bluuebunny/crossref_metadata_2025 huggingface dataset:
Phyto-Fenton remediation of a dichloro-diphenyl-trichloroethane contaminated site in Ha Tinh Province, Vietnam
Authors: Dang Hieu Minh , Vo Cong Huu , Inagaki Yoshihiko , Dao Nhung Thi , Tran Trinh Dinh , Tran Thao Minh , Nguyen Thinh Thi , Ho Hang Thi Thuy , Tran Vien Duc , Sakakibara Yutaka
Published: September 2022

A field trial was conducted at a site in Cam Binh commune, Ha Tinh province, Vietnam, highly contaminated with organo-pesticides. The phyto-Fenton process was applied to remove pesticide residues in soils. In addition to magnetite (Fe3O4) materials added to the soils, fertilizers and elicitors for oxidative burst were also added in the different experimental treatments. Dichloro-diphenyl-trichloroethane (DDT) and isomers were removed in all experimental lots. The removal efficiency was highest in lot B1, a site where only iron materials were added. The removal efficiency and the final content of DDTs in B1 were 98.4% and 0.009 mg kg−1, respectively. In the presence of elicitors, the conversion of DDT to dichloro-diphenyl-dichloroethylene was more favorable. Analysis of soil properties indicated that the phyto-Fenton process can occur at neutral soil pH, and when there are only small changes in soil organic carbon content and cation exchange capacities. Shifts in the composition of the microbial communities were observed. Further studies on the interactions between materials added to soil, plants, and the soil microbiome are needed to understand the mechanism of action of the phyto-Fenton process during soil remediation.
Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks
Authors: Abdi Jafar , Mazloom Golshan
Published: September 2022

Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.
Phylogeny and evolution of the genus Cervus (Cervidae, Mammalia) as revealed by complete mitochondrial genomes
Authors: Mackiewicz Paweł , Matosiuk Maciej , Świsłocka Magdalena , Zachos Frank E. , Hajji Ghaiet M. , Saveljev Alexander P. , Seryodkin Ivan V. , Farahvash Tarlan , Rezaei Hamid Reza , Torshizi Rasoul Vaez , Mattioli Stefano , Ratkiewicz Mirosław
Published: September 2022

Mitochondrial DNA (mtDNA) lineages are recognized as important components of intra- and interspecific biodiversity, and allow to reveal colonization routes and phylogeographic structure of many taxa. Among these is the genusCervusthat is widely distributed across the Holarctic. We obtained sequences of complete mitochondrial genomes from 13Cervustaxa and included them in global phylogenetic analyses of 71 Cervinae mitogenomes. The well-resolved phylogenetic trees confirmedCervusto be monophyletic. Molecular dating based on several fossil calibration points revealed thatca. 2.6 Mya two main mitochondrial lineages ofCervusseparated in Central Asia, the Western (includingC. hangluandC. elaphus) and the Eastern (comprisingC. albirostris,C. canadensisandC. nippon). We also observed convergent changes in the composition of some mitochondrial genes inC. hangluof the Western lineage and representatives of the Eastern lineage. Several subspecies ofC. nipponandC. hangluhave accumulated a large portion of deleterious substitutions in their mitochondrial protein-coding genes, probably due to drift in the wake of decreasing population size. In contrast to previous studies, we found that the relic haplogroup B ofC. elaphuswas sister to all other red deer lineages and that the Middle-Eastern haplogroup E shared a common ancestor with the Balkan haplogroup C. Comparison of the mtDNA phylogenetic tree with a published nuclear genome tree may imply ancient introgressions of mtDNA between differentCervusspecies as well as from the common ancestor of South Asian deer,Rusa timorensisandR. unicolor, to theCervusclade.
Differential expression of aqueous humor microRNAs in central retinal vein occlusion and its association with matrix metalloproteinases: a pilot study
Authors: Hong Eun Hee , Hwang Mina , Yu Hyoseon , Park Hyun-Hee , Cho Heeyoon , Koh Seong-Ho , Shin Yong Un
Published: September 2022

The aim of this study is to investigate the differential expression of microRNAs (miRNAs) in the aqueous humor (AH) of patients with central retinal vein occlusion (CRVO), and their association with AH matrix metalloproteinase (MMP) activity. Eighteen subjects, including 10 treatment naïve patients with CRVO and 8 control subjects, scheduled for intravitreal injection and cataract surgery, respectively, were included. AH samples were collected at the beginning of the procedure. A microarray composed of 84 miRNAs was performed to identify differentially expressed miRNAs in CRVO AH, which were further analyzed using bioinformatic tools to identify directly related cytokines/proteins. Eight miRNAs (hsa-mir-16-5p, hsa-mir-142-3p, hsa-mir-19a-3p, hsa-mir-144-3p, hsa-mir-195-5p, hsa-mir-17-5p, hsa-mir-93-5p, and hsa-mir-20a-5p) were significantly downregulated in the CRVO group. Bioinformatic analysis revealed a direct relationship among downregulated miRNAs, CRVO, and the following proteins: MMP-2, MMP-9, tumor necrosis factor, transforming growth factor beta-1, caspase-3, interleukin-6, interferon gamma, and interleukin-1-beta. Activities of MMP-2 and -9 in AH were detected using gelatin zymography, showing significant increase in the CRVO group compared to the control group (p < 0.01). This pilot study first revealed that MMP-2 and -9 were directly related to downregulated miRNAs and showed significant increase in activity in AH of patients with CRVO. Therefore, the relevant miRNAs and MMPs in AH could serve as potential biomarkers or therapeutic targets for CRVO.
Comparing two machine learning approaches in predicting lupus hospitalization using longitudinal data
Authors: Zhao Yijun , Smith Dylan , Jorge April
Published: September 2022

