The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

The emergence of prosumers, who actively participate in designing and producing goods, has generated interest in domestically manufactured products. Factors such as innovation, design methods, component reuse, prototyping, and digital fabrication empower prosumer designers to realize their ideas and enhance products to meet their specific design requirements. This article presents a case that combines bioinspired design methods with prosumer characteristics from the perspective of a designer who uses biological research results acting as a prosumer. By analyzing prosumer needs, bioinspired design, and domestic fabrication requirements, designers define the methods that best suit their possibilities. As a result of using biological information, the geometry of Samara seeds, propeller sizing, wind tunnel tests, and subsequent improvements, the custom design of a bioinspired microgenerator has been refined to the final design of a bicycle flashlight. The aim is to popularize a working approach that facilitates the interaction of design methods and reinforces domestic manufacturing processes. Implementing bioinspired design methods within the prosumer context offers a methodological alternative to transform the current economic model, promoting systemic sustainability and the development of proprietary technologies.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

Yttria stabilized zirconia (Y-TZP) ceramics with drastically reduced yttria content have been introduced by different manufacturers aiming at improving the damage tolerance of ceramic components. In this study an alumina doped 1.5Y-TZP was axially pressed, pressureless sintered in air at 1250–1400 °C for 2 h and characterized with respect to mechanical properties, microstructure and phase composition. The material exhibits a combination of high strength of 1000 MPa and a high toughness of 8.5–10 MPa√m. The measured fracture toughness is, however, extremely depending on measurement protocol. Direct crack length measurements overestimate toughness due to trapping effects. The initially purely tetragonal material has a high transformability of > 80 %, the transformation behavior is predominantly dilatoric, measured R-curve related toughness increments are in good agreement with transformation toughness increments derived from XRD data.

Yttria stabilized zirconia materials are frequently used in mechanical engineering and biomedical applications. Demanding loading conditions require materials combining a high level of strength and fracture toughness. A ready to press alumina doped 2 mol% yttria stabilized zirconia powder was shaped by axial pressing and sintering in air at 1250 - 1500 °C for 2 h. The best combination of strength and toughness value of 7.8 MPa√m. Materials sintered in the middle of the chosen temperature range combine fully density, high transformability and small grain size. Toughness measurements by direct crack length measurements delivered unrealistically high fracture toughness values.

The design complexity of the new generation of civil aero-engines results in higher demands on engines’ components, higher component temperature, higher heat generation, and, finally, critical thermal management issues. This paper will propose a methodological approach to create physics-based models for heat loads developed by sources as well as a systematic sensitivity analysis to identify the effects of design parameters on the thermal behaviour of civil aero-engines. The ranges and levels of heat loads generated by heat sources (e.g., accessory gearbox, bearing, pumps, etc.) and heat absorption capacity of heat sinks (e.g., engine fuel, oil, and air) are discussed systematically. The practical research challenges for thermal management system design and development for the new and next generation of turbofan engines will then be addressed through a sensitivity analysis of the heat load values as well as the heat sinks flow rates. The potential solutions for thermal performance enhancements of propulsion systems will be proposed and discussed accordingly.

This research article describes the creation of a mock-up for a Smart Insole prototype that employs Force Sensitive Resistors (FSRs) and an ADXL345 accelerometer. The mock-up is an early step towards realising the concept and functionality of the planned Smart Insole system. The key factors in the mock-up's design, integration, and testing are highlighted, with a focus on the iterative refining and optimisation process. The mock-up, which was carefully planned and implemented, highlights the practicality and possible applications of Smart Insole technology in healthcare, sports performance measurement, and rehabilitation. The paper outlines the development process and implementation of a "Smart Insole" utilizing Force Sensitive Resistors (FSR) and the ADXL345 accelerometer. The project aims to create an innovative wearable technology solution for monitoring foot biomechanics and enhancing human performance. The Smart Insole prototype integrates FSRs strategically placed on the insole to capture pressure distribution across the foot, along with an ADXL345 accelerometer to track foot orientation and movement. Through a combination of hardware integration, firmware development using Arduino IDE, and visualization using Processing application, the Smart Insole offers real-time monitoring and analysis of gait dynamics, pressure distribution, and foot movement.

Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

Companies are increasingly focusing on sustainable business practices. Internal and external stakeholders’ expectations manifest in legal requirements, national and international standards, and market and customer expectations, among other things, must be considered. In addition to profit maximization as the usual target for corporate management, management must consider environmental sustainability aspects such as resource efficiency, greenhouse gas intensity, and a company's emissions behavior. In addition, social aspects relating to the company's employees, the immediate urban environment, the situation in the supply chain, and effects on the market environment must increasingly be considered. Specifically, companies are faced with the chal-lenge of dealing with conflicting objectives regarding the various aspects of sustainability and, if necessary, weighing them up against each other. These trade-offs must be made against the company's socio-economic and ecological environment, corporate strategy, and sustainability goals. This paper provides an overview of current approaches and research gaps on this topic through a literature review. It shows a lack of methods and frameworks to specifically deal with trade-offs and conflicts of goals.

EIMIs face the challenge of adapting to the Paris Agreement and the European Green Deal to achieve carbon neutrality. This change is being driven by consumer interest in low-carbon products, regulatory requirements from climate agreements and investors prioritizing sustainability. This pressure is pushing companies to go beyond short-term financial gains and integrate sustainability into their business. The growing awareness of the environmental impact of business models (BM) has fueled the development of Sustainable Business Models (SBMs). This study aims to link the discussion on the role of SBMs in the drivers of decarbonization, to improve the understanding of how the drivers can be integrated and enable new BMs for EIMIs. Based on a structured literature review, semi-structured interviews were conducted in the EIMIs. The study identifies BM-components that are influenced by these drivers and require sustainable business model innovation (SBMI). The results show that decarbonization drivers have a significant impact on all BM components. Experts emphasize the importance of collaborative approaches and cooperation throughout the value chain. This research elaborates on the importance of systematic analysis to understand how companies can effectively manage decarbonization drivers and suggests in-depth exploration within BM-research to uncover potential SBM design options.

Cameroon introduced the malaria vaccine in its routine immunization programme on 22 January 2024, in the 42 districts out of 200, among the most at risk of malaria. A cross-sectional analysis of data on vaccine key events in the introduction roadmap and vaccine uptake during the first 30 days was conducted. In addition to available gray literature related to the introduction of the malaria vaccine, data on malaria vaccine uptake by vaccination session, collected through a digital platform, were analyzed. A total of 1,893 reports were received from 22 January 2024 to 21 February 2024 from 766 health facilities (85% of overall completeness). Two regions out of 10 recorded less than 80% of completeness. As of 21 February 2024, 13,811 children have received the first dose of the malaria vaccine, including 7,124 girls (51.6%) and 6,687 boys (48.4%). Thirty six percent of children were vaccinated through outreach sessions versus 61.5% through sessions in fixed posts. The overall monthly immunization coverage with the first dose was 37%. Early results have shown positive attitudes towards and acceptance of malaria vaccines. Suboptimal completeness of data reporting and low coverage highlight persistent gaps and challenges in vaccine rollout.

Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

In this paper, a novel microwave catalyst Ni@SiC/CNFs (NSC), C/Ni (NC), and C/SiC (SC) were successfully synthesized by electrostatic spinning method, and proved to degrade methylene blue with high efficiency under microwave irradiation. The results imply that the catalyst Ni@SiC/CNFs with double shell structure gave a 99.99 % removal rate in 90 s for the degradation of methylene blue under microwave irradiation, outperformed the C/Ni and C/SiC and most other reported catalysts in similar studies. On the one hand, the possible mechanism of the methylene blue degradation should be ascribed to that double shell structure increases the polarization source of the material, resulting in excellent microwave absorption properties; and on the other, the in-situ generation of active species OH• and O2 - under microwave radiation and the synergistic coupling effect of metal plasma greatly improved the degradation efficiency of methylene blue. The findings of this study could provide a valuable reference for the green degradation of industrial dye wastewater and its sustainable development process.

