bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning
Authors: Savitha P B , Rai Ayushi , Singh Niharika , Keshav Chinmayi C , Neelambike VG 
Published: December 2023

Abstract
Global climatic changes in the recent era have resulted in a significant global food shortage. Where there is a vast population and a diverse agricultural sector, food security is a major problem. Growing competition for land, water, and energy will undermine our capital food production, and the effect of climate change will continue to be a challenge. The integrated greenhouse and warehouse provide quality and sustainable food production. In this paper an integrated green house with warehouse monitoring hardware implementation, with novel feature of customized disease detection with convolution neural network (CNN) algorithm is presented. The real time monitoring of data of different sensors are displayed on the screen/ dashboard developed by the android application. This aids farmers in making wise choices on crop management and disease detection of greenhouse crops and effective monitoring.
Design and Implementation of IoT based PV Integrated Power Monitoring System
Authors: Brinda S , Singh Rajdeep , Savitha P B , Singh Raunak , Ahmad Tamseel 
Published: December 2023

AbstractThe awareness of usage of power and effective utilization of power is based on intelligent decision, which can help the user as well as supplier. In this paper the design and implementation of IOT based PV integrated system is presented. A hardware prototype is developed with PV module connected to boost converter, Arduino Uno controller, ESP 8266 Wi-Fi module, and Thing speak to monitor a real-time power. The Arduino Uno serves as the central controller, gathering data from smart power meters and PV modules for solar energy generation. The collected data is wirelessly delivered to the ThingSpeak, which provides cloud-based storage, data analysis, and visualization. With these users may remotely monitor usage of power, track solar energy generation, and get notifications for energy utilization.
Using artificial neural networks to model single bead geometries processed by laser-wire direct energy deposition
Authors: Asadi Reza , Queguineur Antoine , Ylä-Autio Aapo , Martikkala Antti , Wiikinkoski Olli , Mokhtarian Hossein , Flores Ituarte Iñigo 
Published: December 2023

Abstract
Wire-feed laser additive manufacturing processes have gained researchers’ attention because of their potential to reduce material waste, guarantee accuracy, increase material quality and density, and produce a wide dimensional range of final products. Nevertheless, printing materials with desired geometrical properties of the beads is still challenging in such processes. This might be attributed to the need for more sufficient experimental data and precise modeling approaches. In this study, an architecture based on Artificial Neural Networks (ANNs) is developed to model the bead geometries (width, height, and area), considering the wire feed rate, laser power, and travel speed as process parameters. A design-of-experiment based on full factorial design is considered for processing single beads with a Fraunhofer coaxial wire-feed laser system. Inconel 625 wire with a diameter of 1.14 mm and stainless steel substrate are utilized as the experimental materials. Geometrical data is obtained using a laser scanner model RA-7525 SE with 0.026mm volumetric accuracy. The beads’ geometrical details are provided as the feeding data for the proposed ANN. For each bead, a length of 10 mm is considered to calculate the average geometrical parameters, which increases the accuracy of the dataset in comparison to the values acquired via a macroscopic picture of the cross-section of each weld bead. A variety of hyperparameters are chosen and compared regarding precision criteria, including Mean Square Error (MSE), to increase the model‘s accuracy. A train-test separation strategy is considered to evaluate the model‘s accuracy on independent data points. The outcome of this research is an ANN-based geometry prediction model that can be utilized to enhance the development of offline path planners and optimize process parameter selection for a precise geometry toward process control.
Multi sensor monitoring of the wire-melt pool interaction in hot-wire directed energy deposition using laser beam
Authors: Kisielewicz A , Mi Y , Sikström F , Ancona A 
Published: December 2023

Abstract
This study investigates the combination of three sensors to improve in-process monitoring of the liquid bridge between the feedstock wire and melt pool in hot-wire Directed Energy Deposition using Laser Beam. The stability of the deposition process relies on the transfer of metal between the molten feedstock wire and melt pool. Therefore, monitoring the condition of the liquid bridge and the interaction between the feedstock wire and melt pool is crucial. By utilizing a laser-optics-integrated visible range optical spectrometer and electrical sensors measuring voltage and current, relevant process changes and indications of instabilities were detected. Combined information from the current sensor and the spectrometer provided a better understanding of the process and helped to identify deviations leading to unstable deposition modes.
Laser welding monitoring with multisensory data fusion: A brief review
Authors: Hsu Li-Wei , Salminen Antti 
Published: December 2023

