bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Nature of Arhopalus ferus (Coleoptera: Cerambycidae: Spondylidinae) Pheromone Attraction
Authors: Kerr Jessica L. , Romo Cecilia M. , O’Connor Brooke , Dickson Georgia , Novoselov Max , Aguilar-Arguello Samuel , Todoroki Christine , Najar-Rodriguez Adriana , Manning Lee-Anne , Twidle Andrew , Barrington Anne , Leclair Gaetan , Mayo Peter , Sweeney Jon
Published: June 2024

Cerambycid species of the Spondylidinae subfamily are distributed worldwide and are known for being prolific invaders that infest conifers. In New Zealand, Arhopalus ferus (Mulsant), the burnt pine longhorn beetle, is well-established and requires monitoring at high-risk sites such as ports, airports, and sawmills as part of the requirements to meet pine log export standards set by the New Zealand Ministry of Primary Industries (MPI). Currently, its surveillance relies on traps baited with host volatiles (i.e., ethanol and α-pinene). We used volatile collections from adult beetles, electroantennograms, and field trapping bioassays to identify the pheromones emitted by the burnt pine longhorn beetle A. ferus and their effects on its behaviour. We show that A. ferus males emit mainly (E)-fuscumol and geranylacetone, as well as the minor components, α-terpinene and p-mentha-1,3,8-triene, and that all four compounds elicit a dose-dependent response in antennae of both sexes. Traps baited with the binary combination of geranylacetone plus fuscumol captured significantly more female A. ferus than did unbaited traps in two of three field experiments. α-Terpinene did not affect A. ferus trap catches and effects of p-mentha-1,3,8-triene on trap catch were not determined. Our findings provide further evidence of the use of fuscumol and geranylacetone as aggregation-sex pheromones by longhorn beetles in the Spondylidinae subfamily, and suggest that their deployment in survey traps may improve the efficacy of A. ferus monitoring in New Zealand and elsewhere.
Generation of realistic virtual adult populations using a model-based copula approach
Authors: Guo Yuchen , Guo Tingjie , Knibbe Catherijne A. J. , Zwep Laura B. , van Hasselt J. G. Coen
Published: June 2024

Incorporating realistic sets of patient-associated covariates, i.e., virtual populations, in pharmacometric simulation workflows is essential to obtain realistic model predictions. Current covariate simulation strategies often omit or simplify dependency structures between covariates. Copula models are multivariate distribution functions suitable to capture dependency structures between covariates with improved performance compared to standard approaches. We aimed to develop and evaluate a copula model for generation of adult virtual populations for 12 patient-associated covariates commonly used in pharmacometric simulations, using the publicly available NHANES database, including sex, race-ethnicity, body weight, albumin, and several biochemical variables related to organ function. A multivariate (vine) copula was constructed from bivariate relationships in a stepwise fashion. Covariate distributions were well captured for the overall and subgroup populations. Based on the developed copula model, a web application was developed. The developed copula model and associated web application can be used to generate realistic adult virtual populations, ultimately to support model-based clinical trial design or dose optimization strategies.
Technology Acceptance When Teaching Climate Change
Authors: Rap Shelley , Blonder Ron
Published: June 2024

Climate change is a pressing global challenge for humanity, which should be adequately represented in the educational system. However, teachers face a significant challenge due to the vast amount of data and information about climate change available in the media. We aimed to identify aspects that affect teachers’ acceptance of technology in general and how technology may help/hinder their teaching of climate change, in particular. Thirty-five chemistry teachers and chemistry educators were exposed to a novel curriculum about climate change that was developed on a digital platform. This paper described the promoting and inhibiting factors regarding adopting technological tools to teach about electric cars within this curriculum. We applied the lenses of the technology acceptance model (TAM) framework to analyze teachers’ responses. Most of the hindering factors concerned the general disadvantages of integrating technology into teaching (e.g., technical malfunctions); therefore, these aspects should be primary addressed to encourage adopting and applying educational technology. However, factors that are specific to teaching climate change in relation to TAM emerged as well. These factors included the critical consumption of digital data, the need to constantly change one’s teaching practices based on the changing data, as well as the social impact of such a tool on the students’ environment. We wish to stress that the TAM can be applied as a framework to identify teachers’ filters and amplifiers that might promote or inhibit transforming theoretical knowledge into practice.
They Are Not All the Same: Defenders of Ethnically Victimized Adolescents
Authors: Bilir Özturk Pinar , Bayram Özdemir Sevgi , Strohmeier Dagmar
Published: June 2024