Chromium (Cr) metal has garnered significant attention in alloy systems owing to its exceptional properties, such as high melting point, low density, and superior oxidation and corrosion resistance. However, its processing capabilities are hindered by the high Ductile-Brittle Transition Temperature (DBTT). Recently, powder bed fusion-laser beam for metals (PBF-LB/M) has emerged as a promising technique, offering the fabrication of net shapes and precise control over crystallographic texture. Nevertheless, research investigating the mechanism underlying crystallographic texture development in pure Cr via PBF-LB/M still needs to be conducted. This study explores the impact of scan speed on relative density and crystallographic texture. At the optimal scan speed, an increase in grain size attributed to epitaxial growth was observed, resulting in the formation of cubic texture. Consequently, a reduction in High Angle Grain Boundaries (HAGB) was achieved, suppressing defects such as cracks and enhancing relative density up to 98.1%. Furthermore, with increasing densification, Vickers hardness also exhibited a corresponding increase. These findings underscore the efficacy of PBF-LB/M for processing metals with high DBTT properties.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

Vertical-axis wind turbines (VAWTs) are receiving more and more attention as they involve simple design, cope better with turbulence and are insensitive to wind direction which has a huge impact on the cost since the yaw mechanism is not needed. However, VAWTs still suffer from low conversion efficiency. As a results tremendous efforts are exerted to improve its efficiency which mainly focus on two methods regardless of whether the study is a CFD simulation or a field test or lab test experiment. An active approach which involves the modification of the rotor itself such as the blade design, angle, trailing and leading edges, inner blades, chord thickness, contra-rotating rotor, etc. While the second approach involves passive techniques where the flow is directed to optimally face the downwind rotor by mounting a guiding vane such as a diffuser or other shapes at the upwind position of the rotor. Among all techniques undertaken, the CRWT rotor technique seems to be the most effective with an output even comparable to that of Horizontal Axis Wind Turbines (HAWT), while the Savonius rotor received more attention compared to other designs of VAWTs. Apart from technological issues, it was also suggested that geographical issues such as proper site siting of the wind turbine rotor at a particular location where a uniform flow could be guaranteed is of paramount importance to ensure an effective conversion capacity of wind turbines.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

In the decarbonization of the steel, cement, and chemical industry in Germany, green hydrogen is expected to play a crucial role. The utilization of green hydrogen in the production processes of said industries requires organizations to modify their business model, requiring sustainable business model innovation (SBMI). Numerous tools and frameworks that support organizations in the process of SBMI have been proposed in the literature in recent years. However, the applicability of these tools and frameworks for steel, cement, and chemical companies that intend to utilize green hydrogen to produce their goods remains unexplored. By conducting a systematic literature review on tools and frameworks for SBMI, a literature and practice review to identify and analyze existing green hydrogen projects of steel, cement, and chemical companies, and an evaluation of the identified tools and frameworks in an evaluation matrix, this paper aims to assess the suitability of SBMI tools and frameworks for steel, cement, and chemical companies that plan to use green hydrogen to produce their goods. Based on the evaluation, the Cambridge Business Model Innovation Process (CBMIP) was identified as the most suitable SBMI framework.

The rational design of magnetic carbon composites, encompassing both composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave absorbing materials (EAMs). In this study, FeCo@CM composites was efficiently fabricated through an advanced microwave plasma assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of -58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.

Business-to-business (B2B) manufacturing companies are increasingly confronted with transformative trends such as sustainability, digitalization, and servitization. These trends are changing how product portfolios are developed, and value contributions are assessed, and therefore have disruptive potential. Dealing with these disruptive factors in Product Portfolio Management (PPM) is a largely unexplored topic. This study presents an empirical-qualitative exploration that contributes significantly to the field. The aim is to clarify the extent to which disruptive factors influence the evaluation and shaping of the product portfolio in B2B manufacturing companies. The Gioia method was used to evaluate 21 semi-structured interviews with experts from leading B2B manufacturing companies. Eight overarching challenges in PPM resulting from the disruptive factors were identified. Based on the eight overarching challenges and their associated causal relationships, two aggregated dimensions of action were derived: (1) increasing speed and flexibility by using generative artificial intelligence (AI) in a defined PPM process and (2) adjusting the product portfolio evaluation to consider various strategic drivers. These two dimensions of action call for future research to overcome the disruptive factors in PPM.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

In the world city theory, most researchers focus on the service sector in the urban economy and less discuss the role of manufacturing. However, the path of only emphasizing financial and corporate service could not fit the sustainability concept. Compared to Anglo-American world city, Global South’s world cities have distinct pathway to be industrialization, tertiarization and globalization. This paper adopted dynamic historic perspective with first-hand materials including in-depth interviews with managers and government officers and second-hand data including yearbook statics and economic census to closely examine the emerging world city-- ‘World Factory’ in Global South, Guangzhou in China, from 1949 to 2015, to emphasize how manufacturing affects the urban globalization through three dimensions, economic, social and spatial dimensions. To make the confirmation of the role of manufacturing in Guangzhou as sustainable world city, we find manufacturing in Guangzhou builds up the basic foundation of export-oriented economy and makes positive effects on urban economic transformation. In addition, manufacturing remains important source of employment and foreign immigration. Along with urbanization and industrialization, urban spatial expansion and aggregation changes with different urban development concept. We provide new insights on multiple globalization on manufacturing for sustainable world city.

Many of the fundamental molecules of life share extraordinary pigment-like optical properties in the long-wavelength UV-C spectral region. These include strong photon absorption and rapid (sub pico-second) dissipation of the induced electronic excitation energy into heat through peaked conical intersections. These properties have been attributed to a ``natural selection'' of molecules resistant to the dangerous UV-C light incident on Earth's surface during the Archean. In contrast, the ``thermodynamic dissipation theory for the origin of life'' argues that, far from being detrimental, UV-C light was, in fact, the thermodynamic potential driving the dissipative structuring of life at its origin. The optical properties were thus the thermodynamic ``design goals'' of microscopic dissipative structuring of organic UV-C pigments, today known as the ``fundamental molecules of life'', from common precursors under this light. This ``UV-C Pigment World'' evolved towards greater solar photon dissipation through more complex dissipative structuring pathways, eventually producing visible pigments to dissipate less energetic, but higher intensity, visible photons, up to wavelengths of the ``red-edge''. The propagation and dispersal of organic pigments, catalyzed by animals, and their coupling with abiotic dissipative processes, such as the water cycle, culminated in the apex photon dissipative structure, today's biosphere.

The aim of the study was the comparison of conventional sintering versus additive manufacturing techniques for β-TCP bioceramics, focusing on mechanical properties and biocompatibility. A "critical" bone defect requires surgical intervention beyond simple stabilization. Autologous bone grafting is the gold standard treatment for such defects but it has its limitations. Alloplastic bone grafting using synthetic materials is becoming increasingly popular. The use of bone graft substitutes has increased significantly and current research was focused on optimizing these substitutes, whereas this study compares two existing manufacturing techniques and the thereby produced β-TCP implants. The 3D-printed β-TCP hybrid structure implant was fabricated using two components, a column structure and a freeze-foam, which were sintered together. The conventionally manufactured ceramics were made by pouring. Both scaffolds were characterized according to porosity, mechanical properties and biocompatibility. The hybrid structure had a total porosity of 74.4 ± 0.5%. Microporous β-TCP implants had a porosity of 43.5 ± 2.4%, while macroporous β-TCP implants had a porosity of 61.81%. Mechanical testing revealed that the hybrid structure had a compressive strength of 10.4 ± 6 MPa, significantly lower than the microporous β-TCP implant’s with 32.9 ± 8.7 MPa. Biocompatibility evaluations showed a steady increase in cell proliferation over time for all β-TCP implants, with minimal cytotoxicity. This study provides valuable insight into the potential of additive manufacturing for β-TCP bioceramics in the treatment of bone defects.

