bluuebunny/crossref_metadata_2025 huggingface dataset:
Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device
Authors: Hata Masahiro , Yanagisawa Takufumi , Miyazaki Yuki , Omori Hisaki , Hirashima Atsuya , Nakagawa Yuta , Eto Mitsuhiro , Yoshiyama Kenji , Kanemoto Hideki , Nyamradnaa Byambadorj , Yoshimoto Shusuke , Ezure Kotaro , Takahashi Shun , Ikeda Manabu
Published: November 2024

Mild cognitive impairment (MCI) and dementia present critical health challenges in aging populations, highlighting the need for prompt and accurate diagnostic methods. Current diagnostic approaches for dementia are constrained by limited access to specialists and the high cost or invasiveness of the diagnostic methods, necessitating the development of widely accessible, cost-effective, and noninvasive alternatives. Electroencephalography (EEG) is a promising, accessible, cost-effective, noninvasive biomarker of dementia. However, traditional EEG systems are limited by their lack of portability and need for skilled technicians. This study proposes a deep-learning-based model to differentiate healthy volunteers (HVs) from patients with dementia-related conditions using data from portable EEG devices. A dataset of 233 participants, including 119 HVs and 114 patients categorized by severity and clinical diagnosis, was analyzed. We developed a customized transformer-based model to analyze the EEG data transformed into frequency-domain features using a short-time Fourier transform. The model was evaluated using 10-fold cross-validation and a hold-out dataset. In cross-validation, the model achieved an area under the curve (AUC) of 0.872 and a balanced accuracy (bACC) of 80.8% in distinguishing HVs from patients. Subgroup analyses by severity and clinical diagnosis yielded AUCs of 0.812 to 0.898 and bACCs of 74.9 to 86.4%, respectively. Comparable performance was observed in the holdout dataset, except for small sample sizes. These findings highlight the potential of portable EEG devices and deep-learning models as practical tools for dementia screening.
Timing and Predictors of Tuberculosis Incidence among Contacts
Authors: Asare-Baah Michael , Lauzardo Michael , Johnston Lori , Dominique Lina , Séraphin Marie Nancy
Published: November 2024

Contact investigations are crucial for tuberculosis (TB) control, yet the temporal dynamics of disease progression among exposed contacts remain poorly understood. Here, we analyzed a retrospective cohort of 44,106 contacts of 6,243 TB cases identified through the Florida Department of Health surveillance system (2009-2023). During the 15-year follow-up period, 454 individuals within this cohort developed active TB disease. Using survival analysis and mixed-effect Cox proportional hazards models, we determined that the median time to TB disease onset was 11 months following the initial contact investigation. The risk of progression to TB disease varied markedly by age and immune status. Children aged 0-5 years showed nearly seven times higher risk of TB disease progression compared to adults 65 years and older (aHR = 6.66, 95% CI: 1.33-33.27). HIV-positive contacts demonstrated a five-fold increased risk (aHR = 4.75, 95% CI: 2.43-9.30) relative to HIV-negative individuals. These findings indicate that the risk of TB transmission and disease progression persists beyond initial contact investigation activities and varies significantly across demographic and clinical subgroups, suggesting the need for extended contact monitoring and age-specific preventive strategies, particularly for young children and immunocompromised individuals.
Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments
Authors: Xia Zongqi , Chikersal Prerna , Venkatesh Shruthi , Walker Elizabeth , Dey Anind , Goel Mayank
Published: November 2024

Background

Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual’s own environment may improve self-monitoring and clinical management for people with MS (pwMS).

Objective

We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers.

Methods

We divide the collected data into discrete periods for each patient. For each prediction period, we first extract patient-level behavioral features from the current period (action features) and the previous period (context features). Then, we apply a machine learning (ML) approach based on Support Vector Machine with Radial Bias Function Kernel and AdaBoost to predict the presence of depressive symptoms (every two weeks) and high global MS symptom burden, severe fatigue, and poor sleep quality (every four weeks).

