The cyclic regeneration of human endometrium is guaranteed by the proliferative capacity of Endometrial Mesenchymal Stromal Cells (E-MSCs). Due to this, the autologous infusion of E-MSCs has been proposed to support endometrial growth in a wide range of gynecological diseases. We aimed to compare two different endometrial sampling methods, the surgical curettage and the Vacuum Aspiration Biopsy Random Assay, and to validate a novel xeno-free method to culture human E-MSCs. Six E-MSCs cell lines were isolated after a mechanical tissue homogenization and cultured using human platelet lysate. E-MSCs were characterized for the colony formation capacity, proliferative potential and multilineage differentiation. The expression of mesenchymal and stemness markers was tested by FACS analysis and Real-Time PCR, respectively. Chromosomal alterations were evaluated by karyotype analysis, whereas tumorigenic capacity and invasiveness were tested by soft agar assay. Both endometrial sampling techniques allowed to efficiently isolate and expand E-MSCs using a xeno-free method preserving their mesenchymal and stemness phenotype, proliferative potential and multi-lineage differentiation ability during the culture. No chromosomal alterations and invasive/tumorigenic capacity were observed. Herein we report the first evidence of efficient E-MSCs isolation and culture in Good Manufacturing Practice compliance conditions, suggesting Vabra endometrial sampling as alternative to surgical curettage.

Access to nasopharyngeal swabs for sampling remain a bottleneck in some regions for COVID-19 testing. This study develops a distributed manufacturing solution using only an open source manufacturing tool chain consisting of two types of open source 3-D printing and batch UV curing, and provides a parametric fully free design of a nasopharyngeal swab. The swab was designed using parametric OpenSCAD in two components (a head with engineered break point and various handles), which has several advantages: i) minimizing print time on relatively slow SLA printers, ii) enabling the use of smaller print volume open source SLA printers, iii) reducing the amount of relatively expensive UV resin, and iv) enabling production of handle on more accessible material extrusion 3-D printers. A modular open source UV LED box was designed, fabricated for $45 and tested for batch curing. Swabs can be fabricated for $0.06-$0.12/swab. The results of the mechanical validation tests showed that the swabs could withstand greater forces than would be expected in normal clinical use. The swabs were also able to absorb a significant amounts of synthetic mucus materials and passed abrasion and handling tests. The results show the open source swab are promising candidates for clinical trials.

In our study we investigated the additive manufacturing (AM) of ceramic-based Functionally Graded Materials (FGM) by the direct AM technology Thermoplastic 3D-Printing (T3DP). Zirconia components with a varying microstructure were additively manufactured by using thermoplastic suspensions with different contents of pore forming agents (PFA) and were co-sintered defect-free. Different materials were investigated concerning their suitability as PFA for the T3DP process. Different zirconia-based suspensions were prepared and used for AM of single- and multi-material test components. All samples were sintered defect-free and in the end we could realize a brick wall-like component consisting of dense (<1% porosity) and porous (approx. 5% porosity) zirconia areas to combine different properties in one component. The T3DP opens the door to AM of further ceramic-based 4D-components like multi-color or multi-material, especially multi-functional components.

As fatigue properties of as-built components of additively manufactured (AM) metals are considerably weaker than those of wrought metals because of rougher surface, post-processing is necessary to improve fatigue properties. To demonstrate improvement of fatigue properties of AM metals by post-processing, powder bed fusion (PBF) using laser sintering (LS) AlSi10Mg, i.e., PBF-LS/AlSi10Mg was treated by “submerged laser peening (SLP)” using a fiber laser and/or a Nd:YAG laser, then evaluated by plane bending fatigue tests. At SLP, “laser ablation (LA)” is generated by a pulsed laser, and a bubble is generated after LA, which behaves like a cavitation bubble that is named as “laser cavitation (LC)”. In the present paper, LA-dominated SLP is called as “laser treatment (LT)”, while LC-collapse-dominated SLP is called as “laser cavitation peening (LCP)”, as impact at LC collapse is used for peening. It was revealed that SLP using the fiber laser was LT rather than LCP. It was demonstrated that the fatigue strength at N = 10^7 was 85 MPa for LCP, and 103 MPa for combined process of blasting (B) + LT + LCP, whereas the fatigue strength of as-built specimen was 54 MPa.

