bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Frugal Innovation Capability in Emerging Markets within the Digitalization Epoch: Exploring the Role of Strategic Orientation and Organizational Ambidexterity
Authors: Sengura Josephat Deusidedith , Mu Renyan , Zhang Jingshu
Published: August 2024

Digitalization has forced emerging market (EM) firms operating in resource-constrained environments to adopt market-driven strategies, particularly frugal innovation, to provide affordable, optimized processes and high-value solutions. However, understanding the mechanisms behind developing frugal innovation capability (FIC) at the firm level in diverse EMs remains limited. From the perspective of the resource-based view, this study added to the existing body of knowledge by exploring how strategic orientation (entrepreneurial orientation (EO) and market orientation (MO)) and organizational ambidexterity (OA) impact the development of FIC in EMs. To empirically validate our theoretical predictions, this study used a cross-sectional survey to collect data from 386 valid respondents from Tanzanian manufacturing firms. The results demonstrate that both EO and MO have a strong and positive relationship with OA and the development of FIC in EMs. In addition, OA partially mediates the relationship of both EO and MO with the development of FIC. Furthermore, our results indicate that MO exerts a more significant impact on the development of FIC than EO in EMs. Managers of manufacturing firms in EMs can use these findings to review their strategic decisions and their exploitative and exploratory approaches to enhance supply chains, develop cost-effective technologies, and produce affordable offerings that cater to the preferences of price-conscious consumers in the digital age.
Spatiotemporal Evaluation and Driving Factor Screening for Regulating and Supporting Ecosystem Service Values in Suzhou–Wuxi–Changzhou Metropolitan Area’s Green Space
Authors: Shi Tailong , Xu Hao , Bai Xuefeng
Published: August 2024

The green space system in metropolitan areas is crucial for maintaining environmental health and stability by regulating and supporting ecosystem service values (ESVs). The Suzhou–Wuxi–Changzhou metropolitan area is located in the core of the Yangtze River Delta, and its green space exemplifies this importance, despite facing challenges from rapid urbanization in past decades. Studying the categories of ESVs and their driving factors can facilitate the comprehension of ESVs’ dynamics, thereby promoting regional sustainable development. In this article, we used the inVEST module to calculate six ESV indicators (soil retention, annual water yield, habitat quality, carbon storage, nitrogen, and phosphorus absorption) of the Suzhou–Wuxi–Changzhou metropolitan area’s green space system from 2015 to 2020 and combined it with the entropy weight method (EWM) to allocate weights for these indicators and evaluate the total value of the ESVs. To address the weakness of the inVEST model in calculating the total value of multiple ESVs, the Xgboost algorithm was combined with PCA methods to screen its main driving factors from numerous measures. Finally, the GWR method was used to reveal the spatial and temporal change in the main driving factors’ impacts on ESVs in the study area over five years. The result shows (1) the spatial distribution of the total value of regulating and supporting ESVs in the Suzhou–Wuxi–Changzhou metropolitan area has become more uneven in 2020 compared with 2015; (2) the most important driving factors include landscape diversity, topographic gradient, economic activity intensity, humidity, and surface temperature; and (3) based on the analysis of GWR results, the study area has an overall increase in regional soil erosion due to the expansion of impervious areas. And some mountainous areas have habitat fragmentation because of incorrect economic activity. This study provides a new perspective for evaluating the sum of multiple types of ESVs and exploring their driving factors, as well as revealing the ecosystem problems of the Suzhou–Wuxi–Changzhou metropolitan area in recent years. It also provides a reference for policymakers to maintain local ecological stability and security.
Bird Diversity in Suburban Greenway Was Driven by Habitat Heterogeneity and Landscape Patterns in Autumn–Winter Seasons—Evidence from Hangzhou Qingshan Lake Greenway
Authors: Tao Yizhou , Hu Wenhao , Wang Wenjing , He Lan , Xu Wenhui
Published: August 2024