Developing a comprehensive understanding of adolescents’ defending behaviors in peer victimization incidents is crucial, as these behaviors are instrumental in preventing victimization in schools. Despite recent efforts to examine various defender subgroups and their characteristics, the heterogeneity in defending behaviors within the context of ethnic victimization remains unclear. To address this gap in knowledge, the current study examined naturally occurring subgroups of defenders in ethnic victimization incidents and investigated whether these subgroups differ in their socio-cognitive skills, class norms, and social status within peer relationships. The sample included adolescents in Sweden (N = 1065; Mage = 13.12, SD = 0.41; 44.5% females). Cluster analysis yielded four distinct subgroups: victim-oriented defenders (41.3%), hybrid defenders (23.5%), bully-oriented defenders (9.8%), and non-defenders (25.4%). Hybrid and victim-oriented defenders had higher levels of perspective taking skills and positive attitudes toward immigrants than non-defenders. All three defender subgroups perceived their classroom climate as more socially cohesive than non-defenders. All four subgroups did not significantly differ in their peer status. These findings emphasize the importance of fostering inclusive class norms and implementing classroom practices that facilitate the development of perspective taking skills among students. Such effort can enhance adolescents’ active defending behaviors in instances of ethnic victimization.
International Tax Law and Profit Repatriation Strategies for Investments in Foreign Subsidiaries
Authors: Kollruss Thomas
Published: June 2024

This study is assigned to international tax law. It elaborates on various innovative strategies for tax-optimized profit repatriation from foreign subsidiaries. As an example, the analysis is carried out against the background of the country constellation USA/European Union. However, the results and strategies can be applied to other country constellations. The elaboration of profit repatriation strategies solves a significant taxation problem for investments in foreign subsidiaries, namely the incurrence of withholding tax in the subsidiary's foreign country of residence. At the same time, a contribution is made to expanding the literature. Tax effective profit repatriation strategies regarding investments in foreign subsidiaries have hardly been derived and discussed in the literature.
Invariant measures on p-adic Lie groups: the p-adic quaternion algebra and the Haar integral on the p-adic rotation groups
Authors: Aniello Paolo , L’Innocente Sonia , Mancini Stefano , Parisi Vincenzo , Svampa Ilaria , Winter Andreas
Published: June 2024

We provide a general expression of the Haar measure—that is, the essentially unique translation-invariant measure—on a p-adic Lie group. We then argue that this measure can be regarded as the measure naturally induced by the invariant volume form on the group, as it happens for a standard Lie group over the reals. As an important application, we next consider the problem of determining the Haar measure on the p-adic special orthogonal groups in dimension two, three and four (for every prime number p). In particular, the Haar measure on $$\text {SO}(2,\mathbb {Q}_p)$$

SO
(
2
,

Q
p

)

is obtained by a direct application of our general formula. As for $$\text {SO}(3,\mathbb {Q}_p)$$

SO
(
3
,

Q
p

)

and $$\text {SO}(4,\mathbb {Q}_p)$$

SO
(
4
,

Q
p

)

, instead, we show that Haar integrals on these two groups can conveniently be lifted to Haar integrals on certain p-adic Lie groups from which the special orthogonal groups are obtained as quotients. This construction involves a suitable quaternion algebra over the field $$\mathbb {Q}_p$$

Q
p

and is reminiscent of the quaternionic realization of the real rotation groups. Our results should pave the way to the development of harmonic analysis on the p-adic special orthogonal groups, with potential applications in p-adic quantum mechanics and in the recently proposed p-adic quantum information theory.
An efficient intrusive deep reinforcement learning framework for OpenFOAM
Authors: Salehi Saeed
Published: June 2024

Recent advancements in artificial intelligence and deep learning offer tremendous opportunities to tackle high-dimensional and challenging problems. Particularly, deep reinforcement learning (DRL) has been shown to be able to address optimal decision-making problems and control complex dynamical systems. DRL has received increased attention in the realm of computational fluid dynamics (CFD) due to its demonstrated ability to optimize complex flow control strategies. However, DRL algorithms often suffer from low sampling efficiency and require numerous interactions between the agent and the environment, necessitating frequent data exchanges. One significant bottleneck in coupled DRL–CFD algorithms is the extensive data communication between DRL and CFD codes. Non-intrusive algorithms where the DRL agent treats the CFD environment as a black box may come with the deficiency of increased computational cost due to overhead associated with the information exchange between the two DRL and CFD modules. In this article, a TensorFlow-based intrusive DRL–CFD framework is introduced where the agent model is integrated within the open-source CFD solver OpenFOAM. The integration eliminates the need for any external information exchange during DRL episodes. The framework is parallelized using the message passing interface to manage parallel environments for computationally intensive CFD cases through distributed computing. The performance and effectiveness of the framework are verified by controlling the vortex shedding behind two and three-dimensional cylinders, achieved as a result of minimizing drag and lift forces through an active flow control mechanism. The simulation results indicate that the trained controller can stabilize the flow and effectively mitigate the vortex shedding.
Synthesis and characterization of novel bis(thiosemicarbazone) complexes and investigation of their acetylcholinesterase and glutathione S-transferase activities with in silico and in vitro studies
Authors: Donmez Melike , Sekerci Memet , Adiguzel Ragip , Oğuz Ercan , Türkan Fikret , Yildiko Umit , Colak Naki
Published: June 2024