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease characterized by flares ranging from mild to life-threatening. Severe flares and complications can require hospitalizations, which account for most of the direct costs of SLE care. This study investigates two machine learning approaches in predicting SLE hospitalizations using longitudinal data from 925 patients enrolled in a multicenter electronic health record (EHR)-based lupus cohort. Our first Differential approach accounts for the time dependencies in sequential data by introducing additional lagged variables between consecutive time steps. We next evaluate the performance of LSTM, a state-of-the-art deep learning model designed for time series. Our experimental results demonstrate that both methods can effectively predict lupus hospitalizations, but each has its strengths and limitations. Specifically, the Differential approach can be integrated into any non-temporal machine learning algorithms and is preferred for tasks with short observation periods. On the contrary, the LSTM model is desirable for studies utilizing long observation intervals attributing to its capability in capturing long-term dependencies embedded in the longitudinal data. Furthermore, the Differential approach has more options in handling class imbalance in the underlying data and delivers stable performance across different prognostic horizons. LSTM, on the other hand, demands more class-balanced training data and outperforms the Differential approach when there are sufficient positive samples facilitating model training. Capitalizing on our experimental results, we further study the optimal length of patient monitoring periods for different prediction horizons.
Impact of old environmental burden in the Spiš region (Slovakia) on soil and home-grown vegetable contamination, and health effects of heavy metals
Authors: Musilová Janette , Franková Hana , Lidiková Judita , Chlpík Juraj , Vollmannová Alena , Árvay Július , Harangozo Ľuboš , Urminská Jana , Tóth Tomáš
Published: September 2022

Due to several centuries of ongoing mining activities, Middle Spiš (Slovakia) is one of the areas with a damaged environment. The contents of Fe, Mn, Zn, Cu, Ni, Pb, Cd, and Hg were determined in the soils and home-grown vegetables (potatoes, carrots, tomatoes). Except for Pb, the contents of heavy metals in the soils of some plots were higher than the limit values. Based on the values of Contamination factor (Cf), Degree of contamination (Cdeg), Geo-accumulation index (Igeo), and Pollution load index (PLI), very high Fe, Cd, and Hg contamination (Cf ≥ 6), very high soil contamination (Cdeg ≥ 20), extremely heavy Fe and Hg contamination (Igeo > 5), resp. moderately pollution to non-pollution (1 < PLI ≤ 2) was found in all plots. In vegetable samples, the maximum levels were exceeded for Cu, Pb, Hg (potato), Pb (carrot, tomato), and Hg (carrot, plot E). Bioaccumulation factor values BAF > 1 were for Cu (carrots, potatoes). Estimated daily intake values for all heavy metals were lower than their tolerable daily intake. Chronic daily intake of heavy metals ranged 2.495E−06 (Hg)—0.1416 (Fe) mg/kg/day. Based on Hazard index values, potato consumption poses a risk (0.8068–1.3057). The results showed that the monitoring of soils and cultivated production is necessary for the investigated area.
Low-cost treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2 for water decolorization
Authors: Kheradmand Asiyeh , Negarestani Mehrdad , Mollahosseini Afsaneh , Shayesteh Hadi , Farimaniraad Hamidreza
Published: September 2022

Dye pollution has always been a serious concern globally, threatening the lives of humans and the ecosystem. In the current study, treated lignocellulosic biomass waste supported with FeCl3/Zn(NO3)2was utilized as an effective composite for removing Reactive Orange 16 (RO16). SEM/EDAX, FTIR, and XRD analyses exhibited that the prepared material was successfully synthesized. The removal efficiency of 99.1% was found at an equilibrium time of 110 min and dye concentration of 5 mg L−1Adsorbent mass of 30 mg resulted in the maximum dye elimination, and the efficiency of the process decreased by increasing the temperature from 25 to 40 °C. The effect of pH revealed that optimum pH was occurred at acidic media, having the maximum dye removal of greater than 90%. The kinetic and isotherm models revealed that RO16 elimination followed pseudo-second-order (R2 = 0.9982) and Freundlich (R2 = 0.9758) assumptions. Surprisingly, the performance of modified sawdust was 15.5 times better than the raw sawdust for the dye removal. In conclusion, lignocellulosic sawdust-Fe/Zn composite is promising for dye removal.
Preparation of coal gangue ceramsite high-strength concrete and investigation of its physico-mechanical properties
Authors: Guan Hongbo , Yu Jitao , Umuhuza Kibugenza Albert Salomon , Sun Qingwei
Published: September 2022

