bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative study of laparoscopic ventral mesh rectopexy versus perineal stapler resection for external full-thickness rectal prolapse in elderly patients: enhanced outcomes and reduced recurrence rates—a retrospective cohort study
Authors: Habeeb T. A. A. M. , Podda M. , Chiaretti M. , Kechagias A. , Lledó J. B. , Kalmoush Abd-Elfattah , Mustafa Fawzy M. , Nassar Mohammed Shaaban , Labib Mohamed fathy , Teama Sobhy rezk ahmed , Elshafey Mohammed Hassan , Elbelkasi Hamdi , Alsaad Mohamed Ibrahim Abo , Sallam Ahmed M. , Ashour Hassan , Mansour Mohamed Ibrahim , Mostafa Abdelshafy , Elshahidy Tamer Mohamed , Yehia Ahmed m. , Rushdy Tamer , Ramadan Alaaedin , Hamed Abd Elwahab M. , Yassin Mahmoud Abdou , Metwalli Abd-Elrahman M. 
Published: April 2024

Abstract
Background
In elderly patients with external full-thickness rectal prolapse (EFTRP), the exact differences in postoperative recurrence and functional outcomes between laparoscopic ventral mesh rectopexy (LVMR) and perineal stapler resection (PSR) have not yet been investigated.

Methods
We conducted a retrospective multicenter study on 330 elderly patients divided into LVMR group (n = 250) and PSR (n = 80) from April 2012 to April 2019. Patients were evaluated before and after surgery by Wexner incontinence scale, Altomare constipation scale, and patient satisfaction questionnaire. The primary outcomes were incidence and risk factors for EFTRP recurrence. Secondary outcomes were postoperative incontinence, constipation, and patient satisfaction.

Results
LVMR was associated with fewer postoperative complications (p < 0.001), lower prolapse recurrence (p < 0.001), lower Wexner incontinence score (p = 0.03), and lower Altomare’s score (p = 0.047). Furthermore, LVMR demonstrated a significantly higher surgery–recurrence interval (p < 0.001), incontinence improvement (p = 0.019), and patient satisfaction (p < 0.001) than PSR. Three and 13 patients developed new symptoms in LVMR and PSR, respectively. The predictors for prolapse recurrence were LVMR (associated with 93% risk reduction of recurrence, OR 0.067, 95% CI 0.03–0.347, p = 0.001), symptom duration (prolonged duration was associated with an increased risk of recurrence, OR 1.131, 95% CI 1.036–1.236, p = 0.006), and length of prolapse (increased length was associated with a high recurrence risk (OR = 1.407, 95% CI = 1.197–1.655, p < 0.001).

Conclusions
LVMR is safe for EFTRP treatment in elderly patients with low recurrence, and improved postoperative functional outcomes.

Trial registration
Clinical Trial.gov (NCT05915936), retrospectively registered on June 14, 2023.
Socio-Behavioural Barriers to Viral Suppression in the Older Adult Population in Rural South Africa
Authors: Chinogurei Chido , Manne-Goehler J. , Kahn K. , Kabudula C. W. , Cornell M. , Rohr J. K. 
Published: April 2024

AbstractSouth Africa has the largest share of people living with HIV in the world and this population is ageing. The social context in which people seek HIV care is often ignored. Apart from clinical interventions, socio-behavioural factors impact successful HIV care outcomes for older adults living with HIV. We use cross-sectional data linked with demographic household surveillance data, consisting of HIV positive adults aged above 40, to identify socio-behavioural predictors of a detectable viral load. Older adults were more likely to have a detectable viral load if they did not disclose their HIV positive status to close family members (aOR 2.56, 95% CI 1.89-3.46), resided in the poorest households (aOR 1.98, 95% CI 1.23-3.18), or were not taking medications other than ART (aOR 1.83, 95% CI 1.02-1.99) likely to have a detectable. Clinical interventions in HIV care must be supported by understanding the socio-behavioural barriers that occur outside the health facility. The importance of community health care workers in bridging this gap may offer more optimum outcomes for older adults ageing with HIV.
Using machine learning to predict artistic styles: an analysis of trends and the research agenda
Authors: Valencia Jackeline , Pineda Geraldine García , Pineda Vanessa García , Valencia-Arias Alejandro , Arcila-Diaz Juan , de la Puente Renata Teodori 
Published: April 2024

AbstractIn the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles.
Comparative analysis of bevacizumab and LITT for treating radiation necrosis in previously radiated CNS neoplasms: a systematic review and meta-analysis
Authors: Gecici Neslihan Nisa , Gurses Muhammet Enes , Kaye Brandon , Jimenez Natasha L. Frontera , Berke Chandler , Gökalp Elif , Lu Victor M. , Ivan Michael E. , Komotar Ricardo J. , Shah Ashish H. 
Published: April 2024

Abstract
Purpose
Radiation necrosis (RN) is a local inflammatory reaction that arises in response to radiation injury and may cause significant morbidity. This study aims to evaluate and compare the efficacy of bevacizumab and laser interstitial thermal therapy (LITT) in treating RN in patients with previously radiated central nervous system (CNS) neoplasms.

