Search

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

Hyperbola fitting is a mainstream interpretation technique used in ground-penetrating radar (GPR) due to its simplicity and relatively low computational requirements. Conventional hyperbola fitting is based on the assumption that the investigated medium is a homogeneous half-space, and that the target is an ideal reflector with zero radius. However, the zero-radius assumption can be easily removed by formulating the problem in a more generalized way that considers targets with arbitrary size. Such approaches were recently investigated in the literature, suggesting that hyperbola fitting can be used not only for estimating the velocity of the medium, but also for estimating the radius of subsurface cylinders, a very challenging problem with no conclusive solution to this day. In this letter, through a series of synthetic and laboratory experiments, we demonstrate that for practical GPR survey, hyperbola fitting is not suitable for simultaneously estimating both the velocity of the medium and the size of the target, due to its inherent nonuniqueness, making the results unreliable and sensitive to noise.

Industry 4.0 promises to increase productivity and flexibility for the manufacturing industry, which translates into greater customer value and lower costs. Lean manufacturing has long championed principles and tools with a focus on value adding activities, elimination of waste and continuous improvement. Even the most successful lean manufacturing firms, in terms of efficiency and quality achieved through waste reduction, will acknowledge there is a lot of opportunity for improvement. This review evaluates how lean principles can support Industry 4.0 in pursuit of greater customer value and manufacturing excellence. Leveraging the opportunities that exist within Industry 4.0 umbrella of technologies can help to further reduce the nine wastes of lean. While large corporations have access to extensive capital markets and can capture economies of scale offered by leading edge technologies of Industry 4.0, small and medium sized enterprises (SMEs) with greater capital restraints face the challenge of justifying Industry 4.0 technologies and cannot risk being at the bleeding edge of technology. Using a case study of a small electronics manufacturing firm, the opportunities, challenges, and the implementation strategy for technology are examined. This paper finds that SMEs pursuit of process efficiencies and waste reduction can be best achieved through a focus on foundational digitalization & data management then taking a stepwise approach towards the cyber-physical systems of Industry 4.0.

Recent advances in manufacturing research have set the stage for the industrial integration of zero-defect manufacturing strategies, aiming towards a more sustainable production paradigm. A systematically deployment guideline to implement zero-defect manufacturing is needed to transform the future cyber-physical factory floor. This paper describes a novel framework based on a well-known cyber-physical architecture that provides advanced information analytics, robust information flows, and data acquisition systems that support defect detection and prediction introduces repair technologies and sets up procedures for preventive maintenance. Through continuous inspection of product and equipment status during the manufacturing process, raw quality and tool health data from different sources is used to obtain key performance indicators. Statistical tools such as cross-correlation are then applied to quantify underlying relationships between product quality specifications and equipment health. The resulting correlations are then used to reduce non-conformance of products manufactured by implementation of preventive maintenance. A unified implementation for zero-defect manufacturing cyber-physical processes eases their integration in future Industry 4.0 facilities and validated in the context of offsite construction manufacturing of steel frame assemblies.

Concrete-infilled hollow flange cold-formed steel (CF-HFCFS) beams have gained attention in the construction practices owing to many benefits in terms of their structural performances and applicability. The concrete infill ensures better structural performance by restraining the buckling instabilities of thin-walled cold-formed steel elements. However, the shear strength characteristics of CF-HFCFS are not systematically explored yet and hence there is a lack of understanding on the shear strength characteristics of CF-HFCFS beams. Therefore, in this research, shear characteristics were investigated through numerical studies by establishing and analysing three-dimensional finite element (FE) models of CF-HFCFS beams. The developed FE models were verified against the experimental data in terms of failure modes, ultimate shear capacities, and load-displacement characteristics. Then a series of parametric analyses were carried out to investigate the shear behaviour of CF-HFCFS beams against the effects of geometrical (steel thickness, beam depth) and mechanical (yield strength of steel and compressive strength of concrete) properties to further verify the shear characteristics of CF-HFCFS. Lightweight normal and lightweight high strength concrete materials were considered as infill. Also, the influence of the concrete infill on the ultimate shear capacity of the CF-HFCFS beams was evaluated through parametric studies. The ultimate shear capacities were compared against the already available design provisions. Consequently, based on the data established through of parametric analyses, modified design provisions are developed to estimate the ultimate shear capacity of CF-HFCFS beams.

Current legislation focuses on reducing the operational carbon impact of buildings. However, the production of materials used in construction generates a considerable amount of carbon, known as embodied carbon, that accounts for a sizeable fraction of the environmental impact of a building during its lifecycle. We present a newly developed tool, pycab, which calculates the embodied carbon of a building directly at the design stage and compares it to the Royal Institute of British Architects (RIBA) 2030 Climate Challenge Target Benchmarks. As input, the tool uses standard Industry Foundation Classes (IFC) files that can be produced directly from existing Building Information Modelling (BIM) software. The pycab tool enables industry professionals to make design stage decisions that reduce the embodied carbon impact of their projects. This research demonstrates one of the many potential uses that digital tools can have in reducing the environmental impact of the construction industry.

