Abstract: Objectives: The mechanism of action of genes related to lactate metabolism in papillary thyroid carcinoma (PTC) is still unclear. In this study, key genes that play a role in PTC were selected from the known genes related to lactate metabolism, and their roles in promoting lactate metabolism in PTC cells were investigated. Methods: Through bioinformatics analysis and cell experiments, the roles of the relevant genes in lactate metabolism and their roles in the occurrence and development of PTC were verified. Results: Through bioinformatics analysis, 12 candidate genes were obtained. Through qRT-PCR experiments, it was confirmed that the expressions of TIMP1 and DPP4 were higher in thyroid papillary carcinoma than in normal PTC cells. By inhibiting the expression of TIMP1 and DPP4 using siRNA, the invasion and proliferation abilities of PTC could be reduced. Compared with normal thyroid cells, the contents of lactic acid and LDHA in PTC cells were higher. Knocking down the expression of TIMP1 and DPP4 would reduce the lactate production ability of PTC cells, and TIMP1 and DPP4 promoted the accumulation of lactate in PTC cells.

Abstract: The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review provides a comprehensive examination of recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions.

Abstract: Triptolide (TPL), a diterpenoid derived from the Chinese medicinal plant Tripterygium wilfordii, exhibits broad-spectrum biological and pharmacological activities, although its clinical translation is limited by systemic toxicity. Recent advances in the development of TPL derivatives have created new therapeutic opportunities. This review summarizes current knowledge of triptolide, with a focus on TPL’s toxicity profile, derivative strategies, and antitumor mechanisms. There are at least ten cancer types studied by research. We also summary the plant origin and traditional uses of the plant, TPL’s pharmacokinetics (PK), and relevant clinical trials against tumors. The main mechanism of TPL antitumor effect is interfere ATPase of XPB via covalently binding to it, as well as inducing rapid depletion of RPB1 via hyperphosphorylation and ubiquitination. We also reviewed systematic toxicity including neuro, cardio, oto, nephron, hepato, digestive hemato and reproductive toxicity. Finally, we searched the clinical trials through three platforms against tumor and concluded that Minnelide has great potential against solid tumor in clinic. By critically evaluating TPL and its derivatives from multiple dimensions, this review aims to inform future strategies that maximize therapeutic efficacy while minimizing adverse effects.

Abstract: The main objective of the study is to reduce the dwell time of wagons at stations and to improve the efficiency of shunting locomotive utilization. The problem has a combinatorial nature, since an increase in the number of loading and unloading fronts leads to a sharp growth in the number of feasible service variants. During the research, a mathematical model describing the servicing process of industrial sidings was developed. The study addressed the problem of determining the optimal sequence of wagon deliveries and the optimal distribution of workload among shunting locomotives. Under conditions where two or more shunting locomotives are used, an optimization method based on the indicator of wagon-hours reduction (σ) was proposed for allocating loading and unloading fronts. Using combinatorial properties, it was shown that many possible allocation variants are symmetric, which allowed the development of a mathematical solution that simplifies the search for an optimal solution. Computational results demonstrated that, at the hypothetical railway station “N-1”, applying the optimal service sequence reduces wagon dwell time by 21% compared with an arbitrary sequence. At the hypothetical station “N-2”, distributing wagon groups between two shunting locomotives improves the efficiency of the servicing process by 26% compared with using a single locomotive. Based on the proposed mathematical model and algorithm, a practical software tool was developed that enables the automatic determination of service sequences for loading and unloading fronts. The software allows the identification of optimal servicing orders, analysis of alternative variants, and evaluation of the efficiency of shunting locomotive utilization.

Abstract: The CO₂-emissions are rapidly rising with new records and the transport sector is considerably contributing to GHG emissions. The critical transition towards electrification and sustainable development demands a radical change in the transport industry. One of many solutions is to analyze the environmental benefits of optimized vehicle production and recycling of the vehicle components after its usable life to reduce dependency on limited raw materials. Electric motor is one of the most crucial powertrain components, yet studies on the overall ecological profile of production and end of its useable life is limited. This study examines the life cycle assessment (LCA) impacts of electric motors used in passenger cars and potential recycling of its materials. The analysis covers production and recycling of components, crucial elements, and permanent magnets. The results show that housing and rotor production have the highest impacts mainly due to steel, aluminum and permanent magnets. The findings discuss e-motor recycling innovations, state-of-the-art methods and emission reduction potentials of recycling. This paper also covers the understanding that a significant transformation to optimize the resource consumption in manufacturing of crucial vehicle powertrain component and reduce waste after end-of-life could bring combined ecological advantages.

