bluuebunny/crossref_metadata_2025 huggingface dataset:
High magnification versus optical magnification in hypospadias surgery: a randomized controlled trial
Authors: Radwan Moemen Mohamed Farouk , Kaddah Sherif Nabhan , ElTagy Gamal Hasan , Abdullateef Khaled S.
Published: November 2023

Background

Hypospadias is the most common congenital abnormality of the urethra affecting live male births. The incidence is rising with the increasing pollution. During the last 50 years, developments in surgical instrumentation and optical systems allowed surgeons to increasingly undertake microsurgical procedures. Optical magnification is essential in pediatric surgery. Magnifying loupes are the most frequently used, although they often cause neck pain to the surgeon. Recent advances led to the development of a compact video microscope (VITOM; Karl Storz Endoscopy GmbH, Tuttlingen, Germany) that displays high-definition magnified images on a flat screen.

Objective

Evaluate VITOM as a potential substitute for loupes in complex open pediatric procedures and explore VITOM as an effective intraoperative teaching modality for open surgery.

Methods

Fifty-two patients enrolled in our study were divided into two groups: Group A (26): high magnification was used either (VITOM Karl Storz Endoscopy GmbH, Tuttlingen, Germany) or (Surgical microscope, Carl Zeiss Microscopy GmbH), along with microsurgical instruments. Vicryl 8-0 suture was used for urethroplasty. Group B (26): patients were operated upon by conventional instruments (fine tip but not microsurgical instruments), sutures, and magnification (3.5×). Vicryl 6-0 suture was used for urethroplasty, with loupes-assisted magnification.

Results

More complications were found in group B. Using VITOM was accompanied by less neck pain, enhanced visualization, and an enhanced teaching environment.

Conclusion

Operating with high magnification tools minimizes post-operative complications in hypospadias surgery. They enhanced identification of the penile anatomy and topography which facilitated reconstruction techniques as well as enhanced the comfort of the surgeon.

Level of evidence

I.
Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concrete
Authors: Sathiparan Navaratnarajah , Jeyananthan Pratheeba
Published: November 2023

Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable approach to achieving sustainability in building practices. This particular kind of concrete has the potential to effectively mitigate the issues associated with high levels of CO2 emissions and embodied energy, which are primarily attributed to the excessive utilization of cement in conventional construction materials. When GSA is utilized as a partial replacement for cement, the strength characteristics of concrete are influenced not only by the quantity of GSA replacement but also by several other factors, including cement content, water-to-cement ratio, coarse aggregate content, fine aggregate content, and curing length. This work demonstrates a predictive model for the compressive strength (CS) of GSA mixed concrete using ML methods. The models were constructed with 297 datasets obtained from published literature. These datasets included various input variables such as cement content, GSA content, fine aggregate content, coarse aggregate content, water need, and curing duration. The output variable included in the models was the CS of concrete. In this study, a set of seven machine learning algorithms was utilized as statistical assessment tools to identify the most precise and reliable model for predicting the CS of GSA mixed concrete. These techniques included linear regression, full quadratic model, artificial neural network, boosted decision tree regression, random forest regression, K nearest neighbors, and support vector regression. The present study evaluated several machine learning models, and it was shown that the random forest regression model had superior performance in forecasting the CS of GSA mixed concrete. The train data’s R2 is 0.91, with RMSE of 2.48 MPa. Similarly, for the test data, the R2 value is 0.89, with an RMSE of 2.42 MPa. The sensitivity analysis results of the random forest regression model indicate that the cement content primarily drives the material’s CS. Subsequently, the curing period and GSA content significantly impact the CS. This work systematically evaluates the CS of GSA mixed concrete, contributing to the existing body of knowledge and practical implementation in this domain.
Clinical features of chronic kidney disease in dogs with the serological presence of Leptospira spp., Ehrlichia canis, and Anaplasma phagocytophilum
Authors: Vázquez-Manzanilla Claire A. , Cárdenas-Marrufo María F. , Gutiérrez-Blanco Eduardo , Jiménez-Coello Matilde , Pech-Sosa Nelly R. , Ortega-Pacheco Antonio
Published: November 2023