Highly permeable zeolite membranes are desirable for fast gas separation in the industry. Reducing the membrane's thickness is deemed to be an optimal solution for permeability improvement. Herein, we report the synthesis route of thin SSZ‐13 zeolite membranes via the conversion of template‐contained surface gels. The synthesis gel is fully crystallized into crack‐free SSZ‐13 membranes assisted with dual templates of N, N, N‐trimethyl‐1‐adamantammonium hydroxide (TMAdaOH) and tetraethylammonium hydroxide (TEAOH). The specific functions of TMAdaOH for structure directing and TEAOH for crystallization regulating are well discussed. Thin surface gel layer is impregnated onto porous alumina with subsequent crystallization into a 500 nm thick membrane. This submicron‐thick membrane exhibits high H2 permeance with 50 × 10−8 mol s−1 m−2 Pa−1 during hydrogen separation. Meanwhile, the separation factors are retained around 23.0 and 31.5 for H2/C2H6 and H2/C3H8, respectively. This approach offers a possibility for obtaining high‐quality zeolite membranes for efficient hydrogen separation.

Nanomaterials such as graphene have been added to various matrices to enhance mechanical, thermal and electrical properties for various applications requiring intricate designs at the micro-scale. At this scale, mechanical micro-machining is utilised as post-processing to achieve high surface quality and dimensional accuracy while still maintaining high productivity. Therefore, in this study, the machinability of polymer nanocomposites in micro-scale (micro-machinability) is investigated. Graphene (0.3 wt%)-reinforced epoxy nanocomposites were fabricated using traditional solution mixing and moulding. The samples were then subjected to micro-milling at various cutting speeds using three different micro-tools, including uncoated, diamond and diamond-like carbon (DLC) tools. Mechanical and thermal properties of nanocomposite were also used to support the discussions. The result indicates that the DLC-coated tool shows better performance than the other tools for less tool wear, improved surface quality and less cutting forces.

Multi-project management and uncertain environment are very common factors, and they bring greater challenges to scheduling due to the increase of problem complexity and response efficiency requirements. In this paper, a novel hyper-heuristic based filtering genetic programming (HH-FGP) framework is proposed for evolving priority rules (PRs) to deal with a multi-project scheduling problem considering stochastic activity duration and new project insertion together, namely the Stochastic Resource Constrained Multi-Project Scheduling Problem under New Project Insertions (SRCMPSP-NPI), within heuristic computation time. HH-FGP is designed to divide traditional evolution into sampling and filtering evolution for simultaneously filtering two kinds of parameters constituting PRs, namely depth range and attribute, to obtain more effective PRs. Based on this, the existing genetic search and local search are improved to meet the depth constraints, and a multi-objective evaluation mechanism is designed to achieve effective filtering. Under the existing benchmark, HH-FGP is compared and analysed with the existing methods to verify its effectiveness.

This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 238 results of carbon steel LiteSteel sections were gathered to train machine learning models including support vector regression (SVR), multi-layer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGB). The cross-validation with 10 folds has been conducted in the training process to avoid over-fitting. The optimal hyperparameter combinations for each machine learning model were found during the hyperparameter tuning process and four performance indicators were used to evaluate the performance of the trained models. The comparison results suggest that all four implemented machine learning models reliably predict the shear capacity of both stainless steel lipped channel sections and carbon steel LiteSteel sections while the implemented SVR algorithm is found to be the best performing model. Moreover, it is shown that the implemented machine learning models exceed the prediction accuracy of the available design equations in estimating the shear capacity of steel channel sections.

In this work, the percentage of polyethylene (PE) and polypropylene (PP) blend is controlled at 50:50 to make the composite by forming a co-continuous structure with hexagonal boron nitride (h-BN). The h-BN is wrapped by thermoplastic elastomer styrene-ethylene-propylene-styrene (SEPS). This approach enables the localized distribution of h-BN at the interface of the co-continuous structure of PE/PP blend and SEPS phases, allowing the construction of a heat conduction path in the SEPS phase, thereby improving the thermal conductivity of PE/PP. Theoretical calculation predicted the localized distribution of SEPS at the interface of the PE/PP blend to form a co-continuous composite structure. The thermal conductivity of the composites can be improved by 57.7% by adding 10 wt% h-BN, presenting a commercial potential of such composites in certain heat dissipation applications.

Texture is required on pavements to provide safe and comfortable ride performance for users. This paper provides the first meaningful analysis of a long-term study of texture data obtained using TRACS (TRAffic Speed Condition Survey) at a site in the UK. TRACS data were collected annually, over a 2 km stretch of motorway from 1995 to 2019. A new data analysis approach utilising time series data with spectral analysis and spatial filtering procedures is presented. The results reveal that the approach enables legacy TRACS laser profile Sensor Measured Texture Depth (SMTD) data to be used to determine long term rates of change in road surface macrotexture. Thus, the technique has unlocked the potential for SMTD data collected annually for 7000 km of the Strategic Road Network in the UK, to inform road maintenance programmes by extrapolation. Additionally, results expose a systematic periodicity occurring each year within the SMTD data studied, corresponding to longitudinal oscillations with wavelengths between 33 and 62 m. The time-invariant periodicity of these oscillations suggests that it is ‘imprinted’ in the early life of the pavement. ‘Imprinting’ may theoretically arise with cyclic tyre loading applied by the suspension systems of heavy vehicles or during road construction.