Results

Between November 16, 2019, and January 24, 2021, 104 pwMS (84.6% women, 93.3% non-Hispanic White, 44.0±11.8 years mean±SD age) from a clinic-based MS cohort completed 12-weeks of data collection, including a subset of 44 pwMS (88.6% women, 95.5% non-Hispanic White, 45.7±11.2 years) who completed 24-weeks of data collection. In total, we collected approximately 12,500 days of passive sensor and behavioral health data from the participants. Among the best-performing models with the least sensor data requirement, ML algorithm predicts depressive symptoms with an accuracy of 80.6% (35.5% improvement over baseline; F1-score: 0.76), high global MS symptom burden with an accuracy of 77.3% (51.3% improvement over baseline; F1-score: 0.77), severe fatigue with an accuracy of 73.8% (45.0% improvement over baseline; F1-score: 0.74), and poor sleep quality with an accuracy of 72.0% (28.1% improvement over baseline; F1-score: 0.70). Further, sensor data were largely sufficient for predicting symptom severity, while the prediction of depressive symptoms benefited from minimal active patient input in the form of response to two brief questions on the day before the prediction point.

Conclusions

Our digital phenotyping approach using passive sensors on smartphones and fitness trackers may help patients with real-world, continuous, self-monitoring of common symptoms in their own environment and assist clinicians with better triage of patient needs for timely interventions in MS (and potentially other chronic neurological disorders).
Inconsistent Rebound of Invasive Pneumococcal Disease in Connecticut following the Initial Phase of the SARS-CoV-2 Pandemic
Authors: Perniciaro Stephanie , Weinberger Daniel M.
Published: November 2024

The incidence of invasive pneumococcal disease (IPD) decreased during the SARS-CoV-2 pandemic and rebounded inconsistently over 2 years, with occasional returns to pre-pandemic levels followed by subsequent declines. We evaluated several explanations including changes in rates of viral infections that could interact with pneumococcus and changes in blood culture practices.
Assessment of the school environment for risk factors for tungiasis in nine counties of Kenya: a cross-sectional survey
Authors: Elson Lynne , Kamau Christopher , Koech Sammy , Muthama Christopher , Gachomba George , Sinoti Erastus , Chondo Elwyn , Mburu Eliud , Wakio Miriam , Lore Jimmy , Maia Marta , Adetifa Ifedayo , Orindi Benedict , Bejon Phillip , Fillinger Ulrike
Published: November 2024

Background

Tungiasis is a neglected tropical skin disease mostly affecting children under 15 years, the elderly and disabled people in the most resource poor populations in Latin America and Sub-Saharan Africa. While most transmission seems to occur at home inside houses, we aimed to identify factors in the school environment that may put children at increased risk of infection.

Methods

As part of a cross-sectional school-based prevalence survey of 21,466 pupils in nine counties of Kenya, observations of school infrastructure were made and the headteacher interviewed in each of 196 schools. In a subset of 97 schools, detailed observations of 322 classrooms were made and 117 teachers interviewed. Mixed effect logistic regression models were used to identify factors associated with tungiasis infection in pupils.

Results

We found a higher odds of tungiasis infection for pupils in schools where more than 400 pupils were enrolled (aOR 2.24, 95% CI 1.12−4.50, p=0.023), where clean water was not always available (aOR 2.28, 95% CI 1.13−4.60, p=0.021); and if the school buildings were in a bad structural condition (aOR 2.15, 95% CI 1.00−4.62, p=0.050). Of the 5,102 pupils in 97 schools, 99% studied in a classroom with a concrete floor and those with a lot of loose soil or sand on top of the concrete floor had a six times higher odds of infection than those on a clean concrete floor (aOR 6.52, 95% CI 1.61−26.35, p=0.008). Only 45% of (head)teachers in affected schools knew they had infected pupils in their school or grade. Those who did, were aware of the impact it was having on the pupils and yet only three of 76 affected schools had conducted a tungiasis intervention activity within the last year.

Conclusion

Most schools do not pose a risk of tungiasis for pupils, instead the home environment is the main risk. However, where school buildings are not well maintained and water not always available a multisectoral approach to control tungiasis is needed involving the Department of Health as well as the school management and the Department of Education.
Divergent patterns of genetic overlap between severe mental disorders and metabolic markers
Authors: van der Meer Dennis , Shadrin Alexey , Stinson Sara , Koch Elise , Rokicki Jaroslav , Rahman Zillur , Bergstedt Jacob , Ottas Aigar , Sonderby Ida , Rodevand Linn , Fuhrer Julian , Quintana Daniel S , Dale Anders , O'Connell Kevin , Djurovic Srdjan , Lehto Kelli , Alver Maris , Milani Lili , Andreassen Ole
Published: November 2024