To enhance the applicability of dual-phase high-entropy alloys (HEAs) like Fe32Cr33Ni29Al3Ti3, fabricated via selective laser melting (SLM), a focus on improving their mechanical properties is essential. As part of this effort, heat treatment was explored. This study compares the microstructure and mechanical properties of the as-printed sample with those cooled in water after undergoing heat treatment at temperatures ranging from 1000 to 1200 ℃ for 1 hour. Both pre- and post-treatment samples reveal a dual-phase microstructure comprising FCC and BCC phases. Although heat treatment led to a reduction in tensile and yield strength, it significantly increased ductility compared to the as-printed sample. This strength-ductility trade-off is related to changes in grain sizes with ultrafine grains enhancing strength and micron grains optimizing ductility, also the influence of the content of FCC/BCC phases and dislocation density. In particular, the sample heat-treated at 1000 °C for 1 hour and then water-cooled exhibited a better combination of strength and ductility, a yield strength of 790 MPa, and an elongation of 13%. This research offers innovative perspectives on crafting dual-phase HEA of Fe32Cr33Ni29Al3Ti3, allowing for tailorable microstructure and mechanical properties through a synergistic approach involving SLM and heat treatment.

Quasi-solid composited electrolytes were produced by LiFSI initiated polymerized poly-(Dioxolane) (PDOL) with garnet-LLZO (Li7La2Zr3O12) nanofibers and used in lithium-metal batteries. Linear sweep voltammetry shows that the initial oxidation voltage of 4.6 V for Gel-10% (G10, 10% wt. LLZO in PDOL) vs Li/Li+. EIS test of stainless steel// stainless steel symmetric cells for Gel-1% (G1), Gel-5% (G5) and Gel-10% (G10) show ion conductivity (5*10-5 S/cm). Li/G10/Li symmetric cells have stable stripping-plating cycling performance over 1300 hours continuously with the current density of 0.1 mA/cm2 at 25 oC, indicating a good interface contact and stable lithium-ion transport pathway through the composite electrolytes. LFP/G10/Li batteries have stable charge-discharge plateau, low polarization voltage of 0.07-volt, energy efficiency of 95% and coulombic efficiency of ~99% at current density of 26.3 mA/g at 25 oC for over 100 cycles.

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution were examined with measurements on the physically formed parts. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45-degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation of part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Higher forming rates were found to result in more defects due to increased slip path lengths and traction magnitudes. Optimal forming speeds should balance rapid shaping within the operating temperature window while avoiding tear. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching, and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate, and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.

COVID-19 pandemic disproportionately affected seven South Asian countries because of rampaging virus and weak health system response. Vaccination commenced in all countries during early 2021, once the vaccines became available in the international market. Vaccines were donated by international development partners, friendly countries and procured utilizing domestic resources and financial support from various agencies. All countries opted for a phasing of the vaccination by prioritizing high risk populations which included health care personnel, front line workers and elderly with comorbidities. Seven vaccines that had WHO emergency authorization usage were utilized in South Asia. India produced more than 2.4 billion doses of AstraZeneca vaccine and has indigenously developed five COVID-19 vaccines. Vaccine inequity was obvious and characterized by fewer people in South Asia vaccinated with single dose vis a vis the developed countries. Vaccine hesitancy was another obstacle that kept the vaccination rates low. Difficult to reach areas in Afghanistan, Bhutan, Maldives, Nepal could not fully access vaccines. South Asian countries need to strengthen their health system for efficient deployment of vaccines in future health emergencies. At the same time the global agencies and developed countries must address the issue of vaccine access to low-income countries to mitigate the impact of pandemic all over the world.

A lead zirconate titanate [PZT;Pb(Zr_0.52Ti_0.48)O₃] layer embedded infrared (IR) detector decorated with wavelength-selective plasmonic crystals has been investigated for high-performance non-dispersive infrared (NDIR) spectroscopy. A plasmonic IR detector with an enhanced IR absorption band has been designed based on numerical simulations, fabricated by conventional microfabrication techniques, and characterized with a broadly tunable quantum cascade laser. The enhanced responsivity of the plasmonic IR detector at specific wavelength band has improved the performance of NDIR spectroscopy and pushed the limit of detection (LOD) by an order of magnitude. In this paper, a 13 fold enhancement in the LOD of a methane gas sensing using NDIR spectroscopy is demonstrated with the plasmonic IR detector.

This study investigates the potential benefits of integrating coarser particle size distributions (PSDs) of 45-106 µm into laser-based powder bed fusion of metals (PBF-LB/M), aiming to reduce costs while maintaining quality standards. Despite the considerable advantages of PBF-LB/M in producing intricate geometries with high precision, the high cost of metal powders remains a barrier to widespread adoption. By exploring the use of coarser PSDs, particularly from electron beam-based powder bed fusion of metals (PBF-EB/M), significant cost-saving opportunities are identified. Through comprehensive powder characterization, process analysis, and mechanical property evaluation, this study demonstrates that PBF-LB/M can effectively utilize coarser powders while achieving comparable mechanical properties to those produced with 20-53 µm PSD. Adaptations in process parameters enable the successful processing of coarser powders, maintaining high-relative density components with minimal porosity. Additionally, market surveys reveal substantial cost differentials between PBF-LB/M and PBF-EB/M powders, indicating 40 % cost reduction potential by integrating coarser PSDs into PBF-LB/M. Overall, this study provides valuable insights into the economic and technical feasibility of printing with coarser powders in PBF-LB/M, offering promising avenues for cost reduction without compromising quality, thus enhancing competitiveness and adoption of the technology in manufacturing applications.

For the purpose of improve the sustainability of products sold in the EU, Digital Product Passports is a mandatory solution. These passports would systematically gather information about products, such as detailing the materials involved to aid recycling and remanufacturing efforts, or recording the emissions produced throughout the manufacturing process. The overall goal is the establishment of a circular economy in the EU. Current research on Digital Product Passports is focused on collating content for sector-specific applications. However, achieving sustainability goals through Digital Product Passports will require new methods and technical solutions for digital collaboration between supply chain participants, especially in the manufacturing sector. Collaborative work on Digital Product Passports requires trust and traceability in data and access management to ensure privacy over the data contained. One way to enable this securely and confidentially is through the use of blockchain technology, in which stored data cannot be concealed, which is key to the traceability of digital product data documentation. This paper therefore presents a concept for the implementation of generically applicable cross-industry blockchain-based digital product passports. To this end, generic supply chain processes have been abstracted and condensed into the architecture of smart contracts. Great attention is paid to the establishment of traceability as well as privacy through the selection of the blockchain technology Secret Network in combination with Gaia-X functionalities. An illustrative use case for digital product passports in manufacturing is used to demonstrate the applicability of the presented concept. In this use case information about the digital nameplate and carbon footprint is exchanged securely and confidentially in a multi-tenant supply chain network based on the Industry 4.0 infomation model of the Asset Administration Shell.

Isothermal interrupted hot compression tests of 5754 aluminum were conducted on Gleeble-3500 thermo-mechanical simulator at temperature 350 °C and 450 °C, strain rate 0.1 s-1 and 1s−1. These tests simulated flat rolling to investigate how softening behaviors respond to controlled parameters, such as deformation temperature, strain rate and delay times. This data allowed the parameters for the hot rolling process to be optimized. The delay times during interrupted compression vary between 5s and 60s. The dynamic softening at each pass and metadynamic recrystallization at the intervals of deformation passes were analyzed in detail. 0.2% offset yield strength is applied to calculate the softening fraction undergoing metadynamic recrystallization. A kinetic model was developed to describe metadynamic recrystallization behaviors of the hot deformed 5754 aluminum alloy. Furthermore, the time constant for 50% recrystallization was expressed as functions related to the temperature and the strain rate. The experimental and calculated results were found to be in close agreement, which verified the developed model.