Chronic kidney disease is commonly diagnosed in dogs, and clinical signs may be aggravated when infected agents are involved. In this case report, 33 dogs with chronic kidney disease were clinically evaluated and serologically tested for Leptospira spp., Ehrlichia canis, and Anaplasma phagocytophilum. The seroprevalence for Leptospira spp. was 39.4%. The most frequent serovars found were Pyrogenes, Canicola, Bratislava and Australis, with serological titers between 1:100 to 1:800. Clinical signs included fever, depression, decreased body condition, vomiting and hematuria. Significant laboratory findings were anemia, leukocytosis, thrombocytopenia, increased liver enzymes, urea and creatinine, hyperbilirubinemia and hyperphosphatemia. All leptospira seronegative dogs were positive for one or both monitored homoparasites (i.e., E. canis and A. phagocytophilum); only three leptospira seropositive dogs were positive for one or both hemoparasites. Findings also suggest that endemic hemoparasites of dogs should be monitored in dogs with a kidney condition for a better clinical picture of the patients and therapeutic approach.
Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort
Authors: Lam Vincent , Sharma Shivam , Gupta Sonali , Spouge John L. , Jordan I. King , Mariño-Ramírez Leonardo
Published: November 2023

Background

Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the USA and has a greater observed prevalence among those who identify as Black or Hispanic.

Methods

This study aimed to assess T2D racial and ethnic disparities using the All of Us Research Program data and to measure associations between genetic ancestry (GA), socioeconomic deprivation, and T2D. We used the All of Us Researcher Workbench to analyze T2D prevalence and model its associations with GA, individual-level (iSDI), and zip code-based (zSDI) socioeconomic deprivation indices among participant self-identified race and ethnicity (SIRE) groups.

Results

The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n = 2311), Black (n = 16,282), Hispanic (n = 16,966), and White (n = 50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest levels of socioeconomic deprivation, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation, both iSDI and zSDI, are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with iSDI and zSDI on T2D. Higher levels of iSDI and zSDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of iSDI and zSDI are negatively associated with T2D at high levels of African and Native American ancestry.

Conclusions

Socioeconomic deprivation is associated with a higher prevalence of T2D in Black and Hispanic minority groups, compared to the majority White group. Nonetheless, socioeconomic deprivation is associated with reduced T2D risk within the Black and Hispanic groups. These results are paradoxical and have not been reported elsewhere, with possible explanations related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.
Trust Matters
Authors: Vevaina Leilah
Published: November 2023

Although numbering fewer than 60,000 in a city of more than 12 million people, Mumbai's Parsi community is one of the largest private landowners in the city due to its network of public charitable trusts. In Trust Matters Leilah Vevaina explores the dynamics and consequences of this conjunction of religion and capital as well as the activities of giving, disputing, living, and dying it enables. As she shows, communal trusts are the legal infrastructure behind formal religious giving and ritual in urban India that influence communal life. Vevaina proposes the trusts as a horoscope of the city—a constellation of housing, temples, and other spaces providing possible futures. She explores the charitable trust as a technology of time, originating in the nineteenth century, one that structures intergenerational obligations for Mumbai's Parsis, connecting past and present, the worldly and the sacred. By approaching Mumbai through the legal mechanism of the trust and the people who live within its bounds as well as those who challenge or support it, Vevaina offers a new pathway into exploring property, religion, and kinship in the urban global South.
The realization of interpersonal meanings in cosmetic Maybelline New York in 2018 advertisements
Authors: Herman Herman , Purba Ridwin , Saputra Nanda
Published: November 2023

Background: Interpersonal meaning is the interaction between the speaker and the listener and the writer and the reader. This study aims to determine the structure of mood and how the structure of mood is found. The purposes of this study are (1) to be more sensitive to the mood structure shown in Maybelline New York cosmetic advertisement. (2) to investigate the reason of why interrogative sentences are more difficult to find than declarative sentences. Through this research, we can see the types of mood structures, and how these mood structures are used. Methods: The type of research approach used is descriptive qualitative. Research in this case describes systematically, factually and accurately facts and causal relationships of the phenomena studied. The sources of data in this study are eight cosmetic Maybelline New York products, namely the Maybelline gel pencil, L’oreal colour riche nail, Maybelline mascara and eyeliner, Maybelline creamy lipmatte and Maybelline eyeshadow, Maybelline eyeliner, Maybelline lipstick, and Maybelline clear smooth all in one. Results: In analyzing the data, the researcher of the 2014 Halliday theory revised by Mathiesse found that there are three types of manifested mood, four types of realized speech function, and mood-based realization of speech function. The moods manifested were declarative (48.64%), interrogative (5.40%) and imperative (45.94%). Speech functions that are realized are statements (48.64%), questions (5.40%), offers (18.91%) and command (24.32%). Conclusions: It can be concluded that declarative sentences are mostly found in Maybelline New York products. Researchers suggest that readers be more careful in using mood structures in cosmetic advertisements so that there are no misunderstandings.
The role and interplay of institutions in water governance in the Central Rift Valley of Ethiopia
Authors: Jibat Endalew , Senbeta Feyera , Zeleke Tesfaye , Hagos Fitsum
Published: November 2023

