bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing outcomes in paraesophageal hernia repair: a novel critical view
Authors: Saleh Zena , Verchio Vincent , Ghanem Yazid K. , Lou Johanna , Hundley Erin , Rouhi Armaun D. , Joshi Hansa , Moccia Mathew C. , Scalia Dominick M. , Lenart Austin M. , Ladd Zachary A. , Minakata Kenji , Shersher David D. 
Published: August 2024

Abstract
Background
The recurrence rate of paraesophageal hernia repair (PEHR) is high with reported rates of recurrence varying between 25 and 42%. We present a novel approach to PEHR that involves the visualization of a critical view to decrease recurrence rate. Our study aims to investigate the outcomes of PEHR following the implementation of a critical view.

Methods
This is a single-center retrospective study that examines operative outcomes in patients who underwent PEHR with a critical view in comparison to patients who underwent standard repair. The critical view is defined as full dissection of the posterior mediastinum with complete mobilization of the esophagus to the level of the inferior pulmonary vein, visualization of the left crus of the diaphragm as well as the left gastric artery while the distal esophagus is retracted to expose the spleen in the background. Bivariate chi-squared analysis and multivariable logistic and linear regressions were used for statistical analysis.

Results
A total of 297 patients underwent PEHR between 2015 and 2023, including 207 with critical view and 90 with standard repair which represents the historic control. Type III hernias were most common (48%) followed by type I (36%), type IV (13%), and type II (2.0%). Robotic-assisted repair was most common (65%), followed by laparoscopic (22%) and open repair (14%). Fundoplications performed included Dor (59%), Nissen (14%), Belsey (5%), and Toupet (2%). Patients who underwent PEHR with critical view had lower hernia recurrence rates compared to standard (9.7% vs 20%, P < .01) and lower reoperation rates (0.5% vs 10%, P < .001). There were no differences in postoperative complications on unadjusted bivariate analysis; however, adjusted outcomes revealed a lower odds of postoperative complications in patients with critical view (AOR .13, 95% CI .05–.31, P < .001).

Conclusion
We present dissection of a novel critical view during repair of all types of paraesophageal hernia that results in reproducible, consistent, and durable postoperative outcomes, including a significant reduction in recurrence and reoperation.
Konservative Kombinationstherapie beim lumbalen Bandscheibenvorfall mit Nervenwurzelreizsyndrom mit mechanischer Physiotherapie (McKenzie), Gabapentin und transforaminalen epiduralen Infiltrationen
Authors: Jäntsch-Rieckert Manuela , Rommel Oliver , Kästner Verena , Maercklin-Rommel Lotte , Jäger Georg 
Published: August 2024

Zusammenfassung
Hintergrund
Die Behandlung des Nervenwurzelreizsyndroms beim lumbalen Bandscheibenvorfall kann chirurgisch oder konservativ erfolgen. Die konservativen Behandlungskonzepte zeigten einen Erfolg bei 90 % der Patienten, in der Mehrzahl der Studien wurde jedoch kein strukturiertes Behandlungskonzept angewendet.

Ziel der Arbeit
In der Studie untersuchten wir den Effekt einer kombinierten konservativen Therapie des Wurzelreizsyndroms mit mechanischer Physiotherapie (McKenzie), Gabapentin sowie transforaminalen epiduralen Kortikosteroidinjektionen bei 40 Patienten während eines 10-tägigen stationären Aufenthalts.

Methoden
Neben der klinischen Untersuchung einschließlich des Straight-leg-raise-Tests und Finger-Boden-Abstands wurde die Schmerzstärke in Ruhe und nach einer Gehstrecke von 5 m erhoben und eine Elektromyographie der Kennmuskeln durchgeführt. Die Oswestry Pain Disability Scale sowie Schmerzschwereskala nach von Korff wurden erhoben und neuropathische Schmerzkomponenten mit der painDETECT-Skala erfasst. Ferner wurden die Dauer der Arbeitsunfähigkeit sowie die Notwendigkeit einer Operation innerhalb des Beobachtungszeitraums erfasst. Die Untersuchungen wurden am Tag der Aufnahme, an den Tagen 3, 6 und 10 sowie 3 Monate nach Entlassung durchgeführt.