In this study, firstly, bis(thiosemicarbazone) ligand [L: 2,2′-(2-(2-(4-methoxyphenyl)hydrazineylidene)cyclohexane-1,3-diylidene)bis(hydrazine-1-carbothioamide)] was synthesized by the condensation reaction of thiosemicarbazide and ketone compound (2-(2-(4-methoxyphenyl)hydrazone)cyclohexane-1,3-dione). The metal complexes were synthesized by the reaction of obtained ligand (L) with CuCl2·2H2O, NiCl2·6H2O, CoCl2·6H2O, and MnCl2·4H2O salts. The structures of synthesized ligand and their complexes were characterized using elemental analysis, IR, UV–Vis, 1H-NMR spectra, 13C-NMR spectra, magnetic susceptibility, mass spectra (LC–MS), thermogravimetry analysis-differential thermal analysis (TGA-DTA), and differential scanning calorimetry techniques. According to the results of the analysis, square plane geometry was suggested for Cu and Co complexes. However, the structures of Ni and Mn complexes were in agreement with octahedral geometry. Molecular docking analysis and pharmacological potential of the compound were evaluated to determine the inhibitory potential against acetylcholinesterase (AChE) and Glutathione-S-transferases (GST) enzymes. The compound exhibited strong binding/docking indices of − 5.708 and − 5.928 kcal/mol for the respective receptors. In addition, L–Ni(II) complex was found to be the most effective inhibitor for AChE enzyme with a Ki value of 0.519. However, with a Ki value of 1.119, L–Cu(II) complex was also found to be an effective inhibitor for the GST enzyme.
Anomalously acting agents: the deployment problem
Authors: Wenger Ingeborg , Ebel Henrik , Eberhard Peter
Published: June 2024

Detecting intentionally antagonistic behavior in robot swarms brings challenges that exceed identifying merely erroneous behavior. We investigate a data-based approach to recognize anomalous and, in particular, antagonistic behavior in robots executing a deployment task. The task requires a robot swarm of variable size and starting positions to be optimally distributed within an arbitrary convex surveillance area. Combining a long short-term memory neural network and a normalizing flow, our approach learns to approximate the probability of a robot action. Thus, actions with low probability density values can be categorized as anomalous. The applicability of the proposed approach is validated on simulated runs containing benevolent, antagonistic, and erroneous robots. Both antagonistic and erroneous robots are detected with more than 90 percent accuracy.
Amino-acid PET as a prognostic tool after post Stupp protocol temozolomide therapy in high-grade glioma patients
Authors: Zinsz Adeline , Ahrari Shamimeh , Becker Jason , Mortada Ali , Roch Veronique , Doriat Louis , Santi Matthieu , Blonski Marie , Taillandier Luc , Zaragori Timothée , Verger Antoine
Published: June 2024

Purpose

This study aimed to evaluate the prognostic performance of amino-acid PET in high-grade gliomas (HGG) patients at the time of temozolomide (TMZ) treatment discontinuation, after the Stupp protocol.

Methods

The analysis included consecutive HGG patients with dynamic [18F]FDOPA PET imaging within 3 months of the end of TMZ therapy, post-Stupp protocol. Static and dynamic PET parameters, responses to RANO criteria for MRI and clinical and histo-molecular factors were correlated to progression-free (PFS).

Results

Thirty-two patients (59.4 [54.0;67.6] years old, 13 (41%) women) were included. Static PET parameters peak tumor-to-background ratio and metabolic tumor volume (respective thresholds of 1.9 and 1.5 mL) showed the best 84% accuracies for predicting PFS at 6 months (p = 0.02). These static PET parameters were also independent predictor of PFS in multivariate analysis (p ≤ 0.05).

