bluuebunny/crossref_metadata_2025 huggingface dataset:
Nitrogen fertilization rates in a subtropical peach orchard: Effect on fruit nutritional value and flavor
Authors: Ames Zilfina Rubio , Brecht Jeffrey K. , Olmstead Mercy A. , Tieman Denise , Sims Charles A.
Published: February 2024

Background

The necessity to help farmers improve yields has resulted in many years of agricultural research focused on productivity and disease resistance, neglecting other areas of fruit quality such as flavor, health benefits, and external appearance. Nitrogen is required for several biochemical processes. However, reducing N fertilization can increase the synthesis of antioxidants and volatile aroma compounds. Four‐N rates (0 (N0), 45 (N1), 90 (N2), 179 (N3), and 269 (N4) kg ha−1) were tested each year from 2011 to 2017 in two peach varieties melting flesh (MF) ‘TropicBeauty’ (TB), a soft texture peach, and non‐melting flesh (NMF) ‘UFSharp’ (UFS), a crispy texture peach, to determine the effect of N on nutritional value and flavor.

Results

The phytochemical composition of the NMF ‘UFSharp’ (UFS) and MF variety ‘TropicBeauty’ (TB) were not cleared affected by N rates. Volatile synthesis was little affected by N. The sensory evaluation showed that consumers preferred MF peaches compared with NMF, because of its juiciness.

Conclusions

Nitrogen fertilization had minor effects on peach fruit phytochemical composition, volatile aroma compounds, and consumer acceptability. The N effect could had been influence by pruning practices, training of the orchard, and the delay of fruit developmental period.
Partial replacement of saturated fats in liver pâté by an olive oil‐in‐water emulsion containing β‐glucan shows no compromise in sensory and storage oxidation of lipids and protein
Authors: Zając Marzena , Kasprzak Mirosław M , Tkaczewska Joanna , Berski Wiktor , Stępień Anna , Okpala Charles Odilichukwu R , Domagała Jacek
Published: February 2024

BACKGROUND

The consumption of olive oil has been shown to have a positive effect on preventing obesity and hypertension. At the same time, it is recommended to avoid processed meat products as they contain saturated fats. The inclusion of highly unsaturated lipids in food products can lead to rapid oxidation and deterioration of sensory characteristics. The objective of the current work was to encapsulate olive oil and incorporate it into traditional Polish liver pâté. The oil‐in‐water emulsions were formulated with varying levels of oat β‐glucan and were evaluated for droplet size, pH, encapsulation efficiency and rheology. The liver pâtés made using the emulsions with and without β‐glucan were then evaluated for pH, texture, colour, lipid and protein oxidation, thermal stability and sensory properties.

RESULTS

The results showed that the oil‐in‐water emulsions had a 100% encapsulation rate of olive oil after 30 days of storage at 4 °C, regardless of the presence of β‐glucan. Although the texture of the emulsion‐enriched liver pâté was different from that of the control, this difference was reduced when β‐glucan was added to the emulsion and then to the pâté matrix.

CONCLUSION

Replacing 50% of animal fat with an olive oil emulsion enriched with β‐glucan did not result in any compromise of sensory properties, increase lipid or protein oxidation. These results suggest that it is possible to replace saturated lipids with omega‐3‐rich olive oil. © 2024 Society of Chemical Industry.
Effect of Dietary Protein Intake from Different Sources on Maternal and Umbilical Cord Plasma Amino Acid Levels
Authors: Zhao Rui , Wang Xinzheng , Liu Hongjuan , Wang Shanshan , Zhou Leilei , Cui Ningning , Guo Shu , Xiong Guoping , Yang Xuefeng , Xiong Ting , Hao Liping
Published: February 2024

Scope

To assess the associations of dietary protein intake from different sources during pregnancy with maternal and umbilical cord plasma amino acid levels.

Methods and results

The study includes 216 pregnant women and 39 newborns from the Tongji Birth Cohort in Wuhan, China. The study examines the levels of 21 amino acids in maternal and cord plasma samples using ultra‐performance liquid chromatography with tandem mass spectrometry. A significant positive relationship is observed between dietary protein intake from refined grains and maternal plasma cysteine levels. Dietary protein intake from dairy products is positively associated with maternal plasma levels of sulfur amino acid (mainly cystine), but negatively associated with maternal plasma levels of glutamic acid. In addition, the study observes that pre‐pregnancy body mass index and parity may be potential determinants of maternal plasma amino acid levels, whereas a history of passive smoking during pregnancy is an important factor influencing cord plasma amino acid levels.

Conclusions

These findings suggest that dietary protein intakes from specific sources during pregnancy may affect maternal plasma levels of amino acids.
Folate Deficiency and/or the Genetic Variant Mthfr677C >T Can Drive Hepatic Fibrosis or Steatosis in Mice, in a Sex‐Specific Manner
Authors: Leclerc Daniel , Christensen Karen E. , Reagan Alaina M. , Keser Vafa , Luan Yan , Malysheva Olga V. , Wasek Brandi , Bottiglieri Teodoro , Caudill Marie A. , Howell Gareth R. , Rozen Rima
Published: February 2024

Scope

Disturbances in one‐carbon metabolism contribute to nonalcoholic fatty liver disease (NAFLD) which encompasses steatosis, steatohepatitis, fibrosis, and cirrhosis. The goal is to examine impact of folate deficiency and the Mthfr677C >T variant on NAFLD.