Background Bipolar disorder (BIP), major depressive disorder (MDD) and schizophrenia (SCZ) are severe mental disorders (SMDs), each associated with poor cardiometabolic health. Mapping the genetic relationships of these highly heritable disorders with blood markers of metabolic activity may uncover biological pathways underlying this important shared clinical feature. Methods We charted global genetic overlap of the three SMDs, type 2 diabetes (T2D), coronary artery disease (CAD), and body mass index (BMI) with 249 circulating metabolic markers through linkage disequilibrium score regression and bivariate Gaussian mixture modeling. We estimated causal relationships, functionally annotated shared genetic variants, and investigated enrichment across diverse brain and body tissues. Results All three SMDs had extensive overlap with the metabolic markers. The pattern of genetic correlations was highly similar between MDD, T2D, CAD, and BMI (Spearmans correlation rs>.93), opposite in direction to the pattern found for SCZ and BIP (MDD-BIP rs=-.74; MDD-SCZ rs=-.83). The metabolic markers had widespread, robust causal effects on the SMDs and cardiometabolic traits. We mapped 1056 genes shared between the individual SMDs and the metabolic markers to disorder-specific processes related to metabolic activity, mitochondrial function, and synaptic processes. These genes were most prominently expressed throughout the brain, heart and liver. Discussion SMDs have strong associations with metabolic markers, whereby MDD has a distinctly different genetic relationship than BIP and SCZ. Our findings suggest that metabolic pathways are involved in the development of SMDs and can play a central role in disentangling disorder-specific etiologies. Our metabolic psychiatry approach has high potential to guide the development of targeted interventions.
Multi-omics analysis in mouse primary cortical neurons reveals complex positive and negative biological interactions between constituent compounds in Centella asiatica
Authors: Chamberlin Steve , Zweig Jonathan A , Neff Cody J , Marney Luke , Choi Jaewoo , Yang Liping , Maier Claudia S , Soumyanath Amala , McWeeney Shannon , Gray Nora E
Published: November 2024

Background

A water extract of the Ayurvedic plantCentella asiatica(CAW) improves cognitive function in mouse models of aging and Alzheimer’s disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plant’s therapeutic benefit.

Methods

Mouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses respectively.

Results

Extensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicate the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8) and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA, and possible compound interactions at this level.

Conclusions

Four gene expression modules and two metabolite modules were altered by the four types of treatments applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons.
KLF7-Regulated ITGA2 as a Therapeutic Target for Inhibiting Oral Cancer Stem Cells
Authors: Qi Xin , Zhou Jiang , Wang Pan , Li Yunyan , Li Haoran , Miao Yuwen , Ma XiaoQing , Luo Xiayan , Zhang Zhiling , He Yanling , shen Wenyi , Zhao Wenquan , Cui Rutao , Li Cang , Zhu Huiyong , Lyu Jiong
Published: November 2024

Cancer stem cells (CSCs) play crucial roles in tumor metastasis, therapy resistance, and immune evasion. Identifying and understanding the factors that regulate the stemness of tumor cells presents promising opportunities for developing effective therapeutic strategies. In this study on oral squamous cell carcinoma (OSCC), we confirmed the key role of KLF7 in maintaining the stemness of OSCC. Using chromatin immunoprecipitation sequencing and dual-luciferase assays, we identified ITGA2, a membrane receptor, as a key downstream gene regulated by KLF7 in the maintenance of stemness. Tumor sphere formation assays, flow cytometry analyses, and in vivo limiting dilution tumorigenicity evaluations demonstrated that knocking down ITGA2 significantly impaired stemness. When bound to its ECM ligand, type I collagen, ITGA2 activates several stemness-related pathways, including PI3K-AKT, MAPK, and Hippo. TC-I 15, which inhibits the ITGA2–collagen interaction, showed a synergistic anti-tumor effect when combined with cisplatin in bothin vitroand xenograft models. In summary, we reveal that the KLF7/ITGA2 axis is a crucial modulator of stemness in OSCC. Our findings suggest that ITGA2 is a promising therapeutic target, offering a novel anti-CSC strategy.

Highlights

1) KLF7 as a key molecule in maintaining oral cancer stemness.

2) ITGA2as a key downstream gene regulated by KLF7 in the maintenance of stemness.

3) ITGA2 interacts with extracellular matrix type I collagen, activating stemness-related pathways and promoting YAP1 nuclear translocation to sustain OCSCs.

