Abstract: Dual-fuel combustion is often proposed for diesel engines as a means to partially replace conventional diesel with cleaner and/or more sustainable alternatives, such as those derived from green hydrogen. However, the low reactivity of these fuels (i.e. methane, hydrogen, and ammonia) often leads to prolonged ignition delay and combustion instability. This challenge could potentially be overcome using nanomaterials, which are additives that could improve reactivity and compensate for autoignition deficiencies. Thus, this study evaluates the effect of carbon nanotubes (CNTs) dispersed in diesel fuel on the autoignition process under dual-fuel operation. CNTs were dispersed at a concentration of 100 mg/L and stabilized with surfactant sodium dodecylbenzene sulfonate (SDBS). The resulting nanofuels were then tested in a constant volume combustion chamber (CVCC) using methane, hydrogen, and ammonia as secondary fuels across various energy substitution ratios and temperatures (535 °C, 590 °C and 650 °C). The results show that the impact of CNTs on ID is negligible and highly temperature dependent. At the lowest tested temperature (535 °C) and 40% methane substitution ratio, only slight reductions in ID were obtained. Nevertheless, this effect vanished at higher temperatures (590 °C and 650 °C). Regarding pressure gradient, the addition of CNTs and SDBS generally induced a decrease in pressure peak of up to 15%. This trend is attributed to the trapping of fuel droplets within the CNT structures, which creates a physical barrier that delays vaporization. These findings suggest that the practical benefits of CNTs-SDBS dispersions in diesel engine operating under dual mode with sustainable low reactivity fuels remains limited since the main engine-related phenomena which could be affected, which is autoignition, is not really enhanced.

Abstract: This study proposes a hybrid framework for investment risk assessment in the Romanian equity market by integrating classical hypothesis testing with machine learning techniques. Using daily data for the BET, BET-FI, and BET-NG indices, the analysis evaluates return behavior, volatility dynamics, and return direction in an emerging market context. Classical inferential tests indicate no statistically significant structural breaks in mean returns or seasonal patterns, while volatility-related measures, particularly intraday price dispersion, exhibit consistent explanatory relevance across indices. Machine learning models, namely Random Forest and XGBoost, are applied to predict returns, short-horizon volatility, and return direction using price-based indicators, sentiment variables, and a purchasing power index (PPI). The results show that XGBoost systematically outperforms Random Forest, especially for return direction and short-term volatility prediction, highlighting the importance of nonlinear modeling in capturing complex market dynamics. However, overall predictive performance remains moderate, reflecting the inherent limits of predictability in volatile and structurally evolving markets. The inclusion of purchasing power information improves interpretability and, in selected cases, volatility prediction at the sectoral level, but does not materially alter aggregate market predictability. Overall, the findings underscore the complementarity of hypothesis testing and machine learning approaches and provide evidence that investment risk in the Romanian equity market is primarily volatility-driven rather than return-driven.

Abstract: As artificial intelligence (AI) technologies advance, traditional craftsmen face new challenges —to innovate using digital tools while preserving cultural authenticity and heritage knowledge. The "hybrid artisan," who strategically integrates AI-based design tools with traditional craft, emerges as a response to this tension. This article addresses research questions on how integrating generative AI technologies into design processes influences: (1) artisans' productivity and product quality; (2) cultural authenticity and heritage preservation; (3) sustainable business models in creative entrepreneurship. The research methodology employs a convergent design with mixed methods, combining: (a) a systematic literature review (PRISMA 2020, n=33 articles, 2022-2025); (b) a qualitative survey (n=13 artisans, Romania; semi-structured questionnaire, 34 items). The literature review identifies three dominant human-AI collaboration models: task-level cooperation, process-level coordination, and system-level co-creation. Diffusion models (LoRA-fine-tuned) and GANs achieve cultural authenticity scores of 73-95%, while reducing design time by 30-70%. Empirical data reveal paradoxes: artisans value authentic creativity and sustainability (30.8% rate sustainability as "extremely important"), but adopt AI cautiously (46.2% unfamiliar with AI tools). Those using AI report 15-40% productivity gains without proportional sales increases, suggesting the market does not yet equally value AI-assisted crafts. The successful "hybrid artisan" model relies on collaborative rather than autonomous AI positioning, explicit cultural safeguards in system design, and transparent communication with consumers about AI involvement. This research provides a framework for policymakers and entrepreneurs integrating digital technologies while maintaining cultural integrity.