Background Institutions can play a key role in coordinating how natural resources are effectively managed and used without over-exploitation. Institutions are laws, policies, and organizational arrangements that permit, forbid or regulate human action. This study aimed to look into the roles of formal and informal institutions, and their interactions in water resources governance in the Central Rift Valley (CRV), Ethiopia. Methods Key informant interviews, focus group discussions, and secondary data sources were employed to collect relevant data. Results The result of the study indicated that the influence of informal institutions on formal institutions or vice versa was insignificant, and unable to change the actions of water users in the CRV. Other limitations observed in water resources governance in the CRV include a lack of actors’ clear roles and responsibilities, absence of meaningful decentralization, limited engagement of key actors in policy development, lack of synergy between the institutions, and absence of enforcement mechanisms. Conclusion Considering the local contexts and community’s traditional knowledge of water governance in water-related policy, rules, and regulations, and enhancing the capacity of local-level institutions, strong interplay among all institutions involved in water governance, and meaningful actors’ engagement were recommended to advance the role of institutions in water resources governance in the CRV and in the country.
Development, implementation, and testing of LGBTQIA+ care curriculum for health science professionals: Research protocol.
Authors: Pai Mamatha Shivananda , Yesodharan Renjulal , Palimar Vikram , Thimmappa Latha , Bhat Bhavana B. , Krishnan M. Nirmal , Shetty Deeksha , Babu Bontha V.
Published: November 2023

Lesbian, Gay, Bisexual, Transgender, Queer, Intersex and Asexual (LGBTQIA+) people struggle to identify a healthcare service that understands their problems and needs. Additionally, healthcare professionals also find it difficult to care for LGBTQIA+ as very little is studied or heard about management. The article presents a protocol for a pilot study aimed at the development of an LGBTQIA+ care curriculum for health science professionals. The study includes Phase I: The development of a curriculum based on a literature review and focus group discussion among LGBTQIA+ individuals, and Phase II: Pilot testing of LGBTQIA+ care curriculum. The study outcome will reflect the improvement in the knowledge of healthcare professionals on LGBTQIA+ care.
The role of resilience as mediator in the relationship between cyberbullying victimisation and depression among university students in Saudi Arabia
Authors: AlShehry Abdullah Ali , Wan Sulaiman Wan Shahrazad , Khairudin Rozainee , Abdullah Nurul-Azza
Published: November 2023

Background The evolution of technology has contributed to the way people communicate with each other; however, it exposes individuals to cyberbullying which has become widespread across many societies. Despite the increased attention given to this internet-related problem, little efforts have been made in non-Western societies like Saudi Arabia. Therefore, this study aimed to investigate the direct impact of cyberbullying victimisation and resilience on depression among students and to explore the mediating role of resilience in the relationship between cyberbullying victimisation and depression. Methods A quantitative survey approach was employed to collect data from 353 undergraduate students in Saudi Arabia. Data were collected using a structured questionnaire consisting of the Cyber-Victim dimension from the Cyberbullying Test, the Resilience Scale (RS), and Beck Depression Inventory (BDI). Structural Equation Modeling (SEM) was conducted using AMOS version 26 to test the hypotheses. Results The findings from the structural model showed that cyberbullying victimisation had a significant and negative direct effect on resilience. Cyberbullying victimisation also had a significant and positive direct effect on depression, while there was a significant and negative direct effect of resilience on depression. The study further found that resilience significantly mediated the relationship between cyberbullying victimisation and depression. Conclusions This study is significant as it is a pioneering work in Saudi Arabia that examines the relationship between cyberbullying victimisation, resilience, and depression among undergraduate students. The findings of this study can help tackle depression among students by increasing resilience when faced with cyberbullying victimisation.
Using multiple outcomes in intervention studies: improving power while controlling type I errors
Authors: Bishop Dorothy V. M.
Published: November 2023