Ergebnisse
Während der Behandlung konnte eine kontinuierliche Schmerzreduktion in Ruhe sowie beim Gehen von 5 m, Verbesserung des Straight-leg-raise-Tests sowie Verminderung des Finger-Boden-Abstands dokumentiert werden. Da die 3 Verfahren mit zeitlichem Abstand eingesetzt wurden, konnte gezeigt werden, dass alle signifikant zur Besserung beitrugen. Alle 3 Verfahren wurden ohne wesentliche Nebenwirkungen vertragen und die anhaltende Besserung bei der Nachuntersuchung nach 12 Wochen bestätigte die Nachhaltigkeit des Konzepts. Bei Aufnahme zeigten 32 % der Patienten eine überwiegend neuropathische Schmerzkomponente, welche nach 3 Monaten auf 7 % reduziert war. In der Elektromyographie fanden sich bei Aufnahme bei 70 % der Patienten Auffälligkeiten. Eine Kraftminderung zeigte sich bei Aufnahme bei 28 Patienten, bei der Kontrolle nur noch bei 7 Patienten. Auch die Lebensqualität war signifikant verbessert und die Patienten konnten 5,8 Wochen nach Behandlungsbeginn die Berufstätigkeit wieder aufnehmen. 3 von 40 Patienten benötigten wegen anhaltender Schmerzen eine Operation.

Konsequenz
Das untersuchte kombinierte Behandlungsprogramm ist effektiv und wird gut toleriert.
Rethinking of BERT sentence embedding for text classification
Authors: Galal Omar , Abdel-Gawad Ahmed H. , Farouk Mona 
Published: August 2024

AbstractText classification is a fundamental task in NLP that is used in several real-life tasks and applications. Large pre-trained language models such as BERT achieve state-of-the-art performance in several NLP tasks including text classification tasks. Although BERT boosts text classification performance, the common way of using it for classification lacks many aspects of its advantages. This work rethinks the way of using BERT final layer and hidden layers embeddings by proposing different aggregation architectures for text classification tasks such as sentiment analysis and sarcasm detection. This research also proposes different approaches for using BERT as a feature extractor without fine-tuning whose performance surpasses its fine-tuning counterpart. It also proposes promising multi-task learning aggregation architectures to improve the performance of the related classification problems. The experiments of the different architectures show that freezing BERT can outperform fine-tuning it for sentiment analysis. The experiments also show that multi-task learning while freezing BERT boosts the performance of yet hard tasks such as sarcasm detection. The best-performing models achieved new state-of-the-art performance on the ArSarcasm-v2 dataset for Arabic sarcasm detection and sentiment analysis. For multi-task learning and freezing BERT, a new SOTA F1-score of 64.41 was achieved for the sarcasm detection with a 3.47% improvement and near SOTA FPN of 75.78 for the sentiment classification. For single-task learning, a new SOTA FPN of 75.26 was achieved for the sentiment with a 1.81% improvement.
Enhancing CNN model classification performance through RGB angle rotation method
Authors: Dogan Yahya , Ozdemir Cuneyt , Kaya Yılmaz 
Published: August 2024

AbstractIn recent years, convolutional neural networks have significantly advanced the field of computer vision by automatically extracting features from image data. CNNs enable the modeling of complex and abstract image features using learnable filters, eliminating the need for manual feature extraction. However, combining feature maps obtained from CNNs with different approaches can lead to more complex and interpretable inferences, thereby enhancing model performance and generalizability. In this study, we propose a new method called RGB angle rotation to effectively obtain feature maps from RGB images. Our method rotates color channels at different angles and uses the angle information between channels to generate new feature maps. We then investigate the effects of integrating models trained with these feature maps into an ensemble architecture. Experimental results on the CIFAR-10 dataset show that using the proposed method in the ensemble model results in performance increases of 9.10 and 8.42% for the B and R channels, respectively, compared to the original model, while the effect of the G channel is very limited. For the CIFAR-100 dataset, the proposed method resulted in a 17.09% improvement in ensemble model performance for the R channel, a 5.06% increase for the B channel, and no significant improvement for the G channel compared to the original model. Additionally, we compared our method with traditional feature extraction methods like scale-invariant feature transform and local binary pattern and observed higher performance. In conclusion, it has been observed that the proposed RGB angle rotation method significantly impacts model performance.
Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving
Authors: López Bernal Sergio , Hidalgo Rogel José Manuel , Martínez Beltrán Enrique Tomás , Quiles Pérez Mario , Martínez Pérez Gregorio , Huertas Celdrán Alberto 
Published: August 2024

AbstractIn response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.
Design of Rock Support in Hard and Layered Rock Masses Using a Hybrid Method: A Study Based on the Construction of the New Skarvberg Tunnel, Norway
Authors: Terron-Almenara Jorge , Skretting Erlend , Holter Karl Gunnar , Høien Are Håvard 
Published: August 2024

