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Natural killer (NK) cells are a promising allogeneic immunotherapy option due to their natural ability to kill tumor cells, and due to their apparent safety. This study describes the development of a GMP-compliant manufacturing protocol for the local production of functionally potent NK cells tailored for high-risk acute myeloid leukemia (AML) and neuroblastoma (NBL) patients. Moreover, the quality control strategy and considerations for product batch specifications in early clinical development are described. The protocol is based on the CliniMACS Prodigy platform and Natural Killer Cell Transduction (NKCT) (Miltenyi Biotec). NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with IL-2 and IL-15 cytokines. Three CliniMACS Prodigy processes demonstrated the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product. Characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, the NK cells exhibited increased killing efficacy against patient-derived CD33 + AML blasts and NBL cells in vitro, suggesting a potential therapeutic benefit in AML and NBL. [ABSTRACT FROM AUTHOR]

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot's workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology. [ABSTRACT FROM AUTHOR]

In this study, a combination of Plackett–Burman and Box–Behnken designs is applied to discover the relationships between the components of rubber compounds and technical specifications. Optimization of rubber compound formulation is realized by support vector regression integrated genetic algorithm to minimize compound cost. Twelve components potentially affecting the technical specifications of rubber compound, which are natural rubber, carbon black, white filler, stearic acid, zinc oxide, antiozonant, antioxidant, process oil, curing retarder, curing agent, and accelerator, are screened through Plackett–Burman design to decide the significant variables. Afterwards, four significant parameters, including carbon black, process oil, curing agent, and accelerator are analyzed using Box–Behnken design to minimize the number of experiments while obtaining the correlation between formulation and specifications. Lastly, a support vector regression integrated genetic algorithm is implemented to predict optimum compound formulation at minimum cost. Highlights: Optimization of rubber compound to reduce the mixture and curing cost.Combination of Plackett–Burman and Box–Behnken designs.Integration of support vector regression to genetic algorithm.Correlations between the amounts of components and technical specifications. [ABSTRACT FROM AUTHOR]

With the current demand of wood products and industrial requirements, plantation owners need to re-think ways in which harvesting is carried out and identify the most efficient harvesting system that also utilizes small diameter parts of stem per industry's product specifications. This paper contributes to the discussion by comparing the harvesting productivity and stem recovery between conventional harvesting (CH) and integrated harvesting (IH) systems. CH is harvesting which does not necessarily align with the log dimensional requirements of the customer and only recovers logs with big diameters while IH is based on harvesting and sorting logs according to need in a single operation so that as much volume and value as possible of the stands is realized. The study was carried out at the Sao Hill Forest Plantation in the Southern Highlands of Tanzania. The harvested compartment was purposively selected and divided into IH and CH plots. A total of 39 trees were randomly selected to be harvested from each plot. The findings portray a significant difference in the stem recovery of 75% for IH and 60% for CH. CH had an average cutting productivity of 16 m3/h as compared to 14 m3/h for IH while skidding productivity was 15.3 m3/h and 24.9 m3/h for IH and CH, respectively. In this study, the IH system resulted in higher production in terms of volume recovered, meaning IH had a significant impact on the production and stem recovery of the standard stem-wood for use in different industries. [ABSTRACT FROM AUTHOR]

An online e-learning platform known as a learning management system (LMS) provides an interface between trainers, students, and administrators toward a common objective of learning. In the market, diverse LMSs with extensive functionalities are available to facilitate e-learning. In this article, we selected some popular LMSs majorly used by most schools and institutions for their teaching-learning process. We compared these LMSs in a discussion based on their technical specifications, technical features, administrative features, general features, and course management-related features. This comparative analysis could help users decide which LMS suits them best, according to their requirements. We also attempted to identify the limitations of these learning management systems so that users and organizations can determine which one is best suited to their needs. [ABSTRACT FROM AUTHOR]

The continual progress pace of the development of the Wi-Fi generations may not fulfill the expected tremendous data growth of future smart-home network devices and applications. Throughout this article, we show a comprehensive design of the futuristic smart-home network that distributes massive data for indoor devices while fully supporting intelligent functions. We start by drawing a vision for various application scenarios of information and communication technologies in smart-home networks beyond the 2030s. Then, we appraise the technical specifications of the candidate intelligent home network. To achieve these technological speculations, we propose a novel holistic networking solution that is efficient and intelligent from service and operation perspectives. Specifically, we detail the proposed network architecture, elements, and enabling technologies. To accelerate the research progress and implementation, we further point out the future research directions. Finally, we discuss the non-technological factors that could affect the future deployment of the proposed network solution. [ABSTRACT FROM AUTHOR]

