TY - CONF
AB - The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.
AU - Grigoryan, Khoren
AU - Fichtler, Timm
AU - Schreiner, Nick
AU - Rabe, Martin
AU - Panzner, Melina
AU - Kühn, Arno
AU - Dumitrescu, Roman
AU - Koldewey, Christian
ID - 45793
KW - Product Management
KW - Data Analytics
KW - Data-Driven Design
KW - Product-related data
KW - Lifecycle Data
KW - Tool-support
T2 - Procedia CIRP 33
TI - Data-Driven Product Management: A Practitioner-Driven Research Agenda
ER -
TY - JOUR
AB - Abstract
Background
Occupational health interventions for leaders are underrepresented in small and medium-sized enterprises (SMEs). When creating and developing effective occupational health interventions, identification of the specific needs of the target group is regarded as an essential step before planning an intervention. Therefore, the aim of this study was (1) to examine the subjectively experienced work-related stressors of leaders in small and medium-sized IT and technological services enterprises, (2) to explore coping behaviors leaders use to deal with the experienced work-related stressors, (3) to investigate resources supporting the coping process and (4) to identify potentially self-perceived consequences resulting from the experienced stressors.
Methods
Ten semi-structured interviews with leaders in small and medium-sized IT and technological services enterprises were conducted. The interviews were transcribed and analyzed with content-structuring qualitative content analysis in accordance to Kuckartz.
Results
Leaders in small and medium-sized IT and technological services enterprises experience various stressors caused by work organization as well as industry-related stressors and other work-related stressors. To address the experienced stressors, leaders apply problem focused coping behaviors (e.g. performing changes on structural and personal level), emotional focused coping behaviors (e.g. balancing activities, cognitive restructuring) as well as the utilization of social support. Helpful resources for the coping process include organizational, social and personal resources. As a result of the experienced work-related stressors, interviewees stated to experience different health impairments, negative effects on work quality as well as neglect of leisure activities and lack of time for family and friends.
Conclusion
The identified experienced work-related stressors, applied coping behaviors, utilized resources and emerging consequences underpin the urgent need for the development and performance of health-oriented leadership interventions for leaders in small and medium- sized IT and technological services. The results of this study can be used when designing a target-oriented intervention for the examined target group.
AU - Dannheim, Indra
AU - Buyken, Anette
AU - Kroke, Anja
ID - 45807
IS - 1
JF - BMC Public Health
KW - Public Health
KW - Environmental and Occupational Health
SN - 1471-2458
TI - Work-related stressors and coping behaviors among leaders in small and medium-sized IT and technological services enterprises
VL - 23
ER -
TY - JOUR
AB - Abstract
Objective:
To systematically review the impact of choice architecture interventions (CAI) on the food choice of healthy adolescents in a secondary school setting. Factors potentially contributing to the effectiveness of CAI types and numbers implemented and its long-term success were examined.
Design:
PUBMED and Web of Science were systematically searched in October 2021. Publications were included following predefined inclusion criteria and grouped according to number and duration of implemented interventions. Intervention impact was determined by systematic description of the reported quantitative changes in food choice and/or consumption. Intervention types were compared with regards to food selection and sustained effects either during or following the intervention.
Setting:
CAI on food choice of healthy adolescents in secondary schools.
Participants:
Not applicable
Results:
Fourteen studies were included; four randomized controlled trials and five each of controlled or uncontrolled pre-post design, respectively. Four studies implemented a single CAI type, with ten implementing > 1. Three studies investigated CAI effects over the course of a school year either by continuous or repeated data collection, while ten studies’ schools were visited on selected days during intervention. Twelve studies reported desired changes in overall food selection, yet effects were not always significant, and appeared less conclusive for longer term studies.
Conclusions:
This review found promising evidence that CAI can be effective in encouraging favorable food choices in healthy adolescents in a secondary school setting. However, further studies designed to evaluate complex interventions are needed.
AU - Schulte, Eva A.
