bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hansen’s Accessibility Theory and Machine Learning: a Potential Merger
Authors: Hadjidimitriou Natalia Selini , Reggiani Aura , Östh John , Mamei Marco 
Published: January 2025

Abstract
Accessibility is a central concept in transport geography, given its relationship with land development. It is defined as "the opportunity that an individual at a given location possesses to take part in a particular activity or set of activities" (Hansen, 1959). Hansen’s Accessibility Model (HAM) can be computed using mobility flows between regions or employment data as a proxy for centres of attraction, coupled with an impedance function that incorporates travel costs. It has been the basis of multiple theoretical and empirical approaches over the years. In the last decades, advances in Machine Learning (ML) have also opened new possibilities for developing innovative approaches in the transport field. The objective of this work is primarily the study of the dynamics of urban accessibility, by considering two interrelated perspectives. Firstly, we aim to explore whether alternative data-based techniques, such as ML, can replicate the behaviour of HAM and thus capture, from data, the underlying theory linked to the spatial interaction model, by embedding the influence of geography, transport network, and socioeconomic factors on accessibility. Secondly, we investigate the feasibility of employing ML where flow data are unavailable, ensuring consistent measurements over time. A combined approach HAM-ML is developed and applied to this aim. As a case study, we examine inter-urban accessibility in two Italian regions, Lombardia and Emilia Romagna, based on socioeconomic and transport data from 2011. The results show the potential of this joint approach, opening new research prospects on accessibility from the theoretical, empirical, and policy viewpoints.
HEM NAEMP: a novel hybrid ensemble model for North Anatolian Fault zone earthquake magnitude prediction
Authors: Özceylan Elif , Karadayı Ataş Pınar 
Published: January 2025

Abstract
The application of machine learning in predicting earthquake magnitudes is crucial due to its ability to process extensive data sets and identify intricate patterns, thereby enhancing the accuracy and timeliness of predictions. This capability is essential for improving readiness and relief techniques against seismic activities. This study introduces a novel hybrid ensemble model, the HEM NAEMP, specifically evolved for predicting earthquake magnitudes along the North Anatolian Fault zone. The model integrates data from both the North Anatolian and San Andreas fault zones-the latter selected due to its tectonic similarity-to develop a comprehensive dataset that includes newly extracted features. The novelty of this study lies in the combination of data from two different fault lines to create a new dataset, the extraction of novel features, and the development of a previously unused model leveraging this dataset and its features. The HEM NAEMP model employs a several of regression algorithms, including k-nearest neighbors, random forest, support vector machine, decision tree and extreme gradient boosting, to effectively predict earthquake magnitude. The evaluation metrics for the model are as follows: mean squared error (MSE) of 0.011, mean absolute error (MAE) of 0.064, root mean squared error (RMSE) of 0.108, mean absolute percentage error (MAPE) of 0.268, R Square

$$(\text {R}^2)$$

(

R
2

)

of 0.92 and training time of 2.44 sec. These results are compared against those from a Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN) and AutoRegressive Integrated Moving Average (ARIMA) models, demonstrating that HEM NAEMP has mostly lower error rates in MAE and MAPE and high score in

$$\text {R}^2$$

R
2

, as well as reduced training time, thereby confirming its viability and efficiency.
Sousveillance Work: Monitoring and Managing-Up in Patrimonial Hollywood
Authors: Dessauer Julia M. 
Published: January 2025

Abstract
How do workers come to know what their bosses need and want? This paper shows how laborers in patrimonial work environments learn to serve their bosses through bottom-up observation, rather than top-down instruction. The author uses the case of assistants in Hollywood to introduce the concept of sousveillance work, which is the labor of monitoring, anticipating, and fulfilling a boss’s mutable needs and wants. Drawing on 60 + interviews with professionals in Hollywood, the author reveals how sousveillance work helps assistants manage-up and mitigate volatility wrought by their patrimonial superiors. The concept of sousveillance work adds to research on labor and uncertainty in creative industries, and also helps to reveal how patrimonial systems are sustained in contemporary work environments.
Relations between trajectories of weight loss and changes in psychological health over a period of 2 years following bariatric metabolic surgery
Authors: Pyykkö Johanna Eveliina , van Olst Nienke , Gerdes Victor E. A. , Almansa Josué , Acherman Yaïr I. Z. , De Brauw Maurits , Groen Albert K. , Nieuwdorp Max , Sanderman Robbert , Hagedoorn Mariët 
Published: January 2025

Abstract

Purpose
This study aimed to identify trajectories of BMI, obesity-specific health-related quality of life (HR-QoL), and depression trajectories from pre-surgery to 24 months post-bariatric metabolic surgery (BMS), and explore their associations, addressing subgroup differences often hidden in group-level analyses.