Abstract: Rising energy costs and strict CO₂ traceability regulations create demand for monitoring energy and CO₂ emissions in manufacturing. This paper presents a framework for modelling component-wise energy models with deployable accuracy. In many factories, power meters log data at a sampling rate of 1–2 Hz, so short start-up peaks of components are underestimated. Manufacturers want to exploit this information to support operational decisions, such as peak shaving and optimising energy contract costs. To enable data-driven decisions with limited measurement infrastructure, energy models must extrapolate component behavior from sparse data. The framework is based on power measurements in accordance with ISO 14955-3, ensuring that the load characteristics required for subsequent modelling are known. The measurements are then segmented, and regressions are fitted for each segment. As a case study considering the mist extractors of two different machine tools, the proposed segmentation achieved determination coefficients (R²) of up to 0.94 in the complex ramp-up phase. The resulting models are compact, interpretable, and suited for energy monitoring on edge devices. The contribution is a reproducible framework for delivering peak-aware, component-level energy models from low-frequency industrial power meter data.

Abstract: Loanword Research on Anglicisms has largely centered on lexical borrowing and phonological adaptation, with comparatively limited attention to morphosyntactic integration in recipient grammars. This study examines the syntactic behavior of single-word Anglicisms in Mexican Spanish, drawing on phonetically classified corpora of 131 monosyllabic Anglicisms with mon-ophthongs extracted from spontaneous speech by Spanish–English bilinguals in the Tijuana–San Diego border region. Building on prior acoustic analyses based on F1 and F2 vowel measure-ments, the study investigates the relationship between phonological adaptation and morphosyn-tactic integration. Results reveal a gradient pattern of incorporation. Anglicisms exhibiting Span-ish-like phonetic properties tend to occupy canonical syntactic positions and show greater com-patibility with Spanish functional morphology, whereas phonetically non-adapted forms more frequently resist morphological marking and display island-like behavior within otherwise Spanish clauses. The analysis examines distribution across nominal, adjectival, and prepositional domains, as well as object positions, enabling a fine-grained assessment of degrees of morpho-syntactic integration. The former is illustrated as follows: (1) Guardo cash ([kaʃ]) por si acaso (2) Si hacen match ([mæʧ]), puede funcionar Adopting a usage-based and contact-oriented perspective for syntactic borrowing (Bybee, 2015), the study is situated within the Matrix Language Frame model (Myers-Scotton, 1993; Muysken, 2000) and recent approaches to insertional borrowing (Poplack & Dion, 2012; Onysko & Win-ter-Froemel, 2011). A central contribution lies in establishing a principled link between morpho-syntactic behavior and an independently motivated phonetic classification, offering convergent evidence for the systematic integration of Anglicisms into Spanish grammar. At a broader ana-lytical level, the study advances debates on syntactic borrowing and contact-induced change by demonstrating that Anglicisms are subject to Spanish morphosyntactic constraints rather than functioning as unconstrained lexical insertions, and by developing an interface-based account of borrowing that captures the gradient nature of grammatical incorporation in contact settings and contributes a corpus-based, empirically grounded perspective to typologies of borrowing in Spanish contact linguistics

Abstract: A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Abstract: The present study examined the effect of Enterococcus durans cell free supernatant (CFS) on interleukin (IL) 8, 10 and 1β gene expressions in the intestinal cell line HT-29 treated with Staphylococcus aureus CFS. HT-29 cells were incubated with E. durans CFS or S. aureus CFS, or S. aureus CFS plus E. durans CFS. All concentrations of E. durans CFS did not show cytotoxicity, while the highest treatment (44.9 μg/mL) with S. aureus CFS induced significant cell death. S. aureus CFS did not modify IL-1β gene expression, while E. durans CFS alone or in combination with S. aureus CFS reduced it. Treatment with S. aureus CFS induced greater expression of the IL-8 gene compared to S. aureus CFS plus E. durans CFS. S. aureus CFS alone or in combination with E. durans CFS increased the expression of the IL-10 gene, while E. durans CFS alone did not modify it. These results suggest a potential protective role of the E. durans secretome in mitigating the inflammatory environment in intestinal cells. This treatment could be useful to protect against possible contact with dangerous soluble microbial products present in food.

Abstract: In structural equation modeling (SEM), model fit is commonly evaluated on a single sample, which limits evidence about out-of-sample generalisability in HR research. This study adapts repeated cross-validation to SEM and introduces a normalised generalisation gap (NG) index to quantify fit deterioration on unseen data relative to training fit. Using employee survey data (N = 1,040), we estimated a model in which quiet quitting and passive quitting predict work engagement and evaluated RMSEA across repeated 10×10 folds. In-sample fit indices were stable across training subsamples, yet test-set fit was consistently weaker. The NG index operationalises this decline as a standardised ratio, enabling transparent reporting of overfitting risk and model transportability. The procedure supports more robust evaluation of measurement models and structural relations in people analytics applications.