4) ITGA2 as a novel anti-CSC target, providing a new strategy to overcome OSCC drug resistance.
Structural basis of gap-filling DNA synthesis in the nucleosome by DNA Polymerase β
Authors: Weaver Tyler M. , Ryan Benjamin J. , Thompson Spencer H. , Hussen Adil S. , Spencer Jonah J. , Xu Zhen , Schnicker Nicholas J. , Freudenthal Bret D.
Published: November 2024

Single-strand breaks (SSBs) are one of the most prevalent forms of DNA damage found in the chromatinized genome and are repaired by direct single-strand break repair (SSBR) or base excision repair (BER). DNA polymerase beta (Pol β) is the primary enzyme responsible for processing the 1-nt gap intermediate in chromatin during SSBR and BER. However, the mechanism used by Pol β to process a 1-nt gap in the context of the nucleosome and chromatin remains poorly understood. Here, we use biochemical assays and cryogenic electron microscopy (cryo-EM) to determine the kinetic and structural basis of gap-filling DNA synthesis in the nucleosome by Pol β. Kinetic analysis identified that gap-filling DNA synthesis in the nucleosome by Pol β is position-dependent, where solvent exposed 1-nt gaps are processed more efficiently than histone-occluded 1-nt gaps. A series of cryo-EM structures of Pol β bound to a solvent-exposed 1-nt gap in the nucleosome reveal a global DNA sculpting mechanism for 1-nt gap recognition, which is mediated by sequential engagement of the Pol β lyase domain and polymerase domain. Finally, cryo-EM structures of Pol β bound to 1-nt gaps at two additional positions in the nucleosomal DNA define the structural basis for position-dependent nucleotide insertion in the nucleosome. This work establishes the mechanism used by Pol β for processing 1-nt gaps in the nucleosome during SSBR and BER, providing fundamental insight into DNA repair in chromatin.
Dual-Isometric Projected Entangled Pair States
Authors: Yu Xie-Hang , Cirac J. Ignacio , Kos Pavel , Styliaris Georgios
Published: November 2024

Efficient characterization of higher dimensional many-body physical states presents significant challenges. In this Letter, we propose a new class of projected entangled pair states (PEPS) that incorporates two isometric conditions. This new class facilitates the efficient calculation of general local observables and certain two-point correlation functions, which have been previously shown to be intractable for general PEPS, or PEPS with only a single isometric constraint. Despite incorporating two isometric conditions, our class preserves the rich physical structure while enhancing the analytical capabilities. It features a large set of tunable parameters, with only a subleading correction compared to that of general PEPS. Furthermore, we analytically demonstrate that this class can encode universal quantum computation and can represent a transition from topological to trivial order.

Published by the American Physical Society
2024
Enhanced Efficiency of Latent Heat Energy Storage by Inclination
Authors: Yang Rui , Howland Christopher J. , Liu Hao-Ran , Verzicco Roberto , Lohse Detlef
Published: November 2024

Latent heat storage (LHS) has emerged as a promising solution for addressing the challenges of large-scale and long-term energy storage, offering a clean and reusable system. Being in the developmental stage, and with only limited theoretical predictions being available, there is a need to enhance the efficiency of LHS systems. In this study, we use numerical simulations to study the melting process of a phase-change material (PCM) in a rectangular domain. A significant enhancement in the melt rate of the PCM is found by a simple inclination of the domain, with a 60% increase in melt rate compared to a standard square unit without inclination through enhanced heat transfer. We establish a theoretical relation for the optimal PCM melt rate, based on the inclination angle and the domain aspect ratio, outlining the optimal conditions for the PCM melt rate, which is solely dependent on the domain geometry. We further propose a heat-transfer model that predicts the optimal aspect ratio for achieving the maximum melt rate as a function of the Rayleigh and Prandtl numbers.

Published by the American Physical Society
2024
Compaction behavior of coarse-grained soil under various vibration frequencies: a DEM study
Authors: Ismail Abubakar Sadiq , Nie Zhihong , Ahmad Abdulaziz , Ali Shamshad , Lai Rengui
Published: November 2024

Purpose

This paper investigates the vibration compaction mechanism and evaluates the impact of vibration frequencies on the stability of coarse-grained soil, aiming to optimize the subgrade filling process.