Manual material handling (MMH) significantly impacts worker health and productivity, often leading to musculoskeletal disorders (MSDs) primarily in the lower back. As a novel assistive technology, exoskeletons may serve as ergonomic tools to mitigate these work-related MSDs. It is essential to examine exoskeletons from the users’ perspectives before their widespread implementation in occupational settings. This study investigates the effectiveness of a passive back-support exoskeleton (BExo) in reducing perceived physical exertion and improving ergonomic safety in manufacturing context. Twenty-two college students were recruited to perform MMH tasks in a controlled lab environment, both with and without the BExo, followed by completing a survey questionnaire on various aspects of the BExo. Using ANOVA, the study analyzed biomechanical exertion across various body parts and tasks. The findings indicate that the BExo substantially alleviates discomfort in the low back, shoulders and knees, thereby, enhancing ergonomic posture and reducing fatigue. These results underscore the potential of passive exoskeletons to boost workers’ safety and efficiency, providing valuable insights for future ergonomic strategies in industrial setting.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

Nowadays there is a need to increase the density of interconnections on printed circuit boards (PCBs). Does this mean that the only way out for quality PCB manufacturing is to proportional increase in precision of equipment, or is there another way? One of the main constraints to increasing the density of PCB interconnections is the transition holes. As the number of conductive layers increases, the number of vias increases and they cover a significant space on the PCB. On the other hand, reducing the size of the vias is limited by the capability of spatial alignment of the PCB stack during manufacturing. There are standards that set limits for the design of contact pads on a PCB (IPC-A-600G, IPC-6012B). However, depending on the precision of production, the contact pads may be of poor quality. This raises the issue of determining the reliability of a contact pad with defined parameters at the design stage, taking into account manufacturing capabilities. This research proposes an analytical method for evaluation of reliability of a via or plated through-hole based on calculation of its probability of production in accordance with the current standards. On the basis of the method a model was developed both for the case of a contact pad without any conductors connected to it (nonfunctional contact pad) and for the real case with a connected conductor. The model estimates the probability of making an acceptable via for a given reliability class depending on parameters such as the conductor width (minimum permissible and usable), drilled hole diameter and pad diameter, as well as the accuracy of the drilling operation. The analysis of the modeling results showed that for the real case, a reduction in the reliability class would insignificantly affect the probability of making an acceptable via due to the tight limitation on the connection place of the conductor and the contact pad. In conclusion, we propose an algorithm for determining the optimal parameters of teardrops to minimize the negative impact of the conductor on the reliability of the vias.

To improve accessibility, this article describes a static, four-legged, walker that can be constructed from materials and fasteners commonly available from hardware stores coupled by open-source 3-D printed joints. The designs are described in detail, shared under an open-source license, and fabricated with a low-cost open-source desktop 3-D printer and hand tools. The resulting device is loaded to failure to determine the maximum load that the design can safely support in both vertical and horizontal failure modes. The experimental results showed the average vertical failure load capacity was 3680±694.3N, equivalent to 375.3±70.8kg of applied weight with the fractured location at the wood dowel handlebars. The average horizontal load capacity was 315.6±49.4N, equivalent to 32.2±5.1kg. The maximum weight capacity of a user of 187.1±29.3kg was obtained, which indicates the open-source walker design can withstand the weight requirements of all genders with a 95% confidence interval that includes a safety factor of 1.8 when considering the lowest deviation weight capacity. The design has a cost at the bottom of the range of commercial walkers and reduces the mass compared to a commercial walker by 0.5kg (19% reduction). It can be concluded that this open-source walker design can aid accessibility in low-resource settings.

