bluuebunny/crossref_metadata_2025_split huggingface dataset:
POC device for rapid oral pH determination based on a smartphone platform
Authors: Arroyo Manuel J. , Escobedo Pablo , Ruiz-García Isidoro , Palma Alberto J. , Santoyo Francisco , Ortega-Muñoz Mariano , Capitán-Vallvey Luis Fermín , Erenas Miguel M. 
Published: February 2024

AbstractSalivary pH serves as a valuable and useful diagnostic marker for periodontal disease, as it not only plays a critical role in disease prevention but also in its development. Typically, saliva sampling is collected by draining and spitting it into collection tubes or using swabs. In this study, we have developed a Point-of-Care (POC) device for in situ determination of oral pH without the need for complex instruments, relying solely on a smartphone as the detection device. Our system utilizes a non-toxic vegetable colourimetric indicator, immobilized on a chitosan membrane located on a disposable stick, enabling direct sampling within the buccal cavity. An ad hoc designed 3D-printed attachment is used to ensure accurate positioning and alignment of the stick, as well as isolation from external lighting conditions. A custom-developed smartphone application captures and automatically processes the image of the sensing membrane, providing the salivary pH results. After optimizing the cocktail composition, the developed sensors demonstrated the capacity to determine pH within a range of 5.4 to 8.1 with a remarkable precision of 0.6%, achieving a very short analysis time of just 1 min. A stability study conducted on the sensing membranes revealed a lifetime of 50 days. To validate the performance of our analytical device, we compared its results against those obtained from a calibrated pH-meter, using a group of individuals. The device exhibited an average error of 2.4% when compared with the pH-meter results, confirming its reliability and accuracy.
Graphical abstract
A novel alkane monooxygenase evolved from a broken piece of ribonucleotide reductase in Geobacillus kaustophilus HTA426 isolated from Mariana Trench
Authors: Nithimethachoke Tanasap , Boonmak Chanita , Morikawa Masaaki 
Published: February 2024

AbstractWe have accidentally found that a thermophilic Geobacillus kaustophilus HTA426 is capable of degrading alkanes although it has no alkane oxygenating enzyme genes. Our experimental results revealed that a putative ribonucleotide reductase small subunit GkR2loxI (GK2771) gene encodes a novel heterodinuclear Mn–Fe alkane monooxygenase/hydroxylase. GkR2loxI protein can perform two-electron oxidations similar to homonuclear diiron bacterial multicomponent soluble methane monooxygenases. This finding not only answers a long-standing question about the substrate of the R2lox protein clade, but also expands our understanding of the vast diversity and new evolutionary lineage of the bacterial alkane monooxygenase/hydroxylase family.
Reaching interactions in virtual reality: the effect of movement direction, hand dominance, and hemispace on the kinematic properties of inward and outward reaches
Authors: Clark Logan , Iskandarani Mohamad El , Riggs Sara 
Published: February 2024

AbstractRecent literature has revealed that when users reach to select objects in VR, they can adapt how they move (i.e., the kinematic properties of their reaches) depending on the: (1) direction they move, (2) hand they use, and (3) side of the body where the movement occurs. In the present work, we took a more detailed look at how kinematic properties of reaching movements performed in VR change as a function of movement direction for reaches performed on each side of the body using each hand. We focused on reaches in 12 different directions that either involved moving inward (toward the body midline) or outward (away from the body midline). Twenty users reached in each direction on both left and right sides of their body, using both their dominant and non-dominant hands. The results provided a fine-grained account of how kinematic properties of virtual hand reaches change as a function of movement direction when users reach on either side of their body using either hand. The findings provide practitioners insights on how to interpret the kinematic properties of reaching behaviors in VR, which has applicability in emerging contexts that include detecting VR usability issues and using VR for stroke rehabilitation.
Forest Landscape Restoration and Its Impact on Social Cohesion, Ecosystems, and Rural Livelihoods: Lessons Learned from Pakistan
Authors: Ullah Ayat 
Published: February 2024

