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A Comparison of Physical Activity Level, Quality of Life, Sleep Quality and Depression Levels in Mothers of Disabled Children With and Without Musculoskeletal Pain
(İstanbul Gelişim Üniversitesi, 2025-11) Yalçın, Gülay; Kayış, Rukiye; 308202
Aim: This study aims to compare physical activity levels, quality of life, sleep quality, and depression levels in mothers of children with disabilities, distinguishing between those with and without musculoskeletal pain. Method: A total of 61 mothers aged 20–45 years participated in the study, including 27 mothers with musculoskeletal pain, as determined by the Visual Analog Scale (VAS), and 34 mothers without musculoskeletal pain. The physical activity levels of the mothers were assessed using the International Physical Activity Questionnaire – Short Form (IPAQ), pain status with the VAS, depression levels with the Beck Depression Inventory (BDI), sleep quality with the Pittsburgh Sleep Quality Index (PSQI), and quality of life with the Nottingham Health Profile (NHP). Results: Body mass index, NHP, PSQI, VAS and number of pregnancies were statistically significantly different between the two groups (p<0.05). There was no statistically significant difference between the two groups in the results of IPAQ, BDI, number of children, number of miscarriages, smoking, presence of care assistants, educational status and economic status (p>0.05). Conclusion: Musculoskeletal pain negatively affects the sleep quality and quality of life of mothers of children with disabilities. Depression levels and physical activity levels were high in both groups. In addition, factors such as sociocultural and number of children are also associated with musculoskeletal pain.
A comprehensive analysis of apricot drying methods via multi-criteria decision making techniques
(Wiley, 2024-10) Abdulvahitoğlu, Aslı; Abdulvahitoğlu, Adnan; Cengiz, Nurten; 382420
Food and food safety have been among the most important issues for people throughout history. Societies have always tried to be self-sufficient in food and have avoided becoming dependent on foreign sources. However, the fact that most foods are seasonal and the increasing population's food consumption have revealed the need to preserve foodstuffs for a long time. The old and well-known method used today for extending shelf life is the drying process. The drying process is preferred over other preservation methods for reasons such as being more economical, easier to transport, having a longer shelf life, more concentrated nutritional value, and containing fewer additives. This ensures that dried foods are of higher quality in terms of physical, chemical, microbial properties, and nutritional values compared to other packaged foods. While the drying process was traditionally done over a long period, technological advancements have led to the production of higher quality and more valuable commercial products in a shorter time. In this study, traditional and technological methods used in drying apricots were compared according to the parameters determined by experts in the field. Since multiple parameters are effective in the comparison, Multi-Criteria Decision Making (MCDM) techniques were used. The optimum apricot drying method was determined by combining the results obtained from different MCDM techniques with the Borda rule.
A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture
(Springer, 2024-09) Gülmez, Burak; 222298
This review paper investigates the utilization of Convolutional Neural Networks (CNNs) for disease detection in potato agriculture, highlighting their pivotal role in efficiently analyzing large-scale agricultural datasets. The datasets used, preprocessing methodologies applied, specific data collection zones, and the efficacy of prominent algorithms like ResNet, VGG, and MobileNet variants for disease classification are scrutinized. Additionally, various hyperparameter optimization techniques such as grid search, random search, genetic algorithms, and Bayesian optimization are examined, and their impact on model performance is assessed. Challenges including dataset scarcity, variability in disease symptoms, and the generalization of models across diverse environmental conditions are addressed in the discussion section. Opportunities for advancing CNN-based disease detection, including the integration of multi-spectral imaging and remote sensing data, and the implementation of federated learning for collaborative model training, are explored. Future directions propose research into robust transfer learning techniques and the deployment of CNNs in real-time monitoring systems for proactive disease management in potato agriculture. Current knowledge is consolidated, research gaps are identified, and avenues for future research in CNN-based disease detection strategies to sustain potato farming effectively are proposed by this review. This study paves the way for future advancements in AI-driven disease detection, potentially revolutionizing agricultural practices and enhancing food security. Also, it aims to guide future research and development efforts in CNN-based disease detection for potato agriculture, potentially leading to improved crop management practices, increased yields, and enhanced food security.