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

This work investigates how the user-controlled parameters of the 3D printing process define temperature conditions on the boundary between layers of the part being fabricated and how these conditions influence the structure and strength of the part. The process studied is fused filament fabrication with a desktop 3D printer and the material utilized is PLA (polylactic acid). As a characteristic of the part strength the fracture load in the case of a three-point bend and calculated related stress were used. During the printing process parts were oriented with the long side along the Z axis, thus, in the bend tests, the maximum stress occurred orthogonally to the layers. During the fabrication process, temperature distribution on the sample surface was monitored with thermal imager. Sample mesostructure was analyzed using SEM. The influence of the extrusion temperature, the intensity of part cooling, the printing speed and the time between printing individual layers were considered. The influence of all the parameters can be expressed through two generalizing factors: the temperature of the previous layer and the flow efficiency, determining the ratio of the amount of extruded plastic to the calculated. A regression model was proposed that describes the effect of the two factors on the printed part strength. Along with interlayer bonding strength, these two factors determine the formation of the part mesostructure (the geometry of the boundaries between individual threads). It is shown that the optimization of the process parameters responsible for temperature conditions makes it possible to approximate the strength of the interlayer cohesion to the bulk material strength.

The current paper is studying the influence of geometrical parameters of the FDM (FFF) 3D printing process on printed part strength for open source desktop 3D printers and the most popular material used for that purpose, i.e. PLA (polylactic acid). The study was conducted using a set of different nozzles (0.4, 0.6 and 0.8 mm) and a range of layer heights from the minimum to maximum physical limits of the machine. To assess print strength, a novel assessment method is proposed. A tubular sample is loaded in the weakest direction (across layers) in a three-point bending fixture. To explain the results obtained, a mesostructure evaluation through SEM scans of the samples were used. A significant influence of geometric process parameters was detected on sample mesostructure and, consequently, on sample strength.

The space shuttle, a revolutionary spacecraft that has played a significant role in human space exploration, was composed of various advanced materials that were carefully selected to meet the extreme demands of spaceflight. This review paper provides a comprehensive examination of the materials historically employed in the construction of space shuttles and explores the latest trends shaping the field. The evolution of space shuttle materials is traced from the inception of the space program to contemporary missions, highlighting key milestones, challenges, and breakthroughs. Emphasis is placed on the critical role that materials play in the overall performance, safety, and sustainability of space shuttles. The paper begins by elucidating the diverse requirements that materials must fulfill in the harsh and complex environment of space, encompassing extreme temperatures, radiation exposure, and mechanical stresses. A detailed analysis of the materials utilized in the fabrication of various shuttle components, such as thermal protection systems, structural elements, and propulsion systems, is presented. Special attention is given to the challenges posed by re-entry and the strategies employed to mitigate heat-related issues. Furthermore, the review explores recent innovations and emerging materials that are reshaping the landscape of space shuttle design. Advancements in nanotechnology, composite materials, and additive manufacturing are discussed in the context of their potential applications for enhancing shuttle performance and reducing mission costs. The paper also addresses the importance of sustainability in space exploration, exploring materials with lower environmental impact and improved recyclability. The review concludes with a forward-looking perspective on the future of materials, considering ongoing research, development, and the potential incorporation of cutting-edge technologies. Insights are provided into how the evolving landscape of materials science may influence the design and manufacturing processes of the next generation of space vehicles. This paper provides an overview of the materials used in the construction of the space shuttle, including their properties, applications, and challenges. The materials used in the space shuttle are critical in ensuring the safety, performance, and longevity of the spacecraft, and understanding their characteristics and performance in space is crucial for the advancement of aerospace engineering.

Vit-106 is a promising zirconium-based alloy with high glass-forming ability and belongs to the so-called bulk metallic glasses (BMG). Workpieces with a size of around one centimeter in all three dimensions can be obtained from a BMG alloy by casting. However, further increasing the cast size decreases the cooling rate and thus induces crystallization. Selective laser melting (SLM) is a well-known technique to overcome the size limitations for BMGs because a workpiece is built by the addition of multiple melt portions where the cooling rate is kept above the critical one. Currently, BMG parts obtained by SLM suffer from partial crystallization. The present work studies the influence of SLM process parameters on partial crystallization of Vit-106 by metallography and the influence of the microstructure on mechanical properties by microhardness and wear resistance testing. Submicron crystalline inclusions are observed in an amorphous matrix of Vit-106 alloy obtained by SLM. The size and the concentration of the inclusions can be controlled by varying the laser scanning speed. It is shown that submicron crystalline inclusions formed in the amorphous matrix during SLM can favorably affect microhardness and wear resistance.

