This study examined the impact of leadership styles on academicians’ and leaders’ burnout in private universities. It also attempted to identify the moderating role of work stress management on the relationship. More specifically, it aimed to identify the impact of transformational leadership, transactional leadership, and servant leadership on academicians’ and leaders’ burnout in Jordanian private universities. Then, it examined the word stress management factor as a moderator to identify whether this factor strengthens or weakens the relationship. In order to attain these goals, the researcher embraced the use of SPSS and AMOS software to analyze the data. The results indicated that leadership styles play important role towards burnout among academicians and academic leaders. To be more precise, transformational leadership, transactional leadership, and servant leadership directly and indirectly influence burnout. Also, the work stress management plays a moderating role in this relationship. Therefore, one can state that transformational and servant leadership helps to decrease burnout and burnout through motivation and encouragement people, whereas transactional leadership can lead to psychological burnout due to the emphasis on rules and incentives without referring to feelings. The poll also discovered that the work stress management indicators such as equal distribution of duties, proper handling of disagreements, and mental health support have a depressive impact on the work stress related to the emotional exhaustion and consequently on the workplace output and well being of the staff.
Digital changes in education systems have made the problem of ensuring equal access to digital resources, infrastructure and learning opportunities more urgent. The purpose of the study is to analyze the formation of digital inclusion in the education system of Kazakhstan and identify factors that determine inequality of participation in the digital educational environment. A mixed design was used, which involved a synthesis of a quasi-experimental approach (pre-/post-test), quantitative questionnaires and qualitative analysis of open-ended responses from students and teachers. The total sample consisted of 380 participants. The results showed an average level of digital inclusion (M = 63.8, SD = 11.4). More than a third of students (34.7%) used a smartphone as their main learning device. This is due to reduced indicators of digital inclusion (t(318) = 5.14, p < .001, d = 0.81). Only 26.9% had a stable Internet connection. ANOVA indicated statistically significant SES gaps (F(2,317) = 28.41, p < .001): students with high SES (M = 71.8) outperformed students with average (M = 64.3) and low SES (M = 57.2). Regression analysis confirmed that digital competence is the strongest predictor of digital inclusion (β = 0.36, p < .001). The study indicated that there are the following barriers to the development of digital inclusion: technical, cognitive-navigational, organizational and psychological. At the same time, the experimental intervention (digital training, LMS standardization, digital tutoring) contributed to the increase in the level of digital inclusion. Therefore, the study made an empirical contribution to the understanding of digital inclusion in Kazakhstan and pointed to the interaction of infrastructural, cognitive, and social factors.
The purpose of this proposed study is to analyze the possibilities of applying artificial intelligence (AI) technologies in creating virtual laboratories for engineering education, identify key technological and pedagogical components of such systems and assess their potential impact on the quality of education. The methodological framework was based on a quasi-experimental design. For this purpose, 124 students were selected and divided into experimental and control groups. Data were collected using the Engineering Skills Assessment, Social Engagement Scale (SES) and AI log files. The analysis was conducted using t-tests. The results noted that students who worked in an AI-supported virtual laboratory demonstrated significantly higher final scores (M = 76.84) compared to the control group (M = 65.37), and the increase in competencies was +18.13 points versus +7.47, respectively. The analysis confirmed the high significance of these differences (t(122) = 4.87, p < 0.001). The conclusions confirm that virtual laboratories, enhanced with adaptive AI algorithms, are an effective tool in engineering education, capable of improving the results of students' practical training.
Higher education institutions in the region face limitations in the traceability, management, and auditing of research proposals due to the use of office forms, scattered files without centralization, and a lack of integration and control. In response to this shortcoming detected at the Technological Units of Santander institution, a web application was implemented to optimize the capture, organization, and evaluation of research ideas, strengthening the institutional research ecosystem and mitigating the problem presented. The overarching aim was to design and implement a technological tool that would ensure efficiency, security, and scalability in the management process of research ideas. The study adopted a qualitative approach with a descriptive, cross-sectional, and non-experimental scope; it was framed within Design Science Research, supported by the Model-View-Controller architecture, as well as technologies including Laravel, PHP, and MariaDB for technological development. The implemented system enabled the automation of key tasks such as code assignment, internal audits, and notifications, which significantly improved traceability and real-time information access. It is concluded that the proposed development constitutes a replicable and relevant solution for higher education institutions facing similar challenges in research management.