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

This review examines additive manufacturing for refractory tungsten (W) and its alloys. It emphasizes the primary challenges and determining factors involved in additive manufacturing pure W, W alloys, and composites. Our focus in this regard extends to both process and alloying strategies designed to address critical issues such as densification, micro-cracking, and mechanical properties in tungsten-based components produced through additive techniques. Throughout the review, we organize existing knowledge and insights into convenient tables, serving as valuable resources for researchers embarking on a deeper exploration of these topics.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

Establishment of protected areas (PAs) is one of the key global conservation strategies that currently cover approximately 15% of the earth’s land surface. Globally, PA networks are designed to curb the growing anthropogenic pressures in areas with high biological diversity. Despite the importance of PAs in conserving the vanishing biodiversity and unique habitats, many of them are in critical condition due to poor governance thus functioning below the expected level. Moreover, in many developing countries, the PA coverage is below the global standard. Recognizing their contemporary role in conservation, governments have recently agreed to expand the global PA coverage to 17% by the year 2020 (Aichi target 11). This book with eight chapters from different regions of the world provides an overview of the PAs governance, institutional mechanisms, conservation benefits, limitations and challenges associated with their respective policy discourse, integrated management, and functional attributes. Protected areas expect to to play an important role in the long rn in conservation and protection of biodiversity and ecosystems particularly in countries where population pressure and habitat loss are high. Regular intervention, political commitment, and effective governance are essential for the sustainability of PAs across the world. Here, we also attempted to shed some light on future development clues for the sustainable management and monitoring of PAs worldwide.

Laser Powder Bed Fusion (L-PBF)-based manufacturing is a growing area of research, with a particular focus on evaluating constraints in printed products. This research emphasises the importance of considering various factors—including mechanical properties and support structures—that influence the final product during the design phase. Specifically, the design of microchannel heat exchangers is highlighted, where all constraints are critical. This article will present a method for analysing channel pressure limitations and report findings about mechanical properties loss from examining tube porosity. This includes a summary of results and the establishment of an experimental device system.

Micro plasma devices (MPD) with power gains are of interest in applications involving operations in the presence of ionizing radiations, in propulsion, in control, amplification of high power electromagnetic waves, and in metamaterials for energy management. Here we review and discuss MPDs with an emphasis on new architectures that have evolved during the past 7 years. Devices with programmable impact ionization rates and programmable boundaries are developed to control the plasma ignition voltage and current to achieve power gain. Plasma devices with 1-10 μm gaps are shown to operate in the sub-Paschen regime in atmospheric pressures where ion-assisted field emission results in a breakdown voltage that linearly depends on the gap distance in contrast to the exponential dependence dictated by the Paschen curve. Small gap devices offer higher operation frequencies at low operation voltages with applications in metamaterial skins for energy management and in harsh environment inside nuclear reactors and in space. In addition to analog plasma devices, logic gates, digital circuits, and distributed amplifiers are also discussed.

A review of quantitative risk assessment (QRA) models of L. monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer’s handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis; highlighting therefore the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally-processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

A novel means of applying radiotherapy in cancer treatment is the application of the radiation dose at a very high intensity for a very short time, FLASH radiotherapy (FLASH-RT). This technique involves exposure of tumors to >40 Gy/s, usually for less than one second. Studies conducted in cell and preclinical models suggest that FLASH-RT seems less damaging to normal tissues from adverse effects, relative to the same overall dose of radiation administered in conventional therapy (CONV-RT) which involves administration of lower levels of radiation repeated intermittently over a protracted period. In contrast, the susceptibility of tumor tissues to FLASH-RT is not diminished relative to CONV-RT. Within solid tumors both modes of dispensation of radiation produce an equivalent degree of cell damage. This differential between normal and malignant material has been found in isolated tissues, animal studies, and more recently in clinical trials. However, the classical radiation concept is that high energy linear transfer radiation (LET) is more damaging than the equivalent total dose of low LET. Thus, the susceptibility of cells should be greater to short-term exposure to high LET. This article discusses the potential reasons that may account for this discrepancy. While the relative protection given to untransformed tissues by FLASH-RT relative to tumor tissue is a major step forward in radiation therapy of cancer, the processes that lie behind this phenomenon are incompletely understood and are considered here.

Users with Autism Spectrum Disorder may find human-computer interaction (HCI) challenging due to a number of symptoms experienced, one of which is difficulty coping with change. Through capturing, comparing and statistically analysing the reactions of autistic and neurotypical users to seven individual design changes, it has been possible to identify the changes that cause significant difficulty in autistic users. These difficulty points were then used to form heuristics that can followed by HCI practitioners in the industry to reduce the negative impact of software interface design change on autistic users, hence making software updates more accessible.

This literature review provides an in-depth exploration of the research conducted on the investigation and measurement of residual stress (RS) in Wire Arc Additive Manufacturing (WAAM) products, particularly focusing on how process parameters influence the phenomenon. RS plays a crucial role in determining the mechanical behavior and structural integrity of WAAM components. This review article gives an understanding of the relationship between process parameters and RS to optimize the WAAM processes and ensure the durability of the final products. It also summarizes key findings, measurement techniques, challenges, and future directions in this evolving field. The review also analyzes a measurement of RS in the products of WAAM as a function of process parameters depending on examining various research studies, techniques, and methodologies employed in their studies. Experimental measuring techniques and numerical analysis of RS to determine the impacts of RS in mechanical response in products of WAAM were discussed. By offering a comprehensive overview of current research trends and insights, this review serves as a valuable resource to guide future investigations, fostering the advancement of WAAM as a robust and efficient manufacturing technology.

The digital model of airborne frequency selective surface radomes (AFSSRs) is the basis of design, simulation analysis, manufacturing, and other related research of AFSSRs. This paper proposes a rapid modeling method of AFSSRs based on dynamic customizable primitives. Firstly, a layered digital model construction scheme of AFSSR is presented based on the typical radome wall structure. Then, according to the characteristics of various surface configurations and complex wireframe information of AFSSRs, the dynamic primitives are raised to express the boundary and contour information of all kinds of radomes. Focusing on the undevelopable characteristics of the aero-dynamic shape surface of the AFSSR, the arrangement solution and mapping method for frequency selective elements on undevelopable surfaces are proposed. Furthermore, the imple-mentation logic of this method on the creation of each layer model and the assembly of the whole machine model is introduced. Finally, according to this method, a rapid modeling system (RMS) is established, and the modeling effect and efficiency of the RMS are demonstrated and compared, which verifies the feasibility and effectiveness of this method.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

In this study, we investigated the features of co-infection with SARS-CoV-2 and nonpathogenic strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-СoV-2 and LEV-8 or EV-A71 in the cell model was shown that a competitive inhibitory effect for these viruses were especially significant against SARS-CoV-2. Pre-infection with enteroviruses in animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tracts and more rapid clearance of the lower respiratory tract from infectious SARS-CoV-2. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection for animals. Additionally, the histological data illustrated that co-infection with nonpathogenic coxsackievirus decreased the level of pathological changes by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of antiviral immune response, which can be associated with significant inhibition of SARS-CoV-2 infection. These results demonstrate that there exists a strong viral interference between the studied nonpathogenic strain of coxsackievirus A7 or enteroviruses A71 and SARS-CoV-2 pathogenic for humans in vitro and in vivo.

The excessive and prolonged use of the electronic devices with digital screens may cause some serious health problems. The ocular surface problems particularly Dry Eye Syndrome may be the most common and preventable one of them. In this study, unfavourable and potentially harmful effects of digital screens on lacrimal system and tear functions were evaluated in 221 participants. Their ocular surface conditions were evaluated by Ocular Surface Disease Index (OSDI) scores and their lacrimal functioning was measured by Schirmer’s test.. A reciprocal and statistically significant relationship was found between dry eye symptoms and OSDI scores. This relationship was more prominent especially in long duration use of smartphones with brighter background lights..