AU - Winkler, Gertrud
AU - Brombach, Christine
AU - Buyken, Anette
ID - 45806
JF - Public Health Nutrition
KW - Public Health
KW - Environmental and Occupational Health
KW - Nutrition and Dietetics
KW - Medicine (miscellaneous)
SN - 1368-9800
TI - Choice architecture interventions promoting sustained healthier food choice and consumption by students in a secondary school setting: A systematic review of intervention studies
ER -
TY - JOUR
AB - Background: Establishing a healthy lifestyle has a great potential to reduce the prevalence of non-communicable diseases (NCDs) and their risk factors. NCDs contribute immensely to the economic costs of the health care system arising from therapy, medication use, and productivity loss. Aim: The aim of this study was to evaluate the effect of the Healthy Lifestyle Community Program (cohort 2; HLCP-2) on medication use and consequently on medication costs for selected NCDs (diabetes, hypertension, and dyslipidemia). Methods: Data stem from a 24-month non-randomised, controlled intervention trial aiming to improve risk factors for NCDs. Participants completed questionnaires at six measurement time points assessing medication use, from which costs were calculated. The following medication groups were included in the analysis as NCD medication: glucose-lowering medications (GLM), antihypertensive drugs (AHD) and lipid-lowering drugs (LLD). Statistical tests for inter- and intra-group comparison and multiple regression analysis were performed. Results: In total, 118 participants (intervention group [IG]: n = 79; control group [CG]: n = 39) were considered. Compared to baseline medication use decreased slightly in the IG and increased in the CG. Costs for NCD medication were significantly lower in the IG than in the CG after 6 ( p = 0.004), 12 ( p = 0.040), 18 ( p = 0.003) and 24 months ( p = 0.008). After multiple regression analysis and adjusting for confounders, change of costs differed significantly between the groups in all final models. Conclusion: The HLCP-2 was able to moderately prevent an increase of medication use and thus reduce costs for medication to treat NCDs with the greatest impact on AHD. Trial registration German Clinical Trials Register DRKS ( www.drks.de ; reference: DRKS00018775).
AU - Kranz, Ragna-Marie
AU - Kettler, Carmen
AU - Anand, Corinna
AU - Koeder, Christian
AU - Husain, Sarah
AU - Schoch, Nora
AU - Buyken, Anette
AU - Englert, Heike
ID - 45810
JF - Nutrition and Health
KW - Nutrition and Dietetics
KW - General Medicine
KW - Medicine (miscellaneous)
SN - 0260-1060
TI - Effect of a controlled lifestyle intervention on medication use and costs: The Healthy Lifestyle Community Program (cohort 2)
ER -
TY - JOUR
AB - A previous follow-up of the GINIplus study showed that breastfeeding could protect against early eczema. However, effects diminished in adolescence, possibly indicating a “rebound effect” in breastfed children after initial protection. We evaluated the role of early eczema until three years of age on allergies until young adulthood and assessed whether early eczema modifies the association between breastfeeding and allergies. Data from GINIplus until 20-years of age (N = 4058) were considered. Information on atopic eczema, asthma, and rhinitis was based on reported physician’s diagnoses. Adjusted Odds Ratios (aOR) were modelled by using generalized estimating equations. Early eczema was associated with eczema (aORs = 3.2–14.4), asthma (aORs = 2.2–2.7), and rhinitis (aORs = 1.2–2.7) until young adulthood. For eczema, this association decreased with age (p-for-interaction = 0.002–0.006). Longitudinal models did not show associations between breastfeeding and the respective allergies from 5 to 20 years of age. Moreover, early eczema generally did not modify the association between milk feeding and allergies except for rhinitis in participants without family history of atopy. Early eczema strongly predicts allergies until young adulthood. While preventive effects of full breastfeeding on eczema in infants with family history of atopy does not persist until young adulthood, the hypothesis of a rebound effect after initial protection cannot be confirmed.
AU - Libuda, Lars
AU - Filipiak-Pittroff, Birgit
AU - Standl, Marie
AU - Schikowski, Tamara
AU - von Berg, Andrea
AU - Koletzko, Sibylle
AU - Bauer, Carl-Peter
AU - Heinrich, Joachim
AU - Berdel, Dietrich
AU - Gappa, Monika
ID - 45813
IS - 12
JF - Nutrients
KW - Food Science
KW - Nutrition and Dietetics
SN - 2072-6643
TI - Full Breastfeeding and Allergic Diseases—Long-Term Protection or Rebound Effects?