The classic Cantor (FeCoCrMnNi) isoatomic high entropy alloy was modified by separate additions of Mo and Nb in an effort to optimize its mechanical properties and sliding wear response. It was found that the introduction of Mo and Nb modified the single phase FCC solid solution structure of the original alloy and led to the formation of new phases such as the BCC solid solution, σ-phase, and Laves, along with the possible existence of intermetallic phases. The overall phase formation sequence was approached by parametric model assessment and solidification considerations. Nanoindentation-based mechanical property evaluation showed that due to the introduction of Mo and Nb; the modulus of elasticity and microhardness were increased. Creep nanoindentation assessment revealed the beneficial action of Mo and Nb in increasing the creep resistance based on the stress sensitivity exponent, strain rate sensitivity, and critical volume for the dislocation nucleation considerations. The power law and power law breakdown were identified as the main creep deformation mechanisms. Finally, the sliding wear response was increased by the addition of Mo and Nb with this behavior obeying Archard’s law. A correlation between microstructure, wear track morphologies, and debris characteristics was also attempted.

Digital twin (DT) is a virtual mirror (representation) of a physical world or a system along its lifecycle. As for a complex discrete manufacturing system (DMS), it is a digital model for emulating or reproducing the functions or actions of a real manufacturing system by giving the system simulation information or directly driven by a real system with proper connections between the DT model and the real-world system. It is a key building block for smart factory and manufacturing under the Industry 4.0 paradigm. The key research question is how to effectively create a DT model during the design stage of a complex manufacturing system and to make it usable throughout the system’s lifecycle such as the production stage. Given that there are some existing discussions on DT framework development, this paper focuses on the modeling methods for rapidly creating a virtual model and the connection implementation mechanism between a physical world production system at a workshop level and its mirrored virtual model. To reach above goals, in this paper, the discrete event system (DES) modeling theory is applied to the three-dimension DT model. First, for formally representing a manufacturing system and creating its virtual model, seven basic elements: controller, executor, processor, buffer, flowing entity, virtual service node and logistics path of a DMS have been identified and the concept of the logistics path network and the service cell is introduced to uniformly describe a manufacturing system. Second, for implementing interconnection and interaction, a new interconnection and data interaction mechanism between the physical system and its virtual model for through-life applications has been designed. With them, each service cell consists of seven elements and encapsulates input/output information and control logic. All the discrete cells are constructed and mapped onto different production-process-oriented digital manufacturing modules by integrating logical, geometric and data models. As a result, the virtual-physical connection is realized to form a DT model. The proposed virtual modeling method and the associated connection mechanism have been applied to a real-world workshop DT to demonstrate its practicality and usefulness.

Currently, damage identification and localization in remanufacturing is a manual visual task. It is time-consuming, labour-intensive. and can result in an imprecise repair. To mitigate this, an automatic vision-based damage localization method is proposed in this paper that integrates a camera in a robotic laser cladding repair cell. Two case studies analyzing different configurations of Faster Region-based Convolutional neural networks (R-CNN) are performed. This research aims to select the most suitable configuration to localize the wear on damaged fixed bends. Images were collected for testing and training the R-CNN and the results of this study indicated a decreasing trend in training and validation losses and a mean average precision (mAP) of 88.7%.

Composite industry increasingly uses natural fibres because of their environment-friendly advantages. These natural fibres may swell during the mould filling process when they absorb resin, and this swelling reduces the porosity and permeability of the preform. Hence, computational modelling of the flow in swelling porous media would be useful to model the different mould filling processes with the swelling effect. This paper demonstrates the possibility of using computational fluid dynamics to study the effect of swelling on liquid composite moulding mould filling in isotropic and orthotropic porous media. An empirical relation for local permeability changes is used to model the flow of resin under constant volume flow rate and constant injection pressure conditions. The flow front locations and inlet pressure predicted by the computational fluid dynamics simulations are in good agreement with the experimental data for 1D rectilinear flow case. Further, to capture the flow patterns, two different arrangements employing point injection are considered. It was observed that the volume fraction of resin in swelling porous medium is 6% less than rigid porous medium at any given time. It was also observed that the location of the inlet and outlet has a considerable effect on the flow front advancement.

In this study, turbulent flow characteristics of CuO-water nanofluid through heat exchanger pipe enhanced with louvered strips are numerically investigated. Nanoparticles volume fraction (ϕ) varied from 0 to 2%. The louvered strips are mounted in single and double geometries. The slant angle (θ) and the Reynolds number (Re) are within 15° − 25° and between 5000 and 14,000, respectively. (RNG) k − ϵ model is employed based on the finite volume technique. The results illustrated that strong flow disturbance between the wall and the louvered strip is the main reason for turbulent kinetic energy increment. Besides, the nanoparticles improve the thermophysical properties of the working fluid, which results in better heat transfer. The Nu number increases 15.6% by using nanofluid instead of water at Re = 14000. The highest thermal enhancement parameter of 1.99 is obtained at Re = 14000 by using double perforated louvered strip with θ = 25°. The recirculating flow inside the holes can significantly improve the thermal performance.