Methods and results

This study uses the new Mthfr677C >T mouse model for the human MTHFR677C >T variant. Mthfr677CC and Mthfr677TT mice were fed control diet (CD) or folate‐deficient (FD) diets for 4 months. FD and Mthfr677TT alter choline/methyl metabolites in liver and/or plasma (decreased S‐adenosylmethionine (SAM):S‐adenosylhomocysteine (SAH) ratio, methyltetrahydrofolate, and betaine; increased homocysteine [Hcy]). FD, with contribution from Mthfr677TT, provokes fibrosis in males. Studies of normal livers reveal alterations in plasma markers and gene expression that suggest an underlying predisposition to fibrosis induced by FD and/or Mthfr677TT in males. These changes are absent or reverse in females, consistent with the sex disparity of fibrosis. Sex‐based differences in methylation potential, betaine, sphingomyelin, and trimethylamine‐N‐oxide (TMAO) levels may prevent fibrogenesis in females. In contrast, Mthfr677TT alters choline metabolism, dysregulates expression of lipid metabolism genes, and promotes steatosis in females.

Conclusion

This study suggests that folate deficiency predisposes males to fibrosis, which is exacerbated by Mthfr677TT, whereas Mthfr677TT predisposes females to steatosis, and reveal novel contributory mechanisms for these NAFLD‐related disorders.
Appropriate dosage, timing, and site of intramuscular injections of brain‐derived neurotrophic factor (BDNF) promote motor recovery after facial nerve injury in rats
Authors: Rink‐Notzon Svenja , Reuscher Jannika , Wollny Laura , Sarikcioglu Levent , Bilmen Süreyya , Manthou Marilena , Gordon Tessa , Angelov Doychin N.
Published: February 2024

Introduction/Aims

Daily intramuscular injections of fibroblast growth factor 2 (FGF2) but not of brain‐derived neurotrophic factor (BDNF) significantly improve whisking behavior and mono‐innervation of the rat levator labii superioris (LLS) muscle 56 days after buccal nerve transection and suture (buccal–buccal anastomosis, BBA). We explored the dose–response of BDNF, FGF2, and insulin growth factor 2 (IGF2) on the same parameters, asking whether higher doses of BDNF would promote recovery.

Methods

After BBA, growth factors were injected (30 μL volume) daily into the LLS muscle over 14, 28, or 56 days. At 56 days, video‐based motion analysis of vibrissal whisking was performed and the extent of mono‐ and poly‐reinnervation of the reinnervated neuromuscular junctions (NMJs) of the muscle determined with immunostaining of the nerve with β‐tubulin and histochemical staining of the endplates with Alexa Fluor 488‐conjugated α‐bungarotoxin.

Results

The dose–response curve demonstrated significantly higher whisking amplitudes and corresponding increased mono‐innervation of the NMJ in the reinnervated LLS muscle at concentrations of 20–30 μg/mL BDNF administered daily for 14–28 days after BBA surgery. In contrast, high doses of IGF2 and FGF2, or doses of 20 and 40 μg/mL of BDNF administered for 14–56 days had no effect on either whisking behavior or in reducing poly‐reinnervation of endplates in the muscle.

Discussion

These data suggest that the re‐establishment of mono‐innervation of whiskerpad muscles and the improved motor function by injections of BDNF into the paralyzed vibrissal musculature after facial nerve injury have translation potential and promote clinical application.
An AI‐generated proteome‐scale dataset of predicted protein structures for the ctenophore Mnemiopsis leidyi
Authors: Moreland R. Travis , Zhang Suiyuan , Barreira Sofia N. , Ryan Joseph F. , Baxevanis Andreas D.
Published: February 2024

This Dataset Brief describes the computational prediction of protein structures for the ctenophore Mnemiopsis leidyi. Here, we report the proteome‐scale generation of 15,333 protein structure predictions using AlphaFold, as well as an updated implementation of publicly available search, manipulation, and visualization tools for these protein structure predictions through the Mnemiopsis Genome Project Portal (https://research.nhgri.nih.gov/mnemiopsis). The utility of these predictions is demonstrated by highlighting comparisons to experimentally determined structures for the light‐sensitive protein mnemiopsin 1 and the ionotropic glutamate receptor (iGluR). The application of these novel protein structure prediction methods will serve to further position non‐bilaterian species such as Mnemiopsis as powerful model systems for the study of early animal evolution and human health.
Landrace diversity and heritage of the indigenous millet crop fonio (Digitaria exilis): Socio‐cultural and climatic drivers of change in the Fouta Djallon region of Guinea
Authors: Burton George , Gori Benedetta , Camara Saidou , Ceci Paolo , Conde Nagnouma , Couch Charlotte , Magassouba Sekou , Vorontsova Maria S. , Ulian Tiziana , Ryan Philippa
Published: February 2024

Societal Impact Statement

White fonio (Digitaria exilis [Kippist] Stapf) is an understudied millet crop, indigenous to West Africa and cultivated in the region largely through traditional practices. This species is climate‐resilient, fast‐growing, nutritionally rich, and provides livelihoods and food security to rural communities. Through collaboration with smallholder farmers in the Fouta Djallon region, Guinea, this study investigates how the diversity and selection of fonio landraces has changed in living memory. This research provides insight into how climatic and socio‐cultural changes affect the cultivation of fonio varieties and other indigenous crops, and why they should be conserved and further involved in rural development programmes.