Background The CONSORT guidelines for clinical trials recommend use of a single primary outcome, to guard against the raised risk of false positive findings when multiple measures are considered. It is, however, possible to include a suite of multiple outcomes in an intervention study, while controlling the familywise error rate, if the criterion for rejecting the null hypothesis specifies that N or more of the outcomes reach an agreed level of statistical significance, where N depends on the total number of outcome measures included in the study, and the correlation between them. Methods Simulations were run, using a conventional null-hypothesis significance testing approach with alpha set at .05, to explore the case when between 2 and 12 outcome measures are included to compare two groups, with average correlation between measures ranging from zero to .8, and true effect size ranging from 0 to .7. In step 1, a table is created giving the minimum N significant outcomes (MinNSig) that is required for a given set of outcome measures to control the familywise error rate at 5%. In step 2, data are simulated using MinNSig values for each set of correlated outcomes and the resulting proportion of significant results is computed for different sample sizes,correlations, and effect sizes. Results The Adjust NVar approach can achieve a more efficient trade-off between power and type I error rate than use of a single outcome when there are three or more moderately intercorrelated outcome variables. Conclusions Where it is feasible to have a suite of moderately correlated outcome measures, then this might be a more efficient approach than reliance on a single primary outcome measure in an intervention study. In effect, it builds in an internal replication to the study. This approach can also be used to evaluate published intervention studies.
Microfoundations of Problem Solving: Attentional Engagement Predicts Problem-Solving Strategies
Authors: Laureiro-Martinez Daniella , Arrieta Jose Pablo , Brusoni Stefano
Published: November 2023

Organizations use a plethora of methods and tools to help their members solve problems effectively. Yet the specifics of how individuals solve problems remain largely unexplored. We propose and test a cognitive model of problem solving that integrates dual process theories into the attention-based view. The model suggests that diverse problem-solving strategies emerge in response to how individuals deliberate. Three studies provide observational and causal evidence in support of our model. The first study explores the strategies managers use to solve problems. We use think-aloud protocols combined with content, sequence, and cluster analyses to extract the key differences in how experienced managers solve problems. Two problem-solving strategies emerge from the data: one emphasizes mental activities related to framing, and the other emphasizes mental activities related to implementation. In the second study, we use a mixed factorial experimental design and mouse-tracking analysis to uncover the causal mechanism that explains the emergence of these two strategies. We then retest our hypotheses in a third, preregistered, study. We find that manipulating attention toward mental activities related to framing increases deliberation aimed at restructuring the problem elements. In contrast, directing attention toward mental activities related to implementation increases deliberation on the potential contingencies and consequences of the solution. Our findings provide empirical evidence about how problems are actually solved and support the idea that attentional processes are malleable enough to affect the choice of problem-solving strategies.

History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.

Funding: For Study 1, funding from EC NEST-2006-PATH-Cul, CID-Cultural and Innovation Dynamics: FP6-043345 is gratefully acknowledged.

Supplemental Material: The supplemental material is available at https://doi.org/10.1287/orsc.2019.13213 .
The Natural Emergence of Category Effects on Rugged Landscapes
Authors: Vashevko Anthony
Published: November 2023

Category theory finds that markets partition producers into categories and producers who do not fit one specific category—or who span multiple categories—perform worse than their single-category peers. The dominant thread of this work argues that this miscategorization penalty arises when cognitive limits of categorization cause individual members of the market’s audience to exclude or denigrate ill-fitting producers. I present a null model of markets in which a miscategorization penalty appears without being caused by a market audience: drawing on cognitive science and research on rugged landscapes, the model shows that producer herding behavior generates a spurious correlation between market outcomes and miscategorizations. The model further predicts the dynamics of categorical emergence and change over time. I establish these results in a simulation and discuss strategies by which this landscape model can be empirically distinguished or integrated with the standard account of an audience-driven penalty.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2020.13770 .
Learning from excellence: a thematic analysis of staff-reported excellence in healthcare from two acute NHS trusts
Authors: Crossingham Gemma , Ord Helen , Hannon Frances , Viira David , Plunkett Adrian
Published: November 2023

Background/Aims

Learning from excellence is a philosophy and practice designed to capture positive events in healthcare in order to improve quality and safety, while also providing positive feedback to staff. The aim of this study was to identify themes in staff-reported excellence to understand the conditions and behaviours that can allow excellence to occur.

Methods

A retrospective thematic analysis was performed with 400 excellence reports (200 per trust), which had been submitted via the learning from excellence reporting systems at two acute NHS trusts.

Results

Five overarching, interdependent themes were identified: 1) pro-social behaviours could have a positive impact on staff experience and patient care; 2) witnessed expertise in technical and non-technical skills were a source of appreciation and gratitude for staff; 3) a positive work ethic could benefit patient experience, staff productivity and morale; 4) staff appreciated personalised patient care that deviates from the norm but results in a positive outcome; and 5) effective leadership and teamwork were often characterised by resilience and excellent interpersonal skills.