Unified Power System (UPS) is an important critical infrastructure in Kazakhstan. Post-Soviet old designs, cyberattacks, natural disasters, and peak operational loads are the key risk factors. To ensure a good economic resilience and national security these structures have to be stable under extreme stress conditions. Therefore, the main objective of this paper is to use Monte Carlo simulations to evaluate the stability of Kazakhstan's smart grid infrastructure, under high-load scenarios. Seven scenarios were chosen in this work. These included a baseline case and two mitigation scenarios (capacity upgrade and demand management). In addition, four stress scenarios were analyzed including winter peaks, cyber-attack, renewable energy integration, and simultaneous transmission component outages. The system failure is defined as the occurrence of load demand exceeding allowable capacity limits. Results revealed the failure probability of the capacity upgrade scenario to be the lowest (1.5%) and the highest for cyber-attacks and simultaneous component outages scenarios (16.9%). The present research paper showed that probabilistic simulations can be used effectively to quantify the vulnerabilities in Kazakhstan's UPS.
Digitalization is now considered as necessary for strengthening development and resilience of any nation. The present study examined the role of digitalization in regional economic resilience in Ukraine in the scenario of disruption and displacement. The study utilized comparative cross-regional dataset. The analysis evaluated variations in digital infrastructure and business-facing e-services in various regions of Ukraine. It also examined other factor than can help in recovery and enhancing resilience. Those factors included SME continuity, employment stability and logistics restoration. Quantitative tables summarized differences in the availability and uptake of e-services for permitting, taxation, reporting and supply-chain coordination. Further, the qualitative interviews with regional administrators and firm representatives provided evidence of operational mechanisms. The first and most notable finding is the very strong positive correlation (r = 0.87, p < 0.01) between the Digitalization Index and the Resilience Index; between E-service uptake and Firm continuity (r = 0.82, p < 0.01). The strong positive correlation confirmed that digitalization intensity was closely associated with post-shock economic recovery at the regional level. The findings indicated those regions with higher levels of digital readiness and specially those offering comprehensive online business registration, reporting and logistics platforms demonstrated faster recovery of enterprise activity and more stable supply flows. However, the areas with limited digital access or low administrative capacity faced longer recovery times and greater firm attrition. The results suggest that digitalization is needed for the structural resilience and it helps in enabling administrative continuity and business reactivation without physical presence.
This study investigated the effectiveness of an AI-supported Biology 3 unit on preventive medicine concepts among third-year secondary female students in Saudi Arabia, employing an experimental design that utilized virtual simulations, interactive activities, and project-based learning. Results indicated that the experimental group achieved significantly higher scores in health awareness and preventive knowledge with a strong effect size, suggesting that such AI tools effectively bolster student health literacy. While the study’s focus on a single, female-only setting necessitates broader replication across different regions to ensure generalizability, it provides valuable experimental evidence for the originality of AI-supported instruction, advocating for the systematic integration of preventive medicine into biology curricula alongside expanded AI tools and continuous teacher development.
Recently, location-based systems (LBS) have been proven to be an essential element of smart cities due to the valuable benefits they provide to users searching for their nearest Points of Interest (PoI), facilitating daily life activities. However, privacy protection is a major concern in LBS, where attackers can apply advanced attacks, such as location homogeneity, semantic location and query analyzing attacks, to infer sensitive information about the private lives of LBS users. Therefore, protection of location privacy as well as query privacy is necessary to increase the trust of users in LBS. To address this issue, we present the Intelligent Comprehensive Privacy Protection (IntCPP) system as an enhancement of our previous work by employing a deep-learning technique. The Foursquare weekly trajectory dataset is selected to train the proposed system using the long short-term memory (LSTM) technique with an efficient pre-processing stage to adopt time-series data to the environment of LSTM. Evidence of the IntCPP system’s superiority is provided through comparison to two intelligent dummy-based systems as well as three traditional dummy-based systems. In terms of accuracy, a (0.05) enhancement degree is achieved, while in terms of entropy, cumulative resistance against attacks, and average cumulative cache hit ratio, (2.0, 100%, 0.17) enhancement degrees are achieved, respectively.