Methods
PubMed, Cochrane, Scopus, and EMBASE databases were screened. Studies of patients with radiation necrosis from primary or secondary brain tumors were included. Indirect meta-analysis with random-effect modeling was performed to compare clinical and radiological outcomes.

Results
Twenty-four studies were included with 210 patients in the bevacizumab group and 337 patients in the LITT group. Bevacizumab demonstrated symptomatic improvement/stability in 87.7% of cases, radiological improvement/stability in 86.2%, and steroid wean-off in 45%. LITT exhibited symptomatic improvement/stability in 71.2%, radiological improvement/stability in 64.7%, and steroid wean-off in 62.4%. Comparative analysis revealed statistically significant differences favoring bevacizumab in symptomatic improvement/stability (p = 0.02), while no significant differences were observed in radiological improvement/stability (p = 0.27) or steroid wean-off (p = 0.90). The rates of adverse reactions were 11.2% for bevacizumab and 14.9% for LITT (p = 0.66), with the majority being grade 2 or lower (72.2% for bevacizumab and 62.5% for LITT).

Conclusion
Both bevacizumab and LITT exhibited favorable clinical and radiological outcomes in managing RN. Bevacizumab was found to be associated with better symptomatic control compared to LITT. Patient-, diagnosis- and lesion-related factors should be considered when choosing the ideal treatment modality for RN to enhance overall patient outcomes.
Bolstering stochastic gradient descent with model building
Authors: Birbil Ş. İlker , Martin Özgür , Onay Gönenç , Öztoprak Figen 
Published: April 2024

AbstractStochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand. Although this tuning process can require large computational costs, recent work has shown that these costs can be reduced by line search methods that iteratively adjust the step length. We propose an alternative approach to stochastic line search by using a new algorithm based on forward step model building. This model building step incorporates second-order information that allows adjusting not only the step length but also the search direction. Noting that deep learning model parameters come in groups (layers of tensors), our method builds its model and calculates a new step for each parameter group. This novel diagonalization approach makes the selected step lengths adaptive. We provide convergence rate analysis, and experimentally show that the proposed algorithm achieves faster convergence and better generalization in well-known test problems. More precisely, SMB requires less tuning, and shows comparable performance to other adaptive methods.
Uncertainty Analysis for Ferromagnetic-Paramagnetic Phase Transition Behavior of Magnetic Materials
Authors: Eger Zekeriya Ender , Acar Pinar 
Published: April 2024

AbstractThis study aims to model the phase transition of ferromagnetic materials using two- (2D) and three-dimensional (3D) Ising models, incorporating long-range magnetic spin-to-spin interactions and the influence of an external magnetic field. The 2D Ising model is investigated mainly for a $$4\times 4$$

4
×
4

domain, while its extension to larger domains is also explored. For the 3D Ising model, the selection of representative volume element is discussed in terms of free energy. An uncertainty quantification formulation is integrated into the Ising model to capture the uncertainties related to the external magnetic field and evaluate their effects on the phase transition from a ferromagnetic to a paramagnetic state. Despite the common use and known effectiveness of the Ising model, there is still no universally accepted exact solution for the 3D domains, making it an ongoing research focus. The research also acknowledges the complexities and limitations arising from experimental methods, despite the insights they provide on the microscopic dynamics of ferromagnetic materials during phase transitions.
Elevation Changes in Restored Marshes at Poplar Island, Chesapeake Bay, MD: II. Modeling the Importance of Marsh Development Time
Authors: Morris James T. , Staver Lorie W. 
Published: April 2024