A product-service system (PSS) has many engineering characteristics (ECs), their design is a critical work in PSS planning, which has an important influence on the cost and quality of PSS. How to design a reasonable PSS-ECs scheme, and evaluate its life cycle cost (LCC) is a challenging task. Aiming at the PSS-ECs design for LCC, this paper proposes a new PSS design method, it first treats and models the design of PSS-ECs as a customer requirements-based constraint satisfaction problem (CSP) for finding an initial set of satisfied PSS-ECs schemes,and then it evaluates these schemes based on Bayesian network (BN)-based LCC estimation model for finding an optimal scheme as a solution. Constructing a BN describing the uncertain relationships between PSS-ECs and LCC is the core of this research. By combining existing R&D data and expert experience, Bayesian estimation and arithmetic averaging are used to estimate the conditional probability in BN. Take a subway bogie and its maintenance service in a Chinese company as an example to verify the proposed method. The results show that the proposed method can effectively solve the problem of PSS-ECs design for LCC, it also shows that this method has positive significance in realizing engineering knowledge consolidation, assisting designers in exploring design space, and improving the rationality of design decisions.

Sustainable solutions on fabricating and using a face mask to block the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) spread during this coronavirus pandemic of 2019 (COVID‐19) are required as society is directed by the World Health Organization (WHO) toward wearing it, resulting in an increasingly huge demand with over 4 000 000 000 masks used per day globally. Herein, various new mask technologies and advanced materials are reviewed to deal with critical shortages, cross‐infection, and secondary transmission risk of masks. A number of countries have used cloth masks and 3D‐printed masks as substitutes, whose filtration efficiencies can be improved by using nanofibers or mixing other polymers into them. Since 2020, researchers continue to improve the performance of masks by adding various functionalities, for example using metal nanoparticles and herbal extracts to inactivate pathogens, using graphene to make masks photothermal and superhydrophobic, and using triboelectric nanogenerator (TENG) to prolong mask lifetime. The recent advances in material technology have led to the development of antimicrobial coatings, which are introduced in this review. When incorporated into masks, these advanced materials and technologies can aid in the prevention of secondary transmission of the virus., Masks are critical during the coronavirus pandemic of 2019 (COVID‐19), and there is a huge demand for them globally. Many countries use cloth masks and 3D‐printed masks as substitutes, but the filtration efficiencies are unqualified. Herein, various new mask technologies and advanced materials, including metal nanoparticles, graphene, metal organic framework (MOF), and triboelectric nanogenerator (TENG) are reviewed, to deal with critical shortages, cross‐infection, and secondary transmission risk of masks.

At extremely low temperatures, Ag2CaV4O12 was easily synthesized using the traditional solid-state approach. With a low relative permittivity (εr) of 7.52, a high quality factor (Q × f) of 48 800 GHz (f = 13.6 GHz), and a temperature coefficient of resonance frequency (τf) of −77.4 ppm/°C, dense ceramics sintered at 480 °C with outstanding microwave dielectric characteristics were attained. By combining with rutile TiO2, a composite ceramic with balanced microwave dielectric properties (τf = 3.2 ppm/°C, εr = 10.96, and Q × f = 49 081 GHz) was achieved. No chemical reaction between Ag2CaV4O12 and silver and aluminum occurred. All of the findings show that Ag2CaV4O12 has the potential to be used as dielectric resonances in wireless communication and as substrates in low-temperature cofired ceramics. Furthermore, the processing at an ultralow temperature of Ag2CaV4O12 shows that it is extraordinarily energy saving from the point of view of fabrication and might allow for room-temperature synthesis by combining with high-energy mechanical milling or sintering using a high pressure such as hot isostatic pressing (HIP), spark plasma sintering (SPS), and cold sintering (CS).

The paper details experimental and optimisation results for the effect of cutting fluid concentration and operating parameters on the average surface roughness (Ra) and tool flank wear (VB) when flooded turning of Ti-6Al-4V using water-miscible vegetable oil-based cutting fluid. Cutting fluid concentration, cutting speed, feed rate and cutting tool were the control variables. Response Surface Methodology (RSM) was employed to develop an experimental design and optimise Ra and VB using linear models. The study revealed that cutting fluid concentration has a little influence on Ra and VB performance while Ra was strongly affected by feed rate and cutting tool type. The developed empirical model also suggested that the best parameters setting to minimise Ra and VB are 5%, 58 m/min, 0.1 mm/rev for cutting fluid concentration, cutting speed and feed rate, respectively, using H13A tool. At this setting, the predicted surface roughness and tool wear were 0.48 and 30 µm, respectively. In the same vein, tool life and micro-hardness tests were performed at the suggested optimum cutting condition with different cutting speeds. A notable decrease in tool life (82.3%) was obtained when a higher cutting speed was used.

Capillary pressure is frequently measured to evaluate the shrinkage performance of concrete but has been limited to pressures

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.