Conclusion

In HGG patients having undergone a Stupp protocol, the absence of significant PET uptake after TMZ constitutes a favorable prognostic factor.
On $$\frac{1}{2}$$-DOF active dampers to suppress multistability vibration of a $$2$$-DOF rotor model subjected to simultaneous multiparametric and external harmonic excitations
Authors: Saeed Nasser A. , Awrejcewicz Jan , Elashmawey Randa A. , El-Ganaini Wedad A. , Hou Lei , Sharaf Mohamed
Published: June 2024

This article addresses the bifurcation characteristics and vibration reduction of a $$2$$

2

-DOF dynamical system simulating the nonlinear oscillation of an asymmetric rotor model subjected to simultaneous multiparametric and external excitations. To suppress the system's vibrations, two $$1/2$$

1
/
2

-DOF active dampers are attached to the system in linear and cubic nonlinear forms via a magnetic coupling actuator. The closed-loop system model is derived as two differential equations with multi-control terms, including cubic, quantic, and septic, coupled nonlinearly to two first-order systems. Applying perturbation theory, the system model is solved, and the autonomous system describing the closed-loop slow-flow dynamics is obtained. Through numerical algorithms, the motion bifurcation is analyzed using various tools such as 2D and 3D bifurcation diagrams, two-parameter stability charts, basins of attraction, orbit plots, and time response profiles. The analytical investigations confirm that the uncontrolled model behaves like a hardening Duffing oscillator with multistability characteristics, displaying simultaneous mono-stable, bi-stable, tri-stable, or quadri-stable periodic oscillations depending on both the asymmetric nonlinearities and angular velocity. Subsequently, the influence of different control parameters is analyzed to determine the threshold between mono and multi-stability conditions. Finally, optimal control parameters are designed to eliminate multistability characteristics and achieve minimum and safe vibration levels.
Analytical insights into a fractional thin-film equation: exact solutions and dynamics
Authors: Yaşar Elif
Published: June 2024

In this work, we examine a quadratic thin film equation with a constant negative absorption term. This equation extends a broad variety of the famous scalar reaction-diffusion equations appearing in nonlinear sciences and is derived from the estimations of lubrication theory to represent thin films of a Newtonian liquid dominated by surface tension effects. It is typically used to describe the behavior of light when it interacts with thin films, such as coatings on lenses or mirrors. The connection between thin film equations and optical quantum mechanics lies in the microscopic interactions between photons and the electrons in the thin film material. Employing the invariant subspace approach, we obtain explicit fractional exact solutions for the time-fractional case of the model containing the Riemann-Liouville derivative operator. Furthermore, we illustrate 3-D and 2-D plots of the obtained exact solutions for a better understanding of the physical phenomena.
Improving the understanding of mobility in cross-border areas using a pooled travel survey
Authors: Delloye Justin , Klein Sylvain , Klein Olivier , Drevon Guillaume , Jacquot Mathieu , Rapp Viviane , Cornelis Eric , Rousseaux Joanna , Schiebel Julien , Besch Guy , Godefroy Stéphane , Gerber Philippe
Published: June 2024

Understanding mobility behaviour in cross-border regions is a major challenge for transport policies and the wellbeing of people living near national borders. However, the available relevant information is often limited to a collection of unharmonized national sources. To overcome this limitation, we propose a methodology for building a pooled travel survey, by merging microdata from travel surveys conducted independently in different cross-border areas of the same cross-border region. Anchored in the Luxembourg functional cross-border region as a study area, this paper provides a general, ready-to-use harmonization methodology for pooling travel surveys. It critically discusses the resulting harmonized values of standard daily mobility indicators in regard to the specificities of the study area. Lastly, it shows that the values for the proportions of trip purposes computed with the pooled survey exhibit substantial differences from values obtained using only local travel surveys (up to 6 percentage points, and up to 33 per cent in the total number of trips). This illustrates the added value of a pooled travel survey compared with an unharmonized collection of local surveys.
Unmanned aerial system plant protection products spraying performance evaluation on a vineyard
Authors: Sassu Alberto , Psiroukis Vasilis , Bettucci Francesco , Ghiani Luca , Fountas Spyros , Gambella Filippo
Published: June 2024

In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted.
Examining the environmental Phillips curve hypothesis in G7 nations: critical insights from wavelet coherence and wavelet causality analysis
Authors: Azimi Mohammad Naim , Rahman Mohammad Mafizur
Published: June 2024

This study aims to examine the emerging Environmental Phillips-Curve (EPC) hypothesis across G7 nations from 1990 to 2022, employing the cross-sectionally augmented autoregressive distributed lags (CS-ARDL), wavelet coherence, and wavelet causality techniques. CS-ARDL analysis reveals negative impacts of the unemployment rate on CO2e, with economic growth exerting positive effects on CO2e over short- and long-term periods. Additionally, renewable energy and technological innovations demonstrate mitigating effects on CO2e, while population is identified as a contributor to CO2e in the long-term. Concurrently, economic policy uncertainty emerges as a significant driver of heightened CO2e over the short- and long-term. The inverse relationship between CO2e and unemployment rate corroborates the validity of the EPC hypothesis within G7 nations. Furthermore, country-specific wavelet coherence and causality analyses unveil varying degrees of co-movement and causal links among variables across diverse frequencies and time intervals. Key findings indicate an out-of-phase nexus between the unemployment rate and CO2e, thus cross-validating the EPC hypothesis. These results underscore the necessity for creative solutions to address the trade-off between CO2e reduction and potential employment impacts. Policymakers must promote green-tech adoption and sustainable practices to mitigate environmental harm while fostering green employment growth. Addressing economic policy uncertainty is imperative to ensure environmental sustainability. G7 nations should enact policies that incentivize green investments through higher capital gains, tax-free investments, and subsidies for environmental technologies to catalyze long-term green employment and growth.
Order effect of an 8-week concurrent training program on the maximal fat oxidation
Authors: Ruiz-Alias Santiago A. , Pérez-Castilla Alejandro , Jaén-Carrillo Diego , García-Pinillos Felipe
Published: June 2024