Developing a clinical practice guideline (CPG) for integrating traditional Chinese medicine (TCM) and Western medicine (WM) requires the accurate identification, collation, and integration of all available evidence on TCM and WM in a comprehensive, meaningful, and resource‐efficient manner. This entails framing appropriate clinical questions, retrieving and synthesizing evidence from multiple resources, and providing concise and complete recommendations for specific diseases. However, some existing CPGs for integrating TCM and WM lack deep and organic integration. As the effective preparation of a CPG for integrating TCM and WM typically involves a complex set of principles, methodology, and steps, we believe that a cohesive, step‐by‐step guide on how to prepare a CPG for integrating TCM and WM is essential. To facilitate the design and development of a robust CPG, we present a clear and concise methodology, outlining relevant principles and procedures, supported by references for guidance. This technical specification aims to simplify the methodology for preparing a CPG for integrating TCM and WM; provide healthcare professionals and researchers with methodologically sound tools; and enhance the quality of CPGs for integrating TCM and WM. This technical specification may help elucidate this complex process, facilitate evaluation of the quality of published CPGs for integrating TCM and WM, and improve the understanding and application of recommendations for the combined and integrated use of TCM and WM in a new system. [ABSTRACT FROM AUTHOR]

The continual progress pace of the development of the Wi-Fi generations may not fulfill the expected tremendous data growth of future smart-home network devices and applications. Throughout this article, we show a comprehensive design of the futuristic smart-home network that distributes massive data for indoor devices while fully supporting intelligent functions. We start by drawing a vision for various application scenarios of information and communication technologies in smart-home networks beyond the 2030s. Then, we appraise the technical specifications of the candidate intelligent home network. To achieve these technological speculations, we propose a novel holistic networking solution that is efficient and intelligent from service and operation perspectives. Specifically,we detail the proposed network architecture, elements, and enabling technologies. To accelerate the research progress and implementation, we further point out the future research directions. Finally, we discuss the non-technological factors that could affect the future deployment of the proposed network solution. [ABSTRACT FROM AUTHOR]

This article studies the practical road performance of recycled materials from construction waste, relying on the paving test section of the supporting project for the Qingdao Cross-Sea Bridge. The research focuses on the construction technology and road performance of using recycled construction waste materials in urban road sub-base construction. Through indoor tests such as sieving and unconfined compressive strength tests, relevant technical indicators were obtained and analyzed. Additionally, periodic core sampling, compaction tests, and rebound deflection tests were conducted on-site according to relevant standards to thoroughly investigate the specific effects of using construction waste in practice and to analyze and evaluate the actual feasibility of the materials for road use. The results indicate that the particle gradation of the construction mix in the test section aligns well with the target gradation, and the dosage of the mixing agent meets the design requirements. The 7-day unconfined compressive strength already satisfied the technical requirements for heavy and extremely heavy traffic on highways as specified in the "Technical Specifications for Construction of Highway Pavement Subbase" (JTG/T F20-2015), with the 14-day strength generally reaching 7 MPa. Core sampling revealed good aggregate gradation, smooth and straight profiles, and the thickness and strength of all parts meet the specifications. The compaction levels met the testing requirements, the surface deflection values showed a decreasing trend, and the deformation resistance was good, consistent with the general development patterns of semi-rigid sub-bases. [ABSTRACT FROM AUTHOR]

Cobalt (Co) is a hazardous element of significant environmental concern, primarily due to its potential leaching toxicity. However, the current assessments of leached Co residues have focused solely on the total cobalt concentration, often overlooking the distinct Co species that contribute to its hazardous nature. This study attempts to determine the impact of cobalt speciation on the toxicity of cobalt and the related hazardous characteristics. The objective of this study is to enhance the understanding of how different Co species influence the environmental toxicity of leached residues. Cobalt speciation is studied by a multivariate analysis including ignitability, reactivity, corrosiveness, acute toxicity, leaching toxicity, and toxic substance concentration. The tested concentrations are compared with the identification standards and technical specifications in China. Co species, particularly cobalt oxide, are identified as the main contributors to the toxicity of the leached Co residue. It is also noted that Co occurrence significantly affects the calculation results of cumulative toxicity, thus impacting the hazard characteristics of leached cobalt residue. The findings benefit risk evaluators and decision makers by offering a new approach for managing leached Co residues and providing a scientific foundation for the development of relevant laws and regulations in China. [ABSTRACT FROM AUTHOR]