VL - 15
ER -
TY - CONF
AB - The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all examples of the same class tend to collapse to a single representation, and the features of different classes tend to separate as much as possible. Neural collapse is often studied through a simplified model, called the unconstrained feature representation, in which the model is assumed to have "infinite expressivity" and can map each data point to any arbitrary representation. In this work, we propose a more realistic variant of the unconstrained feature representation that takes the limited expressivity of the network into account. Empirical evidence suggests that the memorization of noisy data points leads to a degradation (dilation) of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon, we show one mechanism by which different losses lead to different performances of the trained network on noisy data. Our proofs reveal why label smoothing, a modification of cross-entropy empirically observed to produce a regularization effect, leads to improved generalization in classification tasks.
AU - Nguyen, Duc Anh
AU - Levie, Ron
AU - Lienen, Julian
AU - Kutyniok, Gitta
AU - Hüllermeier, Eyke
ID - 31880
T2 - International Conference on Learning Representations, ICLR
TI - Memorization-Dilation: Modeling Neural Collapse Under Noise
ER -
TY - GEN
AB - Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.
AU - Lienen, Julian
AU - Hüllermeier, Eyke
ID - 45814
T2 - arXiv:2305.13764
TI - Mitigating Label Noise through Data Ambiguation
ER -
TY - CONF
AU - Özcan, Leon
AU - Fichtler, Timm
AU - Kasten, Benjamin
AU - Koldewey, Christian
AU - Dumitrescu, Roman
ID - 45812
KW - Digital Platform
KW - Platform Strategy
KW - Strategic Management
KW - Platform Life Cycle
KW - Interview Study
KW - Business Model
KW - Business-to-Business
KW - Two-sided Market
KW - Multi-sided Market
TI - Interview Study on Strategy Options for Platform Operation in B2B Markets
ER -
TY - JOUR
AB - As cognitive function is critical for muscle coordination, cognitive training may also improve neuromuscular control strategy and knee function following an anterior cruciate ligament reconstruction (ACLR). The purpose of this case-control study was to examine the effects of cognitive training on joint stiffness regulation in response to negative visual stimuli and knee function following ACLR. A total of 20 ACLR patients and 20 healthy controls received four weeks of online cognitive training. Executive function, joint stiffness in response to emotionally evocative visual stimuli (neutral, fearful, knee injury related), and knee function outcomes before and after the intervention were compared. Both groups improved executive function following the intervention (p = 0.005). The ACLR group had greater mid-range stiffness in response to fearful (p = 0.024) and injury-related pictures (p = 0.017) than neutral contents before the intervention, while no post-intervention stiffness differences were observed among picture types. The ACLR group showed better single-legged hop for distance after cognitive training (p = 0.047), while the healthy group demonstrated no improvement. Cognitive training enhanced executive function, which may reduce joint stiffness dysregulation in response to emotionally arousing images and improve knee function in ACLR patients, presumably by facilitating neural processing necessary for neuromuscular control.
AU - An, Yong Woo
AU - Kim, Kyung-Min
AU - DiTrani Lobacz, Andrea
AU - Baumeister, Jochen
AU - Higginson, Jill S.