Abstract: Salmonella enterica remains a major threat to animal and human health because of its broad host range, increasing antimicrobial resistance (AMR), and capacity to form biofilms. Biofilm formation enhances bacterial persistence in host tissues, farm environments, food-processing systems, and clinical reservoirs, while also contributing to their tolerance against antibiotics, disinfectants, and other stresses. However, biofilm capacity is not uniform across serovars and is influenced by host adaptation, niche specialization, and accessory genome content. This review synthesizes current knowledge on the relationship between biofilm formation, AMR, and serovar-specific adaptation in Salmonella. It examines biofilm-associated traits across various hosts (e.g., gastrointestinal tract and gallbladder, and environmental (e.g., food-production and clinical) niches, and discusses comparative evidence from genomic, transcriptomic, proteomic, and metabolomic studies. Particular attention is given to the emerging concept of comparative biofilmomics, which integrates phenotypic and multi-omics data across diverse serovars and host sources to identify conserved and niche-specific determinants of persistence. This framework may help define high-risk lineages that couple multidrug resistance (MDR) with enhanced biofilm-forming capacity. A better understanding of these linked traits will support the development of more targeted interventions for controlling persistent Salmonella in veterinary, food production, and public health settings.

Abstract: In a previous work [Bakonyi et al., Eur. Phys. J. Plus 137, 871 (2022)], in-plane magnetoresistance results were reported on a thin strip-shaped foil sample of nanocrys-talline (nc) Ni metal. These studies have been by now complemented with the measure-ment of the temperature dependence of the resistivity as well as the field dependence of the resistivity and the Hall effect on the same sample at 3 K and 300 K in polar magnetic fields up to 140 kOe, i.e., with the magnetic field perpendicular to the strip plane. Due to the strong contribution of the grain-boundary scattering in the nc state, the residual re-sistivity was about 11 % of the room-temperature value. The polar magnetoresistance (PMR) showed a similar behavior to the previously reported transverse magnetoresistance (TMR), yielding an anisotropic magnetoresistance (AMR) value in good agreement with the AMR previously deduced from the in-plane MR data. As to the Hall effect, the results for the ordinary (Ro) and the anomalous (Rs) Hall coefficient fitted rather well into the rather dispersed reported data of bulk Ni at both temperatures. However, a closer look of the Rs values for nc-Ni revealed that at 300 K it is larger and at 3 K it is smaller than the corresponding bulk Ni values obtained on samples with the same zero-field resistivity as our nc-Ni foil. It will be discussed briefly that these deviations may be attributed to the nanocrystalline state containing a large density of grain boundaries.

Abstract: Metal forming processes play a key role in modern manufacturing, but they are characterized by high energy consumption and low overall efficiency. In this context, precise methods for monitoring the operational state and cycle-dependent metrics of manufactured parts are essential to implement energy optimization strategies. This article presents a data-driven and non-intrusive methodology to identify, in real time, the part under production and to estimate both cycle time and energy consumption per part. The method relies exclusively on electrical measurements taken at the main switchboard and at the first process-stage switchboard. These signals are used to calculate electrical quantities such as root mean square (RMS) current and active power, and a machine-learning (ML) approach is proposed to automatically identify the part in production. To this end, time-domain features are extracted directly from the signals, while time-frequency features are extracted using Continuous Wavelet Transform (CWT). These features are employed to train Support Vector Machine (SVM) classifiers optimized via grid search. Experimental results show that the model achieves a test accuracy of 99.9%. Once the production state is identified, the system estimates cycle time and energy per cycle in real time. Approximately 58,000 production cycles, corresponding to several part types were characterized.

Abstract: Artificial intelligence is rapidly transforming education. Tools such as modern AI language models can now generate essays, explain complex concepts, create lesson plans, produce quizzes, and summarize entire textbooks within seconds. For many teachers and institutions, this raises an important question: what is the role of a human educator in an age when machines can instantly provide information? This paper presents an accessible framework that helps schools and colleges integrate artificial intelligence into teaching while preserving the essential human elements of education. Rather than viewing AI as a replacement for teachers, the framework positions AI as a powerful assistant that can support lesson preparation, personalized feedback, and adaptive learning resources. By automating repetitive tasks such as content generation, grading support, and material organization, AI allows educators to focus on what machines cannot easily replicate: mentorship, creativity, ethical reasoning, critical thinking, and inspiration.The framework outlines practical strategies for using AI responsibly in classrooms, including guidelines for AI-assisted lesson planning, student engagement techniques, and safeguards to maintain academic integrity. It also discusses how institutions can prepare both teachers and students for an AI-augmented learning environment by promoting digital literacy, responsible tool usage, and critical evaluation of AI-generated information.Ultimately, the goal of AI-enhanced teaching is not to replace educators, but to empower them. When used thoughtfully, artificial intelligence can reduce administrative workload, expand access to high-quality learning resources, and create more personalized educational experiences. In this vision, AI becomes a supportive partner, while teachers remain the guiding force who cultivate curiosity, wisdom, and human understanding in the classroom.