Additive Manufacturing (AM) has been proving suitable to support or even replace traditional manufacturing in several industries, offering many advantages such as delivery time and reduction in terms of material waste, energy consumption and greenhouse gas (GHG) emissions. This study aimed to carry out a comparative assessment of the life cycle, from gate to gate, in the production of a low alloy carbon steel flange part using ER-90 wire. The methods utilized were Wire and Arc Additive Manufacturing (WAAM) and conventional manufacturing (CM) by forging, and comparative factors were energy demands, GHG emissions and generated solid waste. The total energy consumption in WAAM was 10,239.40 MJ, total carbon footprint in CO2 equivalent (CO2e) was 714.1 kgCO2e kg-1, and generated solid waste was 68.6 kg, respectively, 90%, 95% and 76% lower than consumption calculated in conventional manufacturing.

This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team's main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in manufacturing industries and optimizing poorly or non-digitized industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI's contributions to the field, emphasizing the importance of resource efficiency and system optimization in industrial settings. By addressing the challenges posed by under-digitized infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at local level. A crucial element of the added value is the new channel for feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.

Background (1) In the time of the 4th Industrial Revolution or Industry 4.0, a conglomerate of technical and social inventions, political contexts, socio-cultural circumstances, environmental policies, business models, and economic policies has emerged. Sustainability policy in theory and practice aims to deal with the effects of all these factors and to try to make decisions that ensure both social and economic development sustainably. The question is how to familiarize oneself with the current knowledge about the relationship between Industry 4.0 and sustainability?; (2) Methods: This research utilizes an automated content analysis method to analyses scientific journals, newspapers and magazines. The comparison of results of both research group shows that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development; (3) We found that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers, as well as some latest research journals, include articles of the COVID-19 outbreak and its effect on the economy and the environment. Indeed, the outbreak of the virus brings a new thought to the reorganization of the complex relationships between consumers, businesses and the state; (4) Conclusions: According to the comparison of the analyses of the results, it can is that the analyses of both types of literature, both scientific and professional, shows that there are common topics they write about, which are related to the field of clean production, emissions, renewable energy, climate change, sustainable investments and corporate sustainability. An urgent global issue that extends all over the world is the promotion of energy-saving technologies and reduction of carbon dioxide emissions.

In the evolving landscape of pharmaceutical manufacturing, a comprehensive continuous production process is being crafted for small-scale active pharmaceutical ingredient production. This study focuses on continuous crystallization with separate nucleation and crystal growth units, as well as continuous downstream processing, encompassing filtration, washing, and drying until the formation of free-flowing particles. We introduce a novel continuous nucleator designed based on solubility data and produced via 3D printing, enabling a fast and precise small-scale manufacturing of a nucleator meeting the requirements for nucleation and for the further growth processes. The nucleator was evaluated with regard to its suitability for continuous long-term operation across various coupled crystallizers. As a practical application example it is connected to a slug flow crystallizer to enable high-quality continuous crystallization. Additionally, the full integration of downstream processes using the continuous vacuum screw filter to achieve free-flowing product particles is realized. Even under non-optimized process conditions, with the help of in-situ generation of nuclei free-flowing product particles were successfully obtained. This is particularly useful during drug development when no material is available for seed addition and quickly obtain product for further characterization.