Abstract: Endometriosis is associated with nociceptive pain and with peripheral as well as central sensitization. To relieve nociceptive pain, most symptomatic patients benefits from hormonal therapy, which includes first-line (progestogens and estrogen-progestogen combinations) and second-line (GnRH agonists and antagonists) medications. To reduce venous and arterial thrombotic risk and avoid lesion stimulation, combinations containing bioidentical estrogens should be preferred to those containing ethinyl-estradiol. Irregular bleeding is the main adverse effect of first-line medications, adversely impacting efficacy, tolerability, and adherence. When progestogens and estrogen-progestogens do not improve the quality of life, prompt stepping up to GnRH analogues combined with add-back therapy is indicated. An add-on rather than upfront combination therapy is suggested. Keeping analogues and add-back therapy separate allows for the choice of the compounds that best suit individual patients’ characteristics. The transdermal use of bioidentical estradiol is suggested in combination with both progestogens and GnRH analogues. Similar satisfactory outcomes are achieved with GnRH agonists and antagonists. The evidence on neuromodulatory drugs to treat neuropathic and nociplastic pain is derived from other chronic pain conditions, and it demonstrates a limited effect. The two mainstays of hormonal therapy are i) ovariostasis and ii) amenorrhea. Whenever these are not obtained, and a shift from first-line to second-line medications has not been undertaken, “medical treatment failure” must not be declared. In severely symptomatic adolescents and young women, secondary prevention via ovariostasis and amenorrhea should be promptly pursued to improve quality of life, halt lesion progression, and preserve the reproductive potential.

Abstract: With applications in psychology, security, and human-computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro and macro types, yet existing datasets suffer inconsistent labelling for classes, limited diversity of the databases, and insufficient scale for the currently available datasets. To address these gaps, this work proposes a novel framework combining the Diffusion model with pre-trained CNNs. Leveraging original images from established datasets, CASME Ⅱ, we generate synthetic facial expressions to augment training data, mitigating bias and inconsistency. The synthetic dataset is evaluated using ResNet 50, VGG16 and Inception V3 architectures. Inception V3 trained on the proposed AI-generated dataset and tested using CASME Ⅱ achieved the highest accuracy of 99.48%. VGG-16 with data augmentation applied was trained on CASME Ⅱ and tested on the proposed AI-generated dataset achieved 99.54%. While 30% freezing layers method is utilized, Inception V3 trained on the proposed AI-generated dataset and tested using CASME Ⅱ obtained an accuracy of 99.53%. The data augmentation and freezing layers approaches have significantly improved the performance of the models. Our proposed approaches have achieved state-of-the-art performance and outperformed most of the existing state-of-the-art approaches benchmarked in this study.

Abstract: Knowledge graph completion via link prediction is critical for intelligent equipment maintenance systems to support accurate fault diagnosis and maintenance decision-making. However, existing approaches struggle to simultaneously capture local structural dependencies and perform effective multi-hop reasoning, due to limited receptive fields or inefficient path exploration mechanisms. Traditional path-based methods implicitly assume path symmetry, treating all reasoning chains equally without considering their task-specific relevance. To address this issue, we propose a GAT-guided semantic path reasoning framework that breaks this symmetry through attention-driven asymmetric weighting, integrating local structural encoding with global multi-hop inference. The key innovation lies in a target-guided biased path sampling strategy, which transforms graph attention network (GAT) attention weights into probabilistic transition biases, enabling adaptive exploration of high-quality semantic paths relevant to specific prediction targets. Specifically, GATs are employed to learn importance-aware local representations, which guide biased random walks to efficiently sample task-relevant reasoning paths while breaking the implicit symmetry assumption of uniform path exploration. The sampled paths are encoded and aggregated to form global semantic context representations, which are then fused with local embeddings through a gating mechanism for final link prediction. Extensive experiments on FB15k-237, WN18RR, and a real-world equipment maintenance knowledge graph demonstrate that the proposed method consistently outperforms state-of-the-art baselines, achieving an MRR of 0.614 on the maintenance dataset and 0.485 on WN18RR. Further analysis shows that the learned path attention weights provide interpretable asymmetric reasoning evidence, enhancing transparency and trustworthiness for safety-critical maintenance applications.