AbstractThis paper explores community perspectives on forest landscape restoration (FLR) initiatives and their impacts on ecosystems, livelihoods and social cohesion. The study is based on data collected from 08 focus group discussions (FGDs) conducted in local communities involved in FLR activities. A coding process was used to identify key themes and patterns that shed light on the impacts of FLR. The results show that FLR initiatives have led to social cohesion and conflict resolution, including the establishment of community-based organizations (CBOs), the involvement of community volunteers in various FLR tasks, a cooperative/collaborative approach to forest management, conflict resolution, and the empowerment of local communities. In addition, the results of the study showed that FLRs have successfully regenerated and restored the forest ecosystem, including restoration of biodiversity, plant diversity, improvement of animal populations and livestock health, soil health, and water quality. Ecosystem restoration has improved community livelihoods, including increased crop productivity, access to education, affordable health care, improved economic conditions, and food security. Based on the findings, policy recommendations include strengthening support for community FLR initiatives, promoting stakeholder collaboration, and promoting social equity.
Developing questions to assess and measure patients’ perceived survival benefit from adjuvant endocrine therapy in breast cancer: a mixed methods pilot study
Authors: Tawfik Bernard , Jacobson Kendal , Brown-Glaberman Ursa , Kosich Mikaela , Van Horn M. Lee , Nemunaitis Jacklyn , Dayao Zoneddy , Pankratz V. Shane , Sussman Andrew L. , Guest Dolores D. 
Published: February 2024

AbstractThis mixed method study developed multiple question types to understand and measure women’s perceived benefit from adjuvant endocrine therapy. We hypothesis that patients do not understand this benefit and sought to develop the questions needed to test this hypothesis and obtain initial patient estimates. From 8/2022 to 3/2023, qualitative interviews focused on assessing and modifying 9 initial varied question types asking about the overall survival (OS) benefit from adjuvant endocrine therapy. Subsequent focus groups modified and selected the optimal questions. Patients’ self-assessment of their OS benefit was compared to their individualized PREDICT model results. Fifty-three patients completed the survey; 42% Hispanic, 30% rural, and 47% with income < $39,999 per year. Patients reported adequate health care literacy (61.5%) and average confidence about treatment and medication decisions 49.4 (95% CI 24.4–59.5). From the original 9 questions, 3 modified questions were ultimately found to capture patients’ perception of this OS benefit, focusing on graphical and prose styles. Patients estimated an OS benefit of 42% compared to 4.4% calculated from the PREDICT model (p < 0.001). In this group with considerable representation from ethnic minority, rural and low-income patients, qualitative data showed that more than one modality of question type was needed to clearly capture patients’ understanding of treatment benefit. Women with breast cancer significantly overestimated their 10-year OS benefit from adjuvant endocrine therapy compared to the PREDICT model.
Update in Sachen Umwelt-Rechtsbehelfsgesetz
Authors: Guckelberger Annette 
Published: February 2024

ZusammenfassungDas Enumerationsprinzip des §1 Abs. 1 UmwRG lässt sich nach der Entscheidung des EuGH in
Bezug auf Kfz-Typengenehmigungen nur noch schwer aufrechterhalten. Auch im Hinblick auf die Anerkennungsvoraussetzungen
von Umweltvereinigungen besteht nach einer Entscheidung der Vertragsstaatenkonferenz zur Aarhuskonvention
Änderungsbedarf. Der Beitrag gibt insbesondere einen Überblick zu aktuellen Entwicklungen in
der Rechtsprechung zum UmwRG sowie dem jüngst geänderten §6 UmwRG.
Acceptability of a Randomized Trial of Anti-depressant Medication or Interpersonal Therapy for Treatment of Perinatal Depression in Women with HIV
Authors: Bridget Spelke M. , Okumu Eunice , Perry Nzi R. , Blette Bryan S. , Paul Ravi , Schiller Crystal E. , Ncheka J. M. , Kasaro Margaret P. , Price Joan T. , Meltzer-Brody Samantha , Stringer Jeffrey S. A. , Stringer Elizabeth M. 
Published: February 2024