In this paper, the global dynamics of a hanging fluid conveying cantilevered pipe with a concentrated mass attached at the free end is investigated. The problem is of interest not only for engineering applications, but also because it displays interesting and often surprising dynamical behaviour. The widely used nonlinear models based on the transverse motion of the pipe are not able to accurately capture the dynamical behaviour of the system at very high flow velocities. Thus, a high-dimensional geometrically-exact model is developed for the first time, utilising Hamilton’s principle together with the Galerkin modal decomposition technique. Extensive numerical simulations are conducted to investigate the influence of key system parameters. It is shown that at sufficiently high flow velocities past the first instability (Hopf bifurcation), the system undergoes multiple bifurcations with extremely large oscillation amplitudes and rotations, beyond the validity of third-order nonlinear models proposed to-date. In the presence of an additional tip mass, quasi-periodic and chaotic motions are observed; additionally, it is shown that for such cases an exact model is absolutely essential for capturing the pipe dynamics even at relatively small flow velocities beyond the first instability.

In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.

Inspection processes are becoming more and more popular beyond the manufacturing industry to ensure product quality. Implementing inspection systems in multistage production lines brings many benefits in productivity, quality, and customer satisfaction. However, quantifying the changes necessary to adapt the production to these systems is analytically complicated, and the tools available lack the flexibility to visualize all the inspection strategies available. This paper proposed a discrete-event simulation model that relies on probabilistic defect propagation to quantify the impact on productivity, quality, and material supply at the introduction of inspection processes in a multistage production line. The quantification follows lean manufacturing principles, providing from quite basic quantity and time elements to more comprehensive key performance indicators. The flexibility of discrete-event simulation allows for customized manufacturing and inspection topologies and variability in the tasks and inspection systems used. The model is validated in two common manufacturing scenarios, and the method to analyze the cost-effectiveness of implementing inspection processes is discussed.

Superhydrophobic coatings and slippery liquid-infused porous surfaces (SLIPS) have shown their potentials in self-cleaning, anti-icing, anti-erosion, and antibiofouling applications. Various studies have been done on controlling the droplet impact on such surfaces using passive methods such as modifying the lubricant layer thickness in SLIPS. Despite their effectiveness, passive methods lack on-demand control over the impact dynamics of droplets. This paper introduces a new method to actively control the droplet impact onto superhydrophobic and SLIPS surfaces using surface acoustic waves (SAWs). In this study, we designed and fabricated SLIPS on ZnO/aluminum thin-film SAW devices and investigated different scenarios of droplet impact on the surfaces compared to those on similar superhydrophobic-coated surfaces. Our results showed that SAWs have insignificant influences on the impact dynamics of a porous and superhydrophobic surface without an infused oil layer. However, after infusion with oil, SAW energy could be effectively transferred to the droplet, thus modifying its impact dynamics onto the superhydrophobic surface. Results showed that by applying SAWs, the spreading and retraction behaviors of the droplets are altered on the SLIPS surface, leading to a change in a droplet impact regime from deposition to complete rebound with altered rebounding angles. Moreover, the contact time was reduced up to 30% when applying SAWs on surfaces with an optimum oil lubricant thickness of ∼8 μm. Our work offers an effective way of applying SAW technology along with SLIPS to effectively reduce the contact time and alter the droplet rebound angles.

Air quality indices are measured based on the concentration and size of the particles. But should the shape of these particles be considered as well? Upon inhalation, Air Particulate Matter with a size of less than or equal to 2.5μm (PM2.5) can reach the deepest areas of the respiratory system called alveolar region where the gas exchange with blood circulation happens. In this zone, the walls of almost 500 million tiny alveoli sacs are lined with a tiny layer of fluid which causes a high surface tension and instability in this large air-liquid interface. However, thanks to the lung surfactant, there is no collapse of this region in a healthy human. Therefore, the first barrier that PM2.5 encounter before reaching the blood circulation is the interface covered by a monolayer of lung surfactant molecules. The purpose of this research is to study the interfacial properties of these monolayers interacted with titanium (IV) oxide nanoparticles with two different shapes, spherical and irregular ones, and with the same average size of 20 nm. Dipalmitoyl phosphatidylcholine (DPPC) as one of the main constituents of the pulmonary surfactant was used as the synthetic model. Using Profile Analysis Tensiometry (PAT) as an automatic set-up working based on Young-Laplace equation, we measured the interfacial tension and surface dilatational viscoelasticity in the pendant drop mode. The temperature was constant at 37℃. Four different amplitudes, 1, 2, 5, and 10, for the volume change of drops were used. Moreover, various frequencies, 0.1, 0.125, 0.25, and 0.5 Hz, were applied as the representative of the breathing cycle at different ages for a healthy human. The dependence of the interfacial and mechanical properties of the bespoke monolayers on the shape of the nanoparticles can be considered as an important factor for the environmental authorities.

