bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sustainability assessment of peri-urban organic horticulture — A case study in the United Kingdom
Authors: Ali Mustafa , Koh Lenny , Acquaye Adolf , Leake Jonathan , Nickles Jacob , Evans Toby P. , Roberts Gareth , Kemp Douglas 
Published: December 2023

Abstract
Purpose
There is a growing concern about the resilience and sustainability of horticultural production in the United Kingdom (UK) as a result of high energy costs and insufficient local labour, causing over-reliance on imports. In this study, we present an integrated environmental and economic assessment of organic peri-urban horticulture using primary data from a farm in Sheffield.

Methods
This study includes a farm-to-gate hybrid life cycle assessment (LCA) using the ReCIPE (H) approach for the functional unit of 1-kg tomatoes produced in an unheated polytunnel without supplementary lighting, and 1 kg of field-grown courgettes. All analyses were conducted in SimaPro software using environmental data from the ecoinvent database. Results were compared with those from a systematic literature review of similar studies.

Results
We found that the production of organic tomatoes and courgettes resulted in a global warming potential (GWP) of 0.61 kg CO2-eq and 0.11 kg CO2-eq respectively using a process-based LCA approach. Using a hybrid LCA approach, however, yielded a GWP of 3.53 kg CO2-eq and 1.70 kg CO2-eq for the production of organic tomatoes and courgettes respectively. An additional scenario included farmgate-to-warehouse transportation for both domestic and imported produce from Spain, but found that the GWP of tomatoes in the case study was 1.87 times higher than those from Spain. Economic analysis showed that the marginal increase in the prices of tomatoes and courgettes from the case study farm was 4.6 and 5.15 times less than the market prices.

Conclusion
We conclude that the studied production system is both economically and environmentally sustainable as compared to the existing scenario. Other potential benefits of peri-urban organic horticulture include employment, mental health, community cohesion, which remain to be explored in a future qualitative study. The present study is novel as it appears to be the first application of hybrid LCA to UK horticulture. The findings are highly topical given the recent horticultural supply constraints in the UK.
Smart Diagnosis of Urinary Tract Infections: is Artificial Intelligence the Fast-Lane Solution?
Authors: Naik Nithesh , Talyshinskii Ali , Shetty Dasharathraj K. , Hameed B. M. Zeeshan , Zhankina Rano , Somani Bhaskar K. 
Published: December 2023

Abstract
Purpose of Review
Artificial intelligence (AI) can significantly improve physicians’ workflow when examining patients with UTI. However, most contemporary reviews are focused on examining the usage of AI with a restricted quantity of data, analyzing only a subset of AI algorithms, or performing narrative work without analyzing all dedicated studies. Given the preceding, the goal of this work was to conduct a mini-review to determine the current state of AI-based systems as a support in UTI diagnosis.

Recent Findings
There are sufficient publications to comprehend the potential applications of artificial intelligence in the diagnosis of UTIs. Existing research in this field, in general, publishes performance metrics that are exemplary. However, upon closer inspection, many of the available publications are burdened with flaws associated with the improper use of artificial intelligence, such as the use of a small number of samples, their lack of heterogeneity, and the absence of external validation. AI-based models cannot be classified as full-fledged physician assistants in diagnosing UTIs due to the fact that these limitations and flaws represent only a portion of all potential obstacles. Instead, such studies should be evaluated as exploratory, with a focus on the importance of future work that complies with all rules governing the use of AI.

Summary
AI algorithms have demonstrated their potential for UTI diagnosis. However, further studies utilizing large, heterogeneous, prospectively collected datasets, as well as external validations, are required to define the actual clinical workflow value of artificial intelligence.
Review of substitutive assistive tools and technologies for people with visual impairments: recent advancements and prospects
Authors: Muhsin Zahra J. , Qahwaji Rami , Ghanchi Faruque , Al-Taee Majid 
Published: December 2023

AbstractThe development of many tools and technologies for people with visual impairment has become a major priority in the field of assistive technology research. However, many of these technology advancements have limitations in terms of the human aspects of the user experience (e.g., usability, learnability, and time to user adaptation) as well as difficulties in translating research prototypes into production. Also, there was no clear distinction between the assistive aids of adults and children, as well as between “partial impairment” and “total blindness”. As a result of these limitations, the produced aids have not gained much popularity and the intended users are still hesitant to utilise them. This paper presents a comprehensive review of substitutive interventions that aid in adapting to vision loss, centred on laboratory research studies to assess user-system interaction and system validation. Depending on the primary cueing feedback signal offered to the user, these technology aids are categorized as visual, haptics, or auditory-based aids. The context of use, cueing feedback signals, and participation of visually impaired people in the evaluation are all considered while discussing these aids. Based on the findings, a set of recommendations is suggested to assist the scientific community in addressing persisting challenges and restrictions faced by both the totally blind and partially sighted people.
(Neo)adjuvant approaches: pavement on the road to cure—breast cancer
Authors: Klocker Eva Valentina 
Published: December 2023

