University of Peloponnese

• Firm innovation, digital capabilities and HR skills and practices are inextricably linked. • The participation of firms in GVCs moderates such interlinkages. • A bundle of HR practices and skills enhance digital capabilities which in turn contribute to firm innovation. • Digital capabilities play a stronger orchestrating role for GVC firms introducing product innovations. • Non-GVC firms benefit more in building digital capabilities from implementing a bundle of HR practices. In this study, we argue that the coordination of resources and people is conducive to building digital capabilities that transform innovation processes within firms, especially in a global value chain (GVC). GVC participation expands as well as obscures a firm’s boundaries. Therefore, GVC member-firms’ orchestration needs are greater, and as such, the interlinkages between digital capabilities, HR skills and practices, and innovation outcomes are stronger. Using a sample of 1014 Greek manufacturing firms, we estimate a system of equations to investigate the moderating role of GVC participation in the examined interlinkages. Empirical results confirm that developing HR skills and practices matters in building digital capabilities that consequently shape firms’ innovation outcomes. Digital capabilities have a stronger orchestrating role for GVC member-firms aiming to introduce product innovations, and for nonparticipants, the process of building digital capabilities benefits more from implementing a bundle of HR practices.

Myzus persicae (Sulzer), is a major global pest whose control is challenged by widespread insecticide resistance. This study assessed the resistance status of M. persicae populations collected in Greece (2021-2025), using diagnostic bioassays with insecticides (acetamiprid, flupyradifurone, flonicamid, sulfoxaflor and FliPPER) and a newly developed, highly sensitive droplet digital polymerase chain reaction (ddPCR) panel targeting seven key resistance mutations (vgsc: super-kdr M918T/L and kdr L1014F - pyrethroid resistance; acetyl-CoA carboxylase: A2666V - keto-enol resistance; AChE: MACE S431F - dimethyl carbamates resistance; nAChR: R81T and T74I linked to CYP6CY3 overexpression - resistance to nAChR competitive modulators). Bioassays revealed frequent cross-resistance cases. Notably, we report the first case of resistance to flonicamid. FliPPER (fatty acids potassium salts) showed the highest resistance frequency (50.0%) and sulfoxaflor the lowest (1.7%). The ddPCR analysis (on 634 aphids) confirmed the presence of six resistance mutations. The mutation T74I was almost fixed across all populations (mean resistant allele frequency of 99.9%). Other key mutations (MACE, kdr/super-kdr and R81T) were present at moderate-to-high frequencies, with the M918L super-kdr variant showing a notable increase. Myzus persicae in Greece exhibits a high level of genetic variability, leading to severe and dynamic resistance problems. The widespread metabolic resistance (CYP6CY3 overexpression) which has been found to compromise neonicotinoids and fluryradifurone and the emergence of flonicamid resistance complicate the implementation of integrated pest management. The ddPCR panel provides a valid, evidence-based tool for large-scale monitoring of resistance and decision-making, essential for managing this difficult to control pest. However, bioassays are also useful when molecular markers are not available. © 2026 Society of Chemical Industry.

This paper presents a meticulously conducted and extensive comparative analysis of the absorption patterns of European Structural and Investment Funds (ESIF) throughout the 2014–2020 programming period across a diverse array of European countries, with a particular emphasis on the Greek context. The primary objective of this study is to offer a comprehensive understanding of both shared trends and variations in fund absorption dynamics, thereby providing valuable insights into the intricate factors that shape the utilization of ESIF. Utilizing a robust methodological framework, this research undertakes an exhaustive examination of national and regional data to unravel the multifaceted determinants influencing absorption rates. Key determinants, including macroeconomic conditions, administrative efficiency, institutional capacity, and policy frameworks, are rigorously scrutinized to elucidate their nuanced impacts on ESIF absorption dynamics. Through this methodical approach, the study endeavors to provide nuanced insights into the efficacy of fund allocation mechanisms and the pivotal role played by socio-economic factors in steering regional development trajectories.

This work presents a fast neural surrogate capable of reconstructing fully three-dimensional hemodynamic velocity fields in stenotic and bifurcating microvascular geometries with satisfactory accuracy, avoiding repeated, computationally demanding computational fluid dynamics (CFD) simulations. A Fourier-augmented, coordinate-neural surrogate is presented and assessed for rapid computation of three-dimensional blood-flow fields in a sample geometry. The model is trained on detailed CFD data across a parameter set of stenosis severities that feed a direct mapping from spatial coordinates to velocity components. To mitigate spectral bias and improve accuracy in regions of steep gradients, the input space is embedded with random Fourier features and compared against a conventional multilayer perceptron (MLP) backbone. Predictive ability is assessed upon strict hold-out testing, during which certain arteriolar stenosis cases are excluded from training and treated with the Fourier surrogate. Direct comparison with CFD results reveals that the Fourier MLP achieves nearly CFD fidelity with the coefficient of determination R2 ≥ 0.994 and offers more than 80% reduction in the normalized errors as provided by conventional MLP, with the precise improvement depending on the severity of stenosis. Centerline velocity and cross-sectional profiles further show that the Fourier MLP reconstructs stenosis speed-up and radial profiles more reliably compared to conventional MLP. These results indicate that Fourier feature embedding provides a simple and effective route to robust full-field hemodynamic surrogates for efficient screening of stenosis configurations without resorting to repeated, heavily demanding CFD simulations.