Memorial architecture serves as a memory and transmissions system of cultural memorization and collective identity into a space. Nevertheless, in many cases, architectural semiotics has been at an interpretative level with little translation into overt spatial or modular design reasoning. This paper discusses a semiotic modeling theoretical framework of modular memorial building, which is studied based on experimental design research, in the context of the Kazakhstani culture. The approach combines structural and spatial semiotic analysis, where architectural elements are treated as systems of codes of symbols and functions and cultures that are interrelated. To actualize these meanings, semantic zoning, sign-code classification, and form-meaning mapping have been used in the study, which allow translation of the abstract semiotic structures into the spatial parameters in a systematic way. These parameters are investigated based on modular spatial modeling and design-based research (DBR) based on conceptual memorial prototypes based on Kazakhstani memorial traditions. These results reveal how the architectural form can be analytically built up as a semiotic system and can be effectively translated into modular forms that express memory, ritual and national identity. The work presents a theory-informed methodology to apply to the architectural semiotics and modular design practice and develop semiotic studies into generative and culturally responsive design usage
This study explored the integration of a 7 MW PV solar energy system into the energy system of industrial air compressors to mitigate extensive energy costs in the unstable grid electricity in Taiz Al-Huban, Yemen. A combined approach involving solar PV generation, leakage recovery, and compressor operation control was implemented into Five major food-based companies to enhance system efficiency. Cumulative energy-related data was collected from the proposed companies for several years and then analyzed using PVsyst simulations. Interesting results indicated an energy saving of 20% from repairing of 851 leaking points, with a total energy saving of 1.93 GWh. Simulation results show that the proposed PV system produces 13.8 GWh annually, meeting the compressors’ total demand of 9.7 GWh and generating a 4.1 GWh surplus. The system achieved a performance ratio of 81 %, and the optimal tilt angle was identified as 17.5 degrees. Economic analysis confirms strong feasibility with a 2.3-year payback period under the Yemeni electricity price and significant long-term revenue. Environmentally, the system offsets approximately 229,538 tons of CO₂ over 30 years. Overall, the results demonstrate that integrating solar PV with targeted compressor improvements offers a cost-effective and sustainable solution for industrial energy management in developing regions.
The purpose of the study is to determine the effectiveness of mobile applications in the context of personalized foreign language learning. The study uses a quasi-experimental design with control and experimental groups. The experiment lasted 12 weeks (the experimental group used Duolingo, Busuu, and LingQ). The study involved 300 students from 5 higher education institutions, for whom a foreign language has become a mandatory component of the educational program. A standardized language test, a scale of learning motivation (intrinsic, extrinsic motivation, and amotivation), and a questionnaire of learning autonomy and self-regulated learning were used to collect data. The study's results indicated a positive impact of personalized mobile learning on the overall outcomes of foreign language acquisition and on all analyzed components of foreign language competence. In particular, the study indicated a differentiated effect of different mobile applications. The Duolingo and Busuu programs were effective in developing lexical and grammatical skills. LingQ became important for the development of reading and listening skills. Thus, personalized mobile learning has become an effective tool for enhancing the quality of students' foreign-language training and for developing their motivation and self-regulation.
This study presents the experimental evaluation of a hydraulic braking system equipped with an energy recovery mechanism based on a pressure-accumulator. The aim was to quantify the effectiveness of regenerative braking through comparative tests under three accumulator pre-charge pressures: 500 psi, 800 psi, and 1000 psi. The experimental setup included a custom-built data acquisition system that recorded the dynamic behavior of the system during deceleration phases, with and without the accumulator engaged. The power recovered was assessed by calculating the area under the energy-time curve using numerical integration techniques. Results indicate that the inclusion of the hydraulic accumulator reduced the total energy dissipated by up to 15.24% at 500 psi. As pressure increased, energy recovery efficiency slightly declined, reaching 14.43% at 800 psi and 13.19% at 1000 psi, likely due to internal hydraulic losses and early saturation of the accumulator. These findings demonstrate the technical viability of hydraulic energy recovery systems in mechanical braking applications and highlight the importance of optimal pressure selection for maximizing performance.
Detecting anomalies in roadway environments is a vital application of intelligent transportation systems (ITS). It involves setting up sufficient sensing systems, reading and analyzing generated stream data, and clustering the data. Typically, there are numerous sensors that are movable, which suggests high dimensionality of data with high dynamics. Clustering high-dimensional stream data with relatively high dynamics is not an easy task. Existing stream data clustering creates stages of buffering before enabling the production of legitimate clusters. However, none of the existing clustering methods are adapted to operate effectively for moving vehicles. In this study, we incorporate an offline phase for creating clusters based on the density of core mini-clusters in an existing buffer-based online clustering for evolving data streams (BOCEDAS). We designate it as multi-density data stream (MUDEDS) clustering. The goal is to detect and cluster anomalies in the roadway environment. In addition, we build simulations for various types of anomalies in the roadway environment. Experimental results demonstrate the superiority of MUDEDS when evaluated on passing traffic signal, accident, and slippage types of anomalies compared to benchmarking clustering algorithms.