The millet crop white fonio (D. exilis) isa staple crop feeding thousands of people across West Africa. The Fouta Djallon highlands region of Guinea is a hotspot for its cultivation, with known high genetic diversity.

Our study utilises data from ethnobotanic interviews and plant specimen and seed collections, working with farmers from 15 communities in this region with the aim to investigate the diversity of landraces and popularity of fonio within the current agricultural systems, in the present, and changes over the past 50 years.

A total of 24 named varieties of fonio were recorded and described, along with other commonly cultivated food crops. This includes two lost varieties now no longer cultivated. We also describe the methods for cultivation, grain processing and food preparation.

The priority and popularity of cultivated fonio landraces, and other crops, has changed, due to the consequences of social and environmental change in living memory. The diversity of fonio landraces are maintained as an adaptation to historical climatic changes, and there is an increasing preference towards varieties with a longer growing period and more reliable high yield to the detriment of early season varieties.
Dual Substrate Effect of Silicon Substrate on Thermal Transport Characteristic of (14,14,14)‐Graphyne: Transformation from Conventional Suppressing Role to Abnormal Promoting Role
Authors: Gao Yufei , Zhang Zheyi , Zhang Xiaoliang , Zhou Yanguang , Tang Dawei
Published: February 2024

Herein, (14,14,14)‐graphyne (GY) supported by silicon substrate is chosen to be the research object. The results demonstrate that the increasing distance between substrate and supported materials (dsub‐sup) results in the enhancement of thermal conductivity (TC) of supported GY, and the TC of supported GY is even higher than that of free‐standing GY when dsub‐sup exceeds a certain value, which means substrate plays an abnormal promoting role in the thermal transport in supported materials (SM). This phenomenon breaks the traditional cognition that the increasing dsub‐sup can only lead to the TC of SM approaching that of free‐standing model. The related mechanism can be seen as the combined impact of weak interaction of long‐dsub‐sup substrate and tensile effect led by lattice mismatch between substrate and GY. Combining with phonon analysis, it can be observed that the influence of substrate shows a closer relationship with phonon scattering, i.e., the anharmonicity, especially the anharmonicity of out‐of‐plane direction. The anomalous promoting effect of long‐dsub‐sup can be also attributed to the weaker scattering of out‐of‐plane phonon, especially the reduced four‐order phonon scattering. This research provides a new idea to suppress the negative effect of substrate on heat dissipation in electronic devices.
Clinical evaluation of code‐based algorithms to identify patients with pulmonary arterial hypertension in healthcare databases
Authors: Didden Eva‐Maria , Lu Di , Hsi Andrew , Brand Monika , Hedlin Haley , Zamanian Roham T.
Published: February 2024

Pulmonary arterial hypertension (PAH) is a rare subgroup of pulmonary hypertension (PH). Claims and administrative databases can be particularly important for research in rare diseases; however, there is a lack of validated algorithms to identify PAH patients using administrative codes. We aimed to measure the accuracy of code‐based PAH algorithms against the true clinical diagnosis by right heart catheterization (RHC). This study evaluated algorithms in patients who were recorded in two linkable data assets: the Stanford Healthcare administrative electronic health record database and the Stanford Vera Moulton Wall Center clinical PH database (which records each patient's RHC diagnosis). We assessed the sensitivity and specificity achieved by 16 algorithms (six published). In total, 720 PH patients with linked data available were included and 558 (78%) of these were PAH patients. Algorithms consisting solely of a P(A)H‐specific diagnostic code classed all or almost all PH patients as PAH (sensitivity >97%, specificity <12%) while multicomponent algorithms with well‐defined temporal sequences of procedure, diagnosis and treatment codes achieved a better balance of sensitivity and specificity. Specificity increased and sensitivity decreased with increasing algorithm complexity. The best‐performing algorithms, in terms of fewest misclassified patients, included multiple components (e.g., PH diagnosis, PAH treatment, continuous enrollment for ≥6 months before and ≥12 months following index date) and achieved sensitivities and specificities of around 95% and 38%, respectively. Our findings help researchers tailor their choice and design of code‐based PAH algorithms to their research question and demonstrate the importance of including well‐defined temporal components in the algorithms.
Synthesis and Characterization of Anti‐fouling Ultrafiltration Nanocomposite Membranes Integrated with S‐β Zeolite Nanoparticles for Oily Wastewater Treatment
Authors: Mousavi Fakher , Narimani Mostafa , Emadzadeh Daryoush
Published: February 2024