Using coal gangue (CG) as a building material does not only reduce the disposal of industrial waste and promote the resource utilization of solid waste, but also solves the excessive consumption of sand and stone in construction. This study experimentally investigated calcining ceramisites from CG raw materials and the mechanical properties of CG ceramsite concrete were studied. Additionally, the physical, chemical and composition changes of CG before and after calcination were observed using scanning electron microscopy and X-ray diffraction analysis (XRD). The experimental results reveal that calcination can reduce the density, increase the strength, increase the porosity of CG, and change the microstructure and mineral composition of CG. Finally, there are great differences between coal gangue ceramsite concrete and ordinary concrete in the variation of compressive strength with time and the relationship between elastic modulus and compressive strength. In this paper, the existing formula is modified according to the experimental data.
Thermal stable properties of solid hybrid nanoparticles for mixed convection flow with slip features
Authors: Lund Liaquat Ali , Lashin Maha M. A. , Yashkun Ubaidullah , Guedri Kamel , Khan Sami Ullah , Khan M. Ijaz , Bafakeeh Omar T. , Kumam Poom
Published: September 2022

Following to improved thermal impact of hybrid nanomaterials, wide range applications of such materials is observed in the thermal engineering, extrusion systems, solar energy, power generation, heat transfer devices etc. The hybrid nanofluid is a modified form of nanofluid which is beneficial for improving energy transfer efficiency. In current analysis, the solid nanoparticles aluminium ($$\phi_{{{\text{Al}}_{2} {\text{O}}_{3} }}$$

ϕ

Al
2

O
3

) and copper ($$\phi_{{{\text{Cu}}}}$$

ϕ
Cu

) have been mixed with water to produce a new hybrid nanofluid. The investigation of a steady two-dimensional mixed convection boundary layer flow of the resultant hybrid nanofluid on a vertical exponential shrunk surface in the existence of porous, magnetic, thermal radiation, velocity, and thermal slip conditions is carried out. Exponential similarity variables are adopted to transform the nonlinear partial differential equation into a system of ordinary differential equations which has been then solved by employing the shooting method in Maple software. The obtained numerical results such as coefficient of skin friction $$f^{\prime \prime } \left( 0 \right)$$

f
″

0

, heat transfer rate $$- \theta^{\prime } \left( 0 \right)$$

-

θ
′

0

, velocity $$f^{\prime } \left( \eta \right)$$

f
′

η

and temperature $$\left( {\theta \left( \eta \right)} \right)$$

θ

η

distributions are presented in the form of different graphs. The results revealed that duality exists in solution when the suction parameter $$S \ge S_{ci}$$

S
≥

S

ci

in assisting flow case. Due to non-uniqueness of solutions, a temporal stability analysis needs to be performed and the result indicates that the upper branch is stable and realizable compared to the lower branch.
Engineering Volatile Biomarkers for Disease Detection
Authors: Broza Yoav Y.
Published: September 2022

Biodiagnostics based on volatile organic compounds (VOCs) can be divided into direct and indirect approaches, each of which can be tackled by either selective or bioinspired semiselective/cross-reactive sensing strategies. The current chapter presents and discusses the advances and challenges in harnessing VOCs for clinical diagnostics by using the selective sensing approach. The chapter examines different concepts that rely on the administration of exogenous molecules\substances metabolized into volatile by-products by disease-specific metabolic pathways. It shows how tracer-based methods with stable isotopes, exogenous VOCs and, most recently, synthetically engineered volatile nanoreporters can be harnessed toward improved diagnostics and relate to challenges in the field.
Analytical Approaches for Disease Detection
Authors: Pham Y Lan , Beauchamp Jonathan
Published: September 2022

Analytical technologies represent the core of volatile biomarker detection in human health applications. A wide range of techniques exist, each exhibiting a varying degree of complexity, from comprehensive laboratory-based instrumentation to portable handheld sensor systems. Each tool has unique features that make it suitable for its specified application in the detection of volatile organic compounds (VOCs) as biomarkers in human health. This chapter reviews the most common and applicable analytical approaches for VOC detection in diverse bodily fluids, primarily in exhaled breath, as well as in urine, sweat and feces. Technologies covered in this chapter include gas chromatography–mass spectrometry (GC–MS), including multidimensional GC–MS (GC × GC–MS and GC–GC–MS), real-time mass spectrometry, including proton transfer reaction-mass spectrometry (PTR-MS), selected ion flow tube-mass spectrometry (SIFT-MS) and secondary electrospray ionization-mass spectrometry (SESI-MS), as well as ion mobility spectrometry (IMS) and optical spectroscopy. The chapter covers the basic operating principles of these key techniques in the analysis and detection of VOCs and offers an overview of representative studies that demonstrate their strengths and suitability relating to volatile biomarker detection in human health applications.
Cross-reactive Sensors (or e-Noses)
Authors: Chandrapalan S. , Arasaradnam R. P. , Kvasnik F. , Persaud K. C.
Published: September 2022

An e-Nose is based on having an array of broadly tuned sensors that produce a unique response pattern when exposed to a complex mixture of chemicals, with the pattern being different when the mixture changes or new chemicals are detected. It is now applied to noninvasive screening and diagnostics of patients for many diseases, sniffing breath or bodily secretions. This chapter presents concepts and application of e-noses in addressing one of the most challenging questions – can it help to distinguish high-risk individuals from low-risk before the development of a disease? Representative results indicating the utility of e-noses in this enavcour are presented and discussed.
Nanomaterial-based Sensors for Volatile Biomarkers
Authors: Haick Hossam
Published: September 2022