Abstract
As digital manufacturing is being implemented across industries, the automation of the laser welding process is a crucial step to enhance production efficiency. To monitor the in-situ welding process, there are several approaches to detect the electromagnetic and mechanical waves on various frequencies for comprehending laser beam-material interaction. Five sensing techniques, namely the optical microphone, welding camera, inline coherent imaging, infrared camera, and heat flux sensor, can be employed to identify distinct features in the laser welding process. These features include pore formation, melt pool geometry, weld bead topography, keyhole depth, and thermal distribution. The discussion of a proposed welding system designed with compatibility for multisensory data fusion is included, both on its capabilities and potential challenges, to offer guidance of welding monitoring.
Laser welding of additively manufactured parts - A review
Authors: Parchegani Saeid , Piili Heidi , Ganvir Ashish , Salminen Antti 
Published: December 2023

Abstract
Additive manufacturing (AM) is a process in which parts are built up layer by layer, introducing novel approaches to how parts can be manufactured with less material waste, shorter lead times and lower costs than traditional manufacturing. One of the key advantages of AM over conventional manufacturing is its design flexibility, which enables for manufacturing of parts with highly detailed geometries in one go, leaving out the need for molding, casting, etc. However, due to the chamber size of the machines, the size of AM parts is limited. To overcome this limitation, joining AM parts together or to wrought or cast material has been proposed. Among the various welding technologies, laser welding is considered a suitable candidate for joining AM parts because of its low heat input, resulting in low deformation, high welding speed, and full automation capability. This study will provide a fundamental understanding of laser welding of AM parts by reviewing current research in the field. The possibility of joining most commonly used AM parts such as AlSi10Mg, AISI 316L, Ti6Al4V and Nickel alloy 718 by laser welding are investigated. Furthermore, the effect of laser welding parameters on mechanical and microstructural properties of joined AM parts are discussed.
Using ultrasonic atomization to recycle aluminium bronze chips for additive laser directed energy deposition
Authors: Müller Vinzenz , Fasselt Janek Maria , Kruse Tobias , Klötzer Christian , Kleba-Ehrhardt Rafael , Choma Tomasz , Biegler Max , Rethmeier Michael 
Published: December 2023

Abstract
In the post-processing of large maritime components, a considerable amount of waste in the form of milling and grinding chips is produced. At the same time, additive manufacturing technologies have shown great potential in producing high-volume parts for maritime applications, allowing novel design approaches and short lead times. In this context, this study presents a sustainable approach to recycle and use aluminium bronze waste material, generated during post-processing of large cast ship propellers, as feedstock for laser-powder directed energy deposition. The recycling technology used to produce powder batches is inductive re-melting in combination with ultrasonic atomization. The derived metal powders are characterized using digital image analysis, powder flowability tests, scanning electron microscopy as well as energy dispersive X-ray spectroscopy. Compared to conventional metal powders produced by gas atomization, the recycled material shows excellent sphericity and a powder size distribution with a higher content of finer and coarser particles. Metallographic sections of deposited additively produced specimens show an increased hardness and reduced ductility, but also competitive densities and higher yield and ultimate tensile strength compared to cast material. The process chain shows high potential for the maritime sector to enable circular and sustainable manufacturing.
Blue diode lasers – Understanding and influencing melt pool dynamics in copper
Authors: Heine L. , Schlett M. , Britten S. , Arndt A. , Spurk C. , Hummel M. , Beckmann F. , Moosmann J. 
Published: December 2023

Abstract
Within the last years an increasing demand of copper components was observed, especially in the fields of energy transition, e-mobility, or digitalization. For joining those components, laser welding has become more and more popular. Infrared lasers are commonly used for metal welding. They are characterized by a low absorptivity in solid copper at room temperature. In contrast, lasers in the blue wavelength spectrum are characterized by a significantly higher absorptivity. This facilitates the initial energy input into the material and makes the welding process more efficient. Further, in the liquid state, the absorptivity change in copper is much smaller for the blue wavelength. This results in a continuous heating of the material, a more stable melt pool and a lower amount of spatter. One supplier of lasers in the visible wavelength spectrum is Laserline. In 2023, they launched a 4 kW blue diode laser, the currently highest available power for this wavelength. It makes accessible a broad variety of applications. But the scientific investigations for the blue wavelength at a power of several kilowatt are still limited. It is the paper’s purpose to extend them by analysing welding of copper and its alloys with a blue diode laser. One the one hand, synchrotron trials were performed allowing a look into the material during welding. A special focus was on the transition between heat conduction and deep penetration welding as well as the keyhole behaviour. On the other hand, conventional welding trials were performed analysing the impact of beam shaping on the melt pool dynamics.
Laser-induced reduction of iron ore: Trends of the chemical distribution across the domains
Authors: Kaplan A F H , Fedina T , Brueckner F , Naesstroem H 
Published: December 2023