Featured Application: This paper addresses the practical application of unmanned aerial vehicles (UAVs) in modern transportation systems, mainly focusing on how UAVs can be effectively integrated into intelligent transportation systems (ITSs) to improve efficiency, safety, and decision-making in smart cities. It offers a framework for understanding current advancements and guides future research and development in this evolving field. Integrating unmanned aerial vehicles (UAVs) into intelligent transportation systems (ITSs) will be pivotal in shaping next-generation smart cities. This paper proposes a novel three-layered framework for integrating UAVs into intelligent transportation systems (ITSs) and reviews the current developments, challenges, and future directions in this emerging field. This framework provides a comprehensive overview of the key components of UAV-integrated ITSs, encompassing UAV specifications and deployment strategies, communication networks, and data utilization for traffic management. The first layer explores UAVs' technical specifications, deployment strategies, and trajectory optimization, essential for maximizing UAV performance in transportation contexts. The second layer addresses the communication networks between UAVs and vehicles, along with the use of UAVs for responsive traffic monitoring. This includes the development of robust communication protocols and real-time traffic analysis to enhance system efficiency. The third layer focuses on advanced data collection processing techniques and complexities, reviewing the methods for analyzing the traffic data collected by UAVs for decision-making in transportation management. Moreover, the paper presents the current UAV-enabled ITS implementation, highlighting key challenges and future research directions. By providing a comprehensive overview of UAV-enabled ITSs, this study presents a significant portrayal of the current landscape of UAV integration in ITSs and serves as a foundation for future advancements in smart city infrastructure. [ABSTRACT FROM AUTHOR]

5G cellular network is becoming a preferred choice for future deployments of IoT, due to its flexibility, low latency and high bandwidth. It has introduced small cells like femtocells, picocells and microcells to provide higher bandwidth. However, small cells have less signalling coverage, which leads to frequent handover authentication. Therefore, for seamless mobility in 5G cellular network, the existing handover authentication process, which is referred to as '5G-Handover-AKA' in this paper and illustrated in section 6.9.2.3.2 of the 3GPP technical specification TS 33.501, should be made faster. It also has to be made efficient, considering the resource constraints of IoT devices. In this paper, we propose a blockchain based fast and secure handover authentication scheme for 5G cellular network. Unlike 5G-Handover-AKA, the proposed protocol is designed to be robust against security attacks like false base station attack and key compromise attack. While informal security analysis shows the scheme to be robust against various security attacks like de-synchronization attack, eavesdropping attack, denial of service attack, etc., formal security analysis using BAN logic and Scyther tool shows that the scheme meets all the security goals. Performance analysis of the scheme shows that it achieves its goals with 15.84% lesser communication overhead and 18.08% lesser computation overhead at the resource constrained IoT device in comparison to 5G-Handover-AKA. [ABSTRACT FROM AUTHOR]

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains—such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy—demonstrating the fNIRS-EEG system's potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine. [ABSTRACT FROM AUTHOR]

Color of wine gives information on many aspects of the product, and it is one of the main parameters defining wine quality. Consequently, color is an important attribute described in the Designations of Origin (PDO)'s technical specifications. In this paper, color reference grids for color sensory evaluation of white, rosé and red wines (linked to a specific methodology and controlled illumination) are developed, presented, and validated. The color intensity and color hue parameters of 2353 wines of different types have been analyzed using these reference grids during the period 2021–2023. Considering the results obtained, the usefulness of having a double color measurement (color intensity and color hue) is questioned and discussed. [ABSTRACT FROM AUTHOR]

Background/Objectives: Biological products are emerging as therapeutic management options for intervertebral disc (IVD) degenerative affections and lower back pain. Autologous and allogeneic cell therapy protocols have been clinically implemented for IVD repair. Therein, several manufacturing process design considerations were shown to significantly influence clinical outcomes. The primary objective of this study was to preclinically qualify (chondrogenic potential, safety, resistance to hypoxic and inflammatory stimuli) cryopreserved primary progenitor cells (clinical grade FE002-Disc cells) as a potential cell source in IVD repair/regeneration. The secondary objective of this study was to assess the cell source's delivery potential as cell spheroids (optimization of culture conditions, potential storage solutions). Methods/Results: Safety (soft agar transformation, β-galactosidase, telomerase activity) and functionality-related assays (hypoxic and inflammatory challenge) confirmed that the investigated cellular active substance was highly sustainable in defined cell banking workflows, despite possessing a finite in vitro lifespan. Functionality-related assays confirmed that the retained manufacturing process yielded strong collagen II and glycosaminoglycan (GAG) synthesis in the spheroids in 3-week chondrogenic induction. Then, the impacts of various process parameters (induction medium composition, hypoxic incubation, terminal spheroid lyophilization) were studied to gain insights on their criticality. Finally, an optimal set of technical specifications (use of 10 nM dexamethasone for chondrogenic induction, 2% O2 incubation of spheroids) was set forth, based on specific fine tuning of finished product critical functional attributes. Conclusions: Generally, this study qualified the considered FE002-Disc progenitor cell source for further preclinical investigation based on safety, quality, and functionality datasets. The novelty and significance of this study resided in the establishment of defined processes for preparing fresh, off-the-freezer, or off-the-shelf IVD spheroids using a preclinically qualified allogeneic human cell source. Overall, this study underscored the importance of using robust product components and optimal manufacturing process variants for maximization of finished cell-based formulation quality attributes. [ABSTRACT FROM AUTHOR]