The optical model potential for intermediate-energy nucleon-nucleus scattering (≈ 10–200 MeV) exhibits a significant energy dependence in both the real and imaginary components. The Perey-Buck model associates the local potentials depending on the energy with the non-local Gaussian potentials [F. Perey and B. Buck, Nucl. Phys. 32, 353–380 (1962)]. Still, the microscopic methods show that the imaginary part still has energy dependence due to the real temporal non-locality and the channel couplings among them. The present work mainly considers the energy-dependent nonlocality and thus the extension of semimicroscopic approaches. The real part is obtained through the single-folding of the M3Y-Paris interaction, which is density-dependent, while the imaginary part is phenomenological and includes coupled-channels effects. A range of energy-dependent nonlocality β(E) is introduced, and the results are contrasted with the experimental data from EXFOR for elastic scattering of neutrons and protons by nuclei from ¹²C to ²⁰⁸Pb. The findings indicate that differential cross sections and analyzing powers have been fitted much better, particularly in the backscattering areas; thus, the introduction of energy-dependent non-locality is necessary to explain the dispersive corrections and Pauli effects.
This computational study investigates steady-state current responses in Fused Deposition Modeling (FDM) 3D-printed milli fluidic devices with channel band electrodes under laminar flow conditions. While conventional microfluidic devices are well-characterized using Levich and Thin Layer analytical models, 3D-printed platforms exhibit inherent porosity creating complex current behavior inadequately described by Levich and Thin Layer equations alone. After a mesh convergence study determined appropriate mesh conditions, four device designs considering porous network structure and pore proximity to the electrode were computationally probed. Analysis of these simulations incorporated design and hydrodynamic dimensionless parameters to characterize mass-transport regimes. Previously reported General and Transition analytical models as well as Levich and Thin Layer models were applied for current prediction and mass-transport analysis. As a result, the highest and lowest currents were obtained for a pore continuous to the electrode and a complex pore network structure, respectively. Velocity and concentration profiles reveal that interconnected pore structures and pore – electrode proximity create regions where diffusion, convection, or both transport regimes predominate simultaneously in the same device configuration. While general and transition mass-transport models accurately characterize designs with simpler porous network structures, they diverge under the structural mass-transport constraints of a design with a more complex porous network structure; this identifies critical limitations for existing theory and underscores the need for future framework refinements. Progress in this direction is essential to optimize device design and improve analytical performance in electrochemical sensing applications that employ FDM 3D-printed milli fluidic devices.
The paper depicts a symmetric microstrip coupler designed for WiMax applications in the frequency range of 2.32 - 2.52GHz. The proposed coupler is comprised of four resonator structures attached in parallel to the ports providing lowered isolation and increased insertion loss and coupling factor. Furthermore, the presented microstrip coupler is simulated utilizing the Sonnet Suites software with the numerical approximation of the mathematical derivation of MoM (Method of Moments) for the symmetrical structure. The simulated and fabricated results show high resemblance with the data acquired after the simulation with input match being below -20dB, insertion loss and coupling factor almost -3.0 dB and isolation factor below -50dB. The approximate values of the measured design are -15.48dB, -3.86dB and -46.63dB respectively Moreover, the dielectric substrate utilized for the proposed geometry of the microstrip coupler is FR4 with εr=4.4 with thickness of 1. 55mm.The proposed coupler is relatively inexpensive, reliable in the provided frequency range and could be used in wireless communication applications.