Membrane separation has been proven to be highly effective in oily wastewater treatment, although fouling remains a persistent challenge. This study explored the incorporation of S‐β zeolite nanoparticles into an ultrafiltration membrane, forming a nanocomposite to tackle fouling and enhance water flux recovery. Porous nanoparticles were synthesized using sol‐gel and hydrothermal methods, featuring a remarkable surface area of 450 m2/g and pore sizes ranging from 50 to 175 nm according to BET results. These nanoparticles were integrated into the membranes at various concentrations using the phase inversion technique. The presence of S‐zeolite in the membrane was confirmed through FTIR, EDX, and SEM analyses. The S‐β1 nanocomposite membrane demonstrated exceptional antifouling properties, particularly at higher concentrations of oily wastewater. Its outstanding performance is attributed to its enhanced hydrophilicity and average pore size of 13±3 nm, which prevents fouling formation. Compared to other membranes, S‐β1 exhibited remarkable water flux recovery, reaching 100 % at an oil concentration of 50 ppm and 72 % at 1000 ppm. These results highlight the significant advantages of this nanocomposite in reducing fouling and improving the water flux recovery in oily wastewater treatment.
Amperometric Method for Detecting Paracetamol using a Carbon Paste Electrode Modified with Vanadium(V) Oxide
Authors: Nagles Edgar , Cardenas‐Riojas Andy A. , Anaya‐Roa Fernando , Roldán‐Tello Luz , Hurtado John J.
Published: February 2024

This new and simple amperometric method takes advantage of the electroactivity of V2O5 in the oxidation of paracetamol (PCM) on a carbon paste electrode (CPE). With the modified electrode (VOX/CPE) the anode peak current for PCM was more than 100 % than that of the unmodified electrode. The electrode surface was characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM). The amperograms were measured at +0.70 V (vs Ag/AgCl), and the calibration curve indicated a detection limit of 0.06 μmol/L and a sensitivity of 1.93 μA/μM cm2. The modified electrode was used in the detection of solid drugs such as PCM tablets and children's syrup with acceptable results.
Enantioselective Synthesis of 3‐Allylindoles from Indolin‐2‐imines and MBH Carbonates via Nucleophilic Catalysis
Authors: Chen Lunfeng , Li Pengfei
Published: February 2024

With chiral amine (DHQD)2PHAL as nucleophilic catalyst, asymmetric synthesis of enantioenriched 3‐allylindoles was achieved from indolone‐2‐imines and MBH carbonates. Compared with our previous chiral phosphine catalysis, the chiral amine had exactly the opposite effect in terms of enantioselectivity. Importantly, we could obtain each enantiomer by adjusting the catalyst system.
Single Liquid Aerosol Microparticle Electrochemistry on a Suspended Ionic Liquid Film
Authors: Krushinski Lynn E. , Vannoy Kathryn J. , Dick Jeffrey E.
Published: February 2024

Liquid aerosols are ubiquitous in nature, and several tools exist to quantify their physicochemical properties. As a measurement science technique, electrochemistry has not played a large role in aerosol analysis because electrochemistry in air is rather difficult. Here, a remarkably simple method is demonstrated to capture and electroanalyze single liquid aerosol particles with radii on the order of single micrometers. An electrochemical cell is constructed by a microwire (cylindrical working electrode) traversing a film of ionic liquid (1‐butyl‐1‐methylpyrrolidinium bis(trifluoromethylsulfonyl)imide) that is suspended within a wire loop (reference/counter electrode). An ionic liquid is chosen because the low vapor pressure preserves the film over weeks, vastly improving suspended film electroanalysis. The resultant high surface area allows the suspended ionic liquid cell to act as an aerosol net. Given the hydrophobic nature of the ionic liquid, aqueous aerosol particles do not coalesce into the film. When the liquid aerosols collide with the sufficiently biased microwire (creating a complex boundary: aerosol|wire|ionic liquid|air), the electrochemistry within a single liquid aerosol particle can be interrogated in real‐time. The ability to achieve liquid aerosol size distributions for aerosols over 1 µm in radius is demonstrated.
Polyvinylidene Fluoride Based Piezoelectric Composites with Strong Interfacial Adhesion via Click Chemistry for Self‐Powered Flexible Sensors
Authors: Tu Youlei , Yang Yuliang , Zheng Yu , Guo Shaoyun , Shen Jiabin
Published: February 2024