Microbial community assembly mechanisms are pivotal for understanding the ecological functions of microorganisms in biogeochemical cycling in Earth's ecosystems, yet rarely investigated in the context of deep terrestrial ecology. Here, the microbial communities in the production waters collected from water injection wells and oil production wells across eight oil reservoirs throughout northern China were determined and analyzed by proportional distribution analysis and null model analysis. A 'core' microbiota consisting of three bacterial genera, including Arcobacter, Pseudomonas and Acinetobacter, and eight archaeal genera, including Archaeoglobus, Methanobacterium, Methanothermobacter, unclassified Methanobacteriaceae, Methanomethylovorans, Methanoculleus, Methanosaeta and Methanolinea, was found to be present in all production water samples. Canonical correlation analysis reflected that the core archaea were significantly influenced by temperature and reservoir depth, while the core bacteria were affected by the combined impact of the core archaea and environmental factors. Thermodynamic calculations indicate that bioenergetic constraints are the driving force that governs the enrichment of two core archaeal guilds, aceticlastic methanogens versus hydrogenotrophic methanogens, in low- and high-temperature oil reservoirs, respectively. Collectively, our study indicates that microbial community structures in wells of oil reservoirs are structured by the thermodynamic window of opportunity, through which the core archaeal communities are accommodated directly followed by the deterministic recruiting of core bacterial genera, and then the stochastic selection of some other microbial members from local environments. Our study enhances the understanding of the microbial assembly mechanism in deep terrestrial habitats. Meanwhile, our findings will support the development of functional microbiota used for bioremediation and bioaugmentation in microbial enhanced oil recovery.

Composite materials due to their high strength to density characteristics are widely used in aerospace, automotive, marine applications etc. Recent developments in Additive Manufacturing processes and materials have enabled the manufacture of end-use functional components. This paper investigates the fatigue behaviour of composite parts processed Additive Manufacturing (AM) process Continuous Fibre Fabrication reinforcement (CFF) method. There are existing data for static material properties however they are only a few published papers on the fatigue performance of parts processed by CFF additive manufacturing. This study investigates by physical and digital simulation, the fatigue life of 0, 2, 4, 6, 8 layer carbon fibre reinforced Nylon ASTME606M test samples. This research has determined design for fatigue guidelines for additively manufactured composite materials, for both for the low and high cycle applications.

A simple optic-fiber Fabry-Perot (FP) sensing technique was proposed and experimentally investigated by using polymer material to connect the ends of two singlemode fibers. Four different polymer materials (benzocyclobutene (BCB), UV88 (Relentless, China), Loctite3525 (HenKel, Germany), and NOA68 (Norland, USA)) filling the FP cavity were used to comparatively study the sensing performance of temperature, strain and refractive index. The result shows that the FP sensor with BCB has excellent repeatability with good linear response to temperature in a wide range from room temperature to 250 °C, which is much larger than that of other three materials (2)) and the best repeatability among the four polymer materials.

Adjustment on resonance frequency stability against the sintering temperature of Sr3V2O8 was realized by adjusting the Sr:V mole ratio. Effects of Sr:V ratio on sintering behavior and dielectric properties of Sr3V2O8 were studied. The sintering temperature was sucessfully reduced to 950°C from 1150°C. With increasing vanadium content, both relative permittivity and quality factor decreased, while the temperature coefficient of resonance frequency shifted from positive to negative values. Especially, a near‐zero τf of −1.1 ppm/°C along with a low permittivity (εr) of 9.8 and a quality factor Q × f of 24 120 GHz was successfully achieved in Sr3‐yV2O8‐y ceramic (y = 0.6, sintered at 950°C). The wide compositional and processing adjustment window, favorable dielectric performances, and good chemical compatibility with silver render Sr3‐yV2O8‐y ceramics potential candidates in multilayer electronic devices.

Solar and wind power systems have been prime solutions to the challenges centered on reliable power supply, sustainability, and energy costs for several years. However, there are still various challenges in these renewable industries, especially regarding limited peak periods. Solar–wind hybrid technology introduced to mitigate these setbacks has significant drawbacks and suffers from low adoption rates in many geographies. Hence, it is essential to investigate the challenges faced with these technologies and analyze the viable solutions proposed. This work examined solar–wind hybrid plants’ economic and technical opportunities and challenges. In the present work, the pressing challenges solar–wind hybrids face were detailed through extensive case studies, the case study of enabling policies in India, and overproduction in Germany. Presently, the principal challenges of solar–wind hybrids are overproduction, enabling policies, and electricity storage. This review highlights specific, viable, proposed solutions to these problems. As already recorded in the literature, it was discovered that academic research in this space focuses majorly on the techno-economic and seemingly theoretical aspects of these hybrid systems. In contrast, reports and publications from original equipment manufacturers (OEMs) and engineering, procurement, and construction engineers (EPCs) are more rounded, featuring real-life application and implementation.

The high-fidelity computational fluid dynamics (CFD) simulations of a complete wind turbine model usually require significant computational resources. It will require much more resources if the fluid–structure interactions (FSIs) between the blade and the flow are considered, and it has been the major challenge in the industry. The aeromechanical analysis of a complete wind turbine model using a high-fidelity CFD method is discussed in this paper. The distinctiveness of this paper is the application of the nonlinear frequency domain solution method to analyze the forced response and flutter instability of the blade as well as to investigate the unsteady flow field across the wind turbine rotor and the tower. This method also enables the aeromechanical simulations of wind turbines for various interblade phase angles in a combination with a phase shift solution method. Extensive validations of the nonlinear frequency domain solution method against the conventional time domain solution method reveal that the proposed frequency domain solution method can reduce the computational cost by one to two orders of magnitude.