Design/methodology/approach

This study examines the vibratory compaction behavior of coarse-grained soils through indoor vibration tests and discrete element simulations. Focusing on angular gravel (breccias) of varying sizes, the simulations were calibrated using parameters such as Young’s modulus, restitution and friction coefficients. The analysis highlights how particle shape influences compaction, revealing mesoscopic mechanisms that drive macroscopic compaction outcomes.

Findings

This study investigates the influence of vibration frequency on the compaction behavior of coarse-grained soils using discrete element simulation. By analyzing particle contact and motion, the mesoscopic mechanisms driving compaction are explored. The study establishes a positive linear correlation between contact force anisotropy (Cv) and deformation, demonstrating that higher anisotropy leads to greater structural disruption. Additionally, the increase in sliding contact percentage (SCP) at higher frequencies indicates instability in the skeletal structure, driven by uneven contact force distribution. These findings reveal how frequency-induced stress concentration affects the stability and deformation of the soil skeleton.

Originality/value

This research explores the effect of various vibration frequencies on the compaction behavior of coarse-grained soils, examining microscopic interactions to reveal their impact on soil stability and deformation.
Physics-informed and graph neural networks for enhanced inverse analysis
Authors: Di Lorenzo Daniele , Champaney Victor , Ghnatios Chady , Cueto Elias , Chinesta Francisco
Published: November 2024

Purpose

This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Design/methodology/approach

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Findings

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Originality/value

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.
Do public-private partnerships (PPPs) promote the sustainable development of infrastructure (SDI)? Evidence from China
Authors: Ren Feiwu , Huang Yi , Xia Zihan , Xu Xiangyun , Li Xin , Chi Jiangtao , Li Jiaying , Wang Yanwei , Song Jinbo
Published: November 2024

Purpose

To address challenges such as inadequate funding and inefficiency in public infrastructure construction, PPPs have gained significant global traction. This study aims to comprehensively assess the impacts and mechanisms of PPPs on the SDI and to provide rational policy recommendations based on the findings.

Design/methodology/approach

We collated a dataset from 30 Chinese provinces covering the years 2005–2020 as our research sample. The study’s hypotheses are tested using a double fixed-effects model, a chained mediated-effects model and a multidimensional heterogeneity analysis.

Findings

Our findings indicate that PPPs have a facilitating effect on SDI in general. This boost usually lags behind policy implementation and is cyclical in the time dimension. In the spatial dimension, PPPs contribute significantly to SDI in the eastern and western regions, but not in the central region. From the perspective of the dynamics of economic, social and industrial development, PPPs in economically backward areas are difficult to promote SDI, promote it the most in economically medium regions and are slightly less in economically developed regions than in medium regions. This promotion effect has an inverted U-shaped relationship with social development and diminishes with industrial structure upgrading. Finally, due to the negative relationship between PPPs and social development and between social development and SDI, PPPs are shown to contribute to SDI and are identified as critical paths. However, PPPs suppress SDI by inhibiting economic and industrial development.

Originality/value

This study makes three novel contributions to the existing body of knowledge: (1) we innovatively introduce the United Nations Sustainable Development Goals (SDGs) into the field of infrastructure research, offering fresh perspectives on SDI enhancement; (2) revealing the mechanisms by which PPPs affect SDI through the three dimensions of economic, social and industrial development enabling policymakers to better understand and optimize resource allocation and improve planning, design and management of PPP projects for sustainable infrastructure and (3) we assess the spatiotemporal variances of PPPs’ effects on SDI and the diversity across regions at different social, economic and industrial structures developmental stages, offering critical insights to global decision-makers to devise tailored policy measures.
Implementing assessment protocols in compulsory school for students with intellectual disabilities: challenges and opportunities for teachers educating students with intellectual disabilities
Authors: Klefbeck Kamilla
Published: November 2024

Purpose

This study investigates the impact of integrating structured assessment protocols within the Japanese lesson study (JLS) framework in Swedish Compulsory School for Students with Intellectual Disabilities (CSSID). The purpose is to explore whether such integration can enhance teachers’ professional certainty in assessing and supporting students with intellectual disability (ID), aligning educational practices with inclusive legislative mandates.

Design/methodology/approach

The study utilized structured assessment protocols during lesson study cycles, where teachers observed and discussed research lessons based on predefined learning objectives. Data were collected through questionnaires, assessment protocols and post-lesson discussions, then analyzed using narrative and descriptive statistical methods.