Newcastle Disease (ND) remains a critical disease affecting poultry in sub-Saharan Africa. In some countries, repeated outbreaks have a major impact on local economies and food security. Recently, we developed an adenovirus-vectored vaccine encoding the Fusion protein from an Ethiopian isolate of Newcastle Disease Virus (NDV). The adenoviral vector was designed and a manufacturing process developed in the context of the Livestock Vaccine Innovation Fund initia-tive funded by the International Development Research Centre (IDRC) of Canada. The industrial-ly-relevant recombinant vaccine technology platform is being transferred to the National Veteri-nary Institute (Ethiopia) for veterinary applications. Here, we demonstrate that the instillation of the adenoviral vector through the nasal cavity can confer protection to chickens against a lethal challenge with NDV. A manufacturing process using HEK293 suspension cells cultured in stirred-tank bioreactor for the vaccine production is proposed. Taking into consideration supply chain limitations, options for serum-free media selection have been evaluated. A streamlined downstream process including a filtration, an ultrafiltration and a concentration step was de-veloped. With high volumetric yields (infectious titers up to 5 x 109 TCID50/mL) in the culture su-pernatant, the final formulations were prepared at 1010 TCID50/mL, either in liquid or lyophi-lized forms. The liquid formulation was suitable and safe for mucosal vaccination and was sta-ble for 1 week at 37˚C. Both liquid and lyophilized formulations were stable after 6 months of storage at 4˚C. Overall, a manufacturing process for adenovirus vectored vaccine was developed and protective doses were determined using a convenient route of delivery. Formulation and storage conditions were established, and quality control protocols were implemented.

The paper provides a comprehensive review of the recent advancements and methodologies in Human-Robot Collaboration (HRC) applied to the manufacturing assembly process. In modern manufacturing, the assembly process involves intricate and time-consuming operations, often necessitating flexible manual interventions. However, the cost and stability issues associated with manual labor highlight the need for collaborative solutions integrating humans and robots. HRC, as a viable solution, involves the joint effort of humans and robots in manufacturing tasks, presenting advantages in terms of precision, reproducibility, and cycle time. This review categorizes and discusses methodologies such as task allocation, reinforcement learning, and Cyber-Physical Systems (CPS)-based planning approaches that facilitate HRC in the assembly process. It also explores experiments and future trends to address challenges and enhance efficiency in manufacturing assembly through intelligent collaboration between humans and robots. The objective of this research is to provide insights and directions for further research in HRC to optimize manufacturing processes. By analyzing the existing state-of-the-art and presenting future prospects, this paper aims to guide researchers and practitioners toward more effective implementations of HRC in manufacturing assembly, ultimately leading to improved operational efficiency and productivity.

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

To assist firefighters and other first responders to use their existing equipment for respiration during the COVID-19 pandemic without using single-use, low-supply, masks, this study outlines an open source kit to convert a 3M-manufactured Scott Safety self-contained breathing apparatus (SCBA) into a powered air-purifying particulate respirator (PAPR). The open source PAPR can be fabricated with a low-cost 3-D printer and widely available components for less than $150, replacing commercial conversion kits saving 85% or full-fledged proprietary PAPRs saving over 90%. The parametric designs allow for adaptation to other core components and can be custom fit specifically to fire-fighter equipment, including their suspenders. The open source PAPR has controllable air flow and its design enables breathing even if the fan is disconnected or if the battery dies. The open source PAPR was tested for air flow as a function of battery life and was found to meet NIOSH air flow requirements for 4 hours, which is 300% over expected regular use.

Biomimicry in additive manufacturing often refers to topology optimization and the use of lattice structures, due to the organic shape of the topology-optimized designs, and the lattices often looking similar to many light-weight structures found in nature such as trabecular bone, wood, sponges, coral, to name a few. Real biomimetic design however involves the use of design principles taken in some way from natural systems. In this work we use a methodology whereby high resolution 3D analysis of a natural material with desirable properties is “reverse-engineered” and the design tested for the purpose. This allows more accurate replication of the desired properties, and adaption of the design parameters to the material used for production (which usually differs from the biological material). One such example is the impact-protective natural design of the glyptodont body armour. In this paper we report on the production of body armour models in metal (Ti4Al4V) and analyze the resulting mechanical properties, assessing their potential for impact protective applications. This is the first biomimetic study using metal additive manufacturing to date.