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Optimizing the nitrogen (N) timings and rate can improve nutrient uptake, and nutrient-efficiencies, especially of N in wheat under the changing climate scenario. Climatic stress in the form of high temperature and drought resulted in the decreased crop physiology and, ultimately, grain yield. Taking the example of rainfed wheat, we quantified the impact of N application rates as full and split-dose at three variable sites of rainfed Pothwar, Pakistan by conducting field experiments for two years (2013-14 and 201-15). Treatments include, T1 = Control (No fertilizer applied), full dose of N applied at the time of crop sowing, i.e. T2 = 50 kg N ha^-1, T3 = 100 kg N ha^-1 and T4 = 150 kg N ha^-1 and split application of N at different timings during different stages of the crop called as split application of N, i.e. T5: Application of 50 kg N ha^-1 (15 kg N ha^-1 (Sowing) : 20 kg N ha^-1 (Tillering) :15 kg N ha^-1 (Anthesis), T6: Application of 100 kg N ha^-1 (30 kg N ha^-1 (Sowing): 40 kg N ha^-1 (Tillering) : 30 kg N ha^-1 (Anthesis) and T7: Application of 150 kg N ha^-1(45 kg N ha^-1 (Sowing) : 60 kg N ha^-1 (Tillering) : 45 kg N ha^-1 (Anthesis). Three study sites include viz. Islamabad (High rainfall with optimum temperature), University Research Farm (URF)-Koont (Medium rainfall with moderate temperature), and Talagang (low rainfall with high temperature). Results showed that the highest stomatal conductance (0.80 mole m^-2 sec^-1), net photosynthetic rate (20.07 μmole m^-2s^-1), transpiration rate (9.58 mmole m^-2s^-1), intercellular CO2 concentration (329.25 μmole CO₂ mol^-1 air), SPAD values (58.86 %) and proline contents (35.42 μg g^-1) were obtained for split application of N (T6 = Split N100) compared to control and full dose of N treatments. Among sites, these physiological traits remained highest at Islamabad and lowest at Talagang, while among years, maximum values of the measured parameters were obtained in 2013-14. A similar trend was observed for crop total N, N efficiencies, and agronomic traits of the crop. Our results suggest that optimum N application rate and its suitable timings can help to harvest real benefits of N as in our findings, split dose resulted in the maximum performance of the crop from physiological parameters to the agronomic traits of the rainfed wheat crop.

This study provides an in-depth review of the available literature on metal composite material machining, focusing on traditional machining operations such as turning, milling, shaping, drilling, and grinding. The motivation for the study is that this composite type has emerged as a vital material in applications requiring functionality, resilience, cost-effective operation, low consumption of energy and lightweight. Despite its superior characteristics, the manufacturing complexity and poor machinability of metal matrix composites have been the primary barriers to its adoption. Nevertheless, there is a reduction of exceptional efficiency, increase in surface roughness, tool wear and so on in metal matrix composites throughout machining. As a result, this review gives a comprehensive investigation of metal matrix composite machining and effect on process parameters and procedures. Furthermore, this study distinguishes metal matrix composites towards sustainable manufacturing and the metals employed by the researchers, as well as the gaps discovered for future recommendations in metal-based composites.

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet limited studies have been conducted to optimize these prospects from the perspective of low resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour with a role of green dynamic capability, and environmental orientation. This study is anchored on quantitative research approach, deductive reasoning, and explanatory design. Cross – sectional survey data have been obtained from 382 manufacturing companies in Ghana. Our hypotheses have been tested using Structural Equation Modelling and SMART-PLS version 3.8.9. The study has revealed that smart manufacturing practices significantly and positively relate to pro-environmental behaviour. Moreover, green dynamic capability and environmental orientation moderate the relationship between smart manufacturing and pro-environmental behaviour. The implications of the study include the emergence of a baseline model to guide policymakers, practitioners, and academicians to explain the relationship between smart manufacturing and sustainable manufacturing in the context of developing countries. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption and production as well as actions taken to combat climate change.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

The International Renewable Energy Agency (IRENA) was established in 2009 and in 2024, its membership increased to 168 members plus the European Union countries. The purpose of IRENA is to promote the adoption of renewable energy across all industries in the world. Despite the utilization of renewable energy in sectors like power generation and construction, its application in other industries remains limited. Therefore, a literature review study was conducted to investigate the cost and other spatial constraints associated with renewable energy resources, including modeling and optimization techniques that could offer viable solutions to the future challenges faced by the renewable energy production systems. This paper has summarized the result of this study with a focus on the significance of modeling and optimization techniques in energy conservation to highlight the untapped potential for leveraging renewable energy resources in industrial applications. This paper also examined a few case studies from various regions of the world, demonstrating the promising opportunities for research in the modeling and optimization of renewable energy management systems.

Additive manufacturing processes offer various options to fabricate metal parts, which show numerous ecological and technological advantages compared to formative and subtractive manufacturing methods. Due to quick availability and high accuracy, they are particularly suitable to produce complex prototypes. Metal fused deposition modeling is a relatively new material extrusion additive manufacturing technique based on the widely used fused filament deposition process. This study focuses on the Inconel 625 metal filament from Markforged Inc., it consists of a mixture of metal powder and a binder system, the latter being removed during washing and sintering. In this article, the mechanical properties of printed and sintered Inconel were investigated using tensile testing specimens with three different infill patterns, solid, triangular, and gyroid, printed in flatwise and upright direction. In addition, investigations into the dimensional accuracy of the printed specimens and the sintering shrinkage were carried out. The results showed that neither the dimensional accuracy nor the sintering shrinkage was significantly influenced by the printing orientation. In the context of lightweight engineering, the structures proofed beneficial especially within the elastic region. Generally, triangular infill patterns resulted in higher stiffness, while gyroids led to more ductile specimens. A mass-related evaluation of tensile testing elucidates that with aid of the infill structures weight savings of 40 % resulted in mechanical performance decreased by only 20 % on average.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, the drill bit condition monitoring techniques have been developed based on the wavelet analysis and artificial neural network (ANN) as automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is changed. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. Defining RWD based on its original intent, which is the collection of data at the point of care for clinical purposes, can distinguish it from conventional clinical trial data. RWD can be generated using experimental designs that resemble those used in conventional clinical trials, but with several advantages, such as improved efficiency and a better balance between internal and external validity. RWD can support pharmacovigilance, insights into disease progression, and the development of external control groups. Prospective RWD collection enables evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly, patients with poor performance status (PS), hepatitis, or human immunodeficiency virus-infected patients, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.

Automobile industry is recognized as one of the most influential sectors shaping the global economies, societies, and individual lifestyles. Therefore, fierce competitions among different companies are continuously undergoing, and innovations deserve special attention to improve the competitiveness. In the past several years, additive manufacturing (AM) is emerging as an innovative technology in the applications of automobile industry with significant advantages over traditional techniques. As a result, increasing attention has been attracted to combining AM technology with the development of automobile industry. Currently, many automobile players are optimizing their industrial layout by incorporating with innovative AM techniques, and meanwhile, many research progresses have been achieved to meet the market demand. This article aims at presenting a timely review to conclude the recent advances in the application of AM techniques in automobile industry, focusing on the available AM techniques, printable materials, and the industry applications. The future research opportunities and challenges are also outlooked. Hopefully, this work can be useful to the related researchers, as well as the game players in the industry of this field.

In this paper, we investigate the Green measure for a class of non-Gaussian processes in $\mathbb{R}^{d}$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta,\alpha}$, $0

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an on-line vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density；it also conducts experiments with an on-line vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant temperature stirring system, a walk-in constant temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device's evaluation is U= 0.08kg/m3, k=2.The experimental research on the on-line vibrating tube liquid densitometer can be carried out within the range of (650~1400) kg/m3, (10~70)℃, (0~50)bar the metrological performance of the vibrating tube densitometer can be researched by utilizing the straight tube type and the elbow type on-line vibrating tube densitometer. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system's vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device's conformity. The study verified the device's scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research.