Method
Patients with severe obesity (n = 529) reported their HR-QoL and depression before undergoing BMS, and at 12 and 24 months post-operation. Latent Class Growth Analysis was used to identify trajectories of BMI, HR-QoL and depression.

Results
BMI and HR-QoL improved significantly for all patients from pre-surgery to 24 months post-operation, though some patients deteriorated in their outcomes after 12 months. Three distinct trajectories of BMI were identified: Low (35.4%), Medium (45.5%), and High (19.2%), and of HR-QoL: High (38.4%), Medium (43.4%), and Poor (18.1%). Three trajectories of depression were extracted: Low/none (32.4%), Medium–low (45.3%), and Worsening (22.3%). The association between the trajectories of BMI and depression was significant, but not between the BMI and HR-QoL trajectories. Specifically, the Low BMI trajectory patients were more likely to follow the Worsening depression trajectory and reported poorer preoperative psychological health than the other two BMI trajectories.

Conclusion
Patients following the most favourable weight loss trajectory may not manifest psychologically favourable outcomes (i.e., Worsening depression), and preoperative characteristics do not consistently describe post-surgical BMI trajectories. Clinicians should tend to patients’ mental wellbeing besides weight loss post-BMS. The study findings emphasize the significance of incorporating psychological health as an essential component of surgical outcomes.
Management of nausea and vomiting induced by antibody–drug conjugates
Authors: Farhat Jawhara , Sakai Hitomi , Tsurutani Junji 
Published: January 2025

Abstract
Antibody–drug conjugates (ADCs) are an emerging class of anticancer therapy that combines the specificity and long circulation half-life of monoclonal antibodies with the cytotoxic potency of the payload connected through a chemical linker. The optimal management of toxicities is crucial for improving quality of life in patients undergoing ADCs and for avoiding improper dose reductions or discontinuations. This article focuses on the characteristics and management of nausea and vomiting (NV) induced by three ADCs: trastuzumab deruxtecan (T-DXd), sacituzumab govitecan (SG), and datopotamab deruxtecan (Dato-DXd). We summarize the proposed mechanism of NV, clinical study data on NV, and recommendations from clinical guidelines. We also discuss three prospective studies evaluating prophylactic antiemetic therapy in patients receiving T-DXd, along with future perspectives.
Electrochemical Detection of Miltefosine in Urine Using Amino Functionalised Multi-walled Carbon Nanotubes and [Fe(CN)6]−3/−4 as a Redox Couple
Authors: Adu Darko Kwabena , Nate Zondi , Alake John , Ike Blessing Wisdom , Miya Lungelo , Mohite Sachin Balaso , Chauhan Ruchika , Karpoormath Rajshekhar 
Published: January 2025

Abstract
Miltefosine is an alkyllylosophospholipid analogue used to treat visceral leishmaniasis. Recently, reports have been made of suspected counterfeit miltefosine on the Indian market. With the risk counterfeit drugs pose to drug resistance development, quality control of antileishmanial drugs has become important. Hence, in this study, amino-functionalized multi-walled carbon nanotubes (MWCNT-NH2) were synthesised and characterised using Fourier transform infrared spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Also, electrochemical impedance spectroscopy and cyclic voltammetry were used to study the electrochemical properties of the synthesised MWCNT-NH2. A complex was formed between MWCNT-NH2 and miltefosine (Mil-MWCNT-NH2). Five microliters of Mil-MWCNT-NH2 was drop-cast on glassy carbon electrode, and differential pulse voltammetry studies were carried out to assess the performance of the sensor. Using [Fe(CN)6]-3/-4 as a redox couple, a calibration study was carried out at different concentrations (0–250 µM) to establish the concentration range of the sensor. A linear response was established. With a detection limit of 1 µM, the fabricated sensor is a viable tool for detecting antileishmanial drug miltefosine in urine samples and possible application in quality control of miltefosine against counterfeiting.