This paper investigates the perceptions of Construction Engineering and Management instructors regarding the competencies for Human-Robot Collaboration (HRC) in construction to determine knowledge areas, skills, and abilities prioritized for facilitating effective collaboration between humans and robots in the industry. A two-round Delphi study was employed to evaluate the perceptions of construction instructors regarding HRC competencies. This study's findings revealed that human-robot collaboration knowledge areas prioritized by the instructors include HRC ethics and regulation, robot anatomy and technical specifications, construction robot applications, sensors, and task planning. The instructors prioritized skills such as task planning, application of machine learning algorithms, safety management, human-robot interface proficiency, and effective communication. HRC abilities prioritized include decision-making, continuous learning, critical thinking, attention to detail, analytical aptitude, and adaptability. This research established the competencies prioritized by the instructors for implementing HRC in the construction industry and recommended future research directions. [ABSTRACT FROM AUTHOR]

The United Nations International Multiple Micronutrient Antenatal Preparation (UNIMMAP) of a multiple micronutrient supplement (MMS) for pregnant women is a proven evidence‐based nutrition intervention and is listed on the World Health Organization (WHO) Model List of Essential Medicines. The 2024 revision of the Expert Consensus on an Open‐Access UNIMMAP MMS Product Specification provides both purchasers and manufacturers with unified guidance on the production of UNIMMAP MMS. More specifically, it outlines the minimum requirements for the manufacture of UNIMMAP MMS and provides guidance on quality and regulatory standards based on whether the product is manufactured as a medicinal product or a dietary supplement. The product specification includes ingredients, excipients/food additives, and processing aids used in manufacturing UNIMMAP MMS; stability testing for different climatic zones; packaging and labeling considerations; pharmacopeial standards, manufacturing practices, certificates of analysis, change control, and quality agreement; finished product test specifications, including tablet characterization and purity/strength assay; analytical test methods; and storage and transportation requirements. [ABSTRACT FROM AUTHOR]

Background: Expansion of the aesthetic treatment armamentarium with novel injectable products has greatly improved our ability to reshape and enhance the facial profile. However, full‐face "profiloplasty" with injectables remain a nascent concept and is insufficiently considered in daily practice. While techniques have been widely published for treating the individual facial areas that constitute an attractive profile—including the forehead, nose, lips, jawline, and chin—the literature remains fragmented on more universal methods for profile improvement. Aims: To collate disparate knowledge on profile optimization with injectables, and define a holistic, practical, and user‐friendly approach to profiloplasty and facial contouring. Methods: Consideration is given to key anatomical aspects and the changes that occur with age; patient assessment parameters for planning profiloplasty; and the technical specifications, product selection, and essential safety considerations for minimizing the risk of complications and optimizing the effectiveness of treatment. Results: Relevant case studies are presented. Conclusions: Holistic approaches of this type are essential if we are to maximize the potential of full‐face treatment with injectables—both now and in the future. [ABSTRACT FROM AUTHOR]

Globally, demand for water resources is projected to increase due to climate change and population growth. In many developing contexts, water demand already far exceeds supply and alternatives are already required. Recycled water is increasingly seen as a source of alternative water supply to plug demand-supply gaps. This study examined the factors that influence recycled water acceptance amongst residents of Accra, Ghana. We found that irrespective of the technical specifications on water quality, consumers were only willing to use recycled water for some purposes due to low levels of trust in water service providers because of their inefficiencies and unreliable service provision. Cultural beliefs relating to human waste and the ingestion of waste underlines peoples' acceptability of recycled water. Thus, future water reuse planning must include community engagement at the planning stage to create the level of trust needed for reuse acceptability. [ABSTRACT FROM AUTHOR]