Understanding the spatial heterogeneity of bird community distribution within urban greenways is crucial for optimizing ecological functions and supporting urban biodiversity. While ecological corridors enhance connectivity and biodiversity, specific mechanisms by which landscape composition and configuration influence bird diversity remain unclear. This study examines bird community distribution along the Qingshan Lake Greenway in Hangzhou during autumn and winter, using 17 observation points across wetlands, forests, and mixed habitats. The key findings indicate that wetlands have significantly higher species richness compared to forests and mixed habitats, underscoring wetlands’ critical role in supporting diverse bird communities. Greenways primarily facilitate short-distance bird movement with limited permeability between habitats. Additionally, greenway effectiveness in enhancing bird diversity depends heavily on design and structural characteristics. This research highlights the necessity of incorporating microhabitat design and diverse habitat configurations in greenway planning to enhance ecological connectivity and biodiversity. It provides essential insights for urban planners and ecologists, emphasizing detailed landscape composition and configuration analyses. Future research should include year-round studies and advanced ecological monitoring technologies to validate and expand these findings, ultimately contributing to more effective urban biodiversity conservation and sustainable development strategies.
Determination of Contributing Area Threshold and Downscaling of Topographic Factors for Small Watersheds in Hilly Areas of Purple Soil
Authors: Chen Ruiyin , Zhu Yonggang , Zhang Jun , Wen Anbang , Hu Shudong , Luo Jun , Li Peng
Published: August 2024

The results of topographic factor computations are highly sensitive to the setting of contributing area thresholds when applied to soil erosion modeling to evaluate soil erosion; however, the existing choice of contributing area thresholds is highly arbitrary. Meanwhile, due to regional-scale limitations, lower-resolution DEM data are usually used to calculate topographic factors, and with the fragmentation of land parcels in hilly areas of purple soil, lower-resolution DEM data respond to very limited topographic information. This study focuses on solving the mentioned issues by selecting the Lizixi watershed in a hilly area of purple soil as the research subject. It establishes a relationship equation between the resolution of DEM data and the optimal contributing area threshold. This is achieved by investigating the change in the contributing area threshold with the resolution of DEM data, determining the optimal contributing area threshold for different resolutions of DEM data, and establishing the relationship equation between the resolution of DEM data and the optimal contributing area threshold. Meanwhile, to solve the key problem of fragmented land parcels in the purple soil area, where the low-resolution and medium-resolution DEM data cannot accurately reflect the topographic information, combined with the principle of histogram matching, the downscaling model between the topographic factors under the low-resolution DEM data and the topographic factors under the high-resolution DEM data is established. This study confirms that the scale transformation model developed has a strong simulation effect, and the findings can offer technical assistance for the precise computation of soil erosion in small watersheds in hilly areas of purple soil.
Treatment of the Distal Forearm Fracture by Volar Dual Window Approach
Authors: Wang Wei-Ting , Chen Chiang-Sang
Published: August 2024

Background: Distal forearm fractures were defined as distal radius fractures with concomitant distal ulna fractures, except ulna styloid fractures. Distal forearm fractures are common among geriatric populations, particularly those with osteoporosis. Conventionally, distal forearm fractures are reduced by a double incision approach; however, malreduction and instability of the distal radioulnar joint were not uncommon. We introduced a modified volar dual window approach to treat the distal forearm fracture and evaluate the functional outcomes and complications. Methods: From January 2020 to June 2023, 13 patients with distal forearm fractures underwent open reduction by the modified dual window approach with locking plate fixation. After surgery, splints were applied for two weeks, and the patients underwent postoperative hand therapy for three months. The mean Quick Disabilities of the Arm, Shoulder, and Hand scores, range of motions, grip strength, postoperative radiographic parameters, and complications data were collected. Results: The mean follow-up period was 12.1 months, and the mean age was 52.3 years. Average wrist flexion was 67°, extension 69°, pronation 81°, and supination 79°. Grip strength was 28.3 ± 11.5 kg, which was 88% of the uninjured opposite side. The Visual Analog Scale score during activities was recorded as 0.5 ± 0.9. The mean Quick Disabilities of the Arm, Shoulder, and Hand score was 14 ± 11.5. The postoperative radiographic parameters were as follows: radial height: 10.8 ± 1.7 mm, radial inclination: 22.6 ± 3.7°, volar tilting: 4.0 ± 3.9°, and ulnar variance: −0.4 ± 1.4 mm. All the patients achieved bone union at the final follow-up. Two patients underwent ulnar implant removal due to irritation symptoms. Neither infection, nor neurovascular injury, nor malreduction developed in these patients. Conclusions: The modified volar dual window approach can achieve good wrist function and distal forearm fracture reduction without increasing neurovascular or wound healing complications. This method is an alternative approach for distal forearm fracture, especially in comminuted distal ulna fracture or distal radioulnar joint incongruity.
Morphological and Molecular Characterization of Three Myxosporean Species of the Genera Myxobolus, Henneguya, and Myxidium (Cnidaria: Myxozoa) Infecting Freshwater Fish, Isolated for the First Time in Japan
Authors: Sekiya Mariko , Sakai Haruya , Li Ying-Chun , Rosyadi Imron , Yunus Muchammad , Sato Hiroshi
Published: August 2024