Achieving relatively uniform dispersion in organic–inorganic composites with overwhelming differences in surface energy is a perennial challenge. Herein, novel eliminated polyvinylidene fluoride (EPVDF)/EPVDF functionalized barium titanate nanoparticles (EPVDF@BT) flexible piezoelectric nanogenerators (PENGs) with strong interfacial adhesion are developed via thermal stretching following sequential click chemistry. Thanks to the strong interfacial adhesion, the optimal PENGs containing ultra‐high β‐phase content (97.2%) exhibit not only optimized mechanical and dielectric behaviors but also excellent piezoelectric properties with high piezoelectric output (V = 10.7 V, I = 216 nA), reliable durability (8000 cycles), ultrafast response time (20 ms), and good sensitivity (2.09 nA kPa−1), far outperforming most reported PVDF‐based composites. Furthermore, COMSOL finite element simulations (FEM) confirm that the elevated stress transfer efficiency induced by the strong interfacial adhesion is the main driving force for enhanced piezoelectric performances. For practical applications, self‐powered PENGs can simply but stably capture mechanical energy, drive tiny electronic devices, and serve as potential multifunctional and durable sensors for detecting human physiological motions. This work opens a pioneering avenue to break the trade‐offs between piezoelectric and other properties, which is of great importance for developing self‐powered flexible sensors.
Wide‐Temperature and High‐Rate Operation of Lithium Metal Batteries Enabled by an Ionic Liquid Functionalized Quasi‐Solid‐State Electrolyte
Authors: Deng Yonghui , Zhao Shunshun , Chen Yong , Wan Shuang , Chen Shimou
Published: February 2024

The development of high‐energy‐density solid‐state lithium metal battery has been hindered by the unstable cycling of Ni‐rich cathodes at high rate and limited wide‐temperatures adoptability. In this study, an ionic liquid functionalized quasi‐solid‐state electrolyte (FQSE) is prepared to address these challenges. The FQSE features a semi‐immobilized ionic liquid capable of anchoring solvent molecules through electrostatic interactions, which facilitates Li+ desolvation and reduces deleterious solvent‐cathode reactions. The FQSE exhibits impressive electrochemical characteristics, including high ionic conductivity (1.9 mS cm−1 at 30 °C and 0.2 mS cm−1 at ‐30 °C) and a Li+ transfer number of 0.7. Consequently, Li/NCM811 cells incorporating FQSE demonstrate exceptional stability during high‐rate cycling, enduring 700 cycles at 1 C. Notably, the Li/LFP cells with FQSE maintain high capacity across a wide temperature range, from ‐30 to 60 °C. This research provides a new way to promote the practical application of high‐energy lithium metal batteries.
Accelerating Charge Kinetics in Photocatalytic CO2 Reduction by Modulating the Cobalt Coordination in Heterostructures of Cadmium Sulfide/Metal–Organic Layer
Authors: Su Yanhui , Mu Qiaoqiao , Fan Ningbo , Wei Zhihe , Pan Weiyi , Zheng Zhangyi , Song Daqi , Sun Hao , Lian Yuebin , Xu Bin , Yang Wenjun , Deng Zhao , Peng Yang
Published: February 2024

Artificial photocatalytic CO2 reduction (CO2R) holds great promise to directly store solar energy into chemical bonds. The slow charge and mass transfer kinetics at the triphasic solid–liquid–gas interface calls for the rational design of heterogeneous photocatalysts concertedly boosting interfacial charge transfer, local CO2 concentration, and exposure of active sites. To meet these requirements, in this study heterostructures of CdS/MOL (MOL = metal–organic layer) furnishing different redox Co sites are fabricated for CO2R photocatalysts. It is found that the coordination environment of Co is key to photocatalytic activity. The best catalyst ensemble comprising ligand‐chelated Co2+ with the bipyridine electron mediator demonstrates a high CO yield rate of 1523 µmol h−1 gcat−1, selectivity of 95.8% and TON of 1462.4, which are ranked among the best seen in literature. Comprehensive photochemical and electroanalytical characterizations attribute the high CO2R performance to the improved photocarrier separation and charge kinetics originated from the proper energy band alignment and coordination chemistry. This work highlights the construction of 2D heterostructures and modulation of transition metal coordination to expedite the charge kinetics in photocatalytic CO2 reduction.
Fibrous MXene Synapse‐Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory
Authors: Ren Shuhui , Wang Kaiyang , Jia Xiaotong , Wang Jiuyang , Xu Jikang , Yang Biao , Tian Ziwei , Xia Ruoxuan , Yu Ding , Jia Yunfang , Yan Xiaobing
Published: February 2024

Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception‐memory integration. Though many works about planar‐shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high‐density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as‐prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human‐Computer interaction, and has great potential to build lightweight and comfortable Brain‐Computer interfaces.
Entropy‐Enhanced Multi‐Doping Strategy to Promote the Electrochemical Performance of Na4Fe3(PO4)2P2O7
Authors: Li Guodong , Cao Yongjie , Chen Jiawei , Zhang Kai , Liu Yajing , Zhang Xiue , Wang Yonggang , Wang Fei , Xia Yongyao
Published: February 2024