Coordinate measuring machines are widely used in the industrial field due to their ease of automation. However, estimating the measurement uncertainty is a delicate task, especially when controlling for deviation, given the large number of factors that influence the measurement. A precise estimate of the uncertainty is crucial to avoid incorrect conformity assessments. The purpose of this study is to control geometrical-form tolerance specifications, taking into consideration their associated uncertainty. A surface fitting model based on the least squares criterion is proposed, allowing one to obtain the variance–covariance matrix by iterative calculation according to the Levenberg–Marquard optimization method. The form deviation is then evaluated following the Geometrical Product Specifications (GPS) Standard, and its associated uncertainty is estimated using the guide to the expression of uncertainty in measurement (GUM) propagation of the uncertainty law. Finally, the conformity assessment is performed based on the measured deviation and its associated uncertainty. Different results for the measurement of straightness, flatness, circularity, roundness, and cylindricity are presented and detailed. This model is thereafter validated by a Monte Carlo simulation, and interlaboratory comparisons of the obtained results were performed, which showed satisfactory outcome. This contribution is of great use to manufacturing companies and metrology laboratories, allowing them to meet the normative guidelines, which stipulates that each measurement result must be accompanied by its associated uncertainty. [ABSTRACT FROM AUTHOR]

This study empirically investigates the causal relationship that exist between innovators and internal ecosystem factors driving technological innovation among small and medium-sized enterprises (SMEs) across the European region using the linear equation model. Our analysis is based on firm-level panel data from the European Innovation Survey conducted by the European Union from 2011–2018 with 296 SMEs across Europe. The results show that, human resources, research systems, financial support, firm investments, linkages, and employment impact was positively related to technological innovation performance. However, the outcome of an innovation-free environment and sales impacts as contextual factors had an insignificant influence on SMEs' innovation performance. The results imply that effective human resource approach coupled with good governmental policies are important factors that can increase SMEs' innovation performance. Based on this study results, SMEs' managers can provide effective and sustainable policies that would provide both direct and support roles on the main drivers of regional and economic development. The outcome of this research indicates that firms need to pay greater attention to external factors such as governmental policies governing SMEs, considering customers' needs, product specification and ensuring that they deliver quality product to satisfy consumers. [ABSTRACT FROM AUTHOR]

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results. [ABSTRACT FROM AUTHOR]

The development of cleaner, more environmentally friendly processes in polymerization technology is crucial due to the prevalent use of volatile organic solvents (VOCs), which are harmful and toxic. Future regulations are likely to limit or ban VOCs. This review explores the use of supercritical solvents, specifically supercritical CO2 (scCO2), in polymerization processes. The study focuses on reversible addition‐fragmentation chain‐transfer (RAFT) induced homo‐polymerization of various monomers using specific chain transfer agents (CTAs) in scCO2. RAFT polymerization, a reversible deactivation radical polymerization (RDRP) polymerization, relies heavily on the choice of CTA, which significantly influences the dispersity and molar mass of the resulting polymers. Stabilizers are also crucial in controlling product specifications for polymerizations in supercritical CO2, except for fluor‐based polymers, although they must be removed and preferably recycled to ensure product purity and sustainability. The review notes that achieving high molar mass through RAFT polymerization in scCO2 is challenging due to solubility limits, which lead to polymer precipitation. Despite this, RAFT polymerization in scCO2 shows promise for sustainable, circular production of low molar mass polymers, although these cannot yet be fully considered green products. [ABSTRACT FROM AUTHOR]

Among the steelmaking slag, secondary metallurgy slag (SMS) is the most problematic to be recycled. Several attempts to recover such slag as lime replacement, slag flux, pozzolanic materials have been made for long time with pros and cons. However, the amount of recyclable slag is limited and often their employment requires higher energy demand than traditional materials. Nevertheless, the use of SMS in agriculture is poorly or never considered. In this article, the legal and technical evaluation of SMS as raw material for fertilizers production is investigated. Compliance of technical specification, toxic metals concentration, and leaching behavior allows to confirm the technical feasibility of SMS use as a raw material for fertilizers manufacture. Both from the literature data and the experimental results on 16 industrial SMS samples, the requirements for calcium‐magnesium‐sulfur‐based fertilizers, soil correctives and for sanitizing agricultural sewage sludge, appear fully satisfied. The CaO concentration in SMS (35–60 wt%) is abundantly higher than the requirements (≥15 wt%) and CaO is present in most part as water‐soluble complexes such as calcium aluminates (70 wt%), silicates (10 wt%), and sulfide (4 wt%). The pH of the SMS samples leachate is comparable to that of fresh lime (12.35 vs 12.46), highlighting a better behavior for sewage sludge sanitation with respect to limestone (9.98). The measured toxic metals and leachate elements concentration over the corresponding admittable threshold are always lower than 0.5 and 1.0 (mg kg−1/mg kg−1) for liming materials. Finally, these results lead to officially approve the use of SMS as soil corrective according to the Italian Fertilizer Regulation. [ABSTRACT FROM AUTHOR]