The majority of myxosporean species (Cnidaria: Myxozoa) of the genera Myxobolus (35 species), Henneguya (8 species), and Myxidium (9 species) from freshwater or brackish fish in Japan were recorded more than 30 years ago (accumulatively 81.1% [43/53]). The re-discovery and molecular–genetic characterization of these species is a current research priority. During our myxosporean survey in Japanese freshwater fish, we detected three species that had never been recorded in Japan, but in the Russian Far East (Sakhalin Island, and Maritime Province): Myxobolus tribolodonus sp. n., forming cysts in the gills of Tribolodon sachalinensis (syn. M. marinus sensu Aseeva, 2000; M. marinus sensu Sokolov et Frolova, 2015, recorded from the gills of Pseudaspius (syn. Tribolodon) spp.); Henneguya pungitii Achmerov, 1953, forming cysts in the subcutis of external skin and buccal submucosa of Pungitius sinensis; and Myxidium salvelini Konovalov et Shulman, 1966, in the urinary bladder of Oncorhynchus masou ishikawae. These new isolates were characterized by integrated taxonomic approaches, i.e., myxospore morphology and molecular–genetic characterization of the small subunit ribosomal RNA gene (SSU rDNA). These new isolates were phylogenetically differentiated from any species whose SSU rDNA sequences were deposited in the DNA databases, and concurrently compared with recorded species based on classical morphological criteria. All three species were differentiated from myxosporeans previously recorded in Japan, indicating new distribution records out of the Russian Far East. For reliable species identification, accumulation of at least SSU rDNA sequences of known species worldwide is critically important.
Climate Change Affects the Vulnerability of Belarusian Lakes to External Impact
Authors: Sukhovilo Nina , Novik Aliaksei , Vezhnavets Vasil
Published: August 2024

The investigation of lakes’ vulnerability to external impacts is essential for understanding and potentially mitigating the threats they face. By studying how lakes are affected by external factors, such as pollution, climate change and human activities, we can assess the health of the ecosystem and predict how it may respond to future changes. The purpose of this research is the analysis of climate change’s influence on the vulnerability of Belarusian lakes to external impact. The vulnerability indices were calculated using the randomized aggregate method. The dependences between parameters were defined on the basis of correlation and regression analysis. We investigated the vulnerability of 149 Belarusian lakes. Classifying the lakes allowed us to divide them into three types, with high, medium and low vulnerability to external impact. All the types include 2–3 subtypes. On the basis of classification, we created a zoning scheme for Belarus on the vulnerability of lakes to external impact. A forecast of lake vulnerability for three SSP scenarios to 2100 was conducted. In the SSP1 scenario, the vulnerability of stratified lakes will increase insignificantly. The vulnerability of homothermous lakes will not change. In the SSP2 scenario, the vulnerability of lakes will first increase then decrease. Lakes with high and medium vulnerability will be classed as medium- and low-vulnerable. In the SSP5 scenario, the vulnerability of lakes will decrease more significantly than in the other scenarios.
AbraLlama: Predicting Abraham Model Solute Descriptors and Modified Solvent Parameters Using Llama
Authors: Lang Andrew S. I. D. , Lee Youngmin
Published: August 2024

This study explores the application of fine-tuned large language models for predicting physicochemical properties, specifically focusing on Abraham model solute descriptors (E, S, A, B, V) and modified solvent parameters (e0, s0, a0, b0, v0). By leveraging ChemLLaMA, a specialized version of the LLaMA model for cheminformatics tasks, we developed the AbraLlama-Solvent and AbraLlama-Solute models using curated datasets of experimentally derived solute descriptors and solvent parameters. Our findings demonstrate that AbraLlama-Solvent and AbraLlama-Solute predict modified solvent parameters and solute descriptors with high accuracy, comparable to existing methods. The AbraLlama-Solvent model shows varying prediction accuracy across different solvents, influenced by their position within the chemical space, while the AbraLlama-Solute model consistently predicts solute descriptors with high accuracy. Both models are available as applications on Hugging Face, facilitating easy predictions from SMILES strings. This research highlights the potential of LLMs in chemistry applications, offering practical tools for solvent comparison and expanding the applicability of Abraham solvation equations to a broader range of organic solvents.
The Hepatitis B Virus PreS1/HBsAg Ratio Is a Predictive Marker for the Occurrence of Hepatocellular Carcinoma
Authors: Kosaka Masanari , Fujino Hatsue , Tsuge Masataka , Uchikawa Shinsuke , Ono Atsushi , Murakami Eisuke , Kawaoka Tomokazu , Miki Daiki , Hayes C. Nelson , Oka Shiro
Published: August 2024