Abstract
The reduction of iron ore powder in a laser-induced thermal cycle using several reducing agents was studied. The laser-assisted reduction process resulted in the formation of iron-rich domains, irregularly embedded in a slag matrix, and transitional phases. The appearance of these various chemical phases was categorized and geometrically evaluated with respect to representative dimensions using scanning electron microscopy. The statistical trends of the morphology are presented, in context with trends of the chemical composition across the domains, to gain a better understanding of the mechanisms behind the reduction process. Iron domains were predominately observed in the vicinity of the Si-rich zones, indicating the occurrence of the reduction reaction as a result of the Fe2O3-Si interaction. Furthermore, different appearances of the Fe-rich domains and other phases in the close proximity to iron were analyzed and discussed based on diffusion and coalescence phenomena. The obtained results show that the reduction occurs, but the process is still uncontrolled and only partially understood. Further analysis and experiments are, therefore, needed to investigate the prospects of the proposed method.
Experimental investigation of the polarity-switching process with different bipolar ionic liquid thruster operating frequencies
Authors: WU 吴 Xiangbei 湘蓓 , YANG 杨 Cheng 铖 , LUO 罗 Jiawei 嘉伟 , SHEN 沈 Yan 岩 
Published: December 2023

Abstract
The bipolar ionic liquid thruster employs ionic liquid as a propellant to discharge positively and negatively charged high-energy particles under an alternating current (AC) power source, effectively suppressing electrochemical reaction and ensuring charge neutrality. Determining an optimal AC supply power source frequency is critical for sustained stable thruster operation. This study focuses on the emission characteristics of the ionic liquid thruster under varied AC conditions. The AC power supply was set within the frequency range of 0.5–64 Hz, with eight specific frequency conditions selected for experimentation. The experimental results indicate that the thruster operates steadily within a voltage range of ±1470 to ±1920 V, with corresponding positive polarity current ranging from 0.41 to 4.91 μA and negative polarity current ranging from −0.49 to −4.10 μA. During voltage polarity switching, an emission delay occurs, manifested as a prominent peak signal caused by circuit capacitance characteristics and a minor peak signal resulting from liquid droplets. Extended emission test was conducted at 16 Hz, demonstrating approximately 1 h and 50 min of consistent emission before intermittent discharge. These findings underscore the favorable impact of AC conditions within the 8–16 Hz range on the self-neutralization capability of the ionic liquid thruster.
Updates in the treatment of asthma in pediatrics: A review for pharmacists
Authors: Smith Tara , Mills Kimberly , Cober M Petrea , Fenn Norman E , Hill Carolyn , King Morgan , Pauley Jennifer L , Eiland Lea , Sierra Caroline , Omecene Nicole E 
Published: December 2023

Abstract

Purpose
The purpose of this review is to summarize the management of asthma in children and to highlight different guideline-based approaches. This review also discusses literature regarding the use of corticosteroids, both inhaled and systemic, as well as biologic agents, in asthma management.

Summary
Asthma is a common chronic respiratory condition in the pediatric population and has evolved into a highly patient-specific disease. Of the 2 main asthma guidelines, one developed by the National Asthma Education and Prevention Program was recently published as a focused update in 2020. The other, from the Global Initiative for Asthma, focuses on a global strategy for management and prevention, with the most recent update in 2023. Both reports discuss diagnosis, assessment, and treatment of asthma in adults and children. Treatment is designed as a stepwise approach in both reports, although there are key differences. This article focuses on gaps in these guidelines, including the use of bronchodilators and inhaled corticosteroids with single maintenance and reliever therapy and long-acting muscarinic antagonists in children. It also reviews treatment in children under 5 years of age, although recommendations are limited due to a lack of evidence in this age group. Finally, this review discusses considerations for emerging treatments, including biologics, for patients who are difficult to treat.

Conclusion
New treatment strategies and agents have emerged in the treatment of pediatric asthma. Pharmacists play a key role in providing education about, dispensing, and recommending the newest evidence-based treatment options for children.
Evaluation of the impact of a clinic infusion pharmacist on the retention of infusion therapy
Authors: Harsh Andrew , Gabbert Jared , Peek Grayson 
Published: December 2023

Abstract

Purpose
To evaluate the impact of an embedded clinic infusion pharmacist on the retention of outpatient infusion therapy.