In this work SiC-Ti3AlC2 and TiC-Ti3AlC2 composites produced by additive manufacturing are investigated. The fused granules fabrication (FGF) method as one of the material extrusion additive manufacturing (MEAM) technology is used to obtain composite samples. Composites with different ratios between components and different powder:polymer ratios are investigated. The technological features of the additive formation of composites are investigated, as well as their structure and properties. The optimal sintering temperature to form the best mechanical properties for both composites is 1300 °C. The composites have a regulatable porosity. Ti3AlC2 content, sintering temperature, and polymer content in the feedstock are the main parameters that regulates the porosity of FGF samples.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

This study explored differences in student-athletes’ symptoms of depression, anxiety, and stress pre- to post-COVID-19 pandemic. The WHO reported a 25% increase in depression and anxiety rates worldwide with young people disproportionately affected. Student-athletes face many stressors related to their sporting and academic feats but what is not known is how the COVID-19 pandemic affected their experiences of mental illness. A multiple cohort cross-sectional study design was employed, and data collected using physical and online surveys. Participants (M age = 19.98 years, SD = 1.50) were recruited from UK universities (N = 807; 427 pre-pandemic cohort, 380 post-pandemic cohort). Results revealed statistically significant differences in mean depression, anxiety, and stress scores between cohorts. Scores for the post-pandemic cohort were significantly higher than pre-pandemic, suggesting a worsening of symptom severity. Distributions of student-athletes across categories of symptom severity also worsened for depressive and anxiety symptoms post-pandemic and were skewed towards more severe categories. Symptoms of de-pression, anxiety, and stress were a concern pre-pandemic. Rates are higher in the post-pandemic cohort, suggesting a worsening of symptoms. This data adds to evidence on student-athletes’ symptoms of mental illness by exploring a UK sample and comparing scores pre- and post-pandemic.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

This study explores the potential to reach a circular economy for post-consumer recycled polyethylene terephthalate (rPET) packaging and bottles by using it as a distributed recycling for additive manufacturing (DRAM) feedstock. Specifically, rPET is processed using only an open source toolchain with fused particle fabrication (FPF) or fused granular fabrication (FGF) processing. In this study, first the impact of granulation, sifting and heating (and their combination) is quantified on the shape and size distribution of the rPET flakes. Then feeding studies were performed to see if they could be printed through an external feeder or needed to be direct printed with a hopper using two Gigabot X machines, one with extended part cooling and one without. Print settings were optimized based on thermal characterization and for the latter which was shown to print rPET directly from shredded water bottles mechanical testing is performed. The results showed that geometry was important for extended feeding tubes and direct printed using a hopper. Further there is a wide disparity in the physical properties of rPET from water bottles depending on source and the history of the material. Future work is needed to enable water bottles to be used as a widespread DRAM feedstock.

Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts cleaning machine in the metalworking industry.

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

The influence of short term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength; tensile strength and impact strength in dry and wet conditions. After short term water exposure; the maximum microhardness; tensile; flexural and impact toughness values were observed at 0.1 wt% MLG. The microhardness increased up to 50.3%; tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples; the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at concentration of 0.1 wt% to produce considerable improvement in mechanical properties of produced hybrid nanocomposites.

Low carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental concerns. LCSCM technologies face a number of challenges that limit their adoption. Eleven hurdles were selected from the literature as well as the recommendations of business and academic experts. These barriers are defined by a number of factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using Multi criteria decision making (MCDM) techniques such as interpretive structural modeling (ISM) and decision-making trail and evaluation laboratory (DEMATEL). MICMAC analysis is used to estimate the sensitivity and priority of barriers based on dependency power and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt 'LCSCM techniques' in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers.The objective of this paper is the Evaluation and selection of most influential of Barriers to Adopt Low Carbon Supply Chain Management (LCSCM) in the Indian Manufacturing Sector is interrelated.

Abstract. The remarks by the Supreme Leader of the Islamic Revolution on the toll of the famines that afflicted occupied Iran in World War I and World War II indicate a desire to incorporate these calamities in Iran’s national narrative and collective memory. His criticism of famine historiography and stated toll numbers, and the unfolding catastrophic famine in Gaza, justify and necessitate a fresh look at each famine’s historiography and toll. This article re-estimates the toll of each famine by including many overlooked and neglected primary sources and in ways not previously done. It builds a trajectory of Iran’s demographic development from 1810 to 1944, and derives the toll of each famine. The World War II famine and typhus epidemics had claimed 5.5 million. World War I famine was also more deadly, though the 1869-73 famine was not far behind. At least 15.5 million Iranians died in the two world wars, a fact nowhere reflected in the history of the conflicts. Iran’s 1944 population of 12 million was unchanged from that of 1850 and a century of population growth obliterated

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Africa, home to the world's second-largest population of approximately 1.3 billion, grapples with significant challenges in meeting its medical needs, particularly in accessing quality healthcare services and products. The continent faces a continuous onslaught of emerging infectious diseases, exacerbating the strain on its already fragile public health infrastructure. The emergence of SARS-Cov2 has further exposed the deficiencies in Africa's public health response, ranging from limited diagnostic capabilities to difficulties in vaccine distribution. Compounding these challenges is vaccine hesitancy, which is prevalent in many regions due to varying levels of understanding and trust regarding vaccines. The disparities in vaccine literacy across the continent stem from a complex interplay of factors, including low literacy rates and a history of medical exploitation and misinformation campaigns. These factors have led to a pervasive distrust in medical institutions and healthcare providers among certain African communities. Despite concerted efforts by national governments, private entities, and international partners to address these challenges and improve vaccine literacy, significant hurdles persist. This manuscript examines the current state of education in vaccinology and related sciences in Africa, as well as the continent's efforts to address educational needs in vaccine development and manufacturing. It evaluates initiatives aimed at strengthening vaccine education and literacy, highlighting successful approaches and ongoing challenges. By assessing the progress made and identifying remaining obstacles, this review offers insights into how Africa can enhance its capacity in vaccinology and accelerate its contributions to global health security.

Industry 4.0 (I4.0) epitomizes the nexus of technological evolution and manufacturing process enhancements, underpinned by four critical pillars: Cyber-Physical Systems (CPS), Digital Twins (DT), the Industrial Internet of Things (IIoT), and Cloud Computing (CC). Through the lens of the German Reference Architectural Model Industrie (RAMI) 4.0, these components are integral to fostering Smart Manufacturing (SM) practices. In conjunction, the Asset Administration Shell (AAS) facilitates seamless inter-company communication and enables adaptability to fluctuating market demands. This paper delves into the structural intricacies of AAS and SM within the RAMI 4.0 framework, unveiling three distinct implementation strategies. Furthermore, it meticulously scrutinizes challenges that hinder the full realization of I4.0 potential, such as interoperability, security concerns, and standardized communication protocols. By examining these models and identifying current barriers, the study illuminates pathways for future research, particularly in enhancing the integration of AAS and SM systems, ensuring robust security measures, and advocating for the development of universal standards. This exploration aims to contribute to the ongoing discourse on I4.0, proposing avenues for advancing manufacturing technologies and operational frameworks in alignment with the principles of RAMI 4.0.

Background: This study aimed to identify the association between occupational stress and depression-well-being by proposing a comprehensive and flexible job burden-capital model with its corresponding hypotheses. Methods: For this research, 1618 valid samples were gathered from the electronic manufacturing service industry in Hunan Province, China; self-rated questionnaires were administered to participants for data collection after obtaining their written consent. The proposed model was fitted and tested through structural equation model analysis. Results: Single-factor correlation analysis results indicated that coefficients between all items and dimensions had statistical significance. The final model demonstrated satisfactory global goodness of fit (CMIN/DF=5.37, AGFI=0.915, NNFI=0.945, IFI=0.952, RMSEA=0.052). Both the measurement and structural models showed acceptable path loadings. Job burden and capital were directly associated with depression and well-being or indirectly related to them through personality. Multi-group structural equation model analyses indicated general applicability of the proposed model to basic features of such a population. Gender, marriage and education led to differences in the relation between occupational stress and health outcomes. Conclusions: The job burden-capital model of occupational stress-depression and well-being was found to be more systematic and comprehensive than previous models.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

Analyzing BRCA1/2 tumor pathogenic variants (TPVs) in epithelial tubal/ovarian cancers (EOC) has become an essential part of the diagnostic workflow in many centers to guide treatment options and genetic cascade testing. However, there is no standardization of testing procedures, including techniques, gene assays, or sequencers used, and data on the execution of tumor tests remains scarce. Therefore, we evaluated characteristics of BRCA1/2 tumor testing in advanced-stage EOC with real-world national data. Pathology reports of patients diagnosed with EOC in 2019 in the Netherlands were obtained from the Dutch Pathology Registry (PALGA), and data regarding histological subtype and BRCA1/2 tumor tests were extracted. A total of 999 patients with advanced-stage EOC were included. Tumor tests were performed for 502 patients (50.2%) and BRCA1/2 TPVs were detected in 14.7%. Of all tests, 48.6% used hybrid capture techniques and 26.5% used PCR-based techniques. More than half of the tests (55.0%) analyzed other genes in addition to BRCA1/2. Overall, this study highlights the heterogeneity in the execution of BRCA1/2 tumor tests. Despite a lack of evidence of quality differences, we emphasize that adequate reporting and internal and external quality monitors are essential for high-quality implementation and execution of reliable BRCA1/2 tumor testing, which is crucial for identifying all patients with BRCA1/2 TPVs.