A modern approach to data privacy with federated learning
(Maltepe University, 2023) Kalkavan, Ziya Can; Şahinaslan, Ender; Şahinaslan, Önder; 122635
Today, information technologies and their usage areas are increasing day by day. Advanced technologies such as the internet of things, smart devices and applications, machine learning and arti cial intelligence are a driving force in the spread of their usage areas. The increase in prevalence and use also increases the production and sharing of data. This increase causes various security problems and concerns in terms of data privacy. Therefore, a balance has to be struck between the need for data sharing and its security. For this purpose, the use of federated learning methods has been examined. Traditional data sharing methods focus on centralized solutions for the processing of private and sensitive data of data subjects, but this causes various problems and raises concerns in the sharing of sensitive data. In the federated learning model, it trains locally without data sharing. It has a distributed arti cial intelligence approach that can run di erent resources together. Thus, it o ers an alternative solution that can help address data privacy concerns arising from the traditional method. In this study, the basic principles, usage areas, advantages and difficulties of federated learning, which is also accepted as a modern approach in data privacy, are discussed. The data and examples obtained in the study will be presented.
A Multi-Criteria Decision-Making Approach to Enhancing Border Security Against Irregular Migration
(Manisa Celal Bayar Üniversitesi, 2025-12) Abdulvahitoğlu, Adnan; Güçlüten, Çağrı; Tunca, Hakan Ömer; 382420
International migration has become a fundamental phenomenon shaping social, political, and economic structures in the globalized world. The increase in human mobility is directly linked to the strengthening of global connections in areas such as economy, technology, culture, and education. Since the Second World War, poverty, unemployment, and political oppression have driven individuals toward countries offering better living conditions. The European Union (EU) member states and the United States are among the primary destinations for migration. The recent surge in irregular migration has led these countries to adopt stricter and more protectionist policies. Located along the EU’s transit route, Türkiye is a strategic actor in border security, with the prevention of irregular migration constituting a key policy objective. This study employs the Stepwise Weight Assessment Ratio Analysis (SWARA) method to evaluate border security systems and identify priority measures. Based on the assessments of nine experts with an average of 15 years of field experience, the three most critical components were identified as border patrol activities, the use of unmanned aerial vehicles, and internal security patrols in border provinces. The findings provide a concrete roadmap for policymakers in terms of resource allocation, operational planning, and technological investment.
A novel hybrid MCDM framework combining TOPSIS, PROMETHEE II, and VIKOR for peach drying method selection
(Elsevier, 2024-11) Gülmez, Burak; 222298
The selection of optimal drying technologies for peach processing presents a complex decision-making challenge due to multiple conflicting criteria. This study introduces a novel hybrid multi-criteria decision-making (MCDM) framework combining TOPSIS, VIKOR, and PROMETHEE II methods to evaluate eight drying technologies. The evaluation was conducted across twelve criteria, encompassing product quality, operational efficiency, economic factors, and environmental impact. Data were collected from five industry experts through structured matrices. The results demonstrate that vacuum drying emerged as the optimal technology, maintaining the top position in 75 % of sensitivity scenarios. Freeze drying and heat pump drying consistently ranked among the top three alternatives across all methods. The correlation analysis revealed strong agreement between VIKOR and PROMETHEE II rankings (0.857), while TOPSIS provided complementary insights. Sensitivity analysis identified energy consumption, investment cost, and nutritional retention as the most critical factors influencing technology selection. The findings indicate that advanced drying technologies significantly outperform traditional methods in terms of overall performance. This research provides a comprehensive framework for evidence-based decision-making in food processing technology selection and establishes quantitative benchmarks for future technology evaluations in the fruit drying industry.
A qualitative examination of career adaptation of LGBT+ individuals
(Springer, 2026-02) Çohadar, Esra; Karacan Özdemir, Nurten; 392810
This study explores LGBT+ (Lesbian, Gay, Bisexual, Transgender, and other sexual and gender minority identities) individuals’ career adaptation processes through the career construction model of career construction theory. Employing a qualitative, phenomenological design, data were collected from 15 participants via snowball sampling using a personal information form and semistructured interviews. Content analysis revealed 14 themes and 31 categories across four dimensions: adaptivity, adaptability (concern, control, curiosity, confidence), adapting responses, and adaptation results. Participants reported challenges in career decision-making and exploration. Some preferred working in socially accepting countries. Coming out influenced the effectiveness of coping strategies in job search and the workplace.