AbstractHard rock formations with layered, anisotropic geological structures are rather common in Norway. The design of tunnel rock support under such ground conditions has been traditionally done with an empirical approach and assisted with engineering rock mass classification. In most cases, rock stability has been achieved with the use of fiber-reinforced sprayed concrete combined with rock bolts, sometimes supplemented with ribs of reinforced sprayed concrete (RRS) and bolt spiling. However, the discontinuous character and more complex ground behavior of layered rock can lead to design challenges as experienced during the construction of the new Skarvberg highway tunnel in Northern Norway. A significant portion of the tunnel exhibited consistent overbreak, delamination problems and a tunnel failure. In this article, the ground behavior and rock support performance at different locations of the tunnel are investigated to find a basis for design optimization of rock support under similar ground conditions. For this purpose, a thorough site investigation program was carried out, which formed the basis for further assessments and analyses with a hybrid design methodology and the numerical code UDEC (Itasca). As a result, the main challenges of classification systems to capture ground behavior and derive optimal support design in layered rocks were identified. As such, this study has investigated design optimization possibilities, which resulted in the development of, among others, a specific ground behavior classification for layered grounds and a new, optimized RRS-support concept for layered rock masses of very poor quality (e.g., Q ~ 0.1–1).
Community detection in social networks using machine learning: a systematic mapping study
Authors: Nooribakhsh Mahsa , Fernández-Diego Marta , González-Ladrón-De-Guevara Fernando , Mollamotalebi Mahdi 
Published: August 2024

AbstractOne of the important issues in social networks is the social communities which are formed by interactions between its members. Three types of community including overlapping, non-overlapping, and hidden are detected by different approaches. Regarding the importance of community detection in social networks, this paper provides a systematic mapping of machine learning-based community detection approaches. The study aimed to show the type of communities in social networks along with the algorithms of machine learning that have been used for community detection. After carrying out the steps of mapping and removing useless references, 246 papers were selected to answer the questions of this research. The results of the research indicated that unsupervised machine learning-based algorithms with 41.46% (such as k means) are the most used categories to detect communities in social networks due to their low processing overheads. On the other hand, there has been a significant increase in the use of deep learning since 2020 which has sufficient performance for community detection in large-volume data. With regard to the ability of NMI to measure the correlation or similarity between communities, with 53.25%, it is the most frequently used metric to evaluate the performance of community identifications. Furthermore, considering availability, low in size, and lack of multiple edge and loops, dataset Zachary’s Karate Club with 26.42% is the most used dataset for community detection research in social networks.
The molecular profile of gastric intraepithelial foveolar type neoplasia based on somatic copy number alterations and multiple mutation analysis
Authors: Sugai Tamotsu , Uesugi Noriyuki , Osakabe Mitsumasa , Yamamoto Ryuya , Hamada Koichi , Honda Michitaka , Yanagawa Naoki , Suzuki Hiromu 
Published: August 2024

Abstract
Background
Gastric foveolar type neoplasia is a rare histological variant of gastric tumors. It is very difficult to differentiate between benign and malignant intraepithelial foveolar neoplasia (IFN). Although limited molecular alterations have been identified in IFNs, somatic copy number alterations (SCNAs), which are linked to tumor progression, have not been systematically evaluated in IFN.

Methods
The aim of the present study was to comprehensively examine SCNAs using a SNP array in 37 cases of IFN, compared with intestinal type dysplasia, including 39 low grade (LGD) and 32 high grade dysplasia (HGD) cases. In addition, gene mutations were evaluated using a gene panel. Finally, we attempted to determine molecular profiles using a hierarchical clustering analysis.

Results
Two patterns could be categorized according to the SCNAs in 108 tumors examined: high (subgroup 1) and low (subgroup 2) frequencies of SCNAs. Although IFN and LGD were associated with subgroup 2, HGD was found in both subgroups. The median numbers of total SCNAs and copy number gains were higher in IFN or HGD than in LGD. In addition, the IFN genotype was characterized by altered genes located at 4p13–4q35.2, including RAP1GDS1 and LEF1, which may be associated with IFN development. Finally, no significant mutations were found in IFNs using a gene panel.

Conclusions
The current molecular profiles of IFN may help elucidate the mechanisms of IFN development.
Portfolio optimization for sustainable investments
Authors: Varmaz Armin , Fieberg Christian , Poddig Thorsten 
Published: August 2024

AbstractIn mean-variance portfolio optimization, multi-index models often accelerate computation, reduce input requirements, facilitate understanding, and allow easy adjustment to changing conditions more effectively than full covariance matrix estimation in many situations. In this paper, we develop a multi-index model-based portfolio optimization approach that takes into account aspects of the environment, social responsibility and corporate governance (ESG). Investments in assets related to ESG have recently grown, attracting interest from both academic research and investment fund practice. Various literature strands in this area address the theoretical and empirical relation among return, risk and ESG. Our portfolio optimization approach is flexible enough to take these literature strands into account and does not require large-scale covariance matrix estimation. An extension of our approach even allows investors to empirically discriminate among the literature strands. A case study demonstrates the application of our portfolio optimization approach.
Performance analysis of an SPD integrated with a stepped pyramid solar still: experimental study
Authors: Mohammed Ahmed H. , Shmroukh Ahmed N. , Ghazaly Nouby M. , Kabeel Abd Elnaby 
Published: August 2024