SummaryDuring the past few decades, major advances have been achieved in the treatment of early breast cancer (BC) resulting in improvements of inavasive disease free survival and overall survival. New treatment substances have been established in neoadjuvant and adjuvant settings. On the other hand, deescalation strategies are studied to get more tailored treatment concepts.
Impact of Bonding Capital and Bridging Capital on Scientific Results of Research Groups
Authors: Barrios-Hernández Karelis , García-Villaverde Pedro Manuel , Ruiz-Ortega María José 
Published: December 2023

Abstract
This article aims to analyze the impact of bonding capital and bridging capital on the scientific results of research groups. The paper contributes to the literature on social capital by going beyond the idea of a linear relationship and reviews the balance between the advantages and disadvantages of bonding capital and its interaction with bridging capital. An empirical study was carried out with 1127 Colombian university research groups from the field of engineering and technology. Hierarchical regression analysis was used to test the proposed hypotheses. The results show that the benefits of bonding social capital are maintained up to a level of centrality, above which the returns of the groups are reduced, adopting an inverted U shape. Moreover, when the members of the research groups diversify their research activities and fulfil relationships that go beyond the limits of their group, this curvilinear relationship is moderated, improving the opportunity to successfully exploit the social capital derived from dense and cohesive ties. The novel contribution of this study lies in linking different types of social capital —bonding and bridging capital—to test their impact on the results of the research groups and in the proposal and testing of a moderate curvilinear relationship, which allows us to advance in this line of research.
Conceptual Spaces for Conceptual Engineering? Feminism as a Case Study
Authors: Bendifallah Lina , Abbou Julie , Douven Igor , Burnett Heather 
Published: December 2023

AbstractRecently, there has been much research into conceptual engineering in connection with feminist inquiry and activism, most notably involving gender issues, but also sexism and misogyny. Our paper contributes to this research by explicating, in a principled manner, a series of other concepts important for feminist research and activism, to wit, feminist political identity terms. More specifically, we show how the popular Conceptual Spaces Framework (CSF) can be used to identify and regiment concepts that are central to feminist research, focusing especially on feminism in France. According to the CSF, concepts can be represented geometrically, as regions in similarity spaces. A particular strength of the CSF framework is its empirically-focused methodology, which allows researchers to infer the boundaries of concepts from empirical data, thus eliminating the need to strongly rely on intuitions about meanings. This is shown to be especially valuable for the explication of concepts relating to feminist political identity, given that the intuitions of feminist scholars and activists about what would appear to be core concepts in the area tend to be poorly aligned or even conflicting. We report the results from an empirical categorization study conducted among French feminists and show how they support the view that the CSF can contribute to both the conceptual engineering project and our understanding of the structure of social reality.
Order Restricted Randomized Block Designs
Authors: Ozturk Omer , Jarrett Richard , Kravchuk Olena 
Published: December 2023

AbstractField experiments are run under two competing objectives, high precision and minimal cost. The precision can be increased either by using sound experimentation techniques that account for the sources of variation with reasonable statistical models or by increasing the sample size. Large sample sizes usually increase the cost of the experiment and may not be feasible. This paper uses order restricted randomized designs (ORRD) to increase the precision while keeping the sample size and cost of the experiment minimal. The ORRD described here starts with a randomized block design but adds a second layer of blocking by ranking plots within each block. This creates a two-way lay-out, blocks and ranking groups, and uses a restricted randomization to improve the precision of estimation of the treatment parameters. Ranking groups create a correlation structure for within-block units. The restricted randomization uses this correlation structure to reduce the error variance of the experiment. The paper computes the expected mean square for each source of variation in the ORRD design under a suitable linear model. It also provides approximate F-tests for treatment and ranking group effects. The efficiency of the ORRD is investigated through empirical power studies. Finally, an example based on a uniformity field trial illustrates the use of the method in a split-plot experiment. Supplementary material to this paper is provided online.
Generation of iPSC lines (KAIMRCi003A, KAIMRCi003B) from a Saudi patient with Dravet syndrome carrying homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A
Authors: Alowaysi Maryam , Al-Shehri Mohammad , Badkok Amani , Attas Hanouf , Aboalola Doaa , Baadhaim Moayad , Alzahrani Hajar , Daghestani Mustafa , Zia Asima , Al-Ghamdi Khalid , Al-Ghamdi Asayil , Zakri Samer , Aouabdi Sihem , Tegner Jesper , Alsayegh Khaled 
Published: December 2023