This chapter provides an overview of the present and ongoing developments in the field of volatile organic compound (VOC) nanomaterial-based sensors that have been finding use in healthcare. It starts by briefly explaining the advantage of nanomaterials for sensing applications and reviewing the available and quickly progressing VOC sensing devices. There is a discussion of the main trends in material design with special attention given to nanostructuring and nanohybridization, while highlighting emerging sensing materials and strategies. An insightful summary of the field and suggestions of multiple worthy directions in which to choose nanomaterial-based sensors are discussed, while considering the profile of the targeted VOCs and the possible limitations of applying sensing approaches. The perspectives when taking VOC sensors to a new level in the field of diagnostics are made evident.
Wearable Sensors for Detection of Human Health Using Volatile Biomarkers
Authors: Jin H. , Tang N. , Li Q. C. , Xue C. L. , Zhang Y. N. , Zhou Y. , Cui D. X. , Haick H.
Published: September 2022

This chapter focuses on wearable technologies for sensing volatile biomarkers regarding healthcare applications. The advantage of adopting wearable sensors in tracking human volatile organic compounds (VOC) is included, and promising advancements in this area are discussed. Notably, bottleneck concerns in designing wearable VOC sensors and the relevant potential strategy to address the problem will be emphasized.
Switchable molecular functionalization of an STM tip: from a Yu–Shiba–Rusinov Tip to a Kondo tip
Authors: Ayani Cosme G. , Calleja Fabian , Ibarburu Ivan M. , Casado Aguilar Pablo , Nazriq Nana K. M. , Yamada Toyo K. , Garnica Manuela , Vázquez de Parga Amadeo L. , Miranda Rodolfo
Published: September 2022

A Niobium superconducting STM tip is fuctionalised with an electron acceptor molecule, showing the corresponding Yu–Shiba–Rusinov (YSR) states in STS. Applying an external magnetic field allows the controlled switching from a YSR tip to a Kondo tip.
Synthesis, crystal structure, and luminescence properties of a three-dimensional coordination polymer from europium(iii) carbonate hemioxalate hydrate
Authors: Takano Rina , Ishida Takayuki , Ay Burak
Published: September 2022

A new luminescent lanthanide coordination polymer composed of europium(iii), oxalate and carbonate was prepared.
syn-Cryptophanes: macrocyclic compounds with optimized characteristics for the design of 129Xe NMR-based biosensors
Authors: Léonce Estelle , Brotin Thierry , Berthault Patrick
Published: September 2022

A new water-soluble syn-cryptophane shows remarkable xenon binding properties and thereby constitutes an essential platform for 129Xe NMR-based biosensors.
Ruthenium tris(σ-B–H) borate complexes: synthesis, structure, and reactivity
Authors: Wei Yongliang , Yang Xiaowen , Tian Minghui , Wang Xue , Wang Tongdao
Published: September 2022

A facile approach for obtaining stable transition metal tris(σ-B–H) borates in excellent isolated yields under mild conditions is described, and these well-defined tris(σ-B–H) borates could undergo H/D exchange and ligand substitution reactions.
An interactive design for sustainable oxygen capacity in alkali-ion batteries
Authors: Lee Jaewoon , Kim Duho
Published: September 2022

This research paper suggests the interactive design frameworks between sodium- and lithium-ion cathodes by mimicking the critical features shown in sodium-ion cathodes to rationally harness the nonhysteretic and reversible oxygen redox.
The semi-synthetic flavonoid 2′,3′,4′-trihydroxyflavone (2-D08) inhibits both SN-38- and cytokine-evoked increases in epithelial barrier permeability in anin vitrointestinal mucositis model
Authors: Marsh Dylan T. , Smid Scott D.
Published: September 2022

The novel flavonoid 2′,3′,4′-trihydroxyflavone (2-D08) reduces ROS generation and inhibits both SN-38- and cytokine-evoked increases in epithelial permeabilityin vitro.
Polydimethylsiloxane microstructure-induced acoustic streaming for enhanced ultrasonic DNA fragmentation on a microfluidic chip
Authors: Sun Lin , Lehnert Thomas , Gijs Martin A. M. , Li Songjing
Published: September 2022

We present a microstructure-enhanced microfluidic chip for efficient DNA fragmentation, suitable for next-generation sequencing platforms. The improved on-chip performance arises from acoustic streaming generated by oscillating microstructures.
Microfluidic outer blood–retinal barrier model for inducing wet age-related macular degeneration by hypoxic stress
Authors: Lee Seokhun , Kim Seunggyu , Jeon Jessie S.
Published: September 2022

Enhanced in vitro oBRB model condition was developed. It showed CNV under hypoxic condition just as OIR model.
Inertial microfluidics: current status, challenges, and future opportunities
Authors: Xiang Nan , Ni Zhonghua
Published: September 2022

We present the current status, challenges, and future opportunities of inertial microfluidics.
Nanobubble-based anti-hepatocellular carcinoma therapy combining immune check inhibitors and sonodynamic therapy
Authors: Liu Yun , Yang ShiQi , Zhou Qing , Zhou Jun , Li Jinlin , Ma Yao , Hu Bing , Liu Chaoqi , Zhao Yun
Published: September 2022

The strategy of nanobubble-based anti-hepatocellular carcinoma therapy combining immune check inhibitors and sonodynamic therapy.
Recent advances in free radical cyclizations of 2-alkenyl benzaldehydes to synthesize benzocycloketones
Authors: Chen Pu , Ji Xiaochen , Tang Shi , Deng Guo-Jun , Huang Huawen
Published: September 2022