AU - Rosen, Jeffrey
AU - Swanik, Charles Buz
ID - 45824
IS - 13
JF - Healthcare
KW - Health Information Management
KW - Health Informatics
KW - Health Policy
KW - Leadership and Management
SN - 2227-9032
TI - Cognitive Training Improves Joint Stiffness Regulation and Function in ACLR Patients Compared to Healthy Controls
VL - 11
ER -
TY - CONF
AB - Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
AU - KOUAGOU, N'Dah Jean
AU - Heindorf, Stefan
AU - Demir, Caglar
AU - Ngonga Ngomo, Axel-Cyrille
ED - Pesquita, Catia
ED - Jimenez-Ruiz, Ernesto
ED - McCusker, Jamie
ED - Faria, Daniel
ED - Dragoni, Mauro
ED - Dimou, Anastasia
ED - Troncy, Raphael
ED - Hertling, Sven
ID - 33734
KW - Neural network
KW - Concept learning
KW - Description logics
T2 - The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023)
TI - Neural Class Expression Synthesis
VL - 13870
ER -
TY - GEN
AB - Knowledge bases are widely used for information management on the web,
enabling high-impact applications such as web search, question answering, and
natural language processing. They also serve as the backbone for automatic
decision systems, e.g. for medical diagnostics and credit scoring. As
stakeholders affected by these decisions would like to understand their
situation and verify fair decisions, a number of explanation approaches have
been proposed using concepts in description logics. However, the learned
concepts can become long and difficult to fathom for non-experts, even when
verbalized. Moreover, long concepts do not immediately provide a clear path of
action to change one's situation. Counterfactuals answering the question "How
must feature values be changed to obtain a different classification?" have been
proposed as short, human-friendly explanations for tabular data. In this paper,
we transfer the notion of counterfactuals to description logics and propose the
first algorithm for generating counterfactual explanations in the description
logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts
and the candidates with fewest feature changes are selected as counterfactuals.
In case of multiple counterfactuals, we rank them according to the likeliness
of their feature combinations. For evaluation, we conduct a user survey to
investigate which of the generated counterfactual candidates are preferred for
explanation by participants. In a second study, we explore possible use cases
for counterfactual explanations.
AU - Sieger, Leonie Nora
AU - Heindorf, Stefan
AU - Blübaum, Lukas
AU - Ngonga Ngomo, Axel-Cyrille
ID - 37937
T2 - arXiv:2301.05109
TI - Counterfactual Explanations for Concepts in ELH
ER -
TY - GEN
AB - Label noise poses an important challenge in machine learning, especially in
deep learning, in which large models with high expressive power dominate the
field. Models of that kind are prone to memorizing incorrect labels, thereby
harming generalization performance. Many methods have been proposed to address
this problem, including robust loss functions and more complex label correction
approaches. Robust loss functions are appealing due to their simplicity, but
typically lack flexibility, while label correction usually adds substantial
complexity to the training setup. In this paper, we suggest to address the
shortcomings of both methodologies by "ambiguating" the target information,
adding additional, complementary candidate labels in case the learner is not
sufficiently convinced of the observed training label. More precisely, we
leverage the framework of so-called superset learning to construct set-valued
targets based on a confidence threshold, which deliver imprecise yet more
reliable beliefs about the ground-truth, effectively helping the learner to
suppress the memorization effect. In an extensive empirical evaluation, our
method demonstrates favorable learning behavior on synthetic and real-world
noise, confirming the effectiveness in detecting and correcting erroneous
training labels.
AU - Lienen, Julian
AU - Hüllermeier, Eyke
ID - 45244
T2 - arXiv:2305.13764
TI - Mitigating Label Noise through Data Ambiguation
ER -
TY - JOUR
AB - This article presents the potential-dependent adsorption of two proteins, bovine serum albumin (BSA) and lysozyme (LYZ), on Ti6Al4V alloy at pH 7.4 and 37 °C. The adsorption process was studied on an electropolished alloy under cathodic and anodic overpotentials, compared to the open circuit potential (OCP). To analyze the adsorption process, various complementary interface analytical techniques were employed, including PM-IRRAS (polarization-modulation infrared reflection-absorption spectroscopy), AFM (atomic force microscopy), XPS (X-ray photoelectron spectroscopy), and E-QCM (electrochemical quartz crystal microbalance) measurements. The polarization experiments were conducted within a potential range where charging of the electric double layer dominates, and Faradaic currents can be disregarded. The findings highlight the significant influence of the interfacial charge distribution on the adsorption of BSA and LYZ onto the alloy surface. Furthermore, electrochemical analysis of the protein layers formed under applied overpotentials demonstrated improved corrosion protection properties. These studies provide valuable insights into protein adsorption on titanium alloys under physiological conditions, characterized by varying potentials of the passive alloy.