A RESEARCH TO EXAMINATE THE RELATIONSHIP BETWEEN BRAND HATE, NEWOM AND BOYCOTTING BEHAVIOR
(KENAN ÇELİK, 2026-04) Dişli Bayraktar, Eda; Alp, Binnaz; Alp, Mustafa; Kalça, Adem; 409828
This study aims to examine the effect of brand hate on consumers’ boycott behavior by exploring the mediating role of negative word-of-mouth (NeWOM). Data were collected from 396 consumers through an online survey distributed via social media and analyzed using Structural Equation Modeling. The findings reveal that brand hate significantly increases NeWOM; NeWOM influences boycott intention and partially mediates the relationship between brand hate and boycott behavior. Overall, strong feelings of brand hate enhance consumers’ tendency to spread negative opinions and experiences, which directly or indirectly strengthen their intention to boycott; thus, the study contributes to understanding the negative emotional and communicational dimensions of consumer–brand relationships.
A simple magnetic resonance scoring system for predicting suitability for primary anterior cruciate ligament repair
(Erzincan Binali Yıldırım Üniversitesi, 2024-08) Kılızay, Yusuf Onur; Erdoğmuş Gülcan, Server; Yılmaz, Hazal Berfin; Yalçın, Gülay; 334080; 392815; 202217; 308202
Objective: This study developed a simple magnetic resonance imaging (MRI) scoring method to assess the suitability of the anterior cruciate ligament (ACL) remnant for primary repair and aimed to test the success of this scoring method on operative images of patients undergoing early ACL surgery. Methods: The video-recorded operative images of patients who underwent ACL reconstruction and the MRI images of the same patients taken in our hospital were retrieved from the hospital archive. Two surgeons evaluated whether the ACL could be primarily repaired on the video images recorded during the operation. Magnetic resonance primary repairability (MPR) scores and repairability status on video images were compared. Results: The mean age of the patients was 30.4 ± 8.6 years. The evaluation of remnant size on MRI showed moderate agreement between observers (P < .001, Cohen’s kappa = 0.605). The assessment of the repairability score based on MRI and video observation demonstrated substantial agreement between observers (P < .001, Cohen’s kappa = 0.743 and P < .001, Cohen’s kappa = 0.762, respectively). Conclusion: The MR primary repairability score (MPR score) is suitable for use in the decision-making process for the primary repair of the ACL.
A study of the reliability and validity of the Mindfulness Parenting Scale in Infancy and the examination of Mothers' Mindfulness in Pareting in Turkish samples
(UNIV POLITECNICA VALENCIA, EDITORIAL UPV, 2025-10) Sezgin, Elif; 157389
The research aims to assess the reliability and validity of the Mindful Parenting in Infancy Scale (MPIS) for mothers with infants aged 0-24 months and to analyze their mindfulness levels across various variables. The study included 353 mothers from Bursa's Nil & uuml;fer and Osmangazi districts, with data collected in private nurseries and daycare homes between December 2023 and March 2024. Teachers distributed the data collection tools, which included the "Mother and Baby Information Form" and the MPIS, developed by Gartstein (2021). Adaptation permissions were secured, and the scale's language, content, and structure were validated. Reliability was measured using the Cronbach Alpha internal consistency coefficient and item-total correlations. Statistical analyses included independent samples t-test and One-way ANOVA to explore MPIS scores across demographic variables. The Levene test assessed homogeneity, while kurtosis and skewness evaluated normal distribution. The internal consistency coefficient was 0.74, with item-total correlations ranging from 0.35 to 0.49. The findings indicated no significant differences in mindfulness based on mothers' age, education, or family type, but highlighted variations based on the birth order of the baby.
A Symmetry-Based Spherical Fuzzy MCDM Approach for the Strategic Assessment of Alternative Fuels Toward Sustainable Energy Policies
(MDPI, 2025-06) Abdulvahitoğlu, Adnan; 382420
Alternative fuels obtained from renewable sources, providing low greenhouse gas emissions and high energy efficiency, offer significant advantages in terms of sustainability. In addition, the wide applicability of these fuel types in sectors such as housing, transportation, and industry creates significant opportunities in terms of reducing dependence on fossil fuels. Alternative fuels should be evaluated not only according to their environmental contributions but also based on multi-dimensional criteria such as economic cost, technical suitability, sustainability level, fuel properties, infrastructure requirements, and social acceptance. In this context, a comparative analysis of alternative fuel types in terms of various basic parameters is no longer optional, but a necessity. These parameters generally include symmetrical relationships such as balanced trade-offs between economic and environmental dimensions or mutual effects between technical and social criteria. However, they also show variability and uncertainty depending on the fuel type. Therefore, Spherical Fuzzy Multi-Criteria Decision Making (SF-MCDM) methods, which can effectively represent symmetry in membership and hesitation degrees, have been used to achieve more realistic and reliable results in uncertain decision environments. The proposed model provides a systematic and flexible evaluation structure that helps decision makers determine the most appropriate alternative fuel options and contributes to the formation of sustainable energy policies.