Acoustofluidic devices based on surface acoustic waves (SAWs) have been widely applied in biomedical research for the manipulation and separation of cells. In this work, we develop an accessible manufacturing process to fabricate an acoustofluidic device consisting of a SAW interdigital transducer (IDT) and a polydimethylsiloxane microchannel. The IDT is manufactured using a flexible printed circuit board pre-patterned with interdigital electrodes that is mechanically coupled with a piezoelectric substrate. A new microchannel moulding technique is realised by 3D printing on glass slides and is demonstrated by constructing the microchannel for the acoustofluidic device. The flexible clamping mechanism, used to construct the device, allows the reconfigurable binding between the IDT and the microchannel. This unique construction makes the acoustofluidic device capable of adjusting the angle between the microchannel and the SAW propagation, without refabrication, via either rotating the IDT or the microchannel. The angle adjustment is demonstrated by setting the polystyrene microsphere aggregation angle to −5°, 0°, 6°, and 15°. Acoustic energy density measurements demonstrate the velocity of microsphere aggregation in the device can be accurately controlled by the input power. The manufacturing process has the advantages of reconfigurability and rapid-prototyping to facilitate preparing acoustofluidic devices for wider applications.

Solution blow spinning (SBS) is gaining popularity for producing fibres for smart textiles and energy harvesting due to its operational simplicity and high throughput. The whole SBS process is significantly dependent on the characteristics of the attenuation force, i.e., compressed air. Although variation in the fibre morphology with varying air input pressure has been widely investigated, there is no available literature on the experimentally determined flow characteristics. Here, we have experimentally measured and calculated airflow parameters, namely, output air pressure and velocity in the nozzle wake at 12 different pressure values between 1 and 6 bar and 11 different positions (retracted 5 mm to 30 mm) along the centreline. The results obtained in this work will answer many critical questions about optimum protrusion length for the polymer solution syringe and approximate mean fibre diameter for polyvinylidene fluoride (PVDF) at given output air pressure and velocity. The highest output air pressure and velocity were achieved at a distance of 3–5 mm away from the nozzle wake and should be an ideal location for the apex of the polymer solution syringe. We achieved 250 nm PVDF fibres when output air pressure and velocity were 123 kPa and 387 m/s, respectively. Published version

Integration of energy recovery section with thermal desalination systems improves their performance from thermodynamics, economics, and environmental viewpoints. This is because it significantly reduces input energy, heat transfer area, and capital cost requirements. Above all, the system outlet streams can achieve thermal equilibrium with the environment by supplying heat for useful preheating purposes thus reducing the environmental impacts. The plate heat exchangers are generally employed for this purpose as preheaters. The current paper presents a comprehensive investigation and optimization of these heat exchangers for thermal desalination systems applications. An experimentally validated numerical model employing Normalized Sensitivity Analysis and Genetic Algorithm based cost optimization is developed to investigate their performance at assorted operating conditions. The analysis showed that the heat transfer coefficient, pressure drop, and outlet water cost were improved by an increase in feed flow rate. However, with an increased flow rate, the comprehensive output parameter (h/ΔP) decreased due to the high degree increase in pressure drop. Moreover, an increase in the chevron angle reduced the heat transfer coefficient, pressure drop, and water cost. Finally, the optimization lowered the heat transfer area by ~79.5%, capital investment by ~62%, and the outlet cost of the cold stream by ~15.7%. The operational cost is increased due to the increased pressure drop but the overall impact is beneficial as Ctotal of equipment is reduced by ~52.7%.

Purpose: Production industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential. Design/methodology/approach: A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics. Findings: The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential. Originality/value: The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies. Design/methodology/approach: A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics. \ud Findings: The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential. \ud Originality: The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics., Sensors, 21 (8), ISSN:1424-8220

Cross-laminated timber (CLT) is an innovative construction material that has brought advantages over traditional wood structures, reducing cost and lead time of buildings in recent years; yet CLT benefits primarily from offsite construction methods instead of automation or safety, while keeping the human onsite. The few advancements in automation for CLT panels have been in the implementation of dedicated CNC machines. Nevertheless, using CNC machines for machining CLT panels have disadvantages like clamping batches of massive panels with individual profiles, lacking the flexibility to access all acute machining angles, and struggling with the extraction of dust while the cutting spindle moves through large tight spaces. These disadvantages can be overcome with industrial robots’ help, which the construction industry has not been traditionally favorable on their application, giving then the research gap in this study. This paper explores the introduction of a robotic cell for the machining of cross-laminated timber panels. The robotic cell is designed using 3D modeling and validated through motion simulation in a virtual environment. The proposed cell design is based on a minimum viable product and compared against a minimum throughput benchmarked on the Canadian market. This study aims to research the feasibility of CLT’s automated machining by providing clear production characteristics of the designed robotic cell, such as material and tool utilization rates, lead time, or production efficiency.