AbstractPostpartum depression (PPD) affects nearly 20% of postpartum women in Sub-Saharan Africa (SSA), where HIV prevalence is high. Depression is associated with worse HIV outcomes in non-pregnant adults and mental health disorders may worsen HIV outcomes for postpartum women and their infants. PPD is effectively treated with psychosocial or pharmacologic interventions; however, few studies have evaluated the acceptability of treatment modalities in SSA. We analyzed interviews with 23 postpartum women with HIV to assess the acceptability of two depression treatments provided in the context of a randomized trial. Most participants expressed acceptability of treatment randomization and study visit procedures. Participants shared perceptions of high treatment efficacy of their assigned intervention. They reported ongoing HIV and mental health stigma in their communities and emphasized the importance of social support from clinic staff. Our findings suggest a full-scale trial of PPD treatment will be acceptable among women with HIV in Zambia.
Value Judgments in Mathematics: G. H. Hardy and the (Non-)seriousness of Mathematical Theorems
Authors: Weisgerber Simon 
Published: February 2024

AbstractOne of the general criteria G. H. Hardy identifies and discusses in his famous essay A Mathematician’s Apology, Cambridge University Press, Cambridge, 1940) by which a mathematician’s patterns must be judged is seriousness. This article focuses on one of Hardy’s examples of a non-serious theorem, namely that 8712 and 9801 are the only numbers below 10000 which are integral multiples of their reversals, in the sense that $$8712=4\cdot 2178$$

8712
=
4
·
2178

, and $$9801=9\cdot 1089$$

9801
=
9
·
1089

. In the context of a discussion of generality, which he considers an essential quality of seriousness, he explains that there is nothing in this example which “appeals much to a mathematician” and that it is “not capable of any significant generalization.” Interestingly, since the publication of the Apology, more than a dozen papers—including one by the renowned mathematician Neil Sloane—have been published that discuss generalizations of Hardy’s example. By identifying the most important aspect of Hardy’s notion of generality, it is argued that, contrary to the views of several researchers, Hardy’s claim regarding the non-capability of any significant generalization is still tenable. Furthermore, this case study is presented and discussed as an example of the multifaceted nature of mathematical interest.
Statistical inference of inverted Nadarajah–Haghighi distribution under type-II generalized hybrid censoring competing risks data
Authors: Abushal Tahani A. , AL-Zaydi Areej M. 
Published: February 2024

AbstractTahir et al. (J Stat Comput Simul 88(14):2775–2798, 2018) introduced the inverse Nadarajah–Haghighi distribution (INHD) and demonstrated its ability to model positive real data sets with decreasing and upside-down bathtub hazard rate shapes. This article focuses on the inference of unknown parameters using a generalized Type-II hybrid censoring scheme (GT-II HCS) for the INHD in the presence of competing risks. The maximum likelihood (ML) and Bayes approaches are used to estimate the model parameters. Based on the squared error loss function, we compute Bayes estimates using Markov Chain Monte Carlo (MCMC) by applying Metropolis-Hasting (M-H) algorithm. Furthermore, the asymptotic confidence intervals, bootstrap confidence intervals (BCIs) and the highest posterior density (HPD) credible intervals are constructed. Using real data sets and simulation studies, we examined the introduced methods of inference with different sample sizes.
pH dependence of perturbed angular correlation in DOTA chelated $$^{111}$$In measured with ring-shape gamma-ray detectors
Authors: Shimazoe Kenji , Donghwan Kim , Mineo Tomoko , Sato Takeshi , Ohta Seiichi , Tatsumi Toshifumi , Sugiyama Akira , Yamatsugu Kenzo , Nomura Sachiyo , Terabayashi Ryohei , Tomita Hideki , Sonoda Tetsu , Shigekawa Yudai , Yokokita Takuya , Uenomachi Mizuki 
Published: February 2024