Findings

The integration of structured assessment protocols in lesson studies enhanced teachers’ focus and clarity in assessing and supporting students with ID, improving their perceptions of student engagement, interest and understanding. However, challenges in adapting the protocols to varied teaching contexts and student needs limited the overall impact on professional certainty.

Practical implications

This study examines the adaptability of integrating lesson study with assessment protocols in special education settings. It demonstrates that structured assessment protocols used within the lesson study process provide a common focus on academic achievements for students with ID.

Originality/value

This study contributes to the field by demonstrating the potential of modifying traditional lesson study approaches to include structured assessment protocols, particularly in the context of special education. It highlights the need for ongoing professional development and sustainable assessment strategies that support the diverse learning needs of students with ID.
Why anti-corruption projects and programmes fail in some African countries: a case study of an anti-corruption project that drowned in corruption and the lessons learned
Authors: Hope Kempe Ronald
Published: November 2024

Purpose

The pandemic of corruption in Africa reflects the more general climate of the lack of both ethical leadership and appropriate anti-corruption systems found throughout most of the continent. The purpose of this study is to examine the manner in which corruption was perpetrated in an anti-corruption project that was intended to produce outcomes to reduce and control said corruption, in a sub-Saharan Africa country, for sustained development outcomes, including progress in meeting the sustainable development goals.

Design/methodology/approach

Based on a field experience, and drawing on the active participant observation research methodology, buttressed by open-ended interviews, this work provides a qualitative analysis and assessment case study of corruption in the implementation process of the anti-corruption project.

Findings

It is shown that corruption remains a significant barrier to positive development outcomes, especially in those environments where certain social norms and collectivist behaviour are prominent. In such environments, special systems need to be in place for project implementation success even in cases where a project itself is designed to reduce and control that corruption. Key lessons learned include the imperatives of recruiting qualified and ethical personnel and the importance of country ownership, political will and persistent project monitoring including by the citizen beneficiaries of sustainable development projects.

Originality/value

The paper’s value is the insights it provides through the mapping of the key lessons learned for successful implementation of projects and systems to control corruption and improve ethical behaviour in Africa and beyond.
On the edge: a multilevel perspective on innovation complexities and dynamic attractors in the supply network
Authors: Malacina Iryna , Lintukangas Katrina
Published: November 2024

Purpose

In innovation management, the complexity inherited in the supply network may be necessary for success. This study aims to holistically examine innovation complexities and system attractors within a hierarchically nested supply network and explore how they dynamically interact and influence adaptive innovation processes.

Design/methodology/approach

Taking a complexity theory perspective, we employed a methodological bricolage approach using a single case study with multiple embedded units of analysis – namely, a supply network encompassing 36 firms. We drew upon primary data obtained from 42 interviewees and rich secondary data, and we employed a temporal exponential random graph model to examine the micro-foundations of the evolution of the sampled supply network over a decade.

Findings

This study presents a comprehensive overview of the innovation complexities—relational, temporal, dynamic, operational and structural – and how they manifest within a supply network. It also identifies three systemic attractors – point, periodic and strange – and elucidates their relationships with the complexities and their impact on innovative supply network dynamics. The resulting conceptual framework and working propositions provide a detailed perspective on the complex interplay between balanced order and chaos and the potentially unbalanced innovation states within a supply network.

Originality/value

This research offers an in-depth perspective on the innovation complexities and dynamic attractors within a supply network from a holistic, multilevel perspective. It advances complexity theory and deepens the understanding of supply networks as complex adaptive systems.
The role of information flows in trust-based management
Authors: Norkin Svetlana , Johnsen Åge , Byström Katriina
Published: November 2024

Purpose

This paper explores the role of work-related information flows in trust-based management in public sector organizations. Whereas the relationship that trust affects information sharing between employees as such is well known, the opposite relationship where information flows that facilitate access to and sharing of work-related information affect trust is less studied.

Design/methodology/approach

The paper analyses survey data from 481 employees at lower hierarchical levels in home care and schools in the City of Oslo four years after the City Council decided to implement trust-based management, using structural equation modelling (SEM).

Findings

The results from the path models indicate that work-related information flows mediate the impact of trust-based management, contribute to the development of trust in managers and increase employee autonomy.

Practical implications

A practical implication is that work-related information flows seem to be an important design variable in trust reforms. Organizations that plan to implement more trust-based management should therefore pay close attention to how changes in management systems and leadership practices impact work-related information flows.