AU - Duderija, Belma
AU - González-Orive, Alejandro
AU - Ebbert, Christoph
AU - Neßlinger, Vanessa
AU - Keller, Adrian
AU - Grundmeier, Guido
ID - 45828
IS - 13
JF - Molecules
KW - Chemistry (miscellaneous)
KW - Analytical Chemistry
KW - Organic Chemistry
KW - Physical and Theoretical Chemistry
KW - Molecular Medicine
KW - Drug Discovery
KW - Pharmaceutical Science
SN - 1420-3049
TI - Electrode Potential-Dependent Studies of Protein Adsorption on Ti6Al4V Alloy
VL - 28
ER -
TY - CHAP
AU - Keller, Adrian
AU - Grundmeier, Guido
ID - 45829
SN - 9780124095472
T2 - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering
TI - High-speed AFM studies of macromolecular dynamics at solid/liquid interfaces
ER -
TY - CHAP
AU - Kostan, Anastassija
ID - 45833
SN - 9783828877368
T2 - Bedeutung und Implikationen epistemischer Ungerechtigkeit
TI - Die epistemische Gewalt KI-basierter Gesichtserkennung. Wie ein codierter Blick neue Formen der technologisierten Subalternität erschafft
ER -
TY - THES
AB - Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods.
Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit
(3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified?
The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights.
As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.
AU - Kersting, Joschka
ID - 44323
TI - Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining
ER -
TY - JOUR
AB - The aim of the present study is to prove the construct validity of the German versions of the Feeling Scale (FS) and the Felt Arousal Scale (FAS) for a progressive muscle relaxation (PMR) exercise. A total of 228 sport science students conducted the PMR exercise for 45 min and completed the FS, the FAS, and the Self-Assessment Manikin (SAM) in a pre-test–post-test design. A significant decrease in arousal (t(227) = 8.296, p < 0.001) and a significant increase in pleasure (t(227) = 4.748, p < 0.001) were observed. For convergent validity, the correlations between the FS and the subscale SAM-P for the valence dimension (r = 0.67, p < 0.001) and between the FAS and the subscale SAM-A for the arousal dimension (r = 0.31, p < 0.001) were significant. For discriminant validity, the correlations between different constructs (FS and SAM-A, FAS and SAM-P) were not significant, whereas the discriminant analysis between the FS and the FAS revealed a negative significant correlation (r = −0.15, p < 0.001). Together, the pattern of results confirms the use of the German versions of the FS and the FAS to measure the affective response for a PMR exercise.
AU - Thorenz, Kristin
AU - Berwinkel, Andre
AU - Weigelt, Matthias
ID - 45857
IS - 7
JF - Behavioral Sciences
KW - Behavioral Neuroscience
KW - General Psychology
KW - Genetics
KW - Development
KW - Ecology
KW - Evolution
KW - Behavior and Systematics
SN - 2076-328X
TI - A Validation Study for the German Versions of the Feeling Scale and the Felt Arousal Scale for a Progressive Muscle Relaxation Exercise
VL - 13
ER -
TY - JOUR
AU - Thorenz, Kristin
AU - Berwinkel, Andre
AU - Weigelt, Matthias
ID - 45856
IS - 06
JF - Psychology
KW - General Earth and Planetary Sciences
KW - General Environmental Science
SN - 2152-7180
TI - A Validation Study of the German Versions of the Feeling Scale and the Felt Arousal Scale for a Passive Relaxation Technique (Autogenic Training)
VL - 14
ER -
TY - JOUR
AU - Topalović, Elvira
AU - Blachut, Alisa
ID - 45861
JF - Der Deutschunterricht
TI - Grammatische Modelle. Einführung in das Themenheft
VL - 3
ER -
TY - JOUR
AU - Schlosser, Florian
AU - Zysk, Sebastian
AU - Walmsley, Timothy G.
AU - Kong, Lana
AU - Zühlsdorf, Benjamin
AU - Meschede, Henning
ID - 45867
JF - Energy Conversion and Management
KW - Energy Engineering and Power Technology
KW - Fuel Technology
KW - Nuclear Energy and Engineering
KW - Renewable Energy
KW - Sustainability and the Environment
SN - 0196-8904
TI - Break-even of high-temperature heat pump integration for milk spray drying
VL - 291
ER -