Conclusions

The themes identified in this analysis describe skills and behaviours that were recognised as valuable to the healthcare workforce and could be used to prompt enquiry into the conditions that allow success to happen by highlighting what works well in healthcare.
Let's Explore! The Factor, Reliability, and Validity Analyses of Readiness for a Knowledge-Based Economy Among Undergraduate Students
Authors: Nagari Primasa Minerva , Sahid Sheerad , Hussin Muhammad
Published: November 2023

Knowledge-based economy is an economic model students need to be prepared for a future economic model that uses knowledge as its main resource. Therefore, this study developed and validated instruments for constructing knowledge-based economy readiness among undergraduate students. This study used an online questionnaire with 120 respondents of economic education students in educational universities in East Java, Indonesia, for exploratory factor analysis and 417 respondents for confirmatory factor analysis. Then, statistical analysis was conducted using exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. This study first developed five factors of knowledge of economics, readiness for economic challenges, readiness for education, readiness for infrastructure, and readiness for innovation, consisting of 27 items. However, one item was removed because the loading factor was below .50. Consequently, 26 items were retained because the loading factor was significantly greater than .50. The Cronbach's alpha value for each item of the knowledge-based economy readiness construct was >.60 and met all goodness of fit index criteria, which means that it meets the requirements and can measure the construct of knowledge-based economy readiness. Since this study meets the validity and reliability requirements of the constructs leading to knowledge-based economy readiness, these results will help students prepare for the current and future knowledge-based economy. They can be used in developing economic education curricula in higher education.
The iNaturalist Application in Biology Education: A Systematic Review
Authors: Rode Žan , Torkar Gregor
Published: November 2023

The iNaturalist application has been gaining traction in both biodiversity research and education. Increasingly more research is being carried out, looking into using the iNaturalist application in biology education, to promote biodiversity and educate students about this topic. We used the PRISMA systematic literature review method to examine research carried out using the iNaturalist application in biology education by searching Web of Science and Scopus, and including citizen science in the process. We searched through the iNaturalist forum, where users contribute research performed using iNaturalist. We found that an increasing number of articles have been published on this topic over the years. Researchers are using iNaturalist to improve BioBlitz activities, to enhance outdoor education activities, and to help make herbaria. Summarizing the work performed in this area, we suggest guidelines for including and preparing the iNaturalist application in a school context to be used by teachers and researchers alike.
From dematerialising race to distorting decoloniality: development-as-imperialism and Hindu supremacy
Authors: Wilson Kalpana
Published: November 2023

This article explores how race in international development currently operates globally. It is not simply a discursive legacy of colonialism but also inextricably embedded in contemporary processes of imperialist extraction and accumulation, which the architecture of development both legitimises and extends. The article argues for deeper engagement with Marxist thinkers located in the Global South in contemporary theorisations of race in development. In the context of multiple anti-racist and decolonial movements globally, many mainstream development actors are claiming to engage with questions of racism within their organisations. This is explored through an analysis of several texts produced by the British Overseas Network for Development (Bond), a large network of organisations working on international development in Britain, in response to the resurgence of the Black Lives Matter movement since 2020. While contradictions within these texts evidently reflect internal contestations over understandings of racism and anti-racism, they ultimately propose the adoption of diversity-based ‘solutions’ to racism and coloniality in development. These are analysed in the context of a long line of initiatives to relegitimise colonialism and development in order to sustain racialised structures of global capital accumulation. The essentialisation inherent in these approaches facilitates the appropriation of notions of decoloniality by forces of Brahmanical Hindu supremacism in India, which are closely tied to racialised and racialising global corporate capital. The second part of the article explores these processes, highlights the synergies between anti-caste and anti-racist thinking about capital and labour, and argues that Hindu supremacism incorporates, is sustained and strengthened by, and in turn feeds global supremacist ideologies.
MIMO detection using an ALR-CNN in SDM transmission systems
Authors: Zhang Danni , Ma Xinyuan , Lu Shun , Tan Zhongwei
Published: November 2023

Multiple-input multiple-output (MIMO) detectors have been a key technology in communication systems. In this paper, a new MIMO detector is designed by combining the adaptive learning rate (ALR) with the convolutional neural network (CNN) and successfully implementing it in a mode division multiplexing (MDM) optical transmission system. The results show that the training and test accuracy of the signal in the system we proposed reaches 100%. What is more, we used the ALR-CNN to compare the performance with conventional detection algorithms. The results confirm that our DLNN exceeds the conventional MIMO detectors in performance and is able to achieve the ideal QPSK BER level. The minimum difference in the SNR is about 9.5 dB at a BER of the 10−3 order.
Combined fluorescence lifetime and surface topographical imaging of biological tissue
Authors: Hopkinson Charlotte , Matheson Andrew B. , Finlayson Neil , Tanner Michael G. , Akram Ahsan R. , Henderson Robert K.
Published: November 2023