Background

Concurrent training (CT) is a common method used in health-oriented training programs. However, its design needs to be explored in order to inform practitioners about the most effective methods to improve different health-related markers (e.g., maximal fat oxidation [MFO]). Thus, this study aims to determine the order effect of an 8 week CT on the MFO of recreationally trained young adults.

Methods

Twenty participants were allocated in two different groups to perform the CT differing only in the exercise sequence. The endurance training (ET) consisted of 4 to 6 repetitions of 30 s all-out running sprints with 4 min of active recovery. The resistance training (RT) consisted of 4 to 6 sets at 60 to 80% of the one-repetition maximum with 5 to 1 repetition in reserve of back squat and bench press exercises. 15 min of rest were established between exercise modes. Previous and after the CT program, participants performed a graded exercise test where MFO was determined.

Results

There was a significant time effect on MFO (p = 0.044). A moderate increase was observed in both ET + RT (Mean change: 0.11 [− 0.02 to 0.25] g/min; Effect size: 0.61 [− 0.12 to 1.35]) and RT + ET (Mean change: 0.07 [− 0.01 to 0.16] g/min; Effect size: 0.62 [− 0.12 to 1.36]) groups. No significant interaction was observed (p = 0.658).

Conclusions

The 8 week CT program improved the muscle oxidative capacity of recreationally trained young adults regardless of the exercise sequence.
Identification of Patterns of Hospitalizations in Child and Adolescent Mental Health Service
Authors: Urben Sébastien , Golay Philippe , Forte Alberto , Courousse Swen , Kapp Carole , Plessen Kerstin Jessica , Armando Marco
Published: June 2024

Adolescence is a time of increased vulnerability to mental health conditions, which may necessitate hospitalization. This study sought to identify and characterize patterns of adolescent (re-)hospitalizations. The one-year (re-)hospitalization patterns of 233 adolescents were analyzed. The sequences of hospitalization and discharge was examined using cluster analyses. Results revealed five distinct (re-)hospitalization patterns or clusters: Cluster A represented brief hospitalizations with 56 cases (24.03%) averaging 7.71 days; cluster B consisted of repetitive short hospitalizations involving 97 cases (41.63%) with an average of 19.90 days; cluster C encompassed repetitive medium hospitalizations included 66 cases (28.33%) averaging 41.33 days; cluster D included long hospitalizations with 11 cases (4.72%) and an average of 99.36 days; cluster E depicted chronic hospitalizations, accounting for 3 cases (1.29%) with an average stay of 138.67 days. Despite no age-based differences across clusters, distinctions were noted in terms of sex, diagnoses, and severity of clinical and psychosocial difficulties. The study identified characteristics of both regular and atypical adolescent hospitalization users, emphasizing the distribution of hospitalization days and their associated clinical attributes. Such insights are pivotal for enhancing the organization of child and adolescent mental health services to cater to the growing care requirements of this age group.
Zur Wirksamkeit hochschuldidaktischer Weiterbildungsmaßnahmen an deutschen Universitäten und Hochschulen: Eine systematische Überblicksstudie
Authors: Hoffmann Selina , Klinger Melanie , Deutscher Viola
Published: June 2024