Originality/value

This paper contributes to the public management literature and research on trust reforms by identifying the role of work-related information flows as a mediator in trust-based management. The paper provides new insights into the relationship between information flows, trust and autonomy by combining perspectives from public management, information studies and organizational behaviour.
Machine learning-based design of double corrugated steel plate shear walls
Authors: Baboli Nezhadi Elyas , Labibzadeh Mojtaba , Hosseinlou Farhad , Khayat Majid
Published: November 2024

Purpose

In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

Design/methodology/approach

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

Findings

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

Originality/value

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.
Determining the optimal level of automation for cash flow forecasts
Authors: Rossmann Lorenz , Wald Andreas , Gleich Ronald
Published: November 2024

Purpose

The aim of this paper is to develop an approach for identifying the optimal level of automation by maximizing the level of automation and accuracy while addressing problem areas of forecast quality.

Design/methodology/approach

We use a unique set of forecasts planned by six subsidiaries of a multinational corporation to train and test various models. We compare the accuracy of three levels of automation and how they address prevalent forecasting process quality problem areas.

Findings

The findings indicate that accuracy alone is not a sufficient dimension to consider when selecting the optimal level of automation but that forecast process quality areas need to be assessed as well.

Research limitations/implications

The limitations of this work are the inability to study the effects of our tool’s recommendations, the sample originating from a single company, the use of simple statistical methods and the limited number of dimensions to evaluate forecasts.

Practical implications

Firms should apply the structure offered in this paper to target individual components of the cash flow forecasting process when automating it and use it to structure their discussion, planning and implementation of automation.

Originality/value

A novel approach for determining the optimal level of automation for cash flow forecasting combining the human information processing framework of Parasuraman et al. (2000) with the forecast quality problem areas by Fildes and Petropoulos (2015).
Unveiling the dark and scary side of metaverse: an in-depth qualitative investigation
Authors: Kumar Aman , Shankar Amit , Mehrotra Ankit , Yaqub Muhammad Zafar , A. Alzeiby Ebtesam Abdullah
Published: November 2024

Purpose

Metaverse is one of the decade’s most exciting and transformative technological innovations. While the metaverse holds immense promise, it has potential risks and dark sides. This research aims to investigate and identify the crucial dark dimensions associated with the metaverse platforms.

Design/methodology/approach

Employing a qualitative phenomenological methodology, the authors interviewed 45 metaverse users to unravel dark dimensions related to the metaverse. Analyzing the themes extracted from the participants' insights revealed an alignment with the underpinnings of the Technology Threat Avoidance (TTA) theory.

Findings

The findings of this study revealed seven major dark dimensions: addiction and dependency, isolation and loneliness, mental health issues, privacy and security, cyberbullying and harassment, digital identity theft and financial exploitation.

Practical implications

The study helps organizations and metaverse platforms understand the crucial dark dimensions of the metaverse. This study concludes by synthesizing prevalent themes and proposing propositions, offering insights for practical application and policy considerations.

Originality/value

This study provides a deeper understanding of the dark side of the metaverse environment from a user perspective using the underpinnings of TTA theory.
Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics
Authors: Xiang Junming , Fu Shixuan
Published: November 2024

Purpose

The emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.

Design/methodology/approach

This study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.

Findings

The results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.

Originality/value

Through the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.
The NFL as “not for long”: the NFL draft and teams’ discount rates
Authors: Duquette Christopher M. , Cebula Richard J.
Published: November 2024

Purpose

To present a method for calculating the discount rate that teams apply to future-year draft picks relative to current-year draft picks and then apply that method to the actual draft picks and trades over the period 2011–2022.

Design/methodology/approach

The National Football League (NFL) Draft permits teams to trade the selection rights for current-year and future-year draft picks. We seek to calculate the discount rates associated with NFL Draft trades. With this method, we calculate the discount rate for each trade by NFL teams involving a combination of current-year and future-year draft picks since 2011, when the NFL ratified a new collective bargaining agreement that instituted a draft-pick salary scale and capped the length of draftees’ contracts.

Findings

We find that teams' annualized discount rates in trading away future-year draft picks are quite steep, averaging more than 100% per year. These steep discount rates suggest that teams are pressured by market competition to adopt a “win now” approach in devaluing the future draft choices relative to the present draft choices.

Research limitations/implications

The actual discount rate for each trade is not known with certainty when the trade is transacted. This uncertainty arises because the within-round order of future-year picks is determined by teams’ future performance, which is not known at the time of the transaction.