In this work a combined fluorescence lifetime and surface topographical
imaging system is demonstrated. Based around a 126 × 192 time resolved
single photon avalanche diode (SPAD) array operating in time
correlated single-photon counting (TCSPC) mode, both the fluorescence
lifetime and time of flight (ToF) can be calculated on a pixel by
pixel basis. Initial tests on fluorescent samples show it is able to
provide 4 mm resolution in distance and 0.4 ns resolution in lifetime.
This combined modality has potential biomedical applications such as
surgical guidance, endoscopy, and diagnostic imaging. The system is
demonstrated on both ovine and human pulmonary tissue samples, where
it offers excellent fluorescence lifetime contrast whilst also giving
a measure of the distance to the sample surface.
Laser processing of silicon with GHz burst pumped third harmonics for precise microfabrication
Authors: Sakurai Haruyuki , Konishi Kuniaki
Published: November 2023

Femtosecond laser processing has proved to be a valuable tool for various microfabrication applications. In order to further increase the quality and efficiency of femtosecond laser processing, processing with GHz burst mode lasers has gained attention in recent years, where packets of high-repetition rate pulses are used instead of single pulses at the fundamental repetition rate. However, the use of burst-pulses has mainly been limited to the fundamental wavelength of powerful regenerative amplifier systems, often near 1 micrometer wavelength. In this study, we explore the characteristics and potential benefits of further wavelength conversion of burst-pulses emitted at the near-infrared to the ultraviolet region via direct third-harmonic generation. We construct an in-line process evaluation setup with a chromatic confocal sensor, and evaluate the ablation characteristics of the burst-pumped and non-burst processing of silicon. We observe that burst-mode processing has significantly reduced surface roughness and debris, resulting in high-quality laser processing. To demonstrate the utility of such burst-pumped UV processing, we show the successful milling of a spherical structure enabled by in-line surface profile feedback, while similar processing with non-burst conditions did not work. We believe such results show the strong potential of burst laser sources for use in accurate microfabrication of structures with micrometer-scale resolution.
Erbium doping of lithium niobate on insulator using low-temperature ion exchange
Authors: Younesi Mohammadreza , Yang Daniel , Chung Wei-Yuan , Liu Hao-Yun , Kumar Mohit , Abed Omid , Fedotova Anna , Geiss Reinhard , Setzpfandt Frank , Chen Yen-Hung , Pertsch Thomas
Published: November 2023

We propose an optimized low-temperature ion exchange method for fabricating an erbium-doped lithium niobate on insulator (LNOI) substrate. This method ensures the production of high-quality, crack-free substrates. The erbium-doped substrates are characterized spectroscopically in the near-infrared wavelength range. Additionally, we demonstrate deterministic local doping by using a SiO2 mask. This relatively simple, locally selective doping technology can facilitate the implementation of new and practical active building blocks in the LNOI platform, which could be attractive for several applications, like the realization of integrated lasers and amplifiers.
Fourier ptychographic microscopy image stack reconstruction using implicit neural representations
Authors: Zhou Haowen , Feng Brandon Y. , Guo Haiyun , Lin Siyu (Steven) , Liang Mingshu , Metzler Christopher A. , Yang Changhuei
Published: November 2023

Image stacks provide invaluable 3D information in various biological and pathological imaging applications. Fourier ptychographic microscopy (FPM) enables reconstructing high-resolution, wide field-of-view image stacks without z-stack scanning, thus significantly accelerating image acquisition. However, existing FPM methods take tens of minutes to reconstruct and gigabytes of memory to store a high-resolution volumetric scene, impeding fast gigapixel-scale remote digital pathology. While deep learning approaches have been explored to address this challenge, existing methods poorly generalize to novel datasets and can produce unreliable hallucinations. This work presents FPM-INR, a compact and efficient framework that integrates physics-based optical models with implicit neural representations (INRs) to represent and reconstruct FPM image stacks. FPM-INR is agnostic to system design or sample types and does not require external training data. In our experiments, FPM-INR substantially outperforms traditional FPM algorithms with up to a 25-fold increase in speed and an 80-fold reduction in memory usage for continuous image stack representations.
Flexible tuning of multifocal holographic imaging based on electronically controlled metasurfaces
Authors: Zeng Bowen , Li Chenxia , Fang Bo , Hong Zhi , Jing Xufeng
Published: November 2023