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) and the hydrology in the respective catchment of each bay. The optical properties were measured in situ over the last two decades by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8 and Sentinel-3 data. The absorption coefficient of coloured dissolve organic matter at 440 nm, aCDOM (440), and its spectral slope factor, SCDOM, were mostly correlated to natural land cover classes (Wetland, Meadow) acting as sources of CDOM, while Agricultural and Urban classes seem to act as sinks. The Agricultural class was also found to be a sink for suspended particulate organic matter (SPOM) whilst Coniferous and Mixed Forests as well as Meadows acted as a sources. SPOM seems to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land used and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

In this article, two elliptical-ovel-shaped cylindrical dielectric resonator based wideband Graphene inspired reconfigurable metasurface absorber is designed on diffused quartz substrate having a relative permittivity of 3.75 and conductivity 2200Kg/m3 with height of 25μm. This metasurface having gold plated ground in microstrip with 5μm which is essential by calculating skin depth at 2.0THz frequency. Two elliptical ovel shaped patches with Graphene plated having built in potential varies 0-2.5eV and relaxation time 1.0ps at 290oK. By changing the relaxation time of Graphene 0.1ps-0.3ps its resonance frequency also varies i.e., reconfigurable property they provide without adding any active i.e., PIN diode components. The proposed MS is designed having a dimension of 100×100μm2 is designed on a microstrip. This MS works in the range of 1.6-2.38THz having more than 90% absorbance with two transmission zero at 2.1THz and 2.3THz having high Q ˃ 50 which is used to determine the sensitivity where the proposed MS used as a sensor to determine hemoglobin (Blood Glucose level) and urine (Uric acid) by changing their refractive index from 1.34-1.43. Maximum sensitivity achieved at 2000GHz/RIU and average sensitivity for hemoglobin and urine sensing achieved 833.33GHz/RIU, FOM-8.33 and 4277.7733GHz/RIU, FOM-555.55 respectively.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

Magnesium nanoparticles of various mean diameters (53 – 239 nm) were synthesized herein via Pulsed Laser Ablation in Liquid (PLAL) from millimeter sized magnesium powders within iso-propyl alcohol. It was observed via a 3x3 full factorial DOE that the processing parameters can control the nanoparticle distribution to produce three size-distribution types (bimodal, skewed and normal). Ablation times of 2, 5, and 25 minutes where investigated. An ablation time of 2 minutes produced a bimodal distribution with the other types seen at higher periods of processing. Mg nanoparticle UV-Vis absorbance at 204 nm increased linearly with increasing ablation time, indicating an increase in nanoparticle count. The colloidal density (mg/ml) generally increased with increasing nanoparticle mean diameter as noted via increasing UV-vis absorbance. High la-ser scan speeds (within the studied range of 3000 - 3500 mm/s) tend to increase the nanoparticle count/yield. For the first time, the effect of scan speed on colloidal density, UV-vis absorbance and nanoparticle diameter from metallic powder ablation was investigated and is reported herein. The nanoparticles formed dendritic structures after being drop cast on aluminum foil as observed via FESEM analysis. Dynamic light scattering was used to measure the size of the nanoparticles. Magnesium nanoparticles have promising use in the fabrication of wearables, such as in conductive tracks or battery electrodes, owing to their low heat capacity, high melting point and bio-compatibility.

Satisfactory weatherproofing of buildings is vital to maximise their design life and performance which requires the careful design of external sealing technologies. Systems commonly available have served well in conventional construction however with many prefabricated systems emerging in the building industry new and novel means of weatherproofing between panels and modules need to be developed purpose specific to this application. This paper presents a holistic and fundamental methodological approach to Design and Development of waterproof seals and has been applied specific for prefabricated panelised and modular systems. Two purpose specific weatherproof seals are finally presented. Flow charts of the overview of the suggested methodological approach and the processes within which include DfMA that have been incorporated into understanding and developing seals for this practical application. These strategies have enabled a resourceful and holistic set of processes that can be adapted and used for similar forms of product research in new and developing areas of construction such as prefabrication. The design and development process is thoroughly explored and has resulted in an exploration of the technical challenges and potential solutions which take into consideration factors from installation limitations to building tolerances.

