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The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

This paper aims to study the application of high-tolerance and flexible indigenous bacteria and fungi, along with the co-metabolism in recycled paper and cardboard mill (RPCM) wastewater treatment (WWT).

The molecular characterization of isolated indigenous bacteria and fungi was performed by 16S rRNA and 18S rRNA gene sequencing, respectively. Glucose was used as a cometabolic substrate to enhance the bioremediation process.

The highest removal efficiency was achieved for both chemical oxygen demand (COD) and color [78% COD and 45% color removal by Pseudomonas aeruginosa RW-2 (MZ603673), as well as approximately 70% COD and 48% color removal by Geotrichum candidum RW-4 (ON024394)]. The corresponding percentages were higher in comparison with the efficiency obtained from the oxidation ditch unit in the full-scale RPCM WWT plant.

Indigenous P. aeruginosa RW-2 and G. candidum RW-4 demonstrated effective capability in RPCM WWT despite the highly toxic and low biodegradable nature, especially with the assistance of glucose.

An experimental investigation for developing structure-property correlations of hot-rolled E410 steels with different carbon contents, i.e. 0.04wt.%C and 0.17wt.%C metal active gas (MAG) and cold metal transfer (CMT)-MAG weldments was undertaken.

Mechanical properties and microstructure of MAG and CMT-MAG weldments of two E410 steels with varying content of carbon were compared using standardized mechanical testing procedures, and conventional microscopy.

0.04wt.%C steel had strained ferritic and cementite sub-structures in blocky shape and large dislocation density, while 0.17wt.%C steel consisted of pearlite and polygonal ductile ferrite. This effected yield strength (YS), and microhardness being larger in 0.04wt.%C steel, %elongation being larger in 0.17wt.%C steel. Weldments of both E410 steels obtained with CMT-MAG performed better than MAG in terms of YS, ultimate tensile strength (UTS), %elongation, and toughness. It was due to low heat input of CMT-MAG that resulted in refinement of weld metal, and subzones of heat affected zone (HAZ).

A substantial improvement in YS (∼9%), %elongation (∼38%), and room temperature impact toughness (∼29%) of 0.04wt.%C E410 steel is achieved with CMT-MAG over MAG welding. Almost ∼10, ∼12.5, and ∼16% increment in YS, %elongation, and toughness of 0.17wt.%C E410 steel is observed with CMT-MAG. Relatively low heat input of CMT-MAG leads to development of fine Widmanstätten and acicular ferrite in weld metal and microstructural refinement in HAZ subzones with nearly similar characteristics of base metal.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

This paper aims to explore how the owners of less competitive micro-firms (MFs) perceive the “crisis–innovation–change management” triangle. It examines whether their understanding of these overarching entrepreneurship theory principles is inadequate compared to the relevant scientific literature.

This qualitative analysis follows principles based on the inductive method and grounded theory, thickly describing the results from research conducted in a sample of 38 tertiary-sector MFs in the Eastern Macedonia and Thrace region – one of the least developed and competitive areas across Europe. It triangulates the data with 11 respective small firms.

MF owners perceive the crisis as an ostensibly exogenous phenomenon, innovation as something quasi-unattainable – although vaguely significant – and change management as a relatively unknown process. This understanding lies somewhat distant from the extant literature that examines the structural nature of crises, the innovational power to exit profound restructurings and the rebalancing requisite for building new overall organizational methods to survive this internal–external transformation. In essence, the triangle crisis–innovation–change management is a blind spot for the examined MF owners as they ignore its significance as an adaptation mechanism – contrary to several direct competitors.

Based on the reluctance of these individuals to cultivate their systematic business knowledge, it seems unrealistic that they would seek to pay the necessary high price for business consulting in the future. An ideal solution would be to build public entrepreneurship clinics to provide these less dynamic and adaptable organizations with free preliminary or in-depth counseling. The Institute of Local Development-Innovation could aim to provide free consulting services to reinforce organizational physiology by coordinating different socioeconomic actors.

To the best of our knowledge, this empirical research is one of the first to test the comprehension of weaker MFs – less competitive and developed in organizational terms – to the triangle crisis–innovation–change management.