Sodium‐ion batteries (SIBs) have been regarded as promising candidates for large‐scale energy storage system, and their electrochemical performance is determined by the cathode materials. Recently, the polyanion‐type cathode Na4Fe3(PO4)2P2O7 (NFPP) demonstrates decent performance, while there exists promotion space with respect to its cycle stability and rate capability. Herein, an entropy‐enhanced Na4Fe2.95(NiCoMnMgZn)0.01(PO4)2P2O7 (HE‐NFPP) cathode is proposed with improved rate performance (67.1 mAh g−1 at 50 C) and cycle performance (retention of 92.0% after 1000  cycles at 1 C). The enhancement of configuration entropy improves the structural stability of NFPP thermodynamically. In‐situ XRD illustrates the sodium storage mechanism of HE‐NFPP as an imperfect solid solution reaction driven by Fe2+/Fe3+ redox with a low volume change of 4.0% (90.9% of NFPP). Through doping, the structure distortion and abrupt rearrangement are inhibited. Additionally, HE‐NFPP and hard carbon (HC) are utilized to fabricate pouch cell that demonstrates an average working voltage of 3.0 V and a maximum energy density of 165 Wh kg−1 (based on the total mass of active materials). These results highlight the potential for enhancing the high‐rate and long‐cycle performance of NFPP as a promising cathode for SIBs through an entropy‐enhanced multi‐doping strategy.
Influence of Surface Segregation of Tin on the Phosphatability of Cold‐Rolled Steel Sheets
Authors: Park Joongchul , Kim Dong‐Hyun , Lee Seunghwan , Yoo Hyeonseok , Park Kyosun , Cho Jaedong
Published: February 2024

The increasing use of iron scrap to reduce greenhouse gas emissions results in the enrichment of steel with the tramp element Sn. However, the effect of Sn on phosphatase activity remains unclear. Therefore, this study aims to investigate the effect of the Sn content on the phosphatase activity of cold‐rolled steel plates. The phosphatability of the cold‐rolled steel sheets is evaluated by adding several tens of ppm of Sn. The surface characteristics of the materials are analyzed using X‐Ray photoelectron spectroscopy and transmission electron microscopy. Additionally, the reactivities of the materials in the phosphate treatment solution are assessed using an electrochemical evaluation method. Notably, a significant decrease in phosphatase activity is observed, which is attributed to the inhibition of the phosphate reaction by the Sn oxide layers formed on the steel sheet surface, with a layer thickness of a few nanometers. These Sn surface oxides can be removed by pickling with HCl, and the initial phosphate reaction occurs rapidly in areas where the oxides are removed.
Effect of beeswax and petroleum wax on the thermal degradation behavior, kinetics, and thermodynamics of low‐grade coal
Authors: Behera Pragyandeepti , Mahapatra Pabitra Mohan , Sabat Gayatri , Panda Achyut K.
Published: February 2024

The co‐pyrolysis behavior of low‐grade coal with two different feedstocks (beeswax and petroleum wax) at different compositions (w/w) [(75:25), (50:50), (25:75)] is investigated by using a thermogravimetric (TG) analyzer under the non‐isothermal condition at a constant heating rate of 20°C/min from an ambient temperature to 900°C under nitrogen atmosphere. The kinetic and thermodynamic analysis for the thermal decomposition of the sample parameters was carried out by applying the model fitting Coats–Redfern method to the thermogravimetry data of different feedstocks. The activation energy of thermal degradation of coal is found to be 55.8 kJ/mole. A decrease in activation energy is observed by the addition of 25% and 50% waxes in the blended sample and the blended sample with 25% wax and 75% coal has the lowest activation energy. The activation energy for coal‐beeswax blend (75:25) is 35.718 KJ/mol, but it is 34.237 KJ/mol for coal‐petroleum wax blend (75:25). The value of ∆H decreases by the addition of waxes and follows the order (Coal: Feedstock) {(75:25) < (50:50) < (25:75)}, while that of value of ∆S increases by the addition of waxes and follows the order {(75:25) > (50:50) > (25:75)}. However, the value of ΔG does not significantly shift as the blend ratio of the feedstocks is altered. The addition of both feedstocks with the coal sample showed some degree of synergy. The result illustrates that the co‐pyrolysis of coal with wax be energy economical than individual pyrolysis and this study would give precious scope to design a more efficient conversion system.
Rural NEETs: Pathways Through Formal and Non-formal Education
Authors: Flynn Paul , Paabort Heidi , Milenkova Valentina , Bojkovska Katerina , Rocca Antonella , Hačatrjana Liena , Lendzhova Vladislava , Nakova Albena , de Oliveira Rodrigues Marta
Published: February 2024

Formal and non-formal education often constitute the first line of engagement in supporting Rural young people Not in Employment, nor in Education, or Training (NEET). However, it is not always the case that such interventions are widely documented. This chapter aims to uncover best practice interventions for the educational inclusion of rural NEETs building upon an emergent body of work in order to frame the identification of five case studies across Estonia, Italy, Latvia, Albania, Portugal. EU and non-EU member states were included to offer a diverse set of examples. Based on Bronfenbrenner’s bioecological model and data triangulation, elements that prevent rural youth from entering or staying in NEET status were identified. Our work shows that each formal or nonformal education learning intervention or reform mobilizes different levels of the bioecological framework and has an important function in shaping NEETs or at-risk youth support systems. Educational interventions that directly target young people can improve the likelihood of a positive outcome when they are context specific. In addition, these interventions make it possible to see the potential of different educational methods in supporting rural young people, when that contextualization stems directly from the young person’s perspectives and his/her perceived needs.
Reinforcement Learning-Based Approaches in Manufacturing Environments
Authors: Fernández Martínez Andrea , González-Val Carlos , Martín Daniel Gordo , López Alberto Botana , Muros Jose Angel Segura , Llopis Afra Maria Petrusa , Masood Jawad , Muiños-Landin Santiago
Published: February 2024