Diese Arbeit gibt einen Überblick über die Effekte hochschuldidaktischer Weiterbildungsmaßnahmen an deutschen Hochschulen seit Einführung des Qualitätspakts Lehre. Dabei werden insbesondere die Lehrkompetenzen der Dozierenden fokussiert. Die Ergebnisse von dreizehn Primärstudien, welche die Wirksamkeit hochschuldidaktischer Interventionen auf die Lehrkompetenzen untersuchen, werden in einem systematischen Review zusammengefasst und in einem Modell akademischer Lehrkompetenz verortet. Da die Wirkungsdimensionen Selbstwirksamkeitserwartungen von Lehrenden und Studierendenfokussierung eine ausreichende Studienzahl aufweisen, wird für diese zwei Subbereiche auf Basis von quantitativen Meta-Analysen untersucht, ob sich die Wirksamkeit der hochschuldidaktischen Maßnahmen studienübergreifend empirisch absichern lässt. Die Ergebnisse lassen die Schlussfolgerung zu, dass hochschuldidaktische Weiterbildungsmaßnahmen einen positiven Einfluss auf die Entwicklung der Lehrkompetenz haben – zumindest gemessen an der Selbsteinschätzung der Dozierenden. Aufgrund der eingeschränkten Studienlage ist weitere Forschung notwendig, um eine effektive Gestaltung hochschuldidaktischer Maßnahmen evidenzbasiert zu unterstützen.
The Impact of Cholecystectomy in Patients with Post-Bariatric Surgery Hypoglycemia
Authors: Sardão Daniel , Santos-Sousa Hugo , Peleteiro Bárbara , Resende Fernando , Costa-Pinho André , Preto John , Lima-da-Costa Eduardo , Freitas Paula ,   CRI-O Group
Published: June 2024

Background

Metabolic surgery is the foremost treatment for obesity and its associated medical conditions. Nonetheless, post-bariatric hypoglycemia (PBH) emerges as a prevalent complication. PBH pathophysiology implicates heightened insulin and glucagon-like peptide 1 (GLP-1) levels, with bile acids (BA) contributing to GLP-1 release. A plausible association exists between cholecystectomy and PBH, which is attributed to alterations in BA metabolism and ensuing hormonal responses. The objective of this retrospective cohort study was to evaluate the impact of cholecystectomy on PBH pharmacological treatment, diagnostic timelines and metabolic parameters.

Materials and methods

Patients diagnosed with PBH after bariatric surgery were evaluated based on their history of cholecystectomy. Demographic, anthropometric and clinical data were collected. Mixed meal tolerance tests (MMTT) results were compiled to assess metabolic responses.

Results

Of the 131 patients with PBH included in the study, 29 had prior cholecystectomy. The time to PBH diagnosis was similar across groups. Patients with prior cholecystectomy required higher doses of acarbose (p = 0.046), compared to those without prior cholecystectomy. Additionally, MMTT revealed higher insulin (t = 60 min: p = 0.010 and t = 90 min: p = 0.034) and c-peptide levels (t = 60 min: p = 0.008) and greater glycemic variability in patients with prior cholecystectomy (p = 0.049), highlighting the impact of cholecystectomy on glucose metabolism.

Conclusion

Our study offers novel insights into PBH pharmacotherapy, indicating that PBH patients with a history of cholecystectomy require elevated doses of acarbose for symptom control than PBH patients without such surgical history. Furthermore, our findings underscore the pivotal role of hyperinsulinism in PBH aetiology, emphasizing the significance of the BA-GLP-1-insulin axis.

Graphical Abstract
Relationships Between Changes in Serum Ketone Body Levels and Metabolic Effects in Patients with Severe Obesity Who Underwent Laparoscopic Sleeve Gastrectomy
Authors: Umemura Akira , Sasaki Akira , Kumagai Hideki , Tanahashi Yota , Iwasaki Takafumi , Nitta Hiroyuki
Published: June 2024

Background

Serum ketone bodies increase due to dynamic changes in the lipid metabolisms of patients undergoing bariatric surgery. However, there have been few studies on the role of ketone bodies after bariatric surgery. We aimed to clarify the role of and relationship between the changes in serum ketone bodies and weight loss, as well as between those changes and the metabolic effects after laparoscopic sleeve gastrectomy (LSG).

Methods

We recruited 52 patients with severe obesity who underwent LSG. We measured acetoacetic acid (AcAc) and β-hydroxybutyric acid (β-OHB) at the baseline, 1 month, and 6 months after LSG. Subsequently, we compared the changes in the serum ketone bodies with weight-loss effects and various metabolic parameters.

Results

At 1 month after LSG, β-OHB significantly increased (p = 0.009), then significantly decreased 6 months after LSG (p = 0.002). In addition, β-OHB in patients without Type 2 diabetes (T2D) and metabolic dysfunction-associated steatohepatitis (MASH) was notably higher than in patients with T2D at 1 month after LSG (p < 0.001).

In the early phase, both AcAc and β-OHB mainly had strong positive correlations with changes in T2D- and MASH-related parameters. In the middle term after LSG, changes in both AcAc and β-OHB were positively correlated with changes in lipid parameters and chronic kidney disease-related parameters.

Conclusion

We demonstrated that the postoperative surge of ketone bodies plays a crucial function in controlling metabolic effects after LSG. These findings suggest the cause- and consequence-related roles of ketone bodies in the metabolic benefits of bariatric surgery.