The preS1 region of the large hepatitis B virus (HBV) surface protein is a crucial component in HBV infection; however, its impact on the development of hepatocellular carcinoma (HCC) remains unknown. This study investigated the relationship between serum preS1 levels and hepatocarcinogenesis in patients with chronic hepatitis B (CHB). The preS1 levels were measured in 531 patients with CHB without a history of HCC. Among the patients, 293 HBV carriers who had never received nucleotide/nucleoside analog (NA) therapy had their preS1 levels measured at their first visit (non-NA group), and 238 patients who had received NA therapy had their preS1 levels measured at the start of NA administration (NA group). The two groups had no significant differences in hepatitis B surface antigen (HBsAg) levels; however, the NA group’s preS1/HBsAg ratio was significantly higher. The preS1/HBsAg ratio was significantly different between patients with CHB not meeting the NA treatment criteria and patients with chronic hepatitis and cirrhosis who were eligible for NA treatment. The predictors of HCC development were analyzed, and the preS1/HBsAg ratio was identified in both groups. The preS1/HBsAg ratio could predict hepatocarcinogenesis in patients with CHB with or without NA administration.
Mechanical Properties and Mechanism of Geopolymer Cementitious Materials Synergistically Prepared Using Red Mud and Yellow River Sand
Authors: Jin Weizhun , Chen Yiming , Lv Yajun , Jiang Linhua , Bai Weifeng , Zhang Kangjie , Song Caihong , Zhang Xianlei
Published: August 2024

In order to reduce the negative impact on the environment caused by the massive accumulation of red mud (RM) and Yellow River sand (YRS), new alkali-excited granulated blast-furnace slag (GGBS)/RM/YRS (AGRY) geopolymer cementitious materials were prepared by combining RM and YRS with GGBS in different ratios and using sodium silicate as the alkali exciter. The effects of YRS dosage and different curing conditions on the mechanical properties, hydration products, and pore structure of cementitious materials were investigated and analyzed in terms of cost and carbon emissions. The results showed that when the dosage of YRS was 40%, the compressive strength of the prepared AGRY cementitious material could reach 48.8 MPa at 28 d under standard curing, which showed mechanical properties comparable to those of the cementitious material without YRS. The cementitious material has a more compact internal structure, and the combination of RM and YRS promotes the chemical reaction of Al and Si elements and generates the (N, C)-A-S-H gel products, which are the key to the strength enhancement of the cementitious material. In addition, the prepared cementitious material is only 90% of the cement cost for the same strength and has low carbon emission accounting for only 43% of the cement carbon emission. This study not only provides a new way for the resource utilization of RM and YRS, but also contributes an excellent new environmentally friendly material for the construction industry to achieve the goal of low carbon development.
Zirconia and Crofer Joint Made by Reactive Air Brazing Using the Silver Base Paste and Cu-Ti Coating Layer
Authors: Chang Shu-Wei , Shiue Ren-Kae , Huang Liang-Wei
Published: August 2024

This study proposes a method to enhance the airtightness of the joint between the ZrO2 and Crofer alloy using coating technology. With the aid of vacuum sputtering technology, a titanium–copper alloy layer with a thickness between 1.5 μm and 6 μm was first deposited on the surface of ZrO2 and Crofer, respectively. The chemical composition of the deposited reaction layer was 70.2 Cu and 29.8 Ti in at%. Then, using silver as the base material in the reactive air brazing (RAB) process, we explore the use of this material design to improve the microstructure and reaction mechanism of the joint surface between ceramics and metal, compare the effects of different pretreatment thicknesses on the microstructure, and evaluate its effectiveness through air tightness tests. The results show that a coating of Cu-Ti alloy on the ZrO2 substrate can significantly improve bonding between the Ag filler and ZrO2. The Cu-Ti metallization layer on the ZrO2 substrate is beneficial to the RAB. After the brazing process, the coated Cu-Ti layers form suitable reaction interfaces between the filler, the metal, the filler, and the ceramic. In terms of coating layer thickness, the optimized 3 μm coated Cu-Ti alloy layer is achieved from the experiment. Melting and dissolving the Cu-Ti coated layer into the ZrO2 substrate results in a defect-free interface between the Ag-rich braze and the ZrO2. The air tightness test result shows no leakage under 2 psig at room temperature for 28 h. The pressure condition can still be maintained even under high-temperature conditions of 600 °C for 24 h.
Friction and Wear Behavior of Double-Walled Carbon Nanotube-Yttria-Stabilized ZrO2 Nanocomposites Prepared by Spark Plasma Sintering
Authors: Kasperski Anne , Alkattan Dalya , Turq Viviane , Estournès Claude , Laurent Christophe , Weibel Alicia
Published: August 2024