Collaborative filtering is one of the most widely used methods for user rating prediction in recommender systems. To evaluate a collaborative filtering system, rating datasets are typically used, which comprise thousands to millions of records consisting of user–item–rating tuples. Initially, a similarity metric is used to quantify the closeness between each user and every other user in the dataset, typically based on the ratings that each pair of users has given to the same items. Subsequently, the K users having the largest similarity to the target user are used to produce rating predictions, which lead to recommendations. A particularly challenging case arises when the rating dataset is very sparse. In this scenario, it is difficult not only to find users with commonly rated items but also to determine the optimal similarity metric and suitable values for variable K. Setting a small value for K results in extremely low prediction coverage, leading to unsuccessful recommendations, while setting a very large K value increases memory requirements and prediction/recommendation generation time. Through a multiparameter experiment, this work aims to determine the optimal settings for rating predictions when very sparse datasets are used in collaborative filtering recommender systems.

This study examines the impact of heterogeneity in legal origin between partners on the performance of Private Equity (PE) funds. Using a dataset of 3658 buyouts from 2000 to 2016, we show that internationalized PE partnerships, where Limited Partners (LPs) and General Partners (GPs) are from different legal systems, underperform compared to those within a single legal regime. We attribute this effect to challenges in contract enforcement and monitoring. Interestingly, while GP experience does not mitigate this negative effect, LP experience seems to intensify it, possibly due to overconfidence. We further find that funds with civil law GPs and common law LPs perform worse in terms of IRRs and MOIC, whereas the opposite combination only shows a decrease in MOIC. We explore potential explanations for these patterns, including the role of institutional differences, and discuss their implications for theories of PE internationalization and avenues for future research. • We explore how PE Partnerships perform when LPs and GPs are from countries with different legal origins (common vs civil law). • Internationalized PE partnerships with common and civil law partners underperform compared to those that operate under a single legal origin family. • GPs' experience doesn't mitigate this adverse effect, while LPs' experience worsens the negative effects of PE internationalization on fund performance. • The study investigates cultural factors. • We explore the impact of GP and LP experience, and the legal family each partner originates from. We draw out the implications of this for theory and practice.

In recent years, Transformers have become the dominant neural architecture in the fields of natural language processing and computer vision. The generalization of Transformers to graphs, so-called Graph Transformers, have recently emerged as a promising alternative to the successful message passing Graph Neural Networks (MPNNs). While the expressive power of MPNNs has been intensively studied in the past years, that of Graph Transformers is still underexplored. Existing results mostly rely on the employed structural/positional encodings and not on the pure architecture itself. However, gaining an understanding of the strengths and limitations of Graph Transformers would be very useful both for the scientific community and the practitioners. In this paper, we derive a connection between Graph Transformers and the Congested clique, a popular model in distributed computing. This connection allows us to translate theoretical results for different graph problems from the latter to the former. We show that under certain conditions, Graph Transformers with depth 2 are Turing universal. We also show that there exist Graph Transformers that can solve problems which cannot be solved by MPNNs. We empirically investigate whether Graph Transformers and MPNNs with depth 2 can solve graph problems on some molecular datasets. Our results demonstrate that Graph Transformers can generally address the underlying tasks, while MPNNs are incapable of learning any information about the graph.

Background/objectivesThe purpose of this systematic review was to synthesize available human studies, present and weigh the evidence regarding the efficacy of ketogenic dietary interventions (KDIs) for ADPKD, and provide a direction for future research and relevant recommendations.MethodsThree databases were searched and risk of bias (RoB) of the studies was assessed using Cochrane's RoB 2.0, the Newcastle-Ottawa scale and the ROBINS-IΙ tool. The Synthesis Without Meta-analysis (SWiM) extension was used to present the results.ResultsEight studies were identified delivering interventions with ketogenic diets, intermittent fasting, time-restricted feeding, etc. KDIs were generally associated with weight loss and a more favorable anthropometric profile in most interventions (nine). Blood pressure remained unchanged in most interventions (five), similar to blood cholesterol, HDL-cholesterol, triglycerides and LDL concentrations. Regarding renal outcomes, eGFR was either higher (4 interventions) or remained stable post-intervention. htTKV remained unchanged in most of the studies. Evidence for renal structural change was inconsistent and limited by short intervention duration and small sample sizes.ConclusionsThe evidence on KDIs for ADPKD is still limited. However, KDIs, particularly caloric-restriction diets, appear promising tools for managing ADPKD. Current human data support metabolic feasibility more consistently than renal disease modification. The effects of KDIs on renal structural outcomes remain uncertain and longer trials with appropriate comparators, namely the standard of care diet for ADPKD, are required before KDIs can be recommended for ADPKD.

ABSTRACT A novel CMOS‐only voltage reference is proposed in this paper, utilizing neither bipolar junction transistors nor operational amplifiers. To obtain a temperature‐independent voltage at the output, the circuit combines two opposite temperature coefficient (TC) voltages, the proportional‐to‐absolute temperature voltage and the complementary‐to‐absolute temperature voltage. The proposed voltage generator has been designed in a 65‐nm CMOS technology with a supply voltage range from 1.8 to 2.5 V, a voltage reference of 970 mV in a temperature range of −40°C to 120°C and a power supply rejection ratio around −46.5 dB at 100 Hz. The main benefits of the proposed topology are its simple design—consisting of seven MOSFETs and two resistors—a very low TC of approximately 9.67 ppm/°C at the worst‐case corner, and a line sensitivity of 0.0765%/V.