A theoretical and experimental investigation using a multi-criteria decision-making approach to investigate the use of oak cupule biomass as a sustainable corrosion inhibitor for mild steel in acidic solution
(Elsevier, 2025-08) Şişmanoğlu, Sedef; Mert, Mehmet Erman; Doğru Mert, Başak; Abdulvahitoğlu, Adnan; 382420
This study investigates adsorption mechanism and inhibition efficiency of oak cupule (OC) biomass on mild steel (MS) corrosion in an acidic medium through quantum chemical calculations and electrochemical analysis. The chemical composition of OC was characterized using FTIR-ATR, mass spectrometry, and UV–Vis spectroscopy. Electrochemical impedance spectroscopy was conducted at varying inhibitor concentrations, complemented by surface morphology analysis via SEM and contact angle measurements after 168 h of immersion. At 250 ppm, the inhibition efficiency reached 92.5 %, attributed to the presence of tannins and polyphenolic compounds. Adsorption followed the Langmuir isotherm, with an equilibrium constant of 2.36 L mg−1 and a Gibbs free energy change of −12.08 kJ mol−1. Contact angle measurements indicated increased hydrophobicity due to the formation of protective layer. Potential of zero charge (PZC) analysis confirmed electrostatic interactions between OC molecules and the positively charged MS surface in 0.5 M HCl. Molecular insights were obtained using Density Functional Theory (DFT) with the B3LYP/6–311++G(d,p) method, revealing electronic properties such as frontier molecular orbital energies, energy gap and atomic charges. The optimum result was determined by analyzing the experimental findings at different inhibitor concentrations and different durations using Multi Criteria Decision Making (MCDM) methods. It was found that scenario S5 provided the optimum result.
A Zone of Death: Ballardian Necropolitical Sovereignty in Concrete Island
(RumeliYa, 2025-07) Özçelik, Kaya; 351393; Yılmaz, Yakup
This study focuses on J.G. Ballard’s Concrete Island (1974) through the theoretical lens of necropolitics defined by Achille Mbembe to analyse the abandonment and exclusion of certain lives within the infrastructural settings of late modernity. Centring on the protagonist Robert Maitland, stranded in an abandoned interstice of a London motorway - an island within a hyperfunctioning urban framework, the novel details the bitter struggles of an individual to survive. His descent into an utter physical deterioration and psychological disintegration depicts a necropolitical rationale in which the system of the state and its technological mechanisms determine not only who is entitled to live, but more crucially, who can be permitted to die. In this context, this study posits that the motorway island in the novel portrays a necropolitical zone as an uncontrollable space that partially exists within and outside the urban framework, where normative protections and social acknowledgements are put on hold. It is through this marginalisation that Ballard also criticises the immunitarian structures of neoliberal urbanism. Through this spatial marginalisation, Ballard critiques the immunitarian structures of neoliberal urbanism, which prioritise speed, efficiency, and visibility, while transforming certain bodies and lives into throwaways. Putting Maitland’s ongoing dehumanisation process in front of the eyes of the reader, Ballard illustrates how sophisticated infrastructure leads to a desolate environment with human detritus, reminding the reader of Mbembe’s claim that the ultimate expression of sovereign power is verified in its capacity to determine the individuals who are allowed to continue living and who are not. Building on Roberto Esposito’s immunitary paradigm and urban biopolitical theory, this study explores Ballard’s Concrete Island within the contemporary discussions focusing on urban isolation, social exclusion, and the politics of violence regarding the fast-developing modern world.