Radical reactions involving 2-alkenyl benzaldehydes have intensively progressed and give unique access to structurally valuable benzocycloketone compounds.
A novel COOH–GO–COOH–MWNT/pDA/AuNPs based electrochemical aptasensor for detection of AFB1
Authors: Wang Pengfei , Luo Bin , Liu Ke , Wang Cheng , Dong Hongtu , Wang Xiaodong , Hou Peichen , Li Aixue
Published: September 2022

A novel COOH–GO–COOH–MWNT/pDA/AuNPs based electrochemical aptasensor was developed for detection of AFB1.
Highly sensitive benzothiazole-based chemosensors for detection and bioimaging of peroxynitrite in living cells
Authors: Kong Yaqiong , Wu Rong , Wang Xiaodong , Qin Guoxu , Wu Fengyi , Wang Chunyu , Chen Minmin , Wang Nannan , Wang Qian , Cao Duojun
Published: September 2022

Fluorescent probes designed to sense and image peroxynitrite (ONOO−).
Dehydrogenation of the liquid organic hydrogen carrier perhydrodibenzyltoluene – reaction pathway over Pt/Al2O3
Authors: Shi Libin , Qi Suitao , Smith Kevin J. , Alamoudi Majed , Zhou Yiming
Published: September 2022

The catalytic dehydrogenation of perhydro-dibenzyltoluene (H18-DBT) to dibenzyltoluene (DBT) on a Pt/Al2O3 catalyst was investigated using 1H NMR spectroscopy and GC-MS.
Light-driven biocatalytic oxidation
Authors: Yun Chul-Ho , Kim Jinhyun , Hollmann Frank , Park Chan Beum
Published: September 2022

Light-driven catalytic cycles by oxidative enzymes, such as peroxygenases, dehydrogenases or monooxygenases, perform a variety of selective oxyfunctionalization and dehydrogenation reactions with or withdrawal of reducing equivalents.
Impact of a polymer modifier on directing the non-classical crystallization pathway of TS-1 zeolite: accelerating nucleation and enriching active sites
Authors: Zhang Jiani , Bai Risheng , Zhou Yida , Chen Ziyi , Zhang Peng , Li Jiyang , Yu Jihong
Published: September 2022

Polyacrylamide (PAM) was utilized to switch the crystallization pathway of TS-1 zeolite from classical to non-classical by virtue of the specific interaction between PAM and Si/Ti species, thus accelerating nucleation and enriching active sites.
From mixed group 13 cations [M(AlCp*)3]+ (M = Ga/In/Tl) to an Al4+ cluster
Authors: Dabringhaus Philipp , Krossing Ingo
Published: September 2022

Mixed low-valent group 13 cations with formally reduced metal atoms have been prepared via addition of [(AlCp*)4] to the low-valent cation salts of Ga, In and Tl. Addition of an aza-crown ether resulted in isomerization to a novel Al4+ cluster.
Regioselective catalytic carbonylation and borylation of alkynes with aryldiazonium salts toward α-unsubstituted β-boryl ketones
Authors: Zhu Fengxiang , Yin Pengpeng , Wu Xiao-Feng
Published: September 2022

A multi-component procedure on the carbonylative and hydroborative synthesis of β-boryl ketones has been developed with alkynes, B2pin2 and aryldiazonium salts as the substrates and using ethyl acetate as the reagent and solvent.
Roughness tolerant pressure sensitive adhesives made of sticky crumpled sheets
Authors: Elder Theresa , Croll Andrew B.
Published: September 2022

If an adhesive is meant to be temporary, roughness often poses a challenge for design. Crumpling a thin sticky sheet could provide an effective strategy.
Self-assembled materials from cellulose nanocrystals conjugated with a thermotropic liquid crystalline moiety
Authors: Masese Francis K. , Ndaya Dennis , Liu Chung-Hao , Eddy Nicholas , Morales-Acosta M. Daniela , Nieh Mu-Ping , Kasi Rajeswari M.
Published: September 2022

Manipulating molecular and supramolecular interactions within cellulose nanocrystals (CNCs) to introduce different levels of assemblies combined with multiple functionalities is required for the development of degradable smart materials from renewable resources.
Establishing synthesis–composition–property relationships for enhanced and reproducible thermoelectric properties of MgAgSb
Authors: Duparchy Amandine , Millerand Léo , Camut Julia , Tumminello Silvana , Kamila Hasbuna , Deshpande Radhika , Cowley Aidan , Mueller Eckhard , de Boor Johannes
Published: September 2022

Established synthesis–composition–property relationship proving a narrow homogeneity range while revealing that MgAgSb functional properties are governed by secondary phases.
The interfacial aspect of Bi2O3/CeOx heterostructure catalysts for HCOOH production from CO2 electroreduction
Authors: Wang Chaochen , Pang Ruichao , Pan Zhenping , Zhu Yihua , Li Chunzhong , Liu Bin , Shen Jianhua
Published: September 2022

Interfacial effects accelerate electron transfer and stabilize the adsorbate for efficient CO2 conversion.
Functionalized anthrathienothiophenes: synthesis, properties, and integration into OFETs
Authors: Fregoso Garrett , Rupasinghe Gehan S. , Shahi Maryam , Thorley Karl , Parkin Sean , Paterson Alexandra F. , Anthony John
Published: September 2022