Since 2017, the Space Research Centre of the Polish Academy of Sciences in Warsaw, Poland, has measured and gathered over 35,000 hours of observations with the use of the LOw Frequency ARray (LOFAR) radio telescope. This paper outlines the Standard Data Product acquired from the LOFAR PL610 station located in Borówiec, Poland. Within this context, we detail the data products that are accessible, provide metadata descriptions for them, and include an example of the data under both quiet and disturbed ionospheric conditions. [ABSTRACT FROM AUTHOR]

In high-speed rail operations, the pantograph-catenary system is commonly utilised to transmit electricity to the high-speed train. This is achieved through a sliding contact between the pantograph strip and the contact wire of the catenary. The pantograph strip, typically made of softer carbon material, is designed to experience wear, ensuring that the replaceable and cost-effective strip is more likely to deteriorate than the contact wire. However, imperfections in the pantograph strip can adversely affect the interaction performance of the pantograph-catenary system. To quantify the effect of pantograph strip imperfections on the interaction performance, this paper aims to incorporate the measurement data of pantograph strip wear into the numerical simulation of pantograph-catenary interaction. The study collected 58 sets of measurement data on pantograph strip wear from Shanghai Hongqiao station. By employing a well-developed pantograph-catenary model, several numerical simulations were conducted to investigate the dispersion of dynamic behaviours caused by strip wear. The results demonstrate that the inclusion of strip imperfections introduces a certain level of dispersion in the interaction behaviours of the pantograph-catenary system. Specifically, at the operating speed of 300 km/h, strip imperfections can lead to a 16.62% increase in the standard deviation of the contact force and an overall decrease in the minimum contact force. Consequently, it is crucial to update the technical specifications to account for the impact of pantograph strip disturbances. Furthermore, each strip's wear induces a comparable level of dispersion in the contact force standard deviation. However, the contact force on the rear strip typically exhibits greater fluctuation, making it a significant contributor to the overall interaction performance. [ABSTRACT FROM AUTHOR]

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a "grand challenge," thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a "weapon of mass deception" (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of hu mans in ensuring the proper use of ChatGPT for individual and social wellbeing. [ABSTRACT FROM AUTHOR]

Mixed-model assembly is a particular set of production lines assembling a family of product models with similar specifications. Designing paced assembly lines faces two primary problems: balancing and sequencing. The balancing quality is closely associated with the described production sequence. Although these two are problems of one assembly method, they do not occur simultaneously; balancing poses a problem during the line designing, whereas sequencing becomes problematic at the fluctuating demand of markets. The present research presents a balancing and sequencing problem and the proper times to set up the machines between tasks. Unlike a majority of published studies, this paper contains two successive tasks' setup times in dynamic periods, in which periods also impact the flowing period. A mathematical is described with a number of objective functions, reducing the inappropriate assembly line sequence, reducing setup cost, and reducing the inappropriate product balance and the impact of this situation on incomplete tasks. Thus, the literature has presented several metaheuristic algorithms to solve the problems nearly optimally. This study uses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a suitable approach, to create models and solutions. Various problems are designed and compared in different sizes, and the decision variable sensitivity is investigated to prepare managerial intuitions. The findings propose that the presented algorithm can solve the research problems more efficiently. [ABSTRACT FROM AUTHOR]

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas. [ABSTRACT FROM AUTHOR]

The reduction of sodium chloride (NaCl) content, commonly known as salt, in processed meat products is one of the objectives of health organizations and government authorities to achieve healthier products. This reformulation of traditional meat products with protected designations poses more constraints, as they have a more consolidated quality image and less margin for change, since consumers appreciate the products for their unique sensory characteristics. The aim of this work is to present some of the strategies that have been explored to obtain meat products with low sodium content. Information related to the characteristics of traditional meat products with quality marks and geographical indications in different studies is discussed in opposition to the information recorded in their product specifications. It was found that the product specifications of meat products with Portuguese Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) show a wide variation in the NaCl content, much higher than the recommended values. Thus, one of the requirements to be implemented will be the parameterization of NaCl levels and their monitorization by control and certification organizations as a way to ensure product quality. It is also urgent to examine whether healthy innovation strategies may affect the quality of traditional PDO or PGI meat products and whether they can be included in the respective product specifications. [ABSTRACT FROM AUTHOR]

The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand's EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability. [ABSTRACT FROM AUTHOR]