Agricultural Total Factor Productivity in Türkiye: An ARDL Analysis of Macro-Institutional Drivers
(Okur Yazar Derneği, 2025-12) Şengül, Serkan; Karahan Dursun, Pınar; 355807; 414023
This study examines the determinants of agricultural total factor productivity (TFP) in Türkiye over the period 1991–2022 using the ARDL approach. The analysis in-corporates agricultural credit, agricultural CO₂ emissions, human capital (average years of schooling), urbanization, and agricultural value added as explanatory variables. The Bounds test confirms the existence of a cointegration relationship among the variables. The long-run ARDL model results show that agricultural credit and urbanization have negative effects on TFP, while human capital and agricultural value added contribute pos-itively. These signs are also confirmed by the short-run ARDL model. The empirical re-sults indicate that agricultural CO₂ emissions are insignificant in the long run but exert a negative short-run effect, reflecting temporary stress and inefficiencies. Overall, the study provides important policy insights, emphasizing the need for financial reforms, human capital development, rural revitalization, value-chain strengthening, and climate-smart practices to sustain agricultural productivity growth.
An examination of the psychological resilience of preschool children with and without Montessori education
(Wiley, 2024-08) Sezgin, Elif; Şener, Tülay; 157389
This study aims to compare the psychological resilience of children who receive Montessori education with those who do not receive Montessori education(the group attends a school where education is provided according to the Preschool Education Programme of the Ministry of National Education). The study used the Relational Scanning Model Comparison Types to compare the psychological resilience of children who received Montessori education with those who did not. The research involved 99 children aged 3–6 years who received regular preschool and Montessori education during the 2021–2022 academic year. It is reported that no developmental or neurological defects were observed in any of the participants. The study group comprised 50 children aged 3–6 years from a private kindergarten affiliated with National Education in Nilüfer district and 49 children aged 3–6 years from a private Montessori kindergarten in Nilüfer district. According to the study, children who received Montessori education demonstrated comparatively higher levels of psychological resilience than those who did not receive Montessori education. It has been observed that there exists a weak but meaningful correlation between the psychological resilience of children and their social relationships/social performance, particularly in those who have received Montessori education for an extended duration. As a result, it can be inferred that Montessori education positively impacts children's psychological resilience.
An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson’s disease motor progression
(FRONTIERS MEDIA SA, 2026-02) Fırat, Yelda; 41749
Introduction: Predicting Parkinson's disease (PD) motor progression remains challenging despite advances in neuroimaging. Blood-based transcriptomic profiling offers a more accessible and cost-effective alternative. This study aimed to develop and validate a machine learning approach using blood-based transcriptomic data to predict 12-month motor severity in PD and to identify the transcriptomic features and biological pathways most strongly associated with progression. Methods: A Stacking Regressor ensemble model combining three gradient boosting algorithms (XGBoost, LightGBM, CatBoost) was developed using baseline Parkinson's Progression Markers Initiative (PPMI) data (n = 390), integrating blood RNA sequencing (RNA-seq) and clinical features to predict 12-month UPDRS Part III scores. SHapley Additive exPlanations (SHAP) analysis was applied to identify key prognostic features, evaluating seven PD risk genes (SNCA, LRRK2, GBA, PRKN, PINK1, PARK7, VPS35) and pathway scores for mitochondrial dysfunction, neuroinflammation, and autophagy. Results: On an independent test set (n = 78), the model achieved a Coefficient of Determination (R & sup2;) of 0.551 and Mean Absolute Error (MAE) of 6.01. SHAP analysis identified the baseline UPDRS & times; PINK1 interaction (UPDRS_BL & times; PINK1) as the most influential feature (mean |SHAP| = 0.283). Among transcriptomic features, VPS35 (mean |SHAP| = 0.010), GBA, and LRRK2 were most prominent. Mitochondrial dysfunction showed the highest pathway contribution (mean |SHAP| = 0.008). Discussion: The study establishes that machine learning integrating blood transcriptomics and clinical data effectively predicts motor progression in PD. Crucially, the interplay between initial clinical state and specific genetic backgrounds-particularly PINK1-is a more powerful prognostic indicator than any factor alone. This study provides systematic evidence that mitochondrial dysfunction is a dominant prognostic signal for disease progression, nominating key genes and pathways for future mechanistic and therapeutic investigation.