AbstractThe most prevalent form of epileptic encephalopathy is Dravet syndrome (DRVT), which is triggered by the pathogenic variant SCN1A in 80% of cases. iPSCs with different SCN1A mutations have been constructed by several groups to model DRVT syndrome. However, no studies involving DRVT-iPSCs with rare genetic variants have been conducted. Here, we established two DRVT-iPSC lines harboring a homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A gene. Therefore, the derivation of these iPSC lines provides a unique cellular platform to dissect the molecular mechanisms underlying the cellular dysfunctions consequent to CPLX1 and SCN9A mutations.
Comparison of SGLT2 inhibitors vs. DPP4 inhibitors for patients with metabolic dysfunction associated fatty liver disease and diabetes mellitus
Authors: Suzuki Y. , Kaneko H. , Okada A. , Ohno R. , Yokota I. , Fujiu K. , Jo T. , Takeda N. , Morita H. , Node K. , Yasunaga H. , Komuro I. 
Published: December 2023

Abstract
Purpose
This study aimed to examine the potential benefit of sodium-glucose cotransporter 2 (SGLT2) inhibitors for patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and diabetes mellitus (DM) using a real-world database.

Methods
We analyzed individuals with MAFLD and DM newly initiated on SGLT2 or dipeptidyl peptidase 4 (DPP4) inhibitors from a large-scale administrative claims database. The primary outcome was the change in the fatty liver index (FLI) assessed using a linear mixed-effects model from the initiation of SGLT2 or DPP4 inhibitors. A propensity score-matching algorithm was used to compare the change in FLI among SGLT2 and DPP4 inhibitors.

Results
After propensity score matching, 6547 well-balanced pairs of SGLT2 and 6547 DPP4 inhibitor users were created. SGLT2 inhibitor use was associated with a greater decline in FLI than DPP4 inhibitor use (difference at 1-year measurement, − 3.8 [95% CI − 4.7 to − 3.0]). The advantage of SGLT2 inhibitor use over DPP4 inhibitor use for improvement in FLI was consistent across subgroups. The relationship between SGLT2 inhibitors and amelioration of FLI was comparable between individual SGLT2 inhibitors.

Conclusions
Our analysis using large-scale real-world data demonstrated the potential advantage of SGLT2 inhibitors over DPP4 inhibitors in patients with MAFLD and DM.
Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation
Authors: Liu Hongjia , Xiao Yubin , Wu Xuan , Li Yuanshu , Zhao Peng , Liang Yanchun , Wang Liupu , Zhou You 
Published: December 2023

AbstractRadar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.
A linear programming approach to Fuglede’s conjecture in $$\mathbb {Z}_p^3$$
Authors: Malikiosis Romanos Diogenes 
Published: December 2023

AbstractWe present an approach to Fuglede’s conjecture in $$\mathbb {Z}_p^3$$

Z
p
3

using linear programming bounds, obtaining the following partial result: if $$A\subseteq \mathbb {Z}_p^3$$

A
⊆

Z
p
3

with $$p^2-p\sqrt{p}+\sqrt{p}<|A|<p^2$$

p
2

-
p

p

+

p

<

|
A
|

<

p
2

, then A is not spectral.
Who is the human in the machine? Releasing the human–machine metaphor from its cultural roots can increase innovation and equity in AI
Authors: Sutherlin Gwyneth 
Published: December 2023

Abstract
Computer science and cognitive science have a shared past, with many intertwined goals and perspectives. The conceptual metaphor, shaping the discoveries of these fields for decades, has been the human mind–machine. New cross-cultural findings indicate that it is time that we interrogate the origin of the metaphor and develop a more global representation of attributes labeled human. This paper describes a gap in fairness research in cross-cultural bias affecting international participation in the field. It further outlines opportunities to diversify and test core concepts inspiring design and increasing equity. The proposed adaptation would shift our approach to knowledge and technology creation by (1) altering the attributes of the human mind–machine metaphor that define intelligence, memory, categorization, logic, inference, perception, concepts of time and space, concepts of personhood, and other cognitive terms which both fields study; (2) interrogating the universality implied by the conceptual metaphor to both machine and end-user; and (3) seizing the broadened conceptual metaphor to create new math, science, and disrupt the current paradigm scripting the inferences of research findings in computer science and cognitive science. A more globally attuned conceptual metaphor, updated to enfranchise the full membership the term human implies, will increase our collective ability to investigate, describe, and develop new science and technology and increase the equity of those involved in the process.
Computer vision: AI imaginaries and the Massachusetts Institute of Technology
Authors: Law Harry 
Published: December 2023