The capacitor-mismatch among diverse defects caused by the manufacturing process variations significantly affects the linearity of capacitor-array used to implement the capacitive digital-to-analog converter (CDAC) in the successive-approximation-register (SAR) analog-to-digital converter (ADC). Accordingly, the linearity of the SAR ADC is limited by that of capacitor-array, resulting in serious yield loss. This paper proposes an efficient foreground self-calibration technique to enhance the linearity of the SAR ADCs by mitigating the capacitor-mismatch based on the split ADC architecture along with variable capacitors. In this work, two ADC channels (i.e., ADC1 and ADC2) for the split ADC architecture include their capacitive DACs (CDACs) whose binary-weighted capacitor-arrays consist of variable capacitors. A charge-sharing SAR ADC is used for each ADC channel. In the normal operation mode, their digital outputs are averaged to be the final ADC output, as in a conventional split ADC. In the calibration mode, every single binary-weighted capacitor for the two ADCs is sequentially calibrated by making parallel or/and antiparallel connection among two or thee capacitors from the two channels. For instance, because the capacitors of the CDACs ideally exhibit the binary-weighted relation as Cn=2×Cn−1, variable capacitor Cn of ADC1 can be updated to be closest to the sum of Cn−1 of ADC1 and Cn−1 of ADC2 for the calibration. For the process, the two capacitor-arrays of the two ADCs can be reconfigured to be connected to each other, so that Cn of ADC1 can be connected with two of Cn−1 of ADC1 and ADC2 in antiparallel. The two voltages at the top and the bottom plates of the CDAC are compared by a comparator of ADC1, and the comparison results are used to update Cn. This process is iterated, until Cn is in agreement with the sum of two of Cn−1. Finally, all the capacitors can be calibrated in this way to have the binary-weighted relation. The simulation results based on the proposed work with a split SAR ADC model verified that the proposed technique can be practically used, by showing that the total-harmonic-distortion and the signal-to-noise-and-distortion ratio were enhanced by 21.8-dB and 6.4-dB, respectively.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

Thermal sterilization is generally avoided for 3-D printed components because of the relatively low deformation temperatures for common thermoplastics used for material extrusion-based additive manufacturing. 3-D printing materials required for high-temperature heat sterilizable components for COVID-19 and other applications demands 3-D printers with heated beds, hot ends that can reach higher temperatures than polytetrafluoroethylene (PTFE) hot ends and heated chambers to avoid part warping and delamination. There are several high temperature printers on the market, but their high costs make them inaccessible for full home-based distributed manufacturing required during pandemic lockdowns. To allow for all these requirements to be met for under $1,000, the Cerberus – an open source three-headed self-replicating rapid prototyper (RepRap) was designed and tested with the following capabilities: i) 200^oC-capable heated bed, ii) 500^oC-capabel hot end, iii) isolated heated chamber with 1kW space heater core and iv) mains voltage chamber and bed heating for rapid start. The Cereberus successfully prints polyetherketoneketone (PEKK) and polyetherimide (PEI, ULTEM) with tensile strengths of 77.5 and 80.5 MPa, respectively. As a case study, open source face masks were 3-D printed in PEKK and shown not to warp upon widely home-accessible oven-based sterilization.

Crosslinking of proteins for their irreversible immobilization on surfaces is a proven and popular method. However, many protocols lead to random orientation and the formation of undefined or even inactive by-products. Most concepts to obtain a more targeted conjugation or immobilization requires the recombinant modification of at least one binding partner, which is often impractical or prohibitively expensive. Here a novel method is presented, which is based on the chemical preactivation of Protein A or G with selected conventional crosslinkers. In a second step, the antibody is added, which is subsequently crosslinked in the Fc part. This leads to an oriented and covalent immobilization of the immunoglobulin with a very high yield. Protocols for Protein A and Protein G with murine and human IgG are presented. This method may be useful for the preparation of columns for affinity chromatography, immunoprecipitation, antibodies conjugated to magnetic particles, permanent and oriented immobilization of antibodies in biosensor systems, microarrays, microtitration plates or any other system, where the loss of antibodies needs to be avoided, and maximum binding capacity is desired. This method is directly applicable even to antibodies in crude cell culture supernatants, raw sera or protein-stabilized antibody preparations without any purification nor enrichment of the IgG. This new method delivered much higher signals as a traditional method and, hence, seems to be preferable in many applications.