A new approach for establishing the mechanical model of the rubber damping ring was studied numerically and experimentally. Firstly, parameters of Mooney–Rivlin and Prony series models of the rubber material were identified based on ISIGHT integrating with ANSYS and MATLAB, in which the rubber damping ring’s hysteresis loop was obtained by vibration experiment and ANSYS simulation, respectively; meanwhile, the dynamic stiffness and damping were calculated simultaneously by a parameter separation and identification method. Subsequently, the accuracy of the constitutive model parameters was verified experimentally. In the light of this, based on the experimental design and the approximate model method of the joint simulation platform, a mechanical model of dynamic stiffness and damping of the rubber damping ring was established. Finally, the rubber damping ring’s mathematical model was employed to perform a vibration reduction analysis in a multi-span shafting, where the numerical and experimental investigation was conducted, respectively. The results show that the theoretical and experimental error of vibration reduction rate is less than 17%, which verifies the accuracy of the mechanical model of the rubber damping ring.

This research investigated fresh and hardened properties of lightweight self-compacting concrete (LWSCC) incorporated with oil palm shell (OPS) and fly ash (FA). Fresh concrete properties including passing ability, filling ability and segregation resistance were assessed. The properties fulfilled EFNARC guidelines. Incorporation of FA improved fresh properties, particularly filling ability, with the slump flow value increased from 665 mm to 730 mm. As for hardened properties, OPS-aggregate based LWSCC mixes achieved compressive strength of range 18–38 MPa at 28-day age while the splitting tensile strength was in the range of 1.6 to 2.8 MPa. SEM analyses showed good bonding in the interfacial transition zones (ITZ). Micro-pores of OPS were filled by cement hydration products and thus ITZ was enhanced. LWSCC incorporated with OPS, a renewable resource from agricultural waste, and with partial FA replacement, is potentially a sustainable alternative construction material.

An ultra-high sensitivity label-free optical fiber biosensor for inactivated Staphylococcus aureus (S. aureus) detection is proposed and investigated in this study, with additional advantages of robust and stability compared to traditional tapered fiber structure. The proposed fiber biosensor is based on a tapered singlemode- no core-singlemode fiber coupler (SNSFC) structure, where the no core fiber was tapered to small diameter (taper-waist diameter of about 10 μm) and functionalized with the pig immunoglobulin G (IgG) antibody for detection of S. aureus. The measured maximum wavelength shift of the sensor for an S. aureus concentration of 7 × 101 CFU/mL (colony forming unit per milliliter) is 2.04 nm, which is equivalent to a limit of detection (LOD) of 3.1 CFU/mL (a highest LOD reported so far for optical fiber biosensors), considering the maximum wavelength variation of the sensor in phosphate buffered saline (PBS) is ±0.03 nm over 40 min, where 3 times of maximum wavelength variation (3 × 0.03 = 0.09 nm) is defined as measurement limit. The response time of the developed fiber sensor is less than 30 min. The ultra-sensitive biosensor has potential to be widely applied to various areas such as disease, medical diagnostics and food safety inspection.

Borates are promising candidates as dielectric substrate materials in low temperature cofired ceramics technology (LTCC) due to their relative low sintering temperatures and relative permittivities compared to their counterparts. However, synthesizing borates having single-phase is still challenging because of the volatility and hydrophilicity of boron resources. In this work, a compositional design was utilized to synthesize single-phase LiBGeO4 ceramics over a broad temperature range from 600 to 840 °C. Radio-frequency dielectric behaviours featured a strong temperature dependence, especially at high temperatures (>400 °C), which is related to the thermally activated polarizations. LiBGeO4 ceramic sintered at 820 °C has optimum microwave dielectric properties with the relative permittivity (er) of 6.28, a quality factor (Q × f) of 21,620 GHz, and a temperature coefficient of resonance frequency (τf) of -88.7 ppm/°C. LiBGeO4 also showed chemical inertness when cofired with silver (Ag), provided an evidence for its utilization in LTCC technology. Overall, this work provides a strategy for facile synthesis of phase pure borates, via the proposed two-step process to obtain stable boron resources.

In corporate boardrooms around the world, climate change has quickly risen to become a major issue, matching public concern. Recently, corporate management has encountered stakeholder pressure to disclose more information about their carbon profile and their plans to improve it. They have also been challenged to find the appropriate strategy for carbon disclosures, requiring an understanding of the costs and benefits of both carbon improvement initiatives and the reporting of them. Using a unique data set that contains firms listed on the FTSE 350 index on the London Stock Exchange market from 2009 to 2015, we apply the event study method to examine market reaction to carbon disclosures. The results show that investors respond significantly negatively to carbon disclosure announcements via Carbon Disclosure Project (CDP) of FTSE 350 firms. Moreover, for firms working in carbon-intensive industries, investors react to carbon disclosure announcements in a more significantly negative way compared with the main sample. We also find that the study’s main findings are driven by the smaller FTSE 350 firms. Furthermore, a subsample of observations for the financial crisis period of 2007–2008 was analyzed to explore the examined relationship during the crisis. In contrast, a significant positive market reaction to carbon disclosure was found for the 2007–2008 crisis period. Our study’s findings offer fresh insight and updated policy implications for investors, management and sustainability institutions. We recommend management accompanies their carbon disclosures with more explicit statements of reasons for carbon initiatives and the benefits arising from them.