This paper aims to achieve phosphating via optimal features of Mg metal as a suitable base coating, which is considered for other properties such as barrier properties against the passage of several factors.

In this research, in the phosphate bath, immersion time, temperature and the content of sodium nitrite as an accelerator were changed.

As a result, increasing the immersion time of AZ31 Mg alloy samples in the phosphating bath as well as increasing the ratio of sodium dodecyl sulfate (SDS) concentration to sodium nitrite concentration in the phosphating bath formulation increase the mass of phosphating formed per unit area of the Mg alloy. The results of the scanning electron microscope test showed phosphating is not completely formed in short immersion times, which is a thin and uneven layer.

Mg and its alloys are sensitive to galvanic corrosion, which would lead to generating several holes in the metal. As such, it causes a decrease in mechanical stability as well as an unfavorable appearance.

Mg is used in several industries such as automobile and computer parts, mobile phones, astronaut compounds, sports goods and home appliances.

Nevertheless, Mg has high chemical reactivity, so an oxide-hydroxide layer is formed on its surface, which has a harmful effect on the adhesion and uniformity of the coating applied on Mg.

By increasing the ratio of SDS concentration to sodium nitrite concentration in the phosphating bath, the corrosion resistance of the phosphating increases.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

The purpose of this paper is to investigate the effect of NaCl solution with different concentrations on impact-sliding fretting corrosion behavior of Inconel 690TT steam generator heat transfer tubes.

The optical 3D profiler was used to measure the wear profile and calculated the wear volume. Corrosion behavior was studied using open circuit potential monitoring and potentiodynamic polarization testing. The morphologies and elemental distributions of wear scars were analyzed using scanning electron microscopy and energy-dispersive spectroscopy. The synergism of wear and corrosion was analyzed according to the ASTM G119 standard.

The corrosion tendency reflected by OCP and the corrosion current calculated by Tafel both increased with the increase of NaCl concentration. The total volume loss of the material increased with concentration, and it was known from the synergism that the volume loss caused by corrosion-enhanced wear accounted for the largest proportion, while the wear-enhanced corrosion also made a greater contribution to volume loss than tangential fretting corrosion. Through the analysis of the material morphologies and synergism of wear and corrosion, the damage mechanism was elucidated.

The research findings can provide reference for impact-sliding fretting corrosion behavior of Inconel 690TT heat transfer tubes in NaCl solution with different concentrations.

This study aims to investigate the corrosion behavior of cold-rolled Fe₃₅Ni₂₀C_r12Mn_(28-x)Al_x high-entropy alloys (HEAs) using the potentiodynamic polarization technique in 1 M H₂SO₄ acid. Additionally, the influence of molybdenum (Mo) additions as inhibitors and the effect of variations in cold rolling reduction ratios and Al content on corrosion behavior are examined.

Two cold rolling reduction ratios, namely, 50% (R50) and 90% (R90), were examined for the cold-rolled Fe₃₅Ni₂₀Cr₁₂Mn₂₈Al₅ (Al₅) and Fe₃₅Ni₂₀Cr₁₂Mn₂₃Al₁₀ (Al₁₀) HEAs. Mo inhibitor additions were introduced at varying concentrations of 0.3, 0.6 and 0.9 Wt.%. The potentiodynamic polarization technique was used to evaluate the corrosion rates (CRs) under different experimental conditions.

The results indicate that the addition of 0.3 Wt.% Mo in 1 M H₂SO₄ yielded the lowest CR for both R50 and R90, irrespective of the Al content in the HEAs. However, the highest CR was observed at 0.6 Wt.% Mo addition. Furthermore, increasing the concentration of Al resulted in a corresponding rise in the CR. Comparatively, the CR decreased significantly when the cold rolling reduction ratio increased from R50 to R90.

This research provides valuable insights into the intricate relationship between Mo inhibitors, cold rolling reduction ratio, Al content and the resulting corrosion behavior of Fe₃₅Ni₂₀Cr₁₂Mn_(28-x)Al_x HEAs. The comprehensive analysis of corroded HEAs, including surface morphology, compositions and elemental distribution mapping, contributes to the understanding of the corrosion mechanisms and offers potential strategies for enhancing the corrosion behavior of HEAs.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.