Abstract: This study presents a monitoring system designed as an integrated surveillance and decision support tool for the terrestrial and the ocean environments. The developed system integrates in situ ocean sensor for monitoring purposes as well as a real-time communication tool for data transfer combined with a power generating module to sustain power for all modules. The system is applied for a period of around six months in different seasons to detect, identify gradients of radioactivity in the atmosphere. The gross gamma-ray intensity as detected by the system was interpreted in qualitative manner according to the rainfall events. The background gamma-ray spectra during dry periods for different seasons are also discussed in terms of seasonality. The results of the analysis offer actionable insights through existing mechanisms to support authorities in rapid response and policy planning related to marine radioactivity issues.

Abstract: Modern financial markets are increasingly shaped by algorithmic trading systems and artificial intelligence techniques that process large volumes of financial data in real time. However, machine learning–based trading systems often suffer from signal instability and excessive sensitivity to market noise, which may lead to overtrading and increased financial risk. In highly volatile environments such as cryptocurrency markets, the re-liability of trading signals becomes a critical issue for both portfolio allocation and risk management. This study proposes an entropy-filtered machine learning framework designed to en-hance the stability and risk-awareness of algorithmic trading strategies. The proposed approach integrates entropy-based filtering techniques with machine learning classifiers in order to reduce noise in market signals improving the risk-adjusted stability of algo-rithmic trading strategies. Entropy measures are employed as a filtering mechanism that evaluates the informational content of market signals and suppresses unreliable predic-tions generated by the learning model. The empirical analysis is conducted using cryp-tocurrency market data, where the entropy-filtered machine learning framework is ap-plied to trading signal generation and portfolio decision making. The results indicate that the proposed approach improves the stability of trading signals and reduces the occur-rence of false signals compared to conventional machine learning trading models. Moreover, the integration of entropy filtering contributes to a more balanced risk–return profile and enhances the overall robustness of algorithmic trading strategies.The findings suggest that combining information-theoretic measures with machine learning tech-niques represents a promising direction for developing more reliable and risk-aware financial decision systems. The results suggest that entropy-based filtering can substan-tially improve the robustness and risk-awareness of machine learning trading systems, providing a promising direction for future AI-driven financial decision frameworks.

Abstract: Namibia’s rural communities continue to experience limited and unreliable electricity access despite the country’s exceptional solar, wind, and biomass renewable energy re-sources potential. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, re-renewable based alternatives. This paper presents a resource-driven design and multi objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), Carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati using high-resolution, site-specific renewable resource datasets and community-level load forecasts. Results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behavior across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Com-pared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia.

Abstract: Background: Whether inspired oxygen fraction (FiO₂) can modulate the internal meta-bolic cost of supramaximal high-intensity interval training (HIIT) and thereby direct training adaptation remains unclear. We tested whether hyperoxic versus hypoxic ex-posure during Tabata-format HIIT induces distinct adaptive phenotypes. Methods: Twenty-three physically active men completed 3 weeks of supramaximal Tabata HIIT (3 sessions·week⁻¹; 8 × 20 s with 10 s recovery) under hyperoxia (FiO₂ = 0.60, n = 13) or hypoxia (FiO₂ = 0.16, n = 10). Training intensity was regulated to main-tain a comparable internal physiological stimulus rather than an identical external workload. Pre- and post-intervention assessments included maximal oxygen uptake (VO₂max), first and second ventilatory thresholds (VT1, VT2), peak blood lactate, and session rating of perceived exertion (RPE). Post-intervention between-group differences were analysed using ANCOVA adjusted for baseline values; RPE was analysed using a linear mixed-effects model. Results: VO₂max improved in both groups but increased more after hyperoxic training than after hypoxic training (+3.69 vs. +1.50 mL·kg⁻¹·min⁻¹; β = 2.18 mL·kg⁻¹·min⁻¹, 95% CI [1.77–2.59], p < 0.001). Hyperoxia also produced larger gains in VT1 (β = 29.99 W, 95% CI [17.09–42.89], p < 0.001) and VT2 (β = 20.74 W, 95% CI [9.43–32.05], p = 0.001). Peak lactate responses diverged bidirectionally, decreasing in hyperoxia (−0.77 mmol·L⁻¹) and increasing slightly in hypoxia (+0.27 mmol·L⁻¹), with a significant ad-justed between-group effect (β = −1.02 mmol·L⁻¹, 95% CI [−1.47 to −0.57], p < 0.001). RPE declined across sessions in both groups, with a steeper decrease under hyperoxia (Con-dition × Session: β = −0.36, 95% CI [−0.44 to −0.28], p < 0.001). Conclusions: Hyperoxic and hypoxic supramaximal HIIT elicited distinct functional adaptive profiles. Hyperoxia induced greater improvements in aerobic capacity and ventilatory thresholds, reduced peak lactate accumulation, and accelerated the decline in perceived exertion, whereas hypoxia was associated with a more glycolytic response pattern. These findings support the interpretation that FiO₂ acts as a modulator of in-ternal physiological load and shapes the metabolic phenotype of adaptation during su-pramaximal interval training.