Practical implications

In reporting these findings, we acknowledge the limitations of our analysis. The dataset is small, as there are on average between five and six trades per year involving current-year and future-year picks. More observations could have been included by extending the timeframe to before 2011, but we opted against doing so because the NFL’s 2011 CBA changed teams’ draft calculus by imposing a draft-pick salary scale and capping the length of draftees’ contracts. In addition, our discount rates as calculated are ex post facto in that they are calculated after the future-year drafts have been held. While these are the actual discount rates for the trades as transacted, the actual discount rate for each trade is not known with certainty when the trade is transacted. This uncertainty arises because the within-round order of future-year picks is determined by teams’ future performance, which is not known at the time of the transaction. As an aside, we also re-estimated each trade’s discount rate with an adjustment for that uncertainty, and the median discount rate for all 61 trades was still over 100% per year.

Originality/value

Providing insights into NFL Draft management behavior and decision-making for current-year and future-year draft picks since 2011, when the NFL ratified a new collective bargaining agreement that instituted a draft-pick salary scale and capped the length of draftees’ contracts.
The role of ingrown hairs in persistent kerion of children: A clinical study
Authors: Yao Qi‐Hao , Zhi Hui‐Lin , Xia Xiu‐Jiao , Liu Ze‐Hu
Published: November 2024

Tinea capitis, a common public health problem in developing countries, has severe forms such as kerion. However, the underlying mechanisms and standard treatments for persistent cases of tinea capitis or kerion remain controversial. In this work, we investigate the ingrown hairs and corresponding treatment in persistent kerion of children. Children with persistent kerion were enrolled among 312 cases of tinea capitis at the Department of Dermatology, Hangzhou Third People's Hospital from January 2020 to June 2024. The presence of fungal infection was ascertained by direct microscopic examination under calcofluor white staining and routine culture. The structure of the ingrown hairs was observed directly by a dermatoscope, which was subsequently extracted using sterile tools. A total of six cases of persistent kerion among 312 cases of tinea capitis were enrolled. Ingrown hairs were ascertained under dermatoscopy and extracted by minor operation. Except for one patient who continued oral terbinafine, the other five cases were cured by removal alone. Ingrown hairs, induced by fungal infection, may be an aggravating factor of persistent course of tinea capitis. Our study demonstrated that the presence of ingrown hairs could be confirmed through direct dermatoscopy, and patients experienced significant improvement following removal treatment under dermatoscopy.
An evodiamine derivative inhibits osteoclast differentiation and protects against OVX‐induced bone loss in mice
Authors: Liu Chao , Shen Huaxing , Li Huang , Wang Nan , He Shipeng , Ye Guangming , Cong Wei
Published: November 2024

Evodiamine is a biologically active alkaloid extracted from the fruit of the traditional Chinese medicine Evodia rutaecarpa (Juss.) Benth. (Fructus Evodiae, Wuzhuyu). However, due to its lipophilic chemical structure, low water solubility results in poor bio‐availability, which limits its broader application. 3‐Amino‐10‐hydroxyl‐evodiamine (E2) was a water‐soluble derivative of evodiamine with good anti‐tumour bioactivity previously developed by our team; however, its anti‐osteoporosis activity remains unclear. This study demonstrates that E2 inhibits the maturation of osteoclasts and bone resorption promoted by receptor activator of nuclear factor‐κB ligand (RANKL). Mechanistically, E2 reduced RANKL‐induced activation of nuclear factor kappa B (NF‐κB) as well as mitogen‐activated protein kinase (MAPK) pathways, causing the suppression of the expression of genes associated with osteoclasts in vitro. These genes included nuclear factor of activated T cells c1 (NFATc1), tartrate‐resistant acid phosphatase (TRAP), cathepsin k (CTSK) and dendritic cell–specific transmembrane protein (DC‐STAMP). Treatment with E2 in vitro resulted in the attenuation of p‐ERK, p‐JNK, p‐p38 and NFATc1 levels. Furthermore, ovariectomized (OVX) mice treated with E2 showed a decrease in osteoclast formation as well as preservation of bone mass. This study concludes with evidence that E2 decreases osteoclast maturation and bone resorption through the regulation of multiple signalling pathways, thereby exhibiting an osteoprotective role in OVX mice. Consequently, E2 exhibits significant potential as a prospective drug candidate for treating osteoporosis.