AbstractWater scarcity poses a major challenge for the next generation. Additionally, increasing agricultural and industrial activities are creating an unprecedented demand for freshwater supplies throughout Egypt. The Nile River serves as Egypt’s primary water source, accounting for 69.4% of the total water resources. Moreover, Egypt's annual share of water has decreased to approximately 500 m3 per person because of the completion of the Renaissance dam in Ethiopia and the rapid increase in population growth. Solar distillation has been presented as a long-term and promising method for producing edible drinkable water. The present research goal is to increase the daily freshwater output from a stepped pyramid solar still (SPSS) by coupling with a solar parabolic dish (SPD). The proposed system consists of a conventional pyramid solar still (CPSS) and a modified pyramid solar still (MPSS). The main components of an MPSS include an SPSS, a PV panel, an SPD with a spiral coil, a circulating pump, and an Arduino Mega Unit. The developed system was designed, fabricated, and verified from July to August 2023 at the Faculty Engineering, South Valley University. The results of the experiments indicated that the daily productivity of freshwater from MPSS was greater than that from CPSS and reached 6038 and 2612 mL/day, respectively, with an increase in the daily yield of 131%. The peak energy efficiency is 56% for the MPSS and 24% for the CPSS. The peak exergy efficiency is 4.26% for the MPSS and 3.79% for the CPSS. Additionally, the cost analysis of the suggested system revealed that the estimated cost per liter of the proposed system is 0.01635 USD/L.
Human digital twins unlocking Society 5.0? Approaches, emerging risks and disruptions
Authors: Fontes Catarina , Carpentras Dino , Mahajan Sachit 
Published: August 2024

AbstractIndustry 5.0 and Healthcare 5.0 converge towards a human centered society, having technological advancement as a lever. In Society 5.0, decentralized autonomous cities and a convergence of physical and cyberspace are the foundations of the new chapter of society’s development. The idea of creating digital replicas and legitimate representatives of human beings in cyberspace has become a pillar of digitalization. Society 5.0 introduces Human Digital Twins as a central element of Cyber Physical Systems that include human factors or are designed to interact with humans in a personalized fashion. Overall, the HDT and neighboring concepts are applied to depict how humans can be represented in a cyberspace. However, there are clear challenges in determining which human characteristics should take precedence, how much autonomy should be granted to HDTs to optimize their functionality and how to conceptualize the digital environment in which HDTs interact with various entities, including other digital agents and stakeholders with agency and decisional power. To harness similarities and differences of current approaches, we propose a classification of HDTs based on meta-characteristics and ethical implications. We discuss ethical implication by focusing on emerging risks and paradigm shifts and anchor the previous discussion in the vision for Society 5.0, questioning whether societal development relying on disruptive technologies, instead of leading to more human-centered societies might be driving humanized societies away from humanity.
Alteration of nutrient uptake and secondary metabolism connection by foliar application of citrus flavonoids to broccoli plants
Authors: Albaladejo-Marico Lorena , Yepes-Molina Lucia , Carvajal Micaela 
Published: August 2024

AbstractThe nutritional value of broccoli is largely attributed to its abundant secondary metabolites such as phenolic compounds and glucosinolates (GSLs). However, the dynamic relationship between these compounds, including potential synergistic or antagonistic interactions that influence plant physiology and metabolism, remains unclear. In this study, we aimed to elucidate the intricate interplay between phenolic compounds and GSLs in broccoli plants and their consequent effects on primary metabolism and regulatory mechanisms governing water and nutrient uptake. To investigate this, we externally supplied citric phenolic compounds to broccoli plants, and then measured the levels of GSLs and phenolic compounds, along with assessing physiological parameters such as biomass, gas exchange, and nutrient content. Additionally, the expression of genes related to GSLs and phenolics biosynthesis, as well as genes involved in water transport were measured. Our results revealed a complex interrelation between phenolic compounds and GSLs, with phenolic compounds significantly modulating the response of GSLs and influencing the expression of aquaporin genes. This modulation had notable effects on nutrient regulation mechanisms in broccoli plants. Overall, our findings shed light on the regulatory mechanisms underlying the interaction between phenolic compounds, GSLs and growth, providing insights into their roles in plant physiology and metabolism.