Abstract: Background/Aim: Albuminuria is an established marker of endothelial dysfunction and an independent predictor of cardiovascular risk. Polycystic ovary syndrome (PCOS) is associated with early metabolic and vascular abnormalities; however, whether urinary albumin excretion differs across PCOS phenotypes remains unclear. This study aimed to evaluate urinary albumin excretion using the urinary albumin-to-creatinine ratio (U-ACR) across distinct PCOS phenotypes and to examine its association with metabolic parameters. Materials and Methods: In this cross-sectional study, 180 women aged 18-35 years with PCOS and 51 age-matched healthy controls were included. PCOS phenotypes were classified according to the Rotterdam criteria as Phenotype A (n = 96), Phenotype B (n = 19), Phenotype C (n = 35), and Phenotype D (n = 30). Insulin resistance was assessed using the homeostasis model assessment for insulin resistance (HOMA-IR). Urinary albumin and creatinine levels were measured in morning urine samples, and U-ACR was calculated. Results: Age was comparable across all groups. Body mass index, waist circumference, diastolic blood pressure, and HOMA-IR were significantly higher in Phenotype A compared with controls and other phenotypes, indicating a more adverse metabolic profile. Serum creatinine levels were similar across all groups. Despite this unfavorable metabolic profile in Phenotype A, U-ACR was significantly elevated only in Phenotype B compared with controls (p = 0.018) and Phenotype D (p = 0.016). No significant correlations were observed between U-ACR and age, body mass index, or HOMA-IR. When participants were categorized according to U-ACR levels (< 30, 30-299.9, and ≥ 300 mg/g creatinine), no significant differences in category distribution were observed between the total PCOS cohort, phenotype subgroups, and controls. Conclusion: Among PCOS phenotypes, U-ACR elevation was observed exclusively in Phenotype B despite similar renal function markers. Notably, this occurred even though Phenotype A exhibited a more adverse metabolic profile, suggesting a dissociation between metabolic burden and early microvascular involvement across PCOS phenotypes. These findings indicate that vascular risk in PCOS may be phenotype-dependent and support the potential value of phenotype-oriented cardiovascular risk assessment.

Abstract: Artificial Intelligence (AI) has developed over the years from rudimentary systems of symbolic reasoning in the middle of the twentieth century to sophisticated data-driven and generative architectures, which give rise to modern society. The acceleration of machine learning, deep neural networks and large-scale computational infrastructures has turned AI into a basic technology in the economic, social, and societal sectors. This paper investigates the history of the development of AI and critically discusses its influence on society in the 21 st century. Following a narrative review approach, the paper summarises interdisciplinary literature of technological innovation, economic transformation, social change, ethical governance, and sustainability issues.Various findings are found in the analysis. To begin with, AI has greatly increased productivity and operational efficiency in the industry as well as redefining the labor markets and skill requirements. Second, AI-centered systems have enhanced the provision of services in the education, health, transportation, and government sectors, though the issue of bias, privacy, transparency, and accountability continues to be present. Third, the spread of AI to safety-critical systems highlights the value of reliability, regulation, and human-oriented design. Finally, the environmental impact of large-scale AI models represents the necessity of sustainable development practices.The paper concludes that AI is an opportunity for transformation and a governance challenge. The implications to be considered in the future are the emergence of human-focused AI models, the creation of control measures, and the introduction of sustainability indicators into technological change. The fair and responsible implementation of AI will be required in order to maximise the positive impacts on society and reduce the risks in the long term.