Halogen bonding is required in silylethyne-functionalized asymmetrical chromophores to yield crystal packing suitable for efficient transistors.
Origin of polar nanoregions from displacive correlation in relaxor ferroelectric Pb(Mg1/3Nb2/3)O3–PbTiO3
Authors: Wang Zhen , An Zheyi , Zhuang Jian , Li Fei , Ren Wei , Ye Zuo-Guang , Zhang Nan
Published: September 2022

The existence of polar nanoregions (PNRs) which are widely accepted as a key factor contributing to ultrahigh piezoelectricity in relaxor-ferroelectric solid solutions is found to originate from local displacive correlations.
Solution-processed next generation thin film solar cells for indoor light applications
Authors: Mishra Snehangshu , Ghosh Subrata , Boro Binita , Kumar Dinesh , Porwal Shivam , Paul Mrittika , Dixit Himanshu , Singh Trilok
Published: September 2022

Indoor light harvesting solar cells can effectively power the IoT devices. Solution-processable next generation solar cells fuelled by the recent growth in the IoT market present immense potential due to their lightweight and flexible nature.
Music and Speech Perception in Prelingually Deafened Young Listeners With Cochlear Implants: A Preliminary Study Using Sung Speech
Authors: Wheeler Harley J. , Hatch Debora R. , Moody-Antonio Stephanie A. , Nie Yingjiu
Published: September 2022

Purpose:

In the context of music and speech perception, this study aimed to assess the effect of variation in one of two auditory attributes—pitch contour and timbre—on the perception of the other in prelingually deafened young cochlear implant (CI) users, and the relationship between pitch contour perception and two cognitive functions of interest.

Method:

Nine prelingually deafened CI users, aged 8.75–22.17 years, completed a melodic contour identification (MCI) task using stimuli of piano notes or sung speech with a fixed timbre (same word for each note) or a mixed timbre (different words for each note), a speech perception task identifying matrix-styled sentences naturally intonated or sung with a fixed pitch (same pitch for each word) or a mixed pitch (different pitches for each word), a forward digit span test indexing auditory short-term memory (STM), and the matrices section of the Kaufman Brief Intelligence Test–Second Edition indexing nonverbal IQ.

Results:

MCI was significantly poorer for the mixed timbre condition. Speech perception was significantly poorer for the fixed and mixed pitch conditions than for the naturally intonated condition. Auditory STM positively correlated with MCI at 2- and 3-semitone note spacings. Relative to their normal-hearing peers from a related study using the same stimuli and tasks, the CI participants showed comparable MCI at 2- or 3-semitone note spacing, and a comparable level of significant decrement in speech perception across three pitch contour conditions.

Conclusion:

Findings suggest that prelingually deafened CI users show similar trends of normal-hearing peers for the effect of variation in pitch contour or timbre on the perception of the other, and that cognitive functions may underlie these outcomes to some extent, at least for the perception of pitch contour.

Supplemental Material:

https://doi.org/10.23641/asha.21217937
Investigating Narrative Performance in Children With Developmental Language Disorder: A Systematic Review and Meta-Analysis
Authors: Winters Katherine L. , Jasso Javier , Pustejovsky James E. , Byrd Courtney T.
Published: September 2022

Purpose:

Narrative assessment is one potentially underutilized and inconsistent method speech-language pathologists may use when considering a diagnosis of developmental language disorder (DLD). However, narration research encompasses many varied methodologies. This systematic review and meta-analysis aimed to (a) investigate how various narrative assessment types (e.g., macrostructure, microstructure, and internal state language) differentiate children with typical development (TD) from children with DLD, (b) identify specific narrative assessment measures that result in greater group differences, and (c) evaluate participant and sample characteristics that may influence performance differences.

Method:

Electronic databases (PsycINFO, ERIC, and PubMed) and ASHAWire were searched on July 30, 2019, to locate studies that reported oral narrative language measures for both DLD and TD groups between ages 4 and 12 years; studies focusing on written narration or other developmental disorders only were excluded. We extracted data related to sample participants, narrative task(s) and assessment measures, and research design. Group differences were quantified using standardized mean differences. Analyses used mixed-effects meta-regression with robust variance estimation to account for effect size dependencies.

Results:

Searches identified 37 eligible studies published between 1987 and 2019, including 382 effect sizes. Overall meta-analysis showed that children with DLD had decreased narrative performance relative to TD peers, with an overall average effect of −0.82
SD
, 95% confidence interval [−0.99, −0.66]. Effect sizes showed significant heterogeneity both between and within studies, even after accounting for effect size–, sample-, and study-level predictors. Across model specifications, grammatical accuracy (microstructure) and story grammar (macrostructure) yielded the most consistent evidence of TD–DLD group differences.

Conclusions:

Present findings suggest some narrative assessment measures yield significantly different performance between children with and without DLD. However, researchers need to improve consistency of inclusionary criteria, descriptions of sample characteristics, and reporting of correlations between measures to determine which assessment measures reliably distinguish between groups.