AbstractTidal marshes in the Chesapeake Bay are vulnerable to the accelerating rate of sea-level rise (SLR) and subsidence. Restored and created marshes face the same risks as natural marshes, and their resilience to SLR may depend upon appropriate design and implementation. Here, the Coastal Wetland Equilibrium Model (CWEM) was used to assess the resilience of tidal marshes at the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island (PI) in mid-Chesapeake Bay, MD, where dredged material from navigation channels is being used to create new tidal marshes planted with Spartina alterniflora in the low marsh and S. patens in the high marsh. The site is microtidal with low inorganic sediment inputs, where the rate of marsh elevation change is dominated by the production of organic matter and, therefore, is proportional to net ecosystem production (NEP). The model demonstrated the importance of marsh development for surface elevation gain. In created marshes, the buildout of belowground biomass adds volume and results in faster growth of marsh elevation, but the gains slow as the marsh matures. Elevation gain is the lessor of the recalcitrant fraction of NEP sequestered in sediment or the rate of increase in accommodation space. Marshes can keep up with and fill accommodation space with sequestered NEP up to a tipping point determined by the rate of SLR. The PI low marsh platform was forecasted to drown in about 43 years after construction at the current rate of SLR. Marsh loss can be mitigated by periodic thin layer placement (TLP) of sediment. CWEM was used to simulate PI marsh responses to different TLP strategies and showed that there is an optimal design that will maximize carbon sequestration and resilience depending on the trajectory of mean sea level.
Operation and Productivity Monitoring from Sound Signal of Legacy Pipe Bending Machine via Convolutional Neural Network (CNN)
Authors: Kim Eunseob , Mun Daeseong , Jun Martin B. G. , Yun Huitaek 
Published: April 2024

AbstractThis study introduces a non-invasive approach to monitor operation and productivity of a legacy pipe bending machine in real-time based on a lightweight convolutional neural network (CNN) model and internal sound as input data. Various sensors were deployed to determine the optimal sensor type and placement, and labels for training and testing the CNN model were generated through the meticulous collection of sound data in conjunction with webcam videos. The CNN model, which was optimized through hyperparameter tuning via grid search and utilized feature extraction using Log-Mel spectrogram, demonstrated notable prediction accuracies in the test. However, when applied in a real-world manufacturing scenario, the model encountered a significant number of errors in predicting productivity. To navigate through this challenge and enhance the predictive accuracy of the system, a buffer algorithm using the inferences of CNN models was proposed. This algorithm employs a queuing method for continuous sound monitoring securing robust predictions, refines the interpretation of the CNN model inferences, and enhances prediction outcomes in actual implementation where accuracy of monitoring productivity information is crucial. The proposed lightweight CNN model alongside the buffer algorithm was successfully deployed on an edge computer, enabling real-time remote monitoring.
A framework for identification of brain tumors from MR images using progressive segmentation
Authors: Narayana M. V. , Rao J. Nageswara , Shrivastava Sanjeev , Ghantasala G. S. Pradeep , Ioannou Iacovos , Vassiliou Vasos 
Published: April 2024

Abstract
Purpose
This study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies.

Methods
The methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification.

Results
The framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality.

Conclusions
The study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors.

Graphical abstract
Pomegranate seed oil extraction by cold pressing, microwave and ultrasound treatments
Authors: Gök Aslı , Uyar Hilal , Demir Özge 
Published: April 2024

Abstract
In this study, pomegranate seed oil was extracted by microwave-assisted extraction, ultrasound-assisted extraction, and cold pressing techniques. Dimethyl succinate was the solvent of microwave and ultrasound-assisted extraction methods. The optimum conditions were determined by using response surface methodology (RSM, Design Expert software version 7.0 and Box-Behnken design). The ultrasound-assisted extraction technique was found to be superior to the microwave-assisted extraction technique in terms of extraction efficiency. The maximum extraction efficiency of microwave-assisted extraction was 22.01% under the optimized conditions (liquid/solid ratio, 5/1; time, 3 min; and microwave power, 300 W). The maximum extraction efficiency obtained by ultrasound-assisted extraction was 26.31% under the optimized conditions (liquid/solid ratio, 10/1; pulse duration/pulse interval ratio, 1; temperature 60 °C; and time, 20 min). The extraction efficiencies were compared at the optimum conditions with hexane, which is the most used solvent for pomegranate seed oil extraction, and the difference was insignificant. The results of this study are very important, especially in the field of green chemistry and chemical engineering.
Cumulative Exposure to Natural Hazards and Mental Health in China: Are Older People More Vulnerable or More Resilient Than Younger and Middle-Aged Adults?
Authors: Zhou Shuai , Kwan Crystal 
Published: April 2024

AbstractDespite the well-documented impacts of single natural hazards like earthquakes, less is known about the psychological adaptation to multiple natural hazards, particularly in rural areas. This study investigated the associations of multiple natural hazards with depression among Chinese adults. Data were retrieved from the China Family Panel Studies conducted during 2010–2018. With a sample of 11,633 Chinese adults, multilevel logistic regression was employed to examine the relationships between natural hazard exposure and depression in the total sample and different age groups. Overall, experiencing four or more natural hazards was associated with a higher risk of being depressed. Regarding hazard type, the number of hydrometeorological and biological hazards was associated with a higher likelihood of depression, whereas the number of geologic and other hazards was related to a lower risk of depression. Middle-aged adults from villages were more likely to be affected by natural hazard exposure than older and younger adults. The findings of this study show that cumulative exposure to natural hazards can generate lasting effects on depressive symptoms, particularly in middle adulthood. The findings also suggest that older adults from rural areas may have accumulated more resilience to mitigate the adverse well-being effects of hazard events. Policies and interventions should enhance disaster awareness and preparation for aging residents from multi-hazard communities.
A biochar selection method for remediating heavy metal contaminated mine tailings
Authors: Ippolito J. A. , Ducey T. F. , Spokas K. A. , Trippe K. M. , Johnson M. G. 
Published: April 2024