Abstract: This paper presents the concept and implementation of the BIM–CARVER tool, which integrates the CARVER vulnerability assessment methodology (Criticality, Accessibility, Recuperability, Vulnerability, Effect, Recognizability) with an open BIM environment based on the IFC standard. Originally developed by the US military for target analysis, the CARVER methodology has evolved into a defensive tool for protecting critical infrastructure. Traditionally, physical security assessments of buildings are performed manually, separately from the digital model, which contradicts the principles of Security by Design, which assume that security aspects should be taken into account at the early stages of design. As part of research conducted in accordance with the Design Science Research methodology, a plugin for the Bonsai platform (BlenderBIM) was developed, enabling the assignment of vulnerability assessments to individual elements of the IFC model according to six CARVER criteria on a scale of 1-10, visualization of results directly in the modeling environment, and generation of security reports in HTML format. The tool was validated on a set of ten building models of varying purpose and complexity. The results confirmed the effectiveness of the tool in systematically identifying and classifying building elements into four risk categories: critical, important, significant, and insignificant. The developed solution supports designers and security specialists in the proactive identification of threats and enables the comparison of design variants in terms of the aggregated risk level, contributing to the implementation of Security by Design principles in design practice.

Abstract: Understanding the effects of mixing-driven precipitation on solute transport behavior is critical for reactive transport predictions, yet its complexity, arising from the interplay of flow dynamics, solute transport, and geochemical reactions, remains a significant challenge. In particular, mineral precipitation modifies the hydraulic properties of porous media. The impact of this process on the solute transport behavior remains largely unexplored and is crucial for accurate reactive transport predictions. This study presents a controlled laboratory investigation of mixing-driven calcite precipitation (MDP) in an intermediate-scale Hele-Shaw cell, simulating a coarse-sand porous medium. The experiment allowed for direct visualization of the spatiotemporal evolution of precipitation while continuously monitoring hydraulic properties. Self-organized heterogeneities in the precipitate structure were observed, with calcite layers forming symmetric patterns aligned with the main flow, contrasting with the asymmetry predicted by a semi-analytical model under idealized conditions. Tracer tests conducted before and after precipitation demonstrated significant impacts on solute transport, including the emergence of strong anomalous transport features, such as earlier solute arrival, a distinct double peak, and pronounced tailing. These findings highlight the critical role of precipitation-induced heterogeneities in shaping transport behavior, emphasizing the need to integrate these dynamics into reactive transport models for improved predictive accuracy.

Abstract: The rectal mucosa houses a large number of viruses with important roles in shaping the local microbial communities and modulating immune responses which could influence host susceptibility to infection and other diseases. Unique composition of the gut microbiome, including the predominance of clinically significant eukaryotic viruses like herpesviruses, CMV, and human papillomavirus, has been described in both people living with HIV (PLWH) and men who have sex with men (MSM) vulnerable to HIV. Despite these insights, the rectal virome and the clinical implications of virome–bacteriome–immune interactions in the rectal mucosa remain poorly understood. In this review, we synthesize existing data on the composition of the rectal virome, its interactions with the bacteriome and the immune system, and implications on clinical outcomes in people living with or vulnerable to HIV. We also highlight the gaps and research needed to further explore and unravel these relationships.