Supplemental Material:

https://doi.org/10.23641/asha.21200380
An outlier‐resistant approach to observer‐based security control for interval type‐2 T‐S fuzzy systems subject to deception attacks
Authors: Zhang Sunjie , He Jiawei , Yang Yi , Yan Tingli
Published: September 2022

This article focuses on the outlier‐resistant observer‐based controller for the interval type‐2 T‐S fuzzy discrete‐time systems with distributed delays under deception attacks. The deception attack takes place between sensor channel and observer channel. A neoteric guidance for safety performance is constructed to explain the randomly happening spoofing offensive on the control structure. The random events of parameter uncertainty and spoofing offensives are controlled via two sets of Bernoulli stochastic variables with foregone probability distributions. One outlier‐resistant observer‐based security control is constructed to induce an innovation saturation structure in order to alleviate the passive influence on the spoofing offensives about the estimation aspect. The goal of the solution is to design an outlier‐resistant observer‐based security control that can insure boundedness in estimation error while achieving the ideal level of safety. Ultimately, a quantitative value simulation sample is put forward to justify the practicality and validity of the above‐mentioned control project.
Double‐ejection micro‐cathode arc thruster applied to micro‐satellite
Authors: Tian Jia , Liu Wenzheng , Nikiforov Anton , De Geyter Nathalie , Morent Rino
Published: September 2022

An innovative double‐ejection micro‐cathode arc thruster (ACd‐µCAT), which consists of a cylindrical inner anode, a cylindrical outer cathode and two insulating sleeves (an inner insulating sleeve and an outer insulating sleeve), was proposed. The differences in electrical characteristics, plasma parameters and propulsion performance between the newly proposed ACd‐µCAT and a traditional μCAT were examined. Study results showed that compared to the traditional μCAT structure, by using the ACd‐µCAT, the peak value of produced thrust was increased 9.3 times, while the amplitude of the ion current and the ion‐to‐arc ratio were increased 5.4 and 5.9 times (from 1.2% to 7.1%), respectively. In addition, data from Langmuir probe experiments indicated that peak values of the directional propagation speed and density of plasma plume were increased 3.1 times and 4.2 times, respectively. Moreover, plasma plume directional ejection performance was also significantly improved. This study result will provide support for the development of a new‐generation μCAT.
GSGAN: Learning controllable geospatial images generation
Authors: Su Xingzhe , Lin Yijun , Zheng Quan , Wu Fengge , Zheng Changwen , Zhao Junsuo
Published: September 2022

Compared with natural images, geospatial images cover larger area and have more complex image contents. There are few algorithms for generating controllable geospatial images, and their results are of low quality. In response to this problem, this paper proposes Geospatial Style Generative Adversarial Network to generate controllable and high‐quality geospatial images. Current conditional generators suffer the mode collapse problem in geospatial field. The problem is addressed via a modified mode seeking regularization term with contrastive learning theory. Besides, the discriminator network architecture is modified to process global feature information and texture information of geospatial images. Feature loss in the generator is introduced to stabilize the training process and improve generated image quality. Comprehensive experiments are conducted on UC Merced Land Use Dataset, NWPU‐RESISC45 Dataset, and AID Dataset to evaluate all compared methods. Experiment results show our method outperforms state‐of‐the‐art models. Our method not only generates high‐quality and controllable geospatial images, but also enhances the discriminator to learn better representations.
Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation
Authors: Li Zi‐Wei , Xuan Shi‐Bin , He Xue‐Dong , Wang Li
Published: September 2022

Medical image segmentation plays a vital role in computer‐aided diagnosis and intelligent medical treatment. It can preprocess medical images to help doctors better diagnose diseases. Class activation map (CAM) is an important technology in weakly supervised segmentation, which can achieve image segmentation without pixel‐level label training. This technology can well meet the needs of medical image segmentation. However, CAM obtaining is still unperfect due to global average pooling (GAP). GAP will cause important and non‐important regions to be given equal attention during the training process. So, CAM cannot demarcate the boundary of the target regions well. In order to solve this problem, a global weighted average pooling network fusing the grayscale information of medical images is proposed. The proposed network can solve the problem that GAP has the same concern for important regions and non‐important regions of the feature map, because the different weights can be learned for different positions of the feature map before the GAP in the proposed model. At the same time, because of the grayscale difference between the tumor area and the non‐tumor area in the brain tumor image, the low‐level grayscale information of the medical image is fused with the high‐level semantic information extracted by the network to learn the weights. This operation gives full play to the advantages of feature maps of different levels. The experiment results on the popular medical image dataset BraTS2019 show that the proposed method can well improve the performance of CAM and help CAM fit the boundaries of objects. Meanwhile, in the DSC evaluation, the proposed method achieves a score of 64.1%, which is a 4.6% improvement over a recent research method.
Decentralised CAVs based on micro–macro flow control (MiMaFC) strategy for multi‐intersection traffic network
Authors: Zhu Zhengze , Adouane Lounis , Quilliot Alain
Published: September 2022