AbstractApproximately 390,000 abandoned mines across the US pose considerable, pervasive risks to human and environmental health; world-wide the problem is even greater. Lime, organic materials, and other amendments have been used to decrease metal bioavailability (e.g., Cd, Cu, Mn, Ni, Zn) in contaminated mine wastes and to promote plant community establishment for tailings stabilization. Biochar properties (e.g., alkaline pH, metal sorbing capabilities, available nutrients, improved soil water retention) make it a potential amendment for remediating metal contaminated mine tailings. A three-step procedure was developed to identify biochars that were most effective at reducing heavy metal availability, retaining metals, and subsequently selecting biochars for use in a soil amendment laboratory trial to ultimately be utilized in heavy metal contaminated mine land settings: Step (1) a synthetic precipitation leaching procedure extract of mine tailings was produced, representing potentially available metals, and used to identify metal removal properties of 28 different biochars (e.g., made from various feedstocks and pyrolysis or gasification conditions); Step (2) evaluate how well biochars retained previously sorbed metals; and Step (3) laboratory evaluation of the most promising biochars that removed and did not releases metals, applied at 0, 1, 2.5, and 5% (by wt) to mine tailings for reducing metal bioavailability. The reported methodology and results from this study could be used to quickly identify specific biochars and application rates to reduce mine tailings metal availability and aid in future remediation of abandoned mine sites globally.
On the irreducibility of local representations of the Braid group $$B_n$$
Authors: Chreif Mohammad Y. , Dally Malak M. 
Published: April 2024

AbstractWe prove that any homogeneous local representation $$\varphi :B_n \rightarrow GL_n(\mathbb {C})$$

φ
:

B
n

→
G

L
n

(
C
)

of type 1 or 2 of dimension $$n\ge 6$$

n
≥
6

is reducible. Then, we prove that any representation $$\varphi :B_n \rightarrow GL_n(\mathbb {C})$$

φ
:

B
n

→
G

L
n

(
C
)

of type 3 is equivalent to a complex specialization of the standard representation $$\tau _n$$

τ
n

. Also, we study the irreducibility of all local linear representations of the braid group $$B_3$$

B
3

of degree 3. We prove that any local representation of type 1 of $$B_3$$

B
3

is reducible to a Burau type representation and that any local representation of type 2 of $$B_3$$

B
3

is equivalent to a complex specialization of the standard representation. Moreover, we construct a representation of $$B_3$$

B
3

of degree 6 using the tensor product of local representations of type 2. Let $$u_i$$

u
i

, $$i=1,2$$

i
=
1
,
2

, be non-zero complex numbers on the unit circle. We determine a necessary and sufficient condition that guarantees the irreducibility of the obtained representation.
Comprehensive evaluation of early-age hydration and compressive strength development in seawater-mixed binary and ternary cementitious systems
Authors: Rathnarajan Sundar , Cendrowski Krzysztof , Sibera Daniel , Sikora Pawel 
Published: April 2024

AbstractSeawater-mixed concrete (SWC) is a proposed solution for catering to the needs of developing nations facing extremely severe water stress. Recent research works advocate the feasibility of producing SWC by adding supplementary cementitious materials (SCMs) and alternative reinforcements without reducing the engineering properties of the same. However, limited information is available for optimising the type and amount of SCMs in binary and ternary blended SW-mixed cementitious systems for achieving desirable strength development and early-age hydration. A comprehensive study to understand the evolution of heat of hydration and strength up to 28 days was conducted on 31 binder compositions mixed with both fresh water (FW) and seawater (SW). Fly ash, slag, metakaolin, and limestone are the supplementary cementitious materials used with CEM I as a primary binder at a replacement level between 10 and 70%. Isothermal calorimetry results revealed an increase in total heat of hydration and a reduction in setting time with SW-mixed cement pastes compared to their FW-mixed counterparts. Similarly, a significant increase in strength between 0 and 50% was observed in SW-mixed cement pastes. Suitable binder combinations showing an increase in compressive strength and not a significant reduction in strength compared to the CEM I reference mix were identified using the strength improvement factor approach. Binary and ternary blended cementitious, consisting of fly ash, slag, and metakaolin at different replacement levels, are amongst the chosen binder combinations.