Programmable hyper-coded holography has the advantage of being programmable as well as being flexibly modifiable. Digitally coded metamaterials with excellent electromagnetic modulation capability and the ability to control the phase to modulate the spatial radiation field through external excitation in the form of switching can be used to realize low-cost digital arrays. We design a 1-bit encoded programmable metasurface, which is electrically connected to control the PIN diode in the switching state and to switch the condition of each metasurface cell between “0” and “1.” Using the designed programmable metasurface, we can randomly encode the cell structure to realize single-focus focusing, multi-focusing, and simple holographic letter imaging. Based on the nonlinear holographic model, we employ the Gerchberg-Saxton improvement algorithm to modulate the energy distribution at the focus by adjusting the phase distribution. Importantly, we introduce the Fourier convolution principle to regulate the holographic imaging focus flexibly.
Automatic foot ulcer segmentation using conditional generative adversarial network (AFSegGAN): A wound management system
Authors: P. Jishnu , B. K. Shreyamsha Kumar , Jayaraman Srinivasan
Published: November 2023

Effective wound care is essential to prevent further complications, promote healing, and reduce the risk of infection and other health issues. Chronic wounds, particularly in older adults, patients with disabilities, and those with pressure, venous, or diabetic foot ulcers, cause significant morbidity and mortality. Due to the positive trend in the number of individuals with chronic wounds, particularly among the growing elderly and diabetes populations, it is imperative to develop novel technologies and practices for the best practice clinical management of chronic wounds to minimize the potential health and economic burdens on society. As wound care is managed in hospitals and community care, it is crucial to have quantitative metrics like wound boundary and morphological features. The traditional visual inspection technique is purely subjective and error-prone, and digitization provides an appealing alternative. Various deep-learning models have earned confidence; however, their accuracy primarily relies on the image quality, the dataset size to learn the features, and experts’ annotation. This work aims to develop a wound management system that automates wound segmentation using a conditional generative adversarial network (cGAN) and estimate the wound morphological parameters. AFSegGAN was developed and validated on the MICCAI 2021-foot ulcer segmentation dataset. In addition, we use adversarial loss and patch-level comparison at the discriminator network to improve the segmentation performance and balance the GAN network training. Our model outperformed state-of-the-art methods with a Dice score of 93.11% and IoU of 99.07%. The proposed wound management system demonstrates its abilities in wound segmentation and parameter estimation, thereby reducing healthcare workers’ efforts to diagnose or manage wounds and facilitating remote healthcare.
Field validation of different intervention modes for the potential transmission risk of schistosomiasis during post-transmission interruption phase
Authors: Feng Jiaxin , Guo Zhaoyu , Qian Peijun , Wang Wenya , Hu Hehua , Zhang Xia , Xue JingBo , Li Yinlong , Cao Chunli , Hao Yuwan , Li Shizhu
Published: November 2023

Objective

Precision interventions have been proposed in transmission-interrupted areas to further reduce the potential transmission risk of schistosomiasis. This study aimed to evaluate the effects of different interventions modes for potential transmission risk control.

Methods

Three groups of schistosomiasis-endemic villages were selected in Jiangling county, Hubei province. After baseline surveys in 2020, three intervention models were employed in 2021 and 2022. In Model 1, Oncomelania hupensis snail control in key settings and an integrated strategy with an emphasis on the infectious sources managing was employed. In Model 2, an integrated health education-led strategy with an emphasis on infectious source management was employed. In Model 3, only the integrated strategy with an emphasis on infectious source management was employed. The effects of the different intervention models were examined with multiple indicators after 2 years of intervention using the entropy-weighted technique for order of preference by similarity to ideal solution (TOPSIS), rank-sum ratio (RSR) and fuzzy combination model of entropy-weighted TOPSIS and RSR.

Results

Entropy-weighted TOPSIS modeling showed that the Ci values of Model 2 were 0.4434, 0.2759, and 0.3069 in the three pilot villages, Ci values were larger, with top comprehensive ranking. The results of the RSR method showed that the RSR values of Model 2 were 0.75, 0.708, and 0.736 in the three pilot villages, with top comprehensive ranking. The results from the fuzzy combination model of entropy-weighted TOPSIS and RSR showed that implementation of Model 2 resulted in the highest comprehensive ranking among the three models in the three pilot villages under Ci: RSR = 0.1: 0.9, Ci: RSR = 0.5: 0.5 and Ci: RSR = 0.9: 0.1.