Graphical Abstract
Evaluation of irrigation management in different stages of growth on the yield and water productivity in cowpea plant in field and model conditions
Authors: Baradaran-Hezaveh Faranak , Babazadeh Hossein , Amiri Ebrahim , Ebrahimi Hossein 
Published: January 2025

Abstract
The research was conducted in order to evaluate the management of irrigation in different stages of growth on the yield of the cowpea plant using the DSSAT model. The experiment was conducted in the form of a randomized complete block design with three replications in the north of Iran during 2017 and 2018. The main treatments included irrigation with the management of 40, 60, 80, 100 and 120% of the plant’s water requirements (WRs), and the secondary treatment included irrigation in the vegetative, reproductive and complete stages (vegetative and reproductive). The outcomes indicated that the maximum yield of biomass, pod and seeds when supplying 100% of the water supply in 2017 was 4588, 3222 and 1005 kg/ha, respectively, in both vegetative and reproductive stages. In the interaction of year × WR × growth stages, the maximum water use efficiency based on yield of biomass, pod and seeds in 2018 were under irrigated conditions in the vegetative stage and in dry conditions with the average of 3.57, 2.37 and 0.75 kg/m3. The average values of water productivity components based on irrigation, irrigation and rainfall, evapotranspiration, transpiration and evapotranspiration and deep percolation altogether (WPI, WPI + R, WPET, WPT and WPETQ) was obtained as 0.61, 0.41, 0.39, 0.67 and 0.32 kg per cubic meter, respectively. The outcomes of evaluating DSSAT model showed that the values of RMSE and RMSEn in calibration conditions were, respectively, 130 kg/ha and 5.91% for biomass yield and 92 kg/ha and 12.62% for seed yield. In the validation conditions, these indicators were, respectively, 129 kg/ha and 5.29% for biomass yield and 73 kg/ha and 8.61% for seed yield. Based on the results, treating 100% of the water requirements in the vegetative and reproductive stages and with transpiration rate of 188 mm is suggested as suitable management for the studied area.
Is There a Need to Reassess Protein Intake Recommendations Following Metabolic Bariatric Surgery?
Authors: Ben-Porat Tair , Lahav Yair , Cohen Tamara R. , Bacon Simon L. , Buch Assaf , Moizé Violeta , Sherf-Dagan Shiri 
Published: January 2025

Abstract

Purpose of Review
Protein intake is recognized as a key nutritional factor crucial for optimizing Metabolic Bariatric Surgery (MBS) outcomes by preventing protein malnutrition, preserving fat-free mass, and inducing satiety. This paper discusses the current evidence regarding protein intake and its impact on clinical outcomes following MBS.

Recent Findings
There are considerable gaps in the understanding of protein requirements following MBS, as existing guidelines are based on limited and inconsistent reports. This highlights the urgent need for updated clinical practice recommendations grounded in high-quality evidence.

Summary
Further investigation using robust methodologies is essential to address existing research gaps related to the individualization of protein requirements following MBS. Future research should consider factors such as the time elapsed since surgery, the form and quantity of protein consumed, and necessary adjustments for physical activity. Ultimately, in alignment with recent literature, a more specific and personalized dietary protein approach should be examined.
Flourishing as an Aim of Medical Education: Are We Hitting the Target?
Authors: Mansuri Noreen , Zelenski Amy B. 
Published: January 2025

Abstract
In this commentary, we use Reiss and White’s contention of educational aims as a lens to examine the aims of medical education and determine whether the flourishing of medical students is among them. We identify an absence of flourishing and observe descriptions of medical students as finished products of training with an emphasis on professional virtues such as altruism. This emphasis is a compensatory response to professional and cultural shifts during the twentieth century. Anchored by this historical context, we draw on the work of Fielding and Moss to offer a path forward for redefining the aims of medical education.
Using Virtual Reality to Assist Students at Academic Risk in Human Anatomy
Authors: Foley Michael , Lilley Lauren T. , Meyers Lindsay , Armstrong Robert , Fore-Arcand Lisa , McCoy Kelly , Eitel Chad , Clapp Tod R. , Heise Natascha 
Published: January 2025