ARTIFICIAL INTELLIGENCE INDUCED UNEMPLOYMENT FEAR SCALE
(ISARC, 2026-02) Ulusoy, Kaan; İşcan, Orhan; Boz, Dursun; 331826
The insurance sector is currently one of the industries that makes extensive use of artificial intelligence technologies (AI). In operational terms, AI is actively used in a wide range of areas, from sales and marketing processes to customer relationship management and after-sales services, from reporting and claims–compensation management to new product development and information technology applications. Within the “Fintech” concept, which has emerged and gained prominence through the convergence of finance and technology, insurance technologies known as “Insurtech” operate by developing digital solutions aimed at making existing models in the insurance sector more profitable, efficient, and effective. Technological initiatives continue their activities in regions such as Silicon Valley, where R&D activities are supported, benefiting from various incentives and industry collaborations. In the Turkish insurance sector, where similar practices are present, national companies are also observed to be adapting to modern technologies and being influenced by digitalization processes. One of the most prominent concerns arising from the rapid development and deep sectoral penetration of AI technologies is the possibility that AI may replace human labor in the future. In this study, fear of unemployment caused by artificial intelligence was taken as the central focus, and the fear of job loss experienced by individuals working in insurance companies operating in Turkey that use artificial intelligence technologies was examined. As a result of the literature review, no previous scale development study was found in the relevant field; therefore, the need to develop a new scale emerged. In the pilot study, the Artificial Intelligence Induced Fear of Unemployment Scale, consisting of 25 items and developed for the first time in the field, was administered to 100 insurance company employees through convenience sampling on a voluntary basis. As a result of the analyses conducted, it was determined that the factor loadings of the sub-dimensions of the scale named “Managerial Approach”, “Technophobia”, “Self-Efficacy”, and “Adaptation” ranged between 0.579 and 0.908. The developed scale has been introduced to the literature as a tool applicable not only to the insurance sector but also to different sectors.
Assessing competition in the Turkish cement industry: insights from the boone indicator
(Emerald Publishing Limited, 2024-08) Yıldız, Hakan; Köprücü, Yılmaz; Şengül, Serkan; 355807
Purpose – This paper investigates the degree of competitiveness within the Turkish cement industry, employing firm-level quarterly data spanning from 2008 to 2016. Design/methodology/approach – To assess the level and trajectory of competition among Turkish cement firms, we employ the Boone indicator (β) as formulated by Boone (2008). This indicator, rooted in the concept of relative profit differences (RPD), serves as a robust metric for gauging competitive dynamics. According to the ß indicator, firms exhibiting higher relative efficiency are expected to secure greater profits and market shares in a fiercely competitive market. Additionally, we utilize concentration indices for the purpose of revealing comparable findings. Findings – Empirical findings reveal that an enhancement in firms’ efficiency corresponds to a proportionally modest increase in either market share or profits, implying a lower degree of competition within the Turkish cement industry. Although the specific magnitudes of ß estimates exhibit temporal fluctuations, we may conclude that the Turkish cement industry does not conform to the ideals of perfect competition. The concentration indices calculated on the sample also support this result. Research limitations/implications – This research is limited to the Turkish cement companies over the period 2008–2016. Originality/value – The studies measuring the level of competition in the Turkish cement sector are generally based on concentration ratios. In this study, we assess the competition level by using a different methodology based on parametric procedures.
Bibliometric analysis and mapping of the benefits and challenges of cloud ERP systems
(İstanbul Üniversitesi, 2023) Şahinaslan, Önder; Şahin, Ahmet; Şahinaslan, Ender; 122635
Enterprise resource planning is an effective tool in achieving management goals. Cloud ERP systems and applications are platform-independent offerings of this management tool in a cloud environment. This study was carried out to make sense of the advantages, difficulties and relationships of the cloud ERP system with scientific studies. For this purpose, the Scopus, Web of Science and Google Scholar databases and the Publish or Perish, WOSviewer and Excel applications were used. Statistical analysis, text mining, word network association, visual mapping and trend analysis were performed. As a result of the analysis, it was found that the total rate of publications produced in the last 3 years was 43%, the most cited work was Springer (20%) and the country was the USA (10%). It was determined that the three most frequently used keywords were ‘cloud ERP’, ‘ERP system’ and ‘ERP’. A strong correlation was found between ‘study’ and ‘challenge’ in text mining. The challenge was closely related to ‘SMEs’, ‘data’, ‘provider’, ‘technology’, ‘literature’ and ‘cloud environment’. In recent studies, the concept of ‘cloud ERP implementation’ in SMEs has come to the fore.
Bridging the Education–Employment Gap in Europe: An AI-Driven Approach to Skill Matching
(MDPI, 2025-10) Sanguino, Ramón; Çağlarırmak Uslu, Nilgün; Karahan Dursun, Pınar; Özdemir, Caner; Barroso, Ascensión; Sánchez-Hernández, María Isabel; Gaga, Eftade O.; 414023
Education–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.

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