Abstract: We present a four-dimensional, PT-symmetric quaternion-inspired extension of General Relativity formulated in a PT-even Palatini posture. Rather than treating a quaternionic "imaginary metric" as a physical spacetime tensor, we define the physical sector by a PT-even projection operator $\Pi_{PT}$ acting on the action and on composite observables:$$S_{phys} \equiv \Pi_{PT} [S(g, \Gamma, \phi; \epsilon)]$$ so that the measure and all reported observables are real by construction. The fundamental fields are a real (or PT-even) metric $g_{\mu\nu}$, an independent affine connection $\Gamma^{\lambda}_{\mu\nu}$ (allowing torsion), and a single real scalar spurion $\phi$ that controls a dimensionless flux amplitude $\epsilon \equiv 2\phi/M_{pl}$ in the connection/torsion sector. Nontrivial quaternionic/noncommutative effects are parameterized by a white-box operator basis $\Delta\mathcal{L} = \sum_i c_i \mathcal{O}_i(T_{\mu}, S_{\mu}, q_{\lambda\mu\nu}, g; \epsilon)$, built from the irreducible torsion components, with PT selection rules specifying which operators survive in $S_{phys}$.We then adopt a minimal-alignment phenomenology: the same spurion family $\epsilon$ is represented by scale-dependent templates $\epsilon(t)$, $\epsilon(r)$, and $\epsilon(E_T)$, which can be confronted with public data across three regimes:(i) late-time acceleration in SN Ia cosmology;(ii) approximately flat galaxy rotation curves in the SPARC sample;and (iii) missing transverse energy spectra at the LHC.In this work, we emphasize interpretational consistency: the three profiles are not claimed to arise from one universal PDE valid across all scales; instead, they are effective templates corresponding to different kinematic reductions of the same PT-even spurion sector, with scale-dependent effective couplings. The Palatini torsion posture provides an immediate diagnostic interface to stability. Crucially, consistent with the companion symmetry analysis, the PT-even projection imposes a coefficient-locking identity (C3) that enforces equality between the gravitational and kinematic couplings ($K=G$) for tensor modes. This structural locking guarantees exact tensor luminality ($c_T = 1$) at the leading order without parameter tuning, naturally satisfying the stringent GW170817 constraints. This work therefore serves as the phenomenological front-end, applying the strictly bounded PT-even framework to public data.

Abstract: Adult neurogenesis is a highly regulated form of brain plasticity shaped by the interaction between hormonal systems and environmental context. Social experience has emerged as a powerful modulator of neuronal proliferation, differentiation, and survival across the lifespan. This review synthesizes evidence showing how diverse social behaviors—including isolation, social hierarchy, parenting, sexual interaction, social buffering, and social learning—engage neuroendocrine, neurochemical, and stress-related pathways to regulate hippocampal and olfactory neurogenesis. Affiliative and reproductive behaviors generally promote neurogenesis through gonadal hormones, oxytocin, vasopressin, and neurotrophic signaling, whereas chronic isolation or social defeat reliably suppress neurogenic processes, particularly within stress-sensitive regions of the ventral dentate gyrus. Sex differences further shape these effects, reflecting distinct hormonal environments and behavioral strategies. Comparative studies in prairie voles, eusocial mole-rats, nonhuman primates, songbirds, and teleost fish reveal that sociality can either enhance or constrain adult neurogenesis depending on ecological demands and social organization. Together, these findings position adult neurogenesis as a plastic process deeply embedded within the social lives of vertebrates, offering a framework for linking social behavior, brain plasticity, and adaptive function.

Abstract: Background and Objectives: TNM staging is one of the most important prognostic factors in gastrointestinal system cancers. It has been demonstrated that an inflammatory condition develops due to the tumor’s microenvironment in gastrointestinal system cancers. Myeloid-derived cells play a key role in this inflammation. Some of these myeloid derived cells are monocytic cells that express CD66b+ on their surfaces. CD66b+ monocytes were defined as a novel cell subpopulation that is isolated from the peripheral blood of cancer patients. The aim of this study was to investigate the relationship between monocytic CD66b+ levels and disease stage in gastrointestinal system cancers. Materials and Methods: Monocyte CD66b+ levels were measured by collecting peripheral venous blood samples in EDTA tubes from 50 gastric, colorectal, and pancreatic adenocarcinoma patients. The normality of the distributions of numeric variables was analyzed using “Shapiro-Wilk” test, and “Mann-Whitney U” test was used to analyze difference in median and min-max values, and “Spearman’s correlation” test was used for the correlation analyses. Results: A strong positive correlation was found between monocytic CD66b+ levels and disease stage in gastrointestinal system cancers (p<.001). As the disease stage advanced, monocytic CD66b+ levels were found to increase at a statistically significant rate. Conclusion: While whether elevated monocytic CD66b+ levels are a cause or effect in advanced-stage disease is unknown, increased monocytic CD66b+ levels are in parallel with the progression of disease stage. In light of these results, future studies should investigate whether monocytic CD66b+ levels are associated with prognosis and survival in gastrointestinal system cancers.