Conclusion

The integrated health education-led strategy with an emphasis on infectious source management was the optimal model to manage the risk of transmission of schistosomiasis during the post-transmission interruption phase.
Introducing effective parameters for predicting job burnout using a self-organizing method based on group method of data handling neural network
Authors: Fan Tingting , Nazemi Ehsan
Published: November 2023

In addition to affecting people’s bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach’s alpha confirmed the polls’ dependability, and an expert panel confirmed the surveys’ authenticity. The GMDH neural network’s inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network’s output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.
Marsh migration and beyond: A scalable framework to assess tidal wetland resilience and support strategic management
Authors: Stevens Rachel A. , Shull Suzanne , Carter Jamie , Bishop Emily , Herold Nate , Riley Cory A. , Wasson Kerstin
Published: November 2023

Tidal wetlands are critical but highly threatened ecosystems that provide vital services. Efficient stewardship of tidal wetlands requires robust comparative assessments of different marshes to understand their resilience to stressors, particularly in the face of relative sea level rise. Existing assessment frameworks aim to address tidal marsh resilience, but many are either too localized or too general, and few directly translate resilience evaluations to recommendations for management strategies. In response to the deficiencies in existing frameworks, we identified a set of metrics that influence overall marsh resilience that can be assessed at any spatial scale. We then developed a new comprehensive assessment framework to rank relative marsh resilience using these metrics, which are nested within three categories. We represent resilience as the sum of results across the three metric categories: current condition, adaptive capacity, and vulnerability. Users of this framework can add scores from each category to generate a total resilience score to compare across marshes or take the score from each category and refer to recommended management actions we developed based on expert elicitation for each combination of category results. We then applied the framework across the contiguous United States using publicly available data, and summarized results at multiple spatial scales, from regions to coastal states to National Estuarine Research Reserves to finer scale marsh units, to demonstrate the framework’s value across these scales. Our national analysis allowed for comparison of tidal marsh resilience across geographies, which is valuable for determining where to prioritize management actions for desired future marsh conditions. In combination, the assessment framework and recommended management actions function as a broadly applicable decision-support tool that will enable resource managers to evaluate tidal marshes and select appropriate strategies for conservation, restoration, and other stewardship goals.
Object-stable unsupervised dual contrastive learning image-to-image translation with query-selected attention and convolutional block attention module
Authors: Oh Yunseok , Oh Seonhye , Noh Sangwoo , Kim Hangyu , Seo Hyeon
Published: November 2023

Recently, contrastive learning has gained popularity in the field of unsupervised image-to-image (I2I) translation. In a previous study, a query-selected attention (QS-Attn) module, which employed an attention matrix with a probability distribution, was used to maximize the mutual information between the source and translated images. This module selected significant queries using an entropy metric computed from the attention matrix. However, it often selected many queries with equal significance measures, leading to an excessive focus on the background. In this study, we proposed a dual-learning framework with QS-Attn and convolutional block attention module (CBAM) called object-stable dual contrastive learning generative adversarial network (OS-DCLGAN). In this paper, we utilize a CBAM, which learns what and where to emphasize or suppress, thereby refining intermediate features effectively. This CBAM was integrated before the QS-Attn module to capture significant domain information for I2I translation tasks. The proposed framework outperformed recently introduced approaches in various I2I translation tasks, showing its effectiveness and versatility. The code is available at https://github.com/RedPotatoChip/OSUDL
The net effect of wealth on health—Investigating noncommunicable diseases mortality in the context of regional affluence
Authors: Olejnik Alicja , Żółtaszek Agata
Published: November 2023

The wealth-health relationship is not unambiguous and constant. Greater wealth affects individual and population health in opposite ways. Increased risk factors especially raise the probability of noncommunicable diseases (NCDs) impacting a population. Conversely, better healthcare and awareness reduce the chances of developing these diseases or increase the likelihood of treatment and cure. Therefore, this paper aims to assess and quantify the hard-to-grasp overall impact of prosperity on mortality from selected NCDs, allowing us to capture the relevant differences between European regions. In particular, we attempt to estimate the net effect of affluence and the health economic threshold of the GDP-mortality relationship, by developing a dedicated analytical tool based on joinpoint regression and forecasting methods. Our results show that in the case of most investigated diseases in more impoverished regions, a clear pattern reflects mortality rising with prosperity. After crossing the health economic threshold of around 20 thousand euros per capita, the trend changes by stabilising or reversing. The research we present shows that health policy should be more diversified locally to enable health convergence at the national and European regional levels. Moreover, health policy should evolve to prioritise mental and neurological disorders, by improving the resource allocation and increasing public awareness.