Abstract
Learning human anatomy presents a significant challenge for health profession students due to the difficulty in visualizing structures in three dimensions. Virtual reality (VR) has been reported to aid in understanding these relationships. In this study, students at academic risk attended VR sessions alongside their gross anatomy course. Data from post-surveys, observations, and examinations indicated VR participants performed similarly to peers, except in head/neck and pelvis/lower limb exams where performance was lower. Students valued VR for enhancing confidence and understanding. VR shows promise in supplementing anatomy education, particularly for less complex regions, and bridging gaps in traditional learning methods.
On the optimization of fatigue limit in additively manufactured fiber reinforced polymer composites
Authors: Azizian-Farsani Elaheh , Rouhi Moghanlou Mohammad , Mahmoudi Ali , Wilson Peyton J. , Khonsari Michael M. 
Published: January 2025

Abstract
This study uses the Taguchi optimization methodology to optimize the fatigue performance of short carbon fiber-reinforced polyamide samples printed via fused deposition modeling (FDM). The optimal printing properties that maximize the fatigue limit were determined to be 0.075 mm layer thickness, 0.4 mm infill line distance, 50 mm/s printing speed, and 55 °C chamber temperature with layer thickness being the most critical parameter. To qualify fatigue endurance limit, the energy dissipation in uniaxial fatigue was quantified by using hysteresis energy and temperature rise at steady state. From these results, the fatigue limit for a specimen printed with optimized printing parameters was predicted to be 69 and 70 MPa from hysteresis energy and temperature rise at steady state methods, consecutively, and it was experimentally determined to be 67 MPa. This work demonstrates the effectiveness of the Taguchi optimization method when applied to additive manufacturing and the swift ability to predict the fatigue limit of a material with only one specimen to produce optimal additively manufactured components for industrial applications, as validated by experimental fatigue testing.
Assessing Risk in Implementing New Artificial Intelligence Triage Tools—How Much Risk is Reasonable in an Already Risky World?
Authors: Nord-Bronzyk Alexa , Savulescu Julian , Ballantyne Angela , Braunack-Mayer Annette , Krishnaswamy Pavitra , Lysaght Tamra , Ong Marcus E. H. , Liu Nan , Menikoff Jerry , Mertens Mayli , Dunn Michael 
Published: January 2025

Abstract
Risk prediction in emergency medicine (EM) holds unique challenges due to issues surrounding urgency, blurry research-practise distinctions, and the high-pressure environment in emergency departments (ED). Artificial intelligence (AI) risk prediction tools have been developed with the aim of streamlining triaging processes and mitigating perennial issues affecting EDs globally, such as overcrowding and delays. The implementation of these tools is complicated by the potential risks associated with over-triage and under-triage, untraceable false positives, as well as the potential for the biases of healthcare professionals toward technology leading to the incorrect usage of such tools. This paper explores risk surrounding these issues in an analysis of a case study involving a machine learning triage tool called the Score for Emergency Risk Prediction (SERP) in Singapore. This tool is used for estimating mortality risk in presentation at the ED. After two successful retrospective studies demonstrating SERP’s strong predictive accuracy, researchers decided that the pre-implementation randomised controlled trial (RCT) would not be feasible due to how the tool interacts with clinical judgement, complicating the blinded arm of the trial. This led them to consider other methods of testing SERP’s real-world capabilities, such as ongoing-evaluation type studies. We discuss the outcomes of a risk–benefit analysis to argue that the proposed implementation strategy is ethically appropriate and aligns with improvement-focused and systemic approaches to implementation, especially the learning health systems framework (LHS) to ensure safety, efficacy, and ongoing learning.
Berlin Heart EXCOR Implantation in Adult Patients, Outcomes and Complications: Short Report
Authors: Hulman Michal , Artemiou Panagiotis , Durdik Stefan , Lesny Peter , Gasparovic Ivo , Goncalvesova Eva 
Published: January 2025

