the network labels and GraphSAGE are used to learn graph features to obtain the value of network performance for each shortest path. Finally, the path with the best network performance is selected for data transmission. Simulation results show that in the face of complex environments, the proposed algorithm can effectively alleviate network congestion, improve throughput, and reduce end-to-end delay and packet loss rate compared with the traditional shortest-path routing algorithm and the Equal-Cost Multi-Path routing (ECMP).
Predictive Control Strategy for Continuous Production Systems: A Comparative Study with Classical Control Approaches Using Simulation-Based Analysis
Authors: Chindrus Amelia , Copot Dana , Caruntu Constantin-Florin
Published: April 2023

Due to today’s technological development and information progress, an increasing number of physical systems have become interconnected and linked together through communication networks, thus resulting in Cyber-Physical Systems (CPSs). Continuous manufacturing, which involves the manufacture of products without interruption, has become increasingly important in many industries, including the pharmaceutical and chemical industries. CPSs can be used to control and monitor the production process, which is essential in enabling continuous manufacturing. This paper is focused on the modeling and control of physical systems required in tablet production using dry granulation. Tablets are a type of oral dosage form that is commonly used in the pharmaceutical industry. They are solid, compressed forms of medication that are formulated to release the active ingredients in a manner that allows for optimal absorption and efficacy. Thus, a model predictive control (MPC) strategy is applied to a plant model to test the designed controller and to analyze the obtained performances. The simulation results are compared with those obtained using other control algorithms, linear quadratic regulator (LQR) and proportional-integral-derivative (PID), applied to the same plant model. The results showed that the predictive control strategy performed significantly better than the other two control strategies.
From the Imagination to the Reality: Historical Aspects of Rewriting Six Dynasties Buddhist Avadāna Stories
Authors: Li Wei
Published: April 2023

In at least two aspects, Buddhist Avadāna literature shares a strong affinity with Chinese literature. One type of stories can be seen as parallel tales that bear striking resemblances to Chinese tales, while the other type has been assimilated by Chinese writers and transformed into Chinese tales. Regarding the first kind, there are many parallels between Buddhist and Chinese stories throughout the Six Dynasties (222–589), and it was only later that these stories were somehow compiled into collections that brought these parallels to light. As an example of the second type, in linggui zhi 靈鬼志 (The Record of Magical Ghosts) of the Jin Dynasty (265–402), the story of waiguo daoren 外國道人 (“the Foreign Master”) adapts the magical plot in which a man throws up a jug from the story of fanzhi tuhu 梵志吐壺 (“a Brahmin Spits a jug”) in the Buddhist text, yet it changes certain objects of the story to items with Chinese characteristics and develops new meaning. In Xu qixiezhi 續齊諧志 (Further Records of Qixie [Supernatural tales]), the famous e’long shusheng 鵝籠書生 (“the Goose Cage Scholar”, also known as the yangxian shusheng 陽羨書生” (the Scholar from Yangxian)”), takes the same story to another level. The structure of the story is changed, and a number of literati aesthetic interests are added, improving the literary color, smoothing down the language, and making substitutions in the text’s specifics, thus, bolstering the sense of realism and history. Meanwhile, in Liu Yiqing’s 劉義慶 (403–444) Xuanyan ji 宣驗記 (Records Manifest Records of Manifest Miracles), the Avadāna tale yingwu jiuhuo 鸚鵡救火 (“the Parrot Putting Out the Fire”) that he collected is not only associated with Buddhism but can also be seen as a commentary on the turbulent times and a hint of literati optimism if we view it in the context of Liu Yiqing’s Youminglu 幽明錄 (Record of the Hidden and Visible Worlds). The literary elites of the Six Dynasties drew inspiration from Buddhist Avadāna sources and imaginatively mixed them with historical circumstances to create Chinese fiction with new intentions. The rich resources of Avadāna literature from India and the fable tradition in Chinese literature create cultural conditions for these two sources to combine and mutually develop, forming a world of literature with colorful and meaningful stories.
Biblical Perspectives as a Guide to Research on Life’s Origin and History
Authors: Ross Hugh Norman
Published: April 2023

The more than thirty spacetime theorems developed over the past five decades establish that the universe and its spacetime dimensions have emerged from a cause/causal agent beyond the cosmos. Thus, to infer that this cause/causal agent may have intervened in the origin and history of Earth and Earth’s life resides well within the bounds of reason. Meanwhile, proponents of each of the three prevailing naturalistic models (abiogenesis, panspermia, and directed panspermia) for the origin and history of Earth’s life have marshaled arguments and evidence that effectively undermine and refute the other two models. A biblical perspective and approach to Earth’s life can help resolve this impasse. While a superficial and pervasive appeal to divine intervention thwarts scientific advance, so does a rigid adherence to naturalism. A productive way forward is to identify which models (or parts of models), whether naturalistic, theistic, or a combination, most effectively narrow, rather than widen, knowledge gaps, minimize anomalies, offer the most comprehensive and detailed explanation of the data, and prove most successful in predicting scientific discoveries.
Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern
Authors: Chen Jiazheng , Wang Li , Ma Lin , Fan Xinyan
Published: April 2023