The ecological reconstruction and safe utilisation of a large number of tailings ponds are very important. In this study, the accumulation and transportation of cadmium (Cd) and lead (Pb) in the soil-mulberry-silkworm system was evaluated through the mulberry planting and silkworm rearing at a tailings pond in Huayuan County, China. The results revealed that both the bioconcentration factors (BCFs) of mulberry for Cd and Pb were less than 1. The highest translocation factors (TFs) for both Cd and Pb were appeared in the stage from silkworm to excrement, followed by mulberry stem to leaf. The peak concentrations of Cd and Pb in the silkworm excrement were 2.33 mg kg−1 and 6.46 mg kg−1, respectively. Though the Cd and Pb contents in the cocoon shell from Yangjiazhai fitted the Technical Specifications of Ecological Textiles (GB/T 18885–2009, China), the ratio of cocoon shell was reduced by 17.06% under the stress of heavy metals. Developing the sericulture industry could be a reasonable way to utilise the tailings pond. [ABSTRACT FROM AUTHOR]

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Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas. [ABSTRACT FROM AUTHOR]

Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution. [ABSTRACT FROM AUTHOR]

This document discusses the intersection of sustainability and digitalization in manufacturing companies, highlighting the challenges they face in addressing environmental and social impacts. The concept of Maintenance 4.0 is introduced as a way to enhance and advance maintenance processes using technologies such as the Industrial Internet of Things (IIoT), Augmented Reality (AR), Virtual Reality (VR), Big Data Analytics (BDA), and AI. The document also provides an overview of 13 articles included in a special issue on Maintenance 4.0 and sustainability, covering topics such as predictive maintenance, augmented reality, digital twins, and environmental considerations in maintenance activities. The authors emphasize the importance of Industry 4.0 technologies in transforming maintenance processes and supporting sustainable development goals. [Extracted from the article]

Graphene growth on widely used dielectrics/insulators via chemical vapor deposition (CVD) is a strategy toward transfer-free applications of CVD graphene for the realization of advanced composite materials. Here, we develop graphene-skinned alumina fibers/fabrics (GAFs/GAFFs) through graphene CVD growth on commercial alumina fibers/fabrics (AFs/AFFs). We reveal a vapor-surface-solid growth model on a non-metallic substrate, which is distinct from the well-established vapor-solid model on conventional non-catalytic non-metallic substrates, but bears a closer resemblance to that observed on catalytic metallic substrates. The metalloid-catalytic growth of graphene on AFs/AFFs resulted in reduced growth temperature (~200 °C lower) and accelerated growth rate (~3.4 times faster) compared to that obtained on a representative non-metallic counterpart, quartz fiber. The fabricated GAFF features a wide-range tunable electrical conductivity (1-15000 Ω sq−1), high tensile strength (>1.5 GPa), lightweight, flexibility, and a hierarchical macrostructure. These attributes are inherited from both graphene and AFF, making GAFF promising for various applications including electrical heating and electromagnetic interference shielding. Beyond laboratory level preparation, the stable mass production of large-scale GAFF has been achieved through a home-made roll-to-roll system with capacity of 468-93600 m2/year depending on product specifications, providing foundations for the subsequent industrialization of this material, enabling its widespread adoption in various industries. Chemical vapor deposition (CVD) of graphene on widely used insulators can promote its application for functional composite materials. Here, the authors report the large-scale production of graphene alumina fibers/fabrics via a metalloid-catalytic CVD growth process on commercial alumina, showing their application for electrical heating and electromagnetic shielding. [ABSTRACT FROM AUTHOR]

Electronic and electrical waste (e-waste) has increased exponentially due to the humungous growth in the information technology and communication sector. Many multi-metal catalysts are developed by researchers for electronic waste plastic conversion into fuel products and recovery of valuable metals. In the present study, an Alumina-supported catalyst powder with selected transition metals metal oxides was prepared to improve the yield of hydrocarbon liquids. FESEM with energy-dispersive spectroscopy, XRD and laser diffraction particle size analysis were used to characterise catalysts. The shredded e-waste chips to catalyst weight ratio was maintained from 2:1 to 10:1 and studied. The experiments were conducted with a reaction temperature range from 300°C to 500°C. The produced fuel density was 0.88 kg/L, with 0.75 liquid fuel conversion and 71.7% overall liquid fuel yield. The light gas percentage was 4–6%. NMR analysis, bomb calorimetric study and gas chromatography-mass spectroscopy of liquid fuel analysis suggest carbon chain compounds of C3–C20 hydrocarbon, indicating a mixture of petrol and diesel-like liquid products. The catalyst can be tuned to improve liquid fuel conversion. The liquid fuel product is promising for fuel use, however, upgrading treatments are needed to meet standard product specifications. [ABSTRACT FROM AUTHOR]