Double-walled carbon nanotube-yttria-stabilized ZrO2 nanocomposites are prepared by a mixing route followed by Spark Plasma Sintering. The double-walled carbon nanotubes (DWCNTs) have been previously subjected to a covalent functionalization. The nanocomposites present a high densification and show a homogenous dispersion of DWCNTs into a matrix about 100 nm in size. The DWCNTs are well distributed at the matrix grain boundaries but form larger bundles upon the increase in carbon content. The Vickers microhardness of the nanocomposites decreases regularly upon the increase in carbon content. Incorporation of carbon at contents higher than 2 wt.% results in significantly lower friction coefficients, both against alumina and steel balls, possibly because of the elastic deformation of the DWCNTs at the surface of the sample. Their presence also favors a reduction of the steel/ceramic contacts and reduces the wear of the steel ball at high loads. DWCNTs improve wear resistance and reduce friction without incurring any severe damage, contrary to multi-walled carbon nanotubes.
Mechanical and Fatigue Properties of Ti-6Al-4V Alloy Fabricated Using Binder Jetting Process and Subjected to Hot Isostatic Pressing
Authors: Alegre Jesús Manuel , Díaz Andrés , García Ruben , Peral Luis Borja , Lorenzo-Bañuelos Miriam , Cuesta Isidoro Iván
Published: August 2024

Binder jetting 3D printing is an additive manufacturing technique based on the creation of a part through the selective bonding of powder with an adhesive, followed by a sintering process at high temperature to densify the material and produce parts with acceptable properties. Due to the high initial porosity in the material after sintering, which is typically around 5%, post-sintering treatments are often required to increase the material density and enhance the mechanical and fatigue properties of the final component. This paper focuses on the study of the benefits of hot isostatic pressing (HIP) after sintering on the mechanical and fatigue properties of a binder jetting Ti-6Al-4V alloy. Two different HIP processes were considered in this study: one at 920 °C/100 MPa for 4 h, and a second at a higher pressure but lower temperature (HIP-HPLT) at 850 °C/200 MPa for 2 h. The effects of the HIP on the densification, microstructure, mechanical behavior, and fatigue properties were investigated. The results show that the HIP-HPLT process produced a significant increase in the mechanical and fatigue properties of the material compared with the as-sintered parts and even with the conventional HIP process. However, the fatigue and fracture micromechanisms suggest that the oxygen content, which resulted from the decomposition of the binder during the sintering process, played a critical role in the final mechanical properties. Oxygen could reduce the ductility and fatigue life, which deviated from the behavior observed in other additive manufacturing techniques, such as powder bed fusion (PBF).
Advances in Nickel-Containing High-Entropy Alloys: From Fundamentals to Additive Manufacturing
Authors: Gupta Ashish Kumar , Choudhari Amit , Rane Aditya , Tiwari Abhishek , Sharma Prince , Gupta Ashutosh , Sapale Prathamesh , Tirumala Ravi Teja A. , Muthaiah Rajmohan , Kumar Abhishek
Published: August 2024