Graphical Abstract
The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness
Authors: Tsekouras Dimitrios , Gutt Dominik , Heimbach Irina
Published: June 2024

Companies are increasingly introducing conversational reviews—reviews solicited via chatbots—to gain customer feedback. However, little is known about how chatbot-mediated solicitation influences rating valence and review helpfulness compared to conventional online forms. Therefore, we conceptualized these review solicitation media on the continuum of anthropomorphism and investigated how various levels of anthropomorphism affect rating valence and review helpfulness, showing that more anthropomorphic media lead to more positive and less helpful reviews. We found that moderate levels of anthropomorphism lead to increased interaction enjoyment, and high levels increase social presence, thus inflating the rating valence and decreasing review helpfulness. Further, the effect of anthropomorphism remains robust across review solicitors’ salience (sellers vs. platforms) and expressed emotionality in conversations. Our study is among the first to investigate chatbots as a new form of technology to solicit online reviews, providing insights to inform various stakeholders of the advantages, drawbacks, and potential ethical concerns of anthropomorphic technology in customer feedback solicitation.
Gender differences in homicides. A comparative analysis of 106 fatalities in forensic autopsy data
Authors: Walz Cleo , Eifert Steffen , Görg Johanna , Schwarz Clara-Sophie , Steffan Christian , Brettel Hauke , Germerott Tanja
Published: June 2024

While most homicides worldwide are committed against men, women and girls are disproportionately affected by domestic violence and its fatal consequences. The Istanbul Convention mandates the prevention of gender-based violence, particularly against women. This study analyzes the characteristics of male and female victims of homicides to provide a comprehensive understanding of the different situations and ways in which men and women are killed. Files of 106 forensic autopsies of homicide victims aged 14 years and older (51.9% males, 48.1% females) from 2012 to 2019 were reviewed. Demographic data, previous history with a focus on the perpetrator-victim relationship, substance influence, type of violence and law enforcement data were recorded. A gender-separated statistical analysis was carried out. Male victims were killed most often outside their own home (63.6%) by acquaintances or friends (54.5%). They were frequently under the influence of substances (56.4%). The perpetrators suffered from addictive diseases in 49.1%, and 52.7% had prior convictions. In both groups, stab/cut injuries were the most common causes of death, but stab/cut and gunshot injuries occurred more frequently in male victims. Female victims were killed most often in their own homes (76.5%) by intimate partners (62.7%). Compared to male victims, violent asphyxiation and blunt force trauma were more common causes of death. Furthermore, there was a higher frequency of multiple injuries (33.3%). The prevalence of a guilty verdict of the perpetrators was nearly equal in both groups. Gender-specific aspects should be considered when investigating homicides and establishing prevention and intervention strategies for interpersonal violence. Protective concepts are easier to establish in the public sphere than in private homes, which is why homicides against women require special attention in society, law enforcement, and legislation. Significant gender differences were found in the circumstances and the perpetrator-victim relationships of homicides. Gender-specific aspects should be taken into account when investigating homicides and developing prevention and intervention strategies. Homicides against women require special attention in society, law enforcement and legislation.
Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response
Authors: Zhao Shaoling , Lv Qian , Zhang Ge , Zhang Jiangtao , Wang Heqiu , Zhang Jianmin , Wang Meiyun , Wang Zheng
Published: June 2024

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = –0.26–0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18–0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.
Using Artificial Neural Networks and Spectral Indices to Predict Water Availability in New Capital (IKN) and Its’ Surroundings
Authors: Arif Nursida , Toersilowati Laras
Published: June 2024

This study aims to predict water availability in New Capital (IKN) and its surroundings using artificial neural networks and spectral indices as predictors. The study uses Sentinel-2 A imagery from the year 2022 analyzed directly from Google Earth Engine (GEE) to calculate three spectral indices, including the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), and uses these indices as predictors in the artificial neural network model. The study carried out four simulations to determine the best prediction results, and the best results were obtained using ANN parameters: 2 hidden layers (HL); learning rate (LR) 0.01; momentum (M) 0.4; root mean square (RMS) 0.001 and iteration (I) 25,000 with overall accuracy (OA) 97.7% and kappa index 0.96. The results show that the percentage of water availability in the study area is high water/HW (0.51%), vegetation water/VW (20.41%), and non-water/NW (79.08%). The study concludes that artificial neural networks and spectral indices can effectively predict water availability.
An oversampling algorithm of multi-label data based on cluster-specific samples and fuzzy rough set theory
Authors: Liu Jinming , Huang Kai , Chen Chen , Mao Jian
Published: June 2024