Pharmacopeia monographs are not intended to establish biosimilarity. However, the US Food and Drug Administration (FDA) has stopped the US Pharmacopeia (USP) from creating monographs for biological drugs due to the need for side-by-side comparisons with the reference products. The USP can create Biological Product Specifications (BPS), not to be labeled as monographs, based on the analytical testing of reference products and validated test methods that will remove the need for side-by-side analytical testing of biosimilars with reference products. Scientific arguments confirm that this plan is logical and capable of creating global quality standards for biosimilars to allow their interchangeability with other biosimilars. While the regulatory agencies have waived many high-cost biosimilar tests, analytical assessment is the most sensitive test; reducing its cost will further enhance the entry of biosimilars with no clinically meaningful difference. [ABSTRACT FROM AUTHOR]

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are "better" for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel. [ABSTRACT FROM AUTHOR]

Fuel cell vehicles are considered as the direct alternative to fuel vehicles due to their similar driving range and refueling time. The United Nations World Forum for Harmonization of Vehicle Regulations (UN/WP29) released the Global Technical Regulation on Hydrogen and Fuel Cell Vehicles (GTR13) in July 2013, which was the first international regulation in the field of fuel cell vehicles. There exist some differences between GTR13 and the existing safety technical specifications and standards in China. This paper studied the safety requirements of the GTR13 compressed hydrogen storage system, analyzed the current hydrogen storage safety standards for fuel cell vehicles in China, and integrated the advantages of GTR13 to propose relevant suggestions for future revision of hydrogen storage standards for fuel cell vehicle in China. [ABSTRACT FROM AUTHOR]

The prompt emphasizes the importance of identifying victims in a disaster area within 48 hours and highlights the potential benefits of using drones in search and rescue missions. However, the use of drones is limited by factors such as battery life, processing speed, and communication range. To address these limitations, the paper presents a detailed research study on the most effective flying pattern for drones during search and rescue missions. The study utilized energy consumption and coverage area as performance metrics and collected precise images that could be analyzed by the forensic team. The research was conducted using OMNET++ and fieldwork at Pulau Sebang, Melaka, in collaboration with search and rescue agencies in Malaysia. The results suggest that the square flying pattern is the most effective, as it provides the highest coverage area with reasonable energy utilization. Both simulation and fieldwork results showed coverage of 100% and 97.96%, respectively, for this pattern. Additionally, the paper provides technical specifications for rescue teams to use when deploying drones during search and rescue missions. [ABSTRACT FROM AUTHOR]

In the process of industrial production, products with different specifications (i.e., the difference in geometry, process conditions, and machine conditions, etc.) have different quality data distributions, which lead to a decrease in the accuracy of traditional data-driven quality prediction models that require the same quality data distribution. At the same time, due to economic cost factors, obtaining a large amount of accumulated data for different specifications is difficult, and the re-modeling data accumulation of multiple cross-specifications is insufficient. In order to solve the quality prediction problem of production with different data distributions and poor data accumulation, we use the deep transfer learning (DTL) method with unsupervised dynamic domain adaptation (DDA) to transfer the domain invariant features learned from labeled specification products (source domain) to other unlabeled new specification products (target domain). In order to improve the success rate of cross-domain quality prediction, the Wasserstein distance adapter is designed to match appropriate source domain samples and target domain samples to build multiple transfer tasks that are suitable for transfer. At the same time, the dynamic distribution adaptation and dynamic adversarial adaptation are combined to extract the domain invariant features to improve the adaptability of the prediction model for products with new specifications (e.g., size difference) and unlabeled and limited quality data. Finally, a comprehensive experiment is carried out using the actual production data of products with different specifications. The experimental results show that compared with the traditional non-transfer deep learning methods, the MAE, RMSE, and R2 of the proposed DTL method are improved by 18.26%, 16.66%, and 22.48% respectively. Compared with other transfer methods, the MAE, RMSE, and R2 of the DTL proposed in this paper are improved by 10.45%, 10.96%, and 9.72% respectively. [ABSTRACT FROM AUTHOR]

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns. [ABSTRACT FROM AUTHOR]

Electrical machines that can run at high speeds are more and more studied as they can respond to the increasing need of power onboard of aircrafts. However, to allow high-speed operability mechanical handling of the rotating parts need to be insured. In this paper an analytic design process of a novel high-speed induction machine is presented. The analytical magnetic and mechanical models developed are presented and validated with finite element simulations. The magnetic model is based on a classic equivalent electrical diagram of induction machine with a specific adaptation for the rotor leakage inductance as the squirrel cage is buried. The mechanical model is based on a field displacement approach leading to the stress tensor in all the rotating part. A four degrees of freedom vibration analysis model considering gyroscopic effects based on Euler–Lagrange equation allows to identify the critical speeds of the system. It is shown that some geometrical parameters will have opposed effects on the two physics. Thus, an optimization-based coupling between the different physics allows to design rapidly the desired machine regarding any technical specifications as analytical models are being used. [ABSTRACT FROM AUTHOR]