High-entropy alloys (HEAs) are recognized as a class of advanced materials with outstanding mechanical properties and corrosion resistance. Among these, nickel-based HEAs stand out for their impressive strength, ductility, and oxidation resistance. This review delves into the latest advancements in nickel-containing HEAs, covering their fundamental principles, alloy design strategies, and additive manufacturing techniques. We start by introducing HEAs and their unique properties, emphasizing the crucial role of nickel. This review examines the complex relationships between alloy composition, valence electron concentration (VEC), and the resulting crystal structures. This provides insights into design principles for achieving desired microstructures and mechanical properties. Additive manufacturing (AM) techniques like selective laser melting (SLM), electron beam melting (EBM), and laser metal deposition (LMD) are highlighted as powerful methods for fabricating intricate HEA components. The review addresses the challenges of AM processes, such as porosity, fusion defects, and anisotropic mechanical properties, and discusses strategies to mitigate these issues through process optimization and improved powder quality. The mechanical behavior of AM-processed nickel-based HEAs is thoroughly analyzed, focusing on compressive strength, hardness, and ductility. This review underscores the importance of microstructural features, including grain size, phase composition, and deformation mechanisms, in determining the mechanical performance of these alloys. Additionally, the influence of post-processing techniques, such as heat treatment and hot isostatic pressing (HIP) on enhancing mechanical properties is explored. This review also examines the oxidation behavior of nickel-containing HEAs, particularly the formation of protective oxide scales and their dependence on aluminum content. The interplay between composition, VEC, and oxidation resistance is discussed, offering valuable insights for designing corrosion resistant HEAs. Finally, this review outlines the potential applications of nickel-based HEAs in industries such as aerospace, automotive, and energy, and identifies future research directions to address challenges and fully realize the potential of these advanced materials.
Technology for Automated Production of High-Performance Building Compounds for 3D Printing
Authors: Hutyra Adam , Bańkosz Magdalena , Tyliszczak Bożena
Published: August 2024

Three-dimensional printing technology in construction is a rapidly growing field that offers innovative opportunities for design and construction execution. A key component of this process is the automated production of high-performance construction mixtures that meet specific requirements for strength, fluidity, and setting speed. This overview article outlines the history and development of 3D printing technology in the construction industry, describes various printing technologies, and discusses the properties and requirements for construction mixes. Special attention is given to automated systems for batching and mixing ingredients, which increase the precision and efficiency of production. The different types of construction mixes used in 3D printing and the main technical and operational challenges associated with their application are also presented. The article’s conclusions highlight the potential of this technology to revolutionize the construction industry by improving efficiency and reducing costs and project lead times.
Efficient Photodegradation of Thiocyanate Ions in Mining Wastewater Using a ZnO-BiOI Heterojunction
Authors: Ashiegbu Darlington C. , Nkhoesa David , Erasmus Rudolph , Potgieter Herman Johanes
Published: August 2024

Mining industries have long relied on cyanidation as the primary method for gold extraction, but this process generates thiocyanates as a problematic byproduct, posing challenges for wastewater treatment and recycling. The stability of thiocyanates makes their reduction or elimination in mining wastewater difficult. In this study, a p-n heterojunction of ZnO and BiOI was created and evaluated for its ability to photodegrade thiocyanate ions under simulated solar conditions. Various analytical techniques revealed a highly porous structure with a sponge-like morphology and agglomeration in the synthesized heterojunction. The compound exhibited crystalline patterns without impurity peaks, a slight red shift in absorbance, and Type IV isotherm adsorption. The synthesized heterostructure achieved the complete destruction of thiocyanate ions in less than 30 min. The investigation of different process parameters indicated that the destruction of the contaminant by the heterostructure was influenced by the initial thiocyanate concentration, which decreased as the thiocyanate concentration increased. The peak photodestruction reaction was observed at pH 7. By applying a pseudo-first-order kinetic model, it was found that increasing the catalyst mass to 15 mg raised the rate constant from 0.188 to 0.420 min−1, while increasing the pH to 10 led to a 3.5-fold reduction. The strong correlation between the observed data and the predicted values of the pseudo-first-order kinetic model was indicated by the observed (R2) values. The findings of this study hold potential significance for mining industries, as it offers a potential solution for eliminating cyanide and thiocyanates from mining wastewater. The elimination of thiocyanate generation in the cyanidation process is crucial for mining companies, making this study valuable for the industry.
Fatigue Crack Monitoring Method Based on the Lamb Wave Damage Index
Authors: He Muyang , Dong Chengwu , Sun Xiaodan , He Jiayi
Published: August 2024

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure’s service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method.
Mechanical Properties of Eco-Friendly, Lightweight Flax and Hybrid Basalt/Flax Foam Core Sandwich Panels
Authors: Pawlik Marzena , Gunputh Urvashi , Odiyi Daniel , Odofin Sarah , Le Huirong , Wood Paul , Maligno Angelo , Lu Yiling
Published: August 2024