Methods
This was a single-center, pre-post, retrospective and prospective cohort study at a large quaternary care academic medical center. Outpatient infusion administrations were included in this study if they originated from a clinic with an embedded clinic infusion pharmacist and were targeted high-impact medications. The primary outcome was the impact of embedded clinic infusion pharmacists on the number of infusion administrations from before to after implementation. Secondary outcomes included the time from order entry to first infusion, return on investment (ROI), and level of utilization of various infusion centers. Outpatient infusion and injection administrations were divided into 2 cohorts: a preimplementation cohort administered from April 2021 to March 2022 and a postimplementation cohort administered from April 2022 to March 2023.

Results
A total of 12,257 outpatient infusion administrations were included in the study from the inflammatory bowel disease, Vanderbilt Eye Institute, and neurology clinics. As the embedded infusion pharmacists began working within the respective clinics, a statistically significant increase could be seen in the overall administration of high-impact medications (from 5,683 infusions before implementation to 6,574 infusions after implementation; P &lt; 0.001). The ROI for an embedded clinic infusion pharmacist’s services was greater than 2,500%.

Conclusion
Retention of outpatient infusion therapy can be significantly increased with a positive ROI through allocation of pharmacist resources to areas initiating infusions. Health systems can utilize this model to improve patient access to infusion and injection therapies at entity-owned clinics.
MMP3C: anin-silicoframework to depict cancer metabolic plasticity using gene expression profiles
Authors: Chen Xingyu , Deng Min , Wang Zihan , Huang Chen 
Published: December 2023

AbstractMetabolic plasticity enables cancer cells to meet divergent demands for tumorigenesis, metastasis and drug resistance. Landscape analysis of tumor metabolic plasticity spanning different cancer types, in particular, metabolic crosstalk within cell subpopulations, remains scarce. Therefore, we proposed a new in-silico framework, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict tumor metabolic plasticity based on transcriptome data. Next, we performed an extensive metabo-plastic analysis of over 6000 tumors comprising 13 cancer types. The metabolic plasticity within distinct cell subpopulations, particularly interplay with tumor microenvironment, were explored at single-cell resolution. Ultimately, the metabo-plastic events were screened out for multiple clinical applications via machine learning methods. The pilot research indicated that 6 out of 13 cancer types exhibited signs of the Warburg effect, implying its high reliability and robustness. Across 13 cancer types, high metabolic organized heterogeneity was found, and four metabo-plastic subtypes were determined, which link to distinct immune and metabolism patterns impacting prognosis. Moreover, MMP3C analysis of approximately 60 000 single cells of eight breast cancer patients unveiled several metabo-plastic events correlated to tumorigenesis, metastasis and immunosuppression. Notably, the metabolic features screened out by MMP3C are potential biomarkers for diagnosis, tumor classification and prognosis. MMP3C is a practical cross-platform tool to capture tumor metabolic plasticity, and our study unveiled a core set of metabo-plastic pairs among diverse cancer types, which provides bases toward improving response and overcoming resistance in cancer therapy.
AMGDTI: drug–target interaction prediction based on adaptive meta-graph learning in heterogeneous network
Authors: Su Yansen , Hu Zhiyang , Wang Fei , Bin Yannan , Zheng Chunhou , Li Haitao , Chen Haowen , Zeng Xiangxiang 
Published: December 2023

Abstract
Prediction of drug–target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug–target relationship.
Why nature matters: A systematic review of intrinsic, instrumental, and relational values
Authors: Himes Austin , Muraca Barbara , Anderson Christopher B , Athayde Simone , Beery Thomas , Cantú-Fernández Mariana , González-Jiménez David , Gould Rachelle K , Hejnowicz A P , Kenter Jasper , Lenzi Dominic , Murali Ranjini , Pascual Unai , Raymond Christopher , Ring Annalie , Russo Kurt , Samakov Aibek , Stålhammar Sanna , Thorén Henrik , Zent Egleé 
Published: December 2023

Abstract
In this article, we present results from a literature review of intrinsic, instrumental, and relational values of nature conducted for the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, as part of the Methodological Assessment of the Diverse Values and Valuations of Nature. We identify the most frequently recurring meanings in the heterogeneous use of different value types and their association with worldviews and other key concepts. From frequent uses, we determine a core meaning for each value type, which is sufficiently inclusive to serve as an umbrella over different understandings in the literature and specific enough to help highlight its difference from the other types of values. Finally, we discuss convergences, overlapping areas, and fuzzy boundaries between different value types to facilitate dialogue, reduce misunderstandings, and improve the methods for valuation of nature's contributions to people, including ecosystem services, to inform policy and direct future research.