The application of reinforcement learning often faces limitations due to the exploration phase, which can be costly and risky in various contexts. This is particularly evident in manufacturing industries, where the training phase of a reinforcement learning agent is constrained, resulting in suboptimal performance of developed strategies. To address this challenge, digital environments are typically created, allowing agents to freely explore the consequences of their actions in a controlled setting. Strategies developed in these digital environments can then be tested in real scenarios, and secondary training can be conducted using hybrid data that combines digital and real-world experiences.

In this chapter, we provide an introduction to reinforcement learning and showcase its application in two different manufacturing scenarios. Specifically, we focus on the woodworking and textile sectors, which are part of ongoing research activities within two distinct European Research Projects. We demonstrate how reinforcement learning is implemented in a digital context, with the ultimate goal of deploying these strategies in real systems.
Towards Industry 5.0 by Incorporation of Trustworthy and Human-Centric Approaches
Authors: Vyhmeister Eduardo , Castane Gabriel Gonzalez
Published: February 2024

The industrial sector has been a major adopter of new technologies for decades, driving economic and societal progress. The path by which industry embraces new techniques has a significant impact on the environment and society and thus must be guided by principles of sustainability and trustworthiness. In this chapter, we explore the current paradigm in which Industry 4.0 is evolving towards Industry 5.0, where artificial intelligence (AI) and other advance technologies are being used to build services from a sustainable, human-centric, and resilient perspective. We examine how AI can be applied in industry while respecting trustworthy principles and collect information to define how well these principles are adopted. Furthermore, it is presented a perspective on the industry’s approach towards adopting trustworthy AI (TAI), and we propose steps to foster its adoption in an appropriate manner. We also examine the challenges and risks associated with the adoption of AI in industry and propose strategies to mitigate them. This chapter intends to serve researchers, practitioners, and policymakers interested in the intersection of AI, industry, and sustainability. It provides an overview of the latest developments in this field and offers practical guidance for those seeking to promote the adoption of TAI.
Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0
Authors: Hoch Thomas , Martinez-Gil Jorge , Pichler Mario , Silvina Agastya , Heinzl Bernhard , Moser Bernhard , Eleftheriou Dimitris , Estrada-Lugo Hector Diego , Leva Maria Chiara
Published: February 2024

AI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.
Holistic Production Overview: Using XAI for Production Optimization
Authors: Perez-Castanos Sergi , Prieto-Roig Ausias , Monzo David , Colomer-Barbera Javier
Published: February 2024

This chapter introduces the work performed in XMANAI to address the need of explainability in manufacturing AI systems applied to optimize production lines. The XMANAI platform is designed to meet the needs of manufacturing factories, offering them a unified framework to leverage their data and extract valuable insights. Within the project, the Ford use case is focused on forecasting production in a dynamically changing manufacturing line, serving as a practical illustration of the platform capabilities. This chapter focuses on the application of explainability using Hybrid Models and Heterogeneous Graph Machine Learning (ML) techniques. Hybrid Models combine traditional AI models with eXplainable AI (XAI) tools and Heterogeneous Graph ML techniques using Graph Attention (GAT) layers to extract explainability in complex manufacturing scenarios where data that can be represented as a graph. To understand explainability applied to the Ford use case, this chapter describes the initial needs of the scenario, the infrastructure behind the use case and the results obtained, showcasing the effectiveness of this approach, where models are trained in the XMANAI platform. Specifically, a description is given on the results of production forecasting in an engine assembly plant while providing interpretable explanations when deviations from expected are predicted.
Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing
Authors: Lavasa Eleni , Chadoulos Christos , Siouras Athanasios , Etxabarri Llana Ainhoa , Rodríguez Del Rey Silvia , Dalamagas Theodore , Moustakidis Serafeim
Published: February 2024

The field of metrology, which focuses on the scientific study of measurement, is grappling with a significant challenge: predicting the measurement accuracy of sophisticated 3D scanning devices. These devices, though transformative for industries like manufacturing, construction, and archeology, often generate complex point cloud data that traditional machine learning models struggle to manage effectively. To address this problem, we proposed a PointNet-based model, designed inherently to navigate point cloud data complexities, thereby improving the accuracy of prediction for scanning devices’ measurement accuracy. Our model not only achieved superior performance in terms of mean absolute error (MAE) across all three axes (X, Y, Z) but also provided a visually intuitive means to understand errors through 3D deviation maps. These maps quantify and visualize the predicted and actual deviations, which enhance the model’s explainability as well. This level of explainability offers a transparent tool to stakeholders, assisting them in understanding the model’s decision-making process and ensuring its trustworthy deployment. Therefore, our proposed model offers significant value by elevating the level of precision, reliability, and explainability in any field that utilizes 3D scanning technology. It promises to mitigate costly measurement errors, enhance manufacturing precision, improve architectural designs, and preserve archeological artifacts with greater accuracy.
Human-AI Interaction for Semantic Knowledge Enrichment of AI Model Output
Authors: Chala Sisay Adugna , Graß Alexander
Published: February 2024