Greener materials, particularly in sandwich panels, are in increasing demand in the transportation and building sectors to reduce environmental impacts. This shift is driven by strict environmental legislation and the need to reduce material costs and fuel consumption, necessitating the utilisation of more sustainable components in the transportation and construction sectors, with improved load-bearing capabilities and diminished ecological footprints. Therefore, this study aims to analyse and evaluate the structural performance of polyethylene terephthalate (PET) core and flax or basalt/flax FRP sandwich panels as an alternative to conventional synthetic materials. The novel eco-friendly sandwich panels were manufactured using the co-curing technique. Four-point bending, edgewise compression and core shear tests were performed and insights into how the skin properties affect the strength, stiffness and failure mode of specimens were provided. The stress–strain behaviour, facing modulus and strength, flexural rigidity, core shear strength and failure modes were evaluated. The flexural facing modulus of the flax and flax/basalt sandwich skins were found to be 5.1 GPa and 9.8 GPa, respectively. The flexural rigidity of the eco-friendly sandwich panel was compared with published results and demonstrated a promising structural performance. The environmental benefits and challenges were outlined and critically evaluated focusing on transportation and construction applications.
Impact of Silicon Carbide Coating and Nanotube Diameter on the Antibacterial Properties of Nanostructured Titanium Surfaces
Authors: dos Santos Calderon Patricia , Chairmandurai Aravindraja , Xia Xinyi , Rocha Fernanda G. , Camargo Samira Esteves Afonso , Lakshmyya Kesavalu , Ren Fan , Esquivel-Upshaw Josephine F.
Published: August 2024

This study aimed to comprehensively assess the influence of the nanotube diameter and the presence of a silicon carbide (SiC) coating on microbial proliferation on nanostructured titanium surfaces. An experiment used 72 anodized titanium sheets with varying nanotube diameters of 50 and 100 nm. These sheets were divided into four groups: non-coated 50 nm titanium nanotubes, SiC-coated 50 nm titanium nanotubes, non-coated 100 nm titanium nanotubes, and SiC-coated 100 nm titanium nanotubes, totaling 36 samples per group. P. gingivalis and T. denticola reference strains were used to evaluate microbial proliferation. Samples were assessed over 3 and 7 days using fluorescence microscopy with a live/dead viability kit and scanning electron microscopy (SEM). At the 3-day time point, fluorescence and SEM images revealed a lower density of microorganisms in the 50 nm samples than in the 100 nm samples. However, there was a consistently low density of T. denticola across all the groups. Fluorescence images indicated that most bacteria were viable at this time. By the 7th day, there was a decrease in the microorganism density, except for T. denticola in the non-coated samples. Additionally, more dead bacteria were detected at this later time point. These findings suggest that the titanium nanotube diameter and the presence of the SiC coating influenced bacterial proliferation. The results hinted at a potential antibacterial effect on the 50 nm diameter and the coated surfaces. These insights contribute valuable knowledge to dental implantology, paving the way for developing innovative strategies to enhance the antimicrobial properties of dental implant materials and mitigate peri-implant infections.
Virtual Reality for Training in Assembly and Disassembly Tasks: A Systematic Literature Review
Authors: Di Pasquale Valentina , Cutolo Paolo , Esposito Carmen , Franco Benedetta , Iannone Raffaele , Miranda Salvatore
Published: August 2024

The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly enhance the intricacy of these manual tasks. Human involvement is essential in these tasks due to their complexity, necessitating a structured learning process to enhance efficiency and mitigate the learning–forgetting cycle. This study focuses on the utilization of virtual reality (VR) as an innovative training tool to address these challenges. By conducting a systematic literature review (SLR) on the impact of VR on training operators for assembly and disassembly tasks, this paper evaluates the current level of VR application, the used technologies, the operator performance, and the VR benefits and limitations. The analysis reveals a limited but promising application of VR in training, highlighting its potential to improve learning outcomes, productivity, and safety while reducing costs. However, the research also identifies gaps in the practical application of VR for training purposes suggesting a future research agenda to explore its full potential.
Enhanced Graph Representation Convolution: Effective Inferring Gene Regulatory Network Using Graph Convolution Network with Self-Attention Graph Pooling Layer
Authors: Alawad Duaa Mohammad , Katebi Ataur , Hoque Md Tamjidul
Published: August 2024

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks’ intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman’s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into ‘Positive’ and ‘Negative’ categories. ‘Positive’ subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. ‘Negative’ subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease.
A Parallel Approach to Enhance the Performance of Supervised Machine Learning Realized in a Multicore Environment
Authors: Ghimire Ashutosh , Amsaad Fathi
Published: August 2024