Public hospitals in low-resource settings face continuous operational challenges that lead to poor-quality service delivery. Facility Management (FM) as a strategic discipline in well-developed countries enhances efficiency, safety, and sustainability. But in many Low- and Middle-Income Countries (LMICs), especially post-conflict countries such as Iraq, FM practices are fragmented, reactive, and no longer supported by an integrated administrative structure. Several pieces of evidence on FM decision-making and planning were examined to better understand the drivers, obstacles, and effects influencing FM operational performance during its adoption in public hospitals. A systematic search was conducted across Scopus, Web of Science, and PubMed from 2018 to 2025, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. The inclusion criteria used were considered in including thirty-three studies. The critical appraisal Skills Programme (CASP) and the Mixed Methods Appraisal Tool (MMAT) were used to determine the quality of the methodology, and NVivo was used to assess the thematic synthesis. The findings prove that adoption of FM depends on nine overall practices that are proactive maintenance, digital technologies, sustainability practices, safety management, organizational culture and readiness, government initiatives, data management, digital integration, and outcomes of operational performance. It is on these determinants that the current study has offered a context-based FM model that is particularly tailored to the institutional context of Iraq and other post-conflict environment. This model is a novel and workable mechanism of enabling system-level improvement in poor health FM system.
The study aims to work out a reproducible forecasting model to determine the financial sustainability of Ukrainian critical infrastructure businesses during the war. The analysis is performed on the basis of a balanced panel of 72 enterprises on 4 years 2019-2024 in the energy, transport, telecommunications, and water industries, incorporating econometric models (probit, cox survival analysis), machine learning models (Random Forest, XGBoost, and LSTM), and a hybrid ensemble specification. Models are implemented and deployed in a Microsoft Azure cloud computing environment to ensure scalability and security, and enable real-time forecasting. Results indicate that liquidity limitations, leverage, and operational disruption are the leading causes of financial distress, as the incidence of distress has increased by 31% since 2022, compared to 14% during the pre-war period. In terms of predictive performance, XGBoost achieves an out-of-sample AUC of 0.89. At the same time, the hybrid ensemble model outperforms all individual specifications, with an AUC of 0.92, an accuracy of 0.85, and an RMSE of 0.28. Results show that econometric interpretability, with machine-learning predictive power, is significantly better at early warnings. In practice, the framework offers Ukrainian policymakers and infrastructure managers a scalable tool of active risk surveillance, special financial aid, and resilience-oriented decision-making during the recovery.
Financial risk is also a constant menace to the agricultural industry in Ukraine. Still, a key problem with conventional banking methods is that they cannot reflect the risk dynamics specific to the area. This paper questions the prevailing belief that artificial intelligence (AI) is universal, in the sense that it outperforms conventional econometric models in predicting credit interest rate volatility across 25 administrative regions (2015-2020). We find an empirical paradox: under the comparatively constant national level, the simple Linear Regression model performed more effectively than elaborate algorithms, with an accuracy rate of 82.35, which confirms the effectiveness of the principle of parsimony when measured against macroeconomic conditions. Nevertheless, the benefit of AI will be high in economically complex regions. Deep learning (ANN) and gradient boosting models identified non-linear risk patterns that linear models overlooked in agricultural centers such as Kherson and Dnipropetrovsk, further enhancing predictive performance by as much as 10.6 percentage points. These findings are consistent with the Adaptive Markets Hypothesis, which posits that the utility of technology depends on market volatility. Therefore, we suggest a precision banking model: a hybrid model in which stable areas would maintain linear efficiency, whereas shock-affected areas would use AI-powered risk detection to maintain the stability of agricultural credit in the post-war period.
This study aimed to find out whether artificial intelligence (AI) technologies can improve digital mastery among in-training educators in Kazakhstan. It determines the rate of adoption of AI tools, evaluates their role in enhancing digital competencies, and examines the associated challenges and opportunities. Mixed-method approach was employed, which began with a quantitative phase (pre- and post-intervention tests) and concluded with a qualitative phase (interviews). The survey sample comprised 385 participants while 80 participants (40 each) made up the intervention consisting of traditional instruction and AI integrated instruction (ChatGPT and Google Cloud AI). The result showed that prior the intervention, all participants showed low digital literacy and did not adopt AI. However, a 100% AI adoption rate was observed after the intervention, with 75% having moderate to 25% high digital literacy. A paired t-test found significant improvements in digital literacy (M = 25.34 to 35.32, t(19) = 2.12, p < 0.05) and AI proficiency (M = 19.89 to 30.21, t(19) = 1.03, p < 0.05). Lack of institutional support, tool unfamiliarity, and skepticism were some of the problems people encountered. Conversely, the problems with digitally competent users were informed reflection and pedagogic balance. Despite these challenges, both groups recognized the possibilities of individual learning, better classroom result, and team development. Conclusively, targeted AI interventions in teachers training will greatly improve pre-service teacher's digital literacy. Therefore, when applied, close monitoring and ethical strategies should be put in place to enhance academic integrity.