Advanced intersection control systems have been created to alleviate traffic congestion. CAVs may benefit from cooperative navigation in order to address the regular traffic issue. Due to whole uncertainty in transportation network, the conventional motion planning for local areas may lead to undesirable effects in long run. In prior works, a micro–macro flow control (MiMaFC) strategy is used to investigate CAVs' navigation at unsignalised intersections by taking flow velocity and vehicle passing priority into account. To get a better understanding of motion control and how it can be utilised to impact traffic flow behaviour, this study expands the intersection navigation protocol and develops a velocity planning methodology based on the proposed MiMaFC technology. Correspondingly, cooperative navigation protocol used in the addressed architecture is specifically developed for CAVs that continually cross intersections. Further, spatio‐temporal velocity adaption mechanism is presented in this work. Depending on the vehicles' location and speed, CAVs might use either the MiMaFC‐based or self‐interested velocity strategy. Simulation results, which include a congested traffic network, are shown to demonstrate the proposed method's potential. The study found that the suggested motion planning framework may increase urban network mobility over a non‐supervised CAVs system.
Simplified model predictive current control based on fast vector selection method in a VIENNA rectifier
Authors: Song Weizhang , Yang Yang , Jiao Zhu , Xu Shaojie , Dang Chaoliang , Wheeler Pat
Published: September 2022

This paper presents a simplified finite‐control‐set model predictive control (S‐FCS‐MPC) based on fast vector selection method in a three‐level VIENNA rectifier. This method features a high‐power factor, a low input current THD as a well‐controlled DC‐link voltage with fewer redundant vectors and lower computational load. Moreover, the converter with the proposed control technique exhibits a faster dynamic response in terms of input current and DC‐link voltage compared with conventional finite‐control‐set model predictive control (C‐FCS‐MPC). In addition, the average switching frequency can be effectively reduced due to fewer switching times in a subset sector using the proposed method, which means fewer switching losses. Finally, the operation principle of the proposed algorithm has been analysed and an execution time comparison between S‐FCS‐MPC and C‐FCS‐M has been undertaken. The effectiveness of the proposed control technique has been validated using both simulation and experimental results.
A post‐forecast weighing algorithm to improve wind power forecasting capabilities
Authors: Pijnenburg Petrus , Cao Bo , Chang Liuchen , Kilpatrick Ryan , Levy Thomas
Published: September 2022

Wind power generation has had a profound impact on both the green power and traditional power sectors. As a result, wind power forecasting plays an immense role in effectively predicting and providing wind power generated for effective power dispatching for system operators. However, wind power forecasting is a challenging topic with accuracy issues between the predicted power and actual power generation at the point of common coupling. Furthermore, due to the variation of wind, effective dispatching through the utilisation of wind power production forecasting becomes a challenge. This issue is further compounded by the vast amount of data required to train and verify of these forecasting algorithms. This paper presents a fast acting post forecast weighing algorithm designed to evaluate the forecasted power output of a previously developed wind power forecasting package. The developed method is designed to gauge and improve the estimated output forecaster's approach in order to observe performance changes in the algorithm while using minimal data without changing the internal workings of the evaluated forecasting algorithm.
Performance dynamics improvement of a hybrid wind/fuel cell/battery system for standalone operation
Authors: Mossa Mahmoud A. , Echeikh Hamdi , Quynh Nguyen Vu , Bianchi Nicola
Published: September 2022

The present study is concerned with improving the dynamics of a hybrid generation system utilized for feeding an isolated load. The system under study consists of a wind‐driven synchronous generator with permanent magnet type, a fuel cell stack and a storage battery layout used to enhance the system reliability. A detailed design for all system parts is introduced. A new formulated predictive controller is utilized to enhance the performance of synchronous generator in comparison with traditional controllers. The wind turbine power system is designed and adopted a maximum power point tracking (MPPT) strategy to optimally exploit the captured wind energy. An energy management procedure is also considered to balance the power‐sharing between different system units. Extensive performance evaluation analysis is introduced in order to validate the capability of the designed controllers of the generator and fuel cell and check the feasibility of the energy management strategy (EMS) as well. The obtained results approve the capability of the proposed controller with the synchronous generator in achieving better dynamics compared with traditional schemes and confirm the validity of the fuel cell control system in managing the stack power. The results also approve the effectiveness of the designed EMS in preserving a balanced power flow.
Fault diagnosis and location of hydraulic system of domestic civil aircraft based on logic data
Authors: FENG Yunwen , PAN Weihuang , LU Cheng , LIU Jiaqi
Published: September 2022

To study the typical fault diagnosis and fault location technology of the hydraulic system of the domestic civil aircraft, the logic data of the typical fault is constructed according to the formation conditions of the fault. The operation data of the typical fault is collected, and the Bayesian network is used to realize the fault diagnosis and fault components position. First, according to the fault formation conditions in the unit operation manual of a certain type of domestic civil aircraft, referring to the construction method of the logic data, taking the typical fault of the hydraulic system as an example, the fault logic data of the domestic civil aircraft is established to intuitively reflect the logical relationship of the fault formation; secondly, based on the constructed logic data, considering the formation conditions of the fault, a Bayesian network corresponding to the logic data is established, and the logical relationship formed by the fault is represented by the value of the conditional probability distribution; obtain quick access recorder (QAR) data and its parameter information according to the input information of the logic data; finally, according to the established Bayesian network and the obtained QAR data, apply forward reasoning to realize the diagnosis of typical faults of the hydraulic system. Under the condition of partial information, reverse reasoning is applied to locate the faulty components of hydraulic system. The research shows that the proposed method can accurately diagnose faults, and can accurately locate faulty components in complete information, and give the probability of occurrence of potentially faulty components under partial information, which can effectively assist in the location of faulty components. The research work has certain reference significance for improving the fault diagnosis function of the airborne health management system and the ground health management system of domestic civil aircraft.