Machine learning models play a critical role in applications such as image recognition, natural language processing, and medical diagnosis, where accuracy and efficiency are paramount. As datasets grow in complexity, so too do the computational demands of classification techniques. Previous research has achieved high accuracy but required significant computational time. This paper proposes a parallel architecture for Ensemble Machine Learning Models, harnessing multicore CPUs to expedite performance. The primary objective is to enhance machine learning efficiency without compromising accuracy through parallel computing. This study focuses on benchmark ensemble models including Random Forest, XGBoost, ADABoost, and K Nearest Neighbors. These models are applied to tasks such as wine quality classification and fraud detection in credit card transactions. The results demonstrate that, compared to single-core processing, machine learning tasks run 1.7 times and 3.8 times faster for small and large datasets on quad-core CPUs, respectively.
Sentiment Analysis: Predicting Product Reviews for E-Commerce Recommendations Using Deep Learning and Transformers
Authors: Bellar Oumaima , Baina Amine , Ballafkih Mostafa
Published: August 2024

The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers’ perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women’s clothes retailer.
A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm
Authors: Wu Jianwei , Wang Jiaqi , Chen Huanguo
Published: August 2024

Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method’s superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems.
Methodology for Smooth Transition from Experience-Based to Data-Driven Credit Risk Assessment Modeling under Data Scarcity
Authors: Li Hengchun , Lan Qiujun , Xiong Qingyue
Published: August 2024

Credit risk refers to the possibility of borrower default, and its assessment is crucial for maintaining financial stability. However, the journey of credit risk data generation is often gradual, and machine learning techniques may not be readily applicable for crafting evaluations at the initial stage of the data accumulation process. This article proposes a credit risk modeling methodology, TED-NN, that first constructs an indicator system based on expert experience, assigns initial weights to the indicator system using the Analytic Hierarchy Process, and then constructs a neural network model based on the indicator system to achieve a smooth transition from an empirical model to a data-driven model. TED-NN can automatically adapt to the gradual accumulation of data, which effectively solves the problem of risk modeling and the smooth transition from no to sufficient data. The effectiveness of this methodology is validated through a specific case of credit risk assessment. Experimental results on a real-world dataset demonstrate that, in the absence of data, the performance of TED-NN is equivalent to the AHP and better than untrained neural networks. As the amount of data increases, TED-NN gradually improves and then surpasses the AHP. When there are sufficient data, its performance approaches that of a fully data-driven neural network model.
An Enhanced Reptile Search Algorithm for Inverse Modeling of Unsaturated Seepage Parameters in Clay Core Rockfill Dam Using Monitoring Data during Operation
Authors: Huang Zhangxin , Shen Zhenzhong , Xu Liqun , Sun Yiqing
Published: August 2024

The seepage characteristics of clay core walls are crucial for the seepage safety of core rockfill dams, with the permeability coefficient in the unsaturated zone being nonlinear. To accurately determine the unsaturated seepage parameters in clay core rockfill dams, this paper first proposes an enhanced reptile search algorithm (ERSA) by applying three improvement strategies: Arnold’s cat chaotic map, nonlinear evolutionary factor, and adaptive Cauchy–Gaussian mutation with variable weight. Then, by integrating the ERSA with the unsaturated seepage finite element method, an inverse modeling approach is developed. This approach is applied to an actual rockfill dam with operational monitoring data to determine the unsaturated seepage parameters of the clay core. Results indicate that the ERSA outperforms the original RSA in test functions, and the calculation results of the seepage parameters determined through inversion are consistent with the monitoring data, showing an overall mean absolute error of 1.086 m. The inverse modeling approach provides a valuable reference for determining unsaturated seepage parameters in similar clay core rockfill dams.
The Challenges and Advances in Recycling/Re-Using Powder for Metal 3D Printing: A Comprehensive Review
Authors: Lanzutti Alex , Marin Elia
Published: August 2024

This review explores the critical role of powder quality in metal 3D printing and the importance of effective powder recycling strategies. It covers various metal 3D printing technologies, in particular Selective Laser Melting, Electron Beam Melting, Direct Energy Deposition, and Binder Jetting, and analyzes the impact of powder characteristics on the final part properties. This review highlights key challenges associated with powder recycling, including maintaining consistent particle size and shape, managing contamination, and mitigating degradation effects from repeated use, such as wear, fragmentation, and oxidation. Furthermore, it explores various recycling techniques, such as sieving, blending, plasma spheroidization, and powder conditioning, emphasizing their role in restoring powder quality and enabling reuse.