The spatial scaling of patterns and processes is a hot topic of research in landscape ecology, and different scales may yield completely inconsistent results. Therefore, to understand the impact of the scale effect on urban heat island effect, this study analyzes the correlation between surface temperature and landscape index at different spatial scales over Nanjing. The scale effect is calculated thorough curve fitting of the Pearson’s correlation coefficient between ten landscape indices and land surface temperature at different window sizes, and the optimal one is determined. We have found that landscape indices can be divided into exponential and Gaussian landscape indices whose correlation with land surface temperature at different windows conforms to binomial exponential or multi-Gaussian functions, respectively. The optimal window size is approximately 4000–5100 m for exponential landscape indices, 1000–2000 m for aggregation index (AI) and percentage of like adjacencies (PLADJ), 6330 m for contagion (CONTAG) and 4380 m for total edge contrast index (TECI). Moreover, CONTAG and TECI have a high correlation coefficient plateau where the Pearson correlation coefficient is high and changes by less than 0.03 as the window size changes by more than 3000 m, which makes it possible to decrease the window size in order to save the calculation time without an obvious decrease in the Pearson correlation coefficient. To achieve this, we proposed a suitable window selection function so that the window size becomes 4260 m and 2070 m, respectively. The window sizes obtained in this study are just suitable in Nanjing, but the window sizes in other cities can also be obtained by the method in this study. This study provides a reference for future research on the relationship between landscape pattern and land surface temperature and its driving mechanisms, as well as for the impact of urban land use planning on the heat island effect.
Design and Validity of a Smart Healthcare and Control System for Electric Bikes
Authors: Avina-Bravo Eli Gabriel , Sodre Ferreira de Sousa Felipe Augusto , Escriba Christophe , Acco Pascal , Giraud Franck , Fourniols Jean-Yves , Soto-Romero Georges
Published: April 2023

This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output, training time). The developed system aims to monitor the health state of the rider, analyze data in real-time, and provide electric assistance, thus diminishing muscular exertion. Furthermore, such a system can recover the same physiological data used in medical centers and program it into the e-bike to track the patient’s health. System validation is conducted by replicating a standard medical protocol used in physiotherapy centers and hospitals, typically conducted in indoor conditions. However, the presented work differentiates itself by implementing this protocol in outdoor environments, which is impossible with the equipment used in medical centers. The experimental results show that the developed electronic prototypes and the algorithm effectively monitored the subject’s physiological condition. Moreover, when necessary, the system can change the training load and help the subject remain in their prescribed cardiac zone. This system allows whoever needs to follow a rehabilitation program to do so not only in their physician’s office, but whenever they want, including while commuting.
Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms
Authors: Alsaade Fawaz Waselallah , Al-Adhaileh Mosleh Hmoud
Published: April 2023

Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber–physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently. The purpose of this work is to explore the use of machine learning and deep learning methods in defence against cyber threats to autonomous cars. Our primary emphasis is on identifying erroneous information implanted in the data buses of various automobiles. In order to categorise this type of erroneous data, the gradient boosting method is used, providing a productive illustration of machine learning. To examine the performance of the proposed model, two real datasets, namely the Car-Hacking and UNSE-NB15 datasets, were used. Real automated vehicle network datasets were used in the verification process of the proposed security solution. These datasets included spoofing, flooding and replay attacks, as well as benign packets. The categorical data were transformed into numerical form via pre-processing. Machine learning and deep learning algorithms, namely k-nearest neighbour (KNN) and decision trees, long short-term memory (LSTM), and deep autoencoders, were employed to detect CAN attacks. According to the findings of the experiments, using the decision tree and KNN algorithms as machine learning approaches resulted in accuracy levels of 98.80% and 99%, respectively. On the other hand, the use of LSTM and deep autoencoder algorithms as deep learning approaches resulted in accuracy levels of 96% and 99.98%, respectively. The maximum accuracy was achieved when using the decision tree and deep autoencoder algorithms. Statistical analysis methods were used to analyse the results of the classification algorithms, and the determination coefficient measurement for the deep autoencoder was found to reach a value of R2 = 95%. The performance of all of the models that were built in this way surpassed that of those already in use, with almost perfect levels of accuracy being achieved. The system developed is able to overcome security issues in IVNs.
Winter Water Quality Modeling in Xiong’an New Area Supported by Hyperspectral Observation
Authors: Yang Yuechao , Zhang Donghui , Li Xusheng , Wang Daming , Yang Chunhua , Wang Jianhua
Published: April 2023

Xiong’an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters’ content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment.
Research on Synchronous Control of Active Disturbance Rejection Position of Multiple Hydraulic Cylinders of Digging-Anchor-Support Robot
Authors: Ma Tianbing , Guo Xiangxiang , Su Guoyong , Deng Haishun , Yang Ting
Published: April 2023

In order to solve the problems of nonlinearity, uncertainty and coupling of multi-hydraulic cylinder group platform of a digging-anchor-support robot, as well as the lack of synchronization control accuracy of hydraulic synchronous motors, an improved Automatic Disturbance Rejection Controller-Improved Particle Swarm Optimization (ADRC-IPSO) position synchronization control method is proposed. The mathematical model of a multi-hydraulic cylinder group platform of a digging-anchor-support robot is established, the compression factor is used to replace the inertia weight, and the traditional Particle Swarm Optimization (PSO) algorithm is improved by using the genetic algorithm theory to improve the optimization range and convergence rate of the algorithm, and the parameters of the Active Disturbance Rejection Controller (ADRC) were adjusted online. The simulation results verify the effectiveness of the improved ADRC-IPSO control method. The experimental results show that, compared with the traditional ADRC, ADRC-PSO and PID controller, the improved ADRC-IPSO has better position tracking performance and shorter adjusting time, and its step signal synchronization error is controlled within 5.0 mm, and the adjusting time is less than 2.55 s, indicating that the designed controller has better synchronization control effect.