Imbalanced class distributions are common in real-world scenarios, including datasets with multiple labels. One widely acknowledged approach to addressing imbalanced distributions is through oversampling, a technique that both balances the class distribution and improves the effectiveness of classification models. However, when generating synthetic data for multi-label datasets, complexities arise due to the presence of multiple-label sets, which require careful placement and labeling. We propose MLCSMOTE-FRST, an algorithm for synthetic data generation based on label-specific clustering and fuzzy rough set theory. Generation ratios and dependency samples are provided by clusters specific to each label, with a focus on the overall label distribution and the distribution within each cluster. The labels are supported by intra-cluster positive samples, determined using fuzzy rough set theory, which helps to capture the consensus label set. Experimental results on multi-label datasets using four classifiers demonstrate the effectiveness of the proposed method in terms of macro-F1 and micro-F1 scores.
Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications
Authors: Sun Yilin , Wang Huaipeng , Xie Dan
Published: June 2024

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.
Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity
Authors: Yang Yang , Gao Yuchao , Wu Jinran , Ding Zhe , Zhao Shangrui
Published: June 2024

Power systems are pivotal in providing sustainable energy across various sectors. However, optimizing their performance to meet modern demands remains a significant challenge. This paper introduces an innovative strategy to improve the optimization of PID controllers within nonlinear oscillatory Automatic Generation Control (AGC) systems, essential for the stability of power systems. Our approach aims to reduce the integrated time squared error, the integrated time absolute error, and the rate of change in deviation, facilitating faster convergence, diminished overshoot, and decreased oscillations. By incorporating the spiral model from the Whale Optimization Algorithm (WOA) into the Multi-Objective Marine Predator Algorithm (MOMPA), our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies. Furthermore, the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima, thereby generating optimal Pareto solutions. When applied to nonlinear AGC systems featuring governor dead zones, the PID controllers optimized by QQSMOMPA not only achieve 14$$\%$$
%
reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.
Fuzzy Neural Network Model for Intelligent Course Development in Music and Dance Education
Authors: Zhao Lin , Sun Ying , Tian Tian
Published: June 2024

Interactions are mandatory for online or offline music and dance education to improve understandability and learning efficacy. The course designed for such artistic education incorporates multi-point interactions and monotonous presentations. The validation of the key factor: interactivity is thus mandatory for enhancing efficiency. This article introduces an interactivity validation method (IVM) using combined fuzzy neural network (FNN) to aid artistic course development. The output of the existing course and its evaluation criteria are considered in enhancing its grade. The fuzzy performs interaction classification as mandatory and trivial based on the student’s performance. The neural network identifies the chances for maximum performance by increasing or decreasing the interaction rate. If a saturated performance is achieved at a high or low interactivity, then the further course design is performed based on the saturated interactivity factor. The failing factors are used for training the neural network for modifying the interactivity rate from the current course development suggestion. Such a process is keen on classifying and validating the impact of interactivity over artistic course design.
The impact of frailty on rectal prolapse repair: a retrospective analysis of the national inpatient sample for clinical outcomes and health resource utilization
Authors: McKechnie Tyler , Patel Janhavi , Jessani Ghazal , Lee Yung , Amin Nalin , Doumouras Aristithes , Hong Dennis , Eskicioglu Cagla
Published: June 2024

Purpose

Rectal prolapse commonly affects elderly, frail patients. The impact of frailty alone on surgical outcomes for rectal prolapse has not been thoroughly investigated. The aim of this study was to utilize the National Inpatient Sample and the modified frailty index (mFI-11) to compare postoperative outcomes between frail and robust patients undergoing surgery for rectal prolapse.

Methods

We conducted a retrospective population-based cohort study using the Healthcare Cost and Utilization Project (HCUP) NIS from 2015 to 2019. The mFI-11 was utilized to classify patients as frail or robust. The primary outcomes were overall in-hospital postoperative morbidity and mortality. The secondary outcomes included system-specific postoperative morbidity, length of stay (LOS), total in-hospital healthcare cost, and discharge disposition. These were assessed using univariable and multivariable regressions.

Results

A total of 2130 patients, 239 frail (mFI > 0.27) and 1,891 robust patients (mFI < 0.27) who underwent rectal prolapse repair were analyzed. After adjustment, frail patients had a higher rate of in-hospital mortality (OR 10.38, 95% CI 0.65–166.59, p = 0.098) and morbidity (OR 2.18, 95% CI 1.31–3.63, p = 0.003), longer LOS (MD 1.60 days, 95% CI 1.05–2.44, p = 0.028), and greater cost of treatment (MD $15,561.56, 95% CI − 6023.12–37,146.25, p = 0.157) than robust patients.

Conclusion

Frailty increases postoperative morbidity and mortality and cost more to the healthcare system overall for patients undergoing rectal prolapse repair. This retrospective study is limited by selection bias and residual confounding. Consideration of preoperative optimization programs for frail patients undergoing surgery for rectal prolapse is an important next step to mitigate these poor outcomes.