AbstractThe conventional nuclear medical imaging, such as PET and SPECT, visualizes the accumulation of radioisotopes in the human body and has been widely used for diagnosis of cancer and other diseases. Perturbed angular correlation is a powerful sensing tool detecting local electro-magnetic environment and has been used for chemical analysis or nuclear physics research. The use of perturbed angular correlation in nuclear medicine is recently proposed and pH sensing capability with liquid $$^{111}$$

111

In has been confirmed. In more practical applications, DOTA based radioisotope labeled drug delivery is common in nuclear medicine. This study aims to characterize the pH dependance of perturbed angular correlation in Psyche-DOTA chelated $$^{111}$$

111

In with the custom ring-shape gamma-ray detectors. The observed transition in anisotropy of cascade gamma-ray emission can contribute to the detection of a specific phenomenon changing pH inside the human body.
Diving into Clarity: Restoring Underwater Images using Deep Learning
Authors: Martinho Laura A. , Calvalcanti João M. B. , Pio José L. S. , Oliveira Felipe G. 
Published: February 2024

AbstractIn this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.
Artificial Intelligence in Operating Room Management
Authors: Bellini Valentina , Russo Michele , Domenichetti Tania , Panizzi Matteo , Allai Simone , Bignami Elena Giovanna 
Published: February 2024

Abstract
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence’s transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
Does respondent motivation affect item-nonresponse for split-ballot designed survey data? Comparative evidence from the European Social Survey
Authors: Saraç Melike 
Published: February 2024

AbstractSurvey quality would be enhanced if respondents willingly and accurately provided their responses. This seems feasible in ideal interview settings where respondents fully understand questions and provide truthful responses. Therefore, survey outcomes may be impacted by respondents’ feelings during the interview. The purpose of this study is to determine whether there is any relationship between respondent motivation, as operationalized by willingness and ability, and the item-nonresponse level for split-ballot designed questions. The 9th round of the European Social Survey (ESS9), a cross-national survey conducted across Europe, is the source of the data. This study assesses item-nonresponse using “don’t know” and “no answer” response choices for a set of questions designed with the split-ballot technique, which has not been dealt with much. In addition to the percentage distribution of item-nonresponse levels, the correlation analysis is used to understand the relationship between respondent motivation and item-nonresponse descriptively. Multivariable analyses use multiple linear regression modeling to explore the impact of respondent motivation on item-nonresponse, controlling for respondent and interviewer characteristics. Furthermore, bivariate relationships between outcome variable and covariates in the models were assessed using design-adjusted Wald-F tests. The findings pointed out that respondent motivation and item-nonresponse have a significant, negative, and moderate association. According to the complex sample design adjusted estimates of the statistical models, it may be able to reduce item-nonresponse with the increased motivation of respondents. This study offers several recommendations for questionnaire design, interviewing techniques, and interviewer evaluations for each respondent at the end.
Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series
Authors: KARTAL Serkan , IBAN Muzaffer Can , SEKERTEKIN Aliihsan 
Published: February 2024

AbstractThe Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.
Development and Validation of an Optoelectronic Method for the Assessment of Frying Colours of French Fries Based on RGB Colour Values
Authors: Laumer Michaela , Kellermann Adolf , Maidl Franz-Xaver , Hülsbergen Kurt-Jürgen , Ebertseder Thomas 
Published: February 2024

AbstractHigh frying quality in terms of frying colour is an important trait for potatoes meant for French fry production. Determination of frying colour is often performed visually, which might be affected by biases and is therefore less suitable for research. Available laboratory methods also lack the capability to cover the whole sample and distinguish between tuber parts. Additionally, sample destruction is often required for colour analysis. Therefore, a new approach using RGB colour values and the subsequent R/G ratio of French fries to assess frying colour was tested in this research. Over the course of 3 years, 673 samples of the cultivar Innovator were assessed and compared to visual scoring, and some samples were also assessed for glucose content. A high correlation of R2 = 0.8346 between the visual scoring and the optoelectronic assessment was found for 633 samples. The validation data set of 40 samples reached a correlation of R2 = 0.7850 with the implemented model. These results show that French fry frying colour can be described using the R/G ratio. This promising approach is suitable for other researchers as well as industry professionals with further research.