Abstract
This paper explores the way in which computer scientists at the Massachusetts Institute of Technology (MIT) constructed visions of the future in 1960s America to direct the AI and computing research agendas. It argues that MIT computer scientists resisted attempts by the state to control the future of computing by fabricating imaginaries to covertly exert influence over the research environment. The paper examines the impact of the Cold War military–industrial complex on academia, which provided opportunities for research to take place whilst introducing challenges to autonomy. It makes the case that computer scientists such as Marvin Minsky and Fernando J. Corbato carefully shaped narratives across film, television and the media to promote desirable futures centering their own technical approaches. Acknowledging that instruments of the state appealed to the future to guide research towards strategically sensitive areas in the context of Cold War technoscientific contest, it asserts that intensifying ties between military and academic institutions afforded researchers both the latitude and motivation to construct independent visions of the future. In doing so, the paper aims to complicate assumptions about imaginaries as solely tools of governance by highlighting scientists' creativity in navigating institutional constraints to wrest back control of the future.
Semantic Adversarial Attacks on Face Recognition Through Significant Attributes
Authors: Khedr Yasmeen M. , Xiong Yifeng , He Kun 
Published: December 2023

AbstractFace recognition systems are susceptible to adversarial attacks, where adversarial facial images are generated without awareness of the intrinsic attributes of the images in existing works. They change only a single attribute indiscriminately. To this end, we propose a new Semantic Adversarial Attack using StarGAN (SAA-StarGAN), which manipulates the facial attributes that are significant for each image. Specifically, we apply the cosine similarity or probability score to predict the most significant attributes. In the probability score method, we train the face verification model to perform an attribute prediction task to get a class probability score for each attribute. Then, we calculate the degree of change in the probability value in an image before and after altering the attribute. Therefore, we perform the prediction process and then alter either one or more of the most significant facial attributes under white-box or black-box settings. Experimental results illustrate that SAA-StarGAN outperforms transformation-based, gradient-based, stealthy-based, and patch-based attacks under impersonation and dodging attacks. Besides, our method achieves high attack success rates on various models in the black-box setting. In the end, the experiments confirm that the prediction of the most important attributes significantly impacts the success of adversarial attacks in both white-box and black-box settings and could improve the transferability of the generated adversarial examples.
Explainable AI and Law: An Evidential Survey
Authors: Richmond Karen McGregor , Muddamsetty Satya M. , Gammeltoft-Hansen Thomas , Olsen Henrik Palmer , Moeslund Thomas B. 
Published: December 2023

AbstractDecisions made by legal adjudicators and administrative decision-makers often found upon a reservoir of stored experiences, from which is drawn a tacit body of expert knowledge. Such expertise may be implicit and opaque, even to the decision-makers themselves, and generates obstacles when implementing AI for automated decision-making tasks within the legal field, since, to the extent that AI-powered decision-making tools must found upon a stock of domain expertise, opacities may proliferate. This raises particular issues within the legal domain, which requires a high level of accountability, thus transparency. This requires enhanced explainability, which entails that a heterogeneous body of stakeholders understand the mechanism underlying the algorithm to the extent that an explanation can be furnished. However, the “black-box” nature of some AI variants, such as deep learning, remains unresolved, and many machine decisions therefore remain poorly understood. This survey paper, based upon a unique interdisciplinary collaboration between legal and AI experts, provides a review of the explainability spectrum, as informed by a systematic survey of relevant research papers, and categorises the results. The article establishes a novel taxonomy, linking the differing forms of legal inference at play within particular legal sub-domains to specific forms of algorithmic decision-making. The diverse categories demonstrate different dimensions in explainable AI (XAI) research. Thus, the survey departs from the preceding monolithic approach to legal reasoning and decision-making by incorporating heterogeneity in legal logics: a feature which requires elaboration, and should be accounted for when designing AI-driven decision-making systems for the legal field. It is thereby hoped that administrative decision-makers, court adjudicators, researchers, and practitioners can gain unique insights into explainability, and utilise the survey as the basis for further research within the field.