The production of lithium-ion battery cells is characterized by a high degree of complexit due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks such as failure analysis challenging. In this paper, a method is presented, which includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production. Using this model, we are able to conduct root cause analyses as well as analyses of failure propagation. The former support operators in identifying root causes once a cell possesses a specific failure by calculating most-probable explanations matched to the individual battery cell data. The latter enable us to analyze propagation of failures and deviations in the production chain and thus provide support for placement of quality gates, leading to a significant reduction in scrap rate. Moreover, it gives an insight into which process steps are key drivers for which final product characteristics.

Additive manufacturing (AM) or 3D printing is a promising industrial technology that enables rapid prototyping of complex configurations. Powder Bed Fusion (PBF) is one of the most popular AM techniques for metallic materials. Until today, only a few metals and alloys are available for AM, e.g. titanium alloys, the most common of which is Ti-6Al-4V. After optimization of the PBF parameters, with or without post processing such as heat treatment or hot isostatic pressing, the printed titanium alloy can easily reach tensile strengths of over 1100 MPa due to the quickly cooling of AM process. However, attributed to the unique features of metallurgical defects and microstructure introduced by this AM process, their fatigue strength has been low, often only not less than 30% of the tensile strength, especially in very-high-cycle regime, i.e. failure life beyond 107 cycles. Here, we have reviewed the current state of research on the very-high-cycle fatigue (VHCF) of additively manufactured (AMed) titanium alloys, refined the basic quantities of AMed microstructure and metallurgical defects, summarized the main factors limiting their VHCF strengths, and suggested possible ways to improve VHCF performance.

Polyetheretherketone is a promising material for implants due to its good mechanical properties and excellent biocompatibility. Its accessibility to a wide range of applications is facilitated by the ability to process it with an easy-to-use manufacturing process such as Fused Filament Fabrication. The elimination of disadvantages associated with the manufacturing process, such as a poor surface quality, is a main challenge to deal with. As part of the mass finishing process, centrifugal disc finishing has demonstrated good results in surface optimization, making it a promising candidate for post-processing of additively manufactured parts. The objective of this study is to identify the key parameters of the centrifugal disc finishing process on the waviness of additively manufactured PEEK specimen, which has not been investigated yet. Waviness of the specimen was investigated by means of confocal laser scanning microscopy (CLSM), while weight loss was additionally tracked. Six parameters were investigated: type, amount and speed of media, use of compound, amount of water and time. Type of media, time and speed were found to significantly influence waviness reduction and weight loss. Surface electron microscopy images demonstrated the additional effects of deburring and corner rounding. Results on previous studies with specimens made of metal showed similar results. Further investigation is required to optimise waviness reduction and polish parts in a second post-processing step.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

Three-dimensional (3D) printing technologies are transforming the design and manufacture of components and products across a variety of disciplines, however their application in the construction industry is still limited. Material deposition processes can achieve infinite geometries and have advanced from rapid prototyping and model-scale markets to their application in fabricating functional products, large objects and the construction of full-scale buildings. Many international projects have recently been realized and the construction industry is beginning to utilise these dynamic technologies. The potential advantages for integrating 3D printing into house construction are significant, these include the capacity for mass customization of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement, and the use of recycled waste products in layer deposition materials. With the ultimate goal of improving construction efficiency and decreasing building costs, applying Strand7 Finite Element Analysis software, a numerical model was designed specifically for 3D printing in a cement mix incorporated with recycled waste product High Density Polyethylene (HDPE) and found that construction of an arched truss-like roof was structurally feasible without the need for steel reinforcements. The lab sizes prototypes were manufactured based on the destined numerical model by using a 3D printing technology. Currently available 3D printing technologies can be adopted for building construction and this paper discusses the applications, advantages, limitations and future directions of 3D printing as an innovative and viable solution for affordable house construction.

This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

Engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM) and transmission electron microscopy (TEM), the mi-crostructures underneath fracture surfaces were thoroughly investigated in an AM aluminium (AlSi10Mg) alloy with horizontal and vertical building orientation endured very-high-cycle fa-tigue (VHCF) loading under stress ratios R = –1, 0 and 0.5. Two VHCF failure specimens A and B were representatively selected to further SEM examine and TEM samples preparation via FIB milling. Specimen A was horizontally printed and failed at R = –1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack ini-tiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.