Wood is poised to become a material of choice for future construction. When appropriately managed, it is a renewable material with unique mechanical properties. Beyond its inherent sustainability, the use of wood has a crucial role in addressing climate change concerns due a significantly lower energy consumption and emissions during manufacturing and transportation, while simultaneously acting as a carbon sink. Thus, there has been a growing demand for hardwoods for structural applications, including Castanea sativa Mill., the focal point of this investigation. Albeit in a limited capacity, Eurocode 5-2 offers friction coefficients for softwoods, but it falls short for hardwoods which it is a gap to bridge with this research. These coefficients play a critical role in numerical simulations involving friction, enabling the optimization of joints and, by extension, the overall structural integrity. Test samples were evaluated at 18% moisture content, which is typical of Service Class 2, for various orientations of timber-to-timber and timber-to-steel friction. Considering previous friction coefficients at 12% moisture content, the linear variation was evaluated at an intermediate value of 15%. The results provide an experimental database for numerical simulations and highlight the influence of moisture content on the coefficients, which increase linearly along moisture percentages.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

The fetal splenic artery pulsatility index (PI) is a parameter that reflects fetal well-being and has been used as a predictor of adverse pregnancy outcomes. The aim of this study was to investigate the predictive value of splenic artery PI in GDM class A1 cases for NICU admission. In this prospective case controlled study, only sixty single pregnancy cases diagnosed with gestational diabetes mellitus class A1 were evaluated. Fetal splenic artery Doppler parameters Peak systolic velocity (PSV), Pulsatility index (PI), Resistivity index (RI) and End diastolic velocity (EDV) were measured in all cases. The rate of re-quirement of need of neanoatal intensive care unit was noted. In cases with need fetal intensive care, fetal splenic PI index was found to be statistically significantly lower than in healthy cases without it (0.94 ± 0.29 vs. 1.70 ± 0.53, respectively, p

Background: Emerging and re-emerging infectious diseases that infect wildlife, such as African swine fever, avian influenza, and SARS-CoV-2, have highlighted the necessity for wildlife health surveillance (WHS) due to their direct and indirect impacts on wildlife species, ecosystems, domestic animals, and human health. While global policies and guidelines exist, a critical gap remains in local-to-national implementation of WHS systems. A group of local, national, and global actors in WHS have formed a working group to address this gap. Methods and Findings: The working group reports on a theory of change (ToC) developed to implement WHS from local to global scales. Through brainstorming, plenary exercise, and building on peer-reviewed science and existing surveillance systems, we identified six transformative pathways to be implemented via collaborations across scales and contexts: mindset change, policy and investment, user-driven science, user-driven technologies, capacity enhancement, and mobilization of a global community of practice. Interpretation: This ToC serves as a roadmap to develop effective WHS systems that support adaptive management and implementation. WHS is fundamental to understanding the impacts of health threats to biodiversity and human and domestic animal health. This ToC presents an approach to operationalize integration of wildlife health into collaborative One Health surveillance. Funding: The Science for Nature and People Partnership.

We present a case report of a 26-years-old female suffering from Coffin Siris Syndrome, who underwent orthodontic treatment and surgery to solve her malocclusion and improve her aes-thetics and functional occlusion. Methods: The presurgical phase involved multibracket self-ligating attachments, Damon prescription. The patient underwent maxillofacial surgery to correct the severe skeletal malocclusion and relocate the bone bases in the right relationship. The post-surgical orthodontic treatment was performed to complete the alignment. Results: The pa-tient improved her aesthetics and functional abilities. Conclusions: Syndromic patients can un-dergo orthodontictreatment if comorbidities and collaboration allow it. Support and collaboration of families and psychotherapists must be considered, but clinical cases of syndromic patients can be faced and solved. Obviously, each syndromic patient is considered unique, and the risk-benefit ratio must be correctly assessed for each one.

Electromobility promises to efficiently mitigate consequences of increasing traffic volume and its accompanied greenhouse gas emissions. On an individual level, electrified bikes allow emission free electrified mobility at moderate costs and consequently their stock has increased significantly in recent years. This simultaneously increases the demand for spare parts, which are often manufacturer or application-specific, and due to many variants, challenging to provide for the market. This article evaluates powder-based and extrusion-based metal additive manufacturing of a typical electrified bike component to demonstrate an alternative spare parts supply. The investigation demonstrates how these parts can be additively manufactured function equivalent and with sufficient mechanical properties, also taking economical aspects into account. Furthermore, the needed resources and related environmental consequences for metal-based additive manufacturing spare-part production are compared for both process routes. The results show that both routes are capable of producing spare-parts at comparatively same mechanical performance and resource costs, while needed resources such as energy, gases and manufacturing time are significantly lower for powder-based, respectively machine costs for extrusion-based additive manufacturing. Therefore, additive manufacturing offers a promising opportunity to produce parts in small quantities resource efficient and rapidly.

In the realm of metal steel and iron manufacturing, there is a constant drive to optimize production efficiency and enhance the quality of the final products. This abstract explores a paradigm shift in this industry through the utilization of Computational Fluid Dynamics (CFD) as a powerful tool for achieving these goals .The study focuses on various critical aspects of the manufacturing process. Firstly, it delves into three-phase processes such as slag and gas entrainment in liquid steel, which have a significant impact on the overall product quality. Understanding and optimizing these processes is crucial to ensure the desired chemical composition and physical properties of the final metal.The abstract investigates the role of CFD in vacuum degassing, a process that plays a pivotal role in removing impurities, such as hydrogen and oxygen, from the liquid steel. By leveraging CFD to simulate and analyze the fluid flow patterns, researchers and engineers can design more efficient vacuum degassing systems, leading to improved product purity and reduced waste.Another crucial aspect explored in this abstract is the alloy melt and mixing process. By employing CFD simulations, researchers can gain insights into the movement of different alloy components and optimize their distribution within the melt. This aids in achieving the desired alloy composition, ensuring uniformity, and enhancing the mechanical properties of the final product.In addition, the study delves into the movement and flotation of inclusions, which are unwanted impurities present in the metal. CFD can be utilized to model and analyze the behavior of these inclusions, enabling engineers to design efficient strategies for their removal, thereby enhancing the purity and integrity of the final product.Moreover, the abstract considers the impact of melt temperature losses, which can occur during different stages of the manufacturing process. By employing CFD, it becomes possible to understand and mitigate these temperature losses, leading to improved energy efficiency and better control over the manufacturing parameters.Part 1 of this abstract critically evaluates the use of CFD in iron making, analyzing its benefits and limitations. It highlights how CFD can aid in process optimization, improving the understanding of complex phenomena involved in iron making, and ultimately leading to enhanced production efficiency.Part 2 focuses on the application of CFD in steel making and steel operation. It explores the fluid flow dynamics during different stages of steel production, including refining, casting, and rolling. By leveraging CFD, engineers can optimize these processes, leading to improved product quality, reduced defects, and increased productivity.This abstract demonstrates the transformative potential of Computational Fluid Dynamics in metal, steel, and iron manufacturing. By utilizing CFD as a tool to model and analyze complex fluid flow phenomena, researchers and engineers can revolutionize the production process, maximizing efficiency, and ensuring high-quality end products.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

Considering the significance of thermodynamic cycles in the global energy system, it is necessary to develop new general classes of thermodynamic cycles to relieve current energy and environmental problems. Inspired by the relationship between power cycles and refrigeration cycles, we realize that general classes of thermodynamic cycles should occur in pairs with opposite functions. Here we reverse class 1 heating cycles to obtain another new general class of thermodynamic cycles named class 2 heating cycles (HC-2s). HC-2s have two basic forms, and each contains six thermodynamic processes. HC-2s present the simplest and most general approach to utilizing the temperature difference between a medium-temperature heat source and a low-temperature heat sink to achieve efficient high-temperature heating. HC-2s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in the global sustainable energy system.