Modern manufacturing requires developing a framework of AI solutions that capture and process data from various sources including from human-AI collaboration. This chapter tries to describe the concept of domain knowledge fusion in human-AI collaboration for manufacturing. Human interaction with AI is enabled in such a way that the domain expert not only inspects the output of the AI model but also injects engineered knowledge in order to retrain AI models for iterative improvement. Domain knowledge fusion is a technique that involves combining knowledge from multiple domains or sources to produce a more complete solution by augmenting learned knowledge, i.e., the knowledge generated by the AI model with engineered knowledge, i.e., the knowledge provided by the domain expert. The concept developed in this chapter demonstrates how the domain expert interacts with AI systems to observe and decide the veracity of the learned knowledge with respect to the given context. It enables humans to collaborate with AI systems through intuitive interfaces that help domain experts in interpreting insights, validating the findings, and applying domain knowledge to gain a deeper understanding of the data.
Leveraging Semantic Representations via Knowledge Graph Embeddings
Authors: Krause Franz , Kurniawan Kabul , Kiesling Elmar , Martinez-Gil Jorge , Hoch Thomas , Pichler Mario , Heinzl Bernhard , Moser Bernhard
Published: February 2024

The representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.
Self-Reconfiguration for Smart Manufacturing Based on Artificial Intelligence: A Review and Case Study
Authors: Cruz Yarens J. , Castaño Fernando , Haber Rodolfo E. , Villalonga Alberto , Ejsmont Krzysztof , Gladysz Bartlomiej , Flores Álvaro , Alemany Patricio
Published: February 2024

Self-reconfiguration in manufacturing systems refers to the ability to autonomously execute changes in the production process to deal with variations in demand and production requirements while ensuring a high responsiveness level. Some advantages of these systems are their improved efficiency, flexibility, adaptability, and cost-effectiveness. Different approaches can be used for designing self-reconfigurable manufacturing systems, including computer simulation, data-driven methods, and artificial intelligence-based methods. To assess an artificial intelligence-based solution focused on self-reconfiguration of manufacturing enterprises, a pilot line was selected for implementing an automated machine learning method for finding and setting optimal parametrizations and a fuzzy system-inspired reconfigurator for improving the performance of the pilot line. Additionally, a deep learning segmentation model was integrated into the pilot line as part of a visual inspection module, enabling a more efficient management of the production line workflow. The results obtained demonstrate the potential of self-reconfigurable manufacturing systems to improve the efficiency and effectiveness of production processes.
Teaching to the Test: Undermining Academic Rebellion (2020s)
Authors: Gellman Mneesha
Published: February 2024

Advanced Placement US History (APUSH) courses offered in high schools, and the materials learned from them, have important implications for students. Since APUSH courses can be used as college credit, some young people might not even take a subsequent history course when in college—making the curricula in these classes all the more important. This chapter looks specifically at a 2020 textbook titled Fabric of a Nation used for APUSH courses. Out of all the textbooks looked at in this analysis, Fabric of a Nation is undoubtedly the most inclusive and most up to date. It prompts critical thinking and includes more accurate historical context. However, the book is not perfect. It continues to reinforce Native American passivity and doesn’t expand enough on issues of immigration when it comes to Mexican and im/migrant representations.
Introduction
Authors: Zhang Yan
Published: February 2024

This chapter first gives an overview of digital twin, including the development timeline and possible application areas. The features of digital twin are summarized in detail. Then, the concepts, fundamentals, and visions of digital twin are presented.
Digital Twin Models and Networks
Authors: Zhang Yan
Published: February 2024

A digital twin (DT) model reflects one or a group of physical objects that exist in a complex real system to a virtual space. By interconnecting and coordinating multiple independent models, a DT network (DTN) can be built to map the associations and interactions between physical objects. In this chapter, we present DT models in terms of modelling frameworks, modelling methods, and modelling challenges. Then we elaborate the concept of DTNs and compare it with the concept of DT. The communication mechanisms, application scenarios, and open research issues of DTNs are then discussed.
Edge Computing for Digital Twin
Authors: Zhang Yan
Published: February 2024

Mobile edge computing is a promising solution for analysing and processing a portion of data using the computing, storage, and network resources distributed on the paths between data sources and a cloud computing centre. Mobile edge computing thus provides high efficiency, low latency, and privacy protection to sustain digital twin. In this chapter, we first introduce a hierarchical architecture of digital twin edge networks that consists of a virtual plane and a user/physical plane. We then introduce the key communication and computation technologies in the digital twin edge networks and present two typical cooperative computation modes. Moreover, we present the role of artificial intelligence (AI) for digital twin edge networks, and discuss the unique edge association problem.