Abstract
The Berlin Heart EXCOR is a paracorporeal, pulsatile, and pneumatically driven ventricular assist device and can provide durable support to the left ventricle, right ventricle or both ventricles and can be used as bridge to recovery or bridge to transplant. The aim of this short report is to present the outcomes and complications in adult patients who received the paracorporeal biventricular Berlin Heart EXCOR system in our center, as a bridge to transplant strategy. From January 2022 to December 2024 six patients underwent implantation of the device. Three patients died while on support, and 3 patients were successfully transplanted. Despite the high mortality observed, the Berlin Heart EXCOR system is still a reasonable therapeutic option with acceptable post-transplant survival.
Analgesic Protocols in Total Knee Replacement: Early Efficacy and Functional Outcomes
Authors: Matúška Martin , Polan Peter , Rendek Pavol , Halas Matúš , Kokavec Milan , Lacko Marek 
Published: January 2025

Abstract

Objective
Comparison of traditional opioid-based analgesic protocols with multimodal pain management strategies in terms of postoperative pain, opioid consumption, and knee range of motion (ROM).

Materials and Methods
The study included 397 patients (mean age 67.4 ± 8.6 years) undergoing unilateral TKA, divided according to the analgesic protocol: adductor canal block (ACB), periarticular local infiltration anesthesia (LIA), epidural analgesia, and traditional pharmacotherapy. Pain (VAS), opioid consumption (MME), and knee ROM were assessed over 4 postoperative days.

Results
Postoperative VAS scores differ significantly between cohorts at all time points (p < 0.001). The ACB cohort had the lowest VAS scores on the day of surgery (3.30 ± 2.23) and on postoperative day 4 (2.10 ± 1.05), followed by the LIA cohort (2.30 ± 1.12). Epidural analgesia and traditional pharmacotherapy showed higher pain scores. Knee flexion on day 4 was highest in the ACB cohort (92.4° ± 7.2°), followed by the LIA cohort (88.5° ± 8.3°) (p < 0.001). Opioid consumption was lowest in the ACB cohort (6.3 ± 5.8 MME) and highest in the traditional pharmacotherapy cohort (43.1 ± 12.8 MME) (p < 0.001). BMI significantly influenced opioid consumption (p = 0.018), with higher BMI (> 30 kg/m2) which was associated with higher doses. However, BMI, age, and gender had no effect on knee ROM (p > 0.05).

Conclusion
Multimodal protocols, including ACB in particular, improve pain control, reduce opioid consumption, and support faster rehabilitation.
The Net Radiative Effect of the Ill‐Defined Clear‐Sky in the Vicinity of Clouds
Authors: Eytan Eshkol , Gristey Jake J. , Feingold Graham 
Published: January 2025

AbstractClouds play a crucial role in Earth's energy budget resulting in significant deviations from clear‐sky fluxes. This important role, combined with their complexity, makes them a major source of uncertainty in climate projections. Over the past two decades, the radiative signatures of clouds in their surroundings have been extensively studied, highlighting the challenges of defining clouds and distinguishing them from the aerosol embedded in clear‐sky. Accurately defining clouds and aerosol is essential for quantifying their radiative effects and for remote sensing of the surface and atmosphere under clear‐sky conditions. In this study, we combine two of the most mature and sophisticated space‐borne instruments: the MODerate resolution Imaging Spectroradiometer with its high‐resolution multispectral images and the Clouds and the Earth's Radiant Energy System broadband top‐of‐the‐atmosphere radiative flux data. This0 allows us to quantify the net RE of clouds within the clear‐sky around them for the first time. Our findings show that the local RE of clouds over the ocean around noon ranges from −7 to −10 W in the solar band and 1–1.5 W in the longwave infrared. These results suggest that clouds drive a significant portion of the aerosol direct RE. Additionally, the near‐cloud longwave infrared effect is equivalent to an effect of 90 ppm of . We propose to categorize the sky into three regimes—cloudy, cloud‐influenced‐clear‐sky, and far‐field clear‐sky and suggest a path to move beyond a discrete definition of the sky. This will improve our understanding of the role of clouds and aerosol in the climate system, ultimately reducing uncertainties in climate projections.