Thermodynamic cycles are not only the core concepts of thermal science, but also key approaches to energy conversion and utilization. So far, power cycles and refrigeration cycles have been the only two general classes of thermodynamic cycles. While diverse types of systems have been developed to perform thermodynamic cycles, no new general classes of thermodynamic cycles have been proposed. Based on the basic principles of thermodynamics, here we propose and analyze a new general class of thermodynamic cycles named class 1 heating cycles (HC-1s). Two basic forms of HC-1s are obtained by connecting six essential thermodynamic processes in the proper order and forming a thermodynamic cycle. HC-1s present the simplest and most general approach to utilizing the temperature difference between a high-temperature heat source and a medium-temperature heat sink to achieve efficient medium-temperature heating and/or low-temperature cooling. HC-1s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in energy conservation and emission reduction.

Most cervical cancer cases are a result of persistent high-risk Human papillomavirus (HPV) infections. Cervical cancer is prevalent in LMICs especially in SSA where it is second most prevalent cancer. In South Africa, it is the second most common cancer affecting women aged between 15 and 44 years. The host immune response has been shown as an important factor in controlling the progression or regression of high-risk HPV infection of the cervix to cervical cancer. The risk of cervical cancer is known to be influenced by the host's genetic diversity, particularly by immune response-regulating genes such as the Human Leukocyte Antigen (HLA) class I and II genes. HLA class I genes present viral peptides to CD8+ T cells which are restricted and pre-programmed for cytotoxic functions. There is very little known about the HLA genes of South African women and how these influence outcome of HPV infections. This review aims to understand the role and influence of HLA class I genes in HPV clearance, persistence, and CD8+ T cell mediated immunity in African women by examining existing literature in this area. Understanding this role can influence and inform therapeutic vaccine design that is population specific.

3D printing is a non-traditional additive manufacturing process. It is different from the traditional subtractive manufacturing process. It offers exceptional rapid prototyping capabilities and results that traditional subtractive manufacturing methods cannot attain, especially in applications involving curved or intricately shaped components. Despite its advantages, metal 3D printing will face porosity, warpage, and surface roughness issues. These issues will affect the future practical application of the parts indirectly, for example, the structural strength and the parts assembly ca-pability. Therefore, this study compares the qualities of the warpage, weight, and surface rough-ness after milling and grinding processes under the same materials (316L stainless steel) between general rolled steel and 3D additive steel. Experimental results show that 3D printing parts are approximately 13% to 14% lighter than the general rolled steel. The surface roughness performance of 3D printing steel is better than general rolled steel under the same material after milling or grinding processing. The hardness of the 3D printing steel is better than that of the gen-eral rolled steel. The research verifies that 3D additive manufacturing can use surface processing to optimize surface performance and achieve the functions of lightness and hardness.

The objective of this study is to evaluate the effect of constitutive equations on the prediction accuracy for springback in cold stamping with various deformation modes. In this study, two types of yield functions—Hill’48 and Yld2000-2d—were considered to describe yield behavior. Isotropic and kinematic hardening models based on the Yoshida–Uemori model were also adopted to describe hardening behavior. Various material tests (such as uniaxial tension, tension- compression, loading-unloading, and hydraulic bulging tests) were carried out to determine the material parameters of the models. The obtained parameters were implemented in the finite element (FE) simulation to predict springback, and the results were compared with experimental data. U-bending and T-shape drawing were employed to evaluate the springback prediction accuracy. Obviously, the springback prediction accuracy was greatly influenced by constitutive equations. Therefore, it is important to choose appropriate constitutive equations for accurate description of material behaviors in FE simulation.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

By this contribution we would like to briefly highlight the problems in conceiving the “Hydrogen Bond” (HB) as a real short-range, directional, electrostatic, attractive interaction and to reframe its nature through the non-approximated view of condensed matter offered by a Quantum Electro-Dynamic (QED) perspective. We focus our attention on water, as the paramount case to show the effectiveness of this 40-years’ old theoretical background, which depicts water as a two-fluid system (where one of the two phases is coherent). The HB emerge to be the result of the electromagnetic field gradient in the coherent phase of water, whose vacuum level is lower than the non-coherent (gas-like) fraction. Thus, the HB can be properly looked at, i.e. no more as a “dipolar force” among molecules, but as the phenomenological effect of their collective thermodynamic tendency to occupy a lower ground-state, compatibly with temperature and pressure. This perspective allows to account for many “anomalous” behaviours of water and to understand why the calculated energy associated to HB should change when considering two molecules (water-dimer), or the liquid state, or the several kinds of ice. The emergence of a condensed, liquid, phase at room temperature is indeed the consequence of the boson condensation as described in the framework of spontaneous symmetry breakings (SSB). The switch from a still semi-classical Quantum Mechanical (QM) in the 1st quantization to a Quantum Field Theory (QFT) view embedded in the 2nd quantization is advocated for a more realistic and authentic description of water, condensed matter and living systems.

In this study, a comprehensive review on production scheduling based on reinforcement learning (RL) techniques using bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of all pertinent publications contained in the Web of Science database. The study focuses on a wide spectrum of publications spanning the years between 1998 and 2023. The findings and recommendations of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

All life on Earth uses three integrated molecular systems in which genetic information contained in DNA base sequences is transmitted to ribosomes by RNA and a genetic code, then translated into the amino acid sequences of structural and catalytic proteins. Therefore, the most important point for understanding the origin of life is to determine how such systems could emerge from random processes on the early Earth. In this review, two alternatives are compared: the RNA world hypothesis and the [GADV]-protein world hypothesis. [GADV] refers to four amino acids, Gly [G], Ala [A], Asp [D] and Val [V] that are conserved in the amino acid sequences of many common proteins. Here I will argue that the origins of the three primary processes required for life to begin can be better explained by the GADV hypothesis than the RNA world hypothesis. The GADV hypothesis also incorporates a conversion process by which random polymers can evolve into proteins with ordered sequences.

Introduction: maxillary hypoplasia and mandibular asymmetry may be corrected with orthognathic surgery at the completion of growth. Many patients require segmental Le Fort I osteotomies to obtain the most stable results. Unfortunately, if Invisalign is chosen for pre-surgical preparation, ClinCheck © software does not allow for prediction when these complex surgeries are planned. The objective of this paper was to show the potential of aligners beyond its software in most complex cases like multiple-piece maxillary osteotomies in cleft and non-cleft patients. Method: Thirteen patients who underwent pre-surgical treatment with clear aligners-Invisalign (study group) were matched in terms of diagnosis, surgical procedure, and orthodontic complexity with thirteen patients treated using fixed appliances (control group). A technique based on simple superimposition was devised to enhance the software for creating a suitable occlusion for segmental pre-surgical treatments. Virtual curves following the lower arch were employed to guide the correct pre-surgical positions of the upper teeth. The amount of impressions required in both groups to achieve satisfactory pre-surgical alignment of the segmented arches was compared. Results: one or no refinement phase were needed in the Invisalign group to reach an acceptable pre-surgical occlusion while the amount of pre-surgical impressions taken in order to reach adequate coordination with fixed appliance treatment was slightly higher. The average overall treatment duration did not show a statistically significant variation between the groups. Conclusions: it appears that clear aligner could serve as an effective treatment for individuals with skeletal discrepancies, including cleft and craniofacial anomalies, necessitating segmental Le Fort I osteotomies when aided by the suggested superimposition approach during digital pre-surgical movement simulations. More sophisticated softwares will certainly be implemented in the future.