Abstract: Background: HRV is a key biomarker of autonomic nervous system function and cardiovascular health. Emerging evidence suggests that metabolic regulation, particularly glucose variability, may influence autonomic balance even in non-diabetic populations. With the increasing availability of CGM and wearable sensors, the integration of metabolic and cardiovascular signals enables novel data-driven approaches for personalized health monitoring. Objectives: This pilot study aimed to investigate the associations between glucose dynamics, fitness indicators, and HRV in middle-aged amateur endurance athletes, and to evaluate the feasibility of predicting HRV using machine-learning models based on CGM and wearable-derived features. Methods: Ten male endurance athletes (age 39–50 years) were monitored over a two-month period using CGM devices and advanced smartwatches. Daily metrics included average glucose, glucose standard deviation and coefficient of variation, resting heart rate (RHR), nocturnal HRV (RMSSD), blood oxygen saturation (SpO₂), and estimated VO₂max. Anthropometric and biochemical markers (BMI, fat mass, skinfolds, HbA1c, ApoB, vitamin D) were also collected. Random Forest, XGBoost, and LightGBM regression models were trained to predict HRV. Model performance was evaluated using cross-validated R² (R² ranged from 0.12 to 0.28) and normalized mean absolute error (nMAE). Results: Correlation analysis revealed that elevated ApoB, increased fat mass, and higher RHR were strongly associated with lower HRV. Glucose variability showed weaker associations in this cohort. Machine-learning models demonstrated limited predictive accuracy for HRV (R² < 0.30 across models), suggesting that while physiological links exist, the selected features alone are insufficient to fully explain HRV variability in a pilot cohort. Conclusions: In middle-aged endurance athletes, glucose regulation and cardiovascular fitness markers show meaningful associations with autonomic function, but the feasibility of accurately predicting HRV from CGM-derived metrics remains limited in this pilot dataset. These findings support the physiological link between metabolic stability and autonomic balance, while highlighting the need for larger longitudinal datasets and time-series modeling to capture the complex dynamics between glucose fluctuations and cardiac autonomic regulation.

Abstract: We study extreme tail risk in EUR/NOK, USD/NOK, and EUR/USD using an integrated GARCH–Block-Maxima EVT (GEV) framework. Layer A filters time-varying volatility via GARCH(1,1) to obtain standardized residuals; Layer B models monthly block maxima with a GEV distribution. To produce one-day VaR, we map daily confidence levels α to block-maxima probabilities α_B=α^B (with B=21 trading days) and rescale by next-day conditional volatility. Using daily data from 2015–2025, we find heavy tails across all pairs, with EUR/NOK exhibiting the heaviest tail (larger ξ ̂) and USD/NOK the largest extreme-scale. Back tests show reliable 99% VaR across pairs (coverage not rejected; no systematic clustering), while 95% reveals under-coverage for EUR/USD and mild clustering for USD/NOK—consistent with Block-Maxima’s emphasis on far tails. The framework is transparent and auditable; for moderate-tail control (95%), a light PoT overlay or t/skew-t innovations can improve calibration. Our results document economically meaningful cross-pair differences in NOK risk and provide a practical pipeline from filtered returns to daily far-tail capital metrics.

Abstract: Early orbital predictions for the near-Earth asteroid 2001 CA21 — based on 2015 JPL Horizons data — revealed a trajectory with an eccentricity of 0.777, a perihelion of 0.373 AU, and an aphelion extending to 2.967 AU. While subsequent refinements altered the asteroid’s actual orbit, these initial parameters provided a valuable reference template for designing rapid Earth–Mars transfers. By anchoring transfer-plane geometry to the CA21 orbital solution, we identified novel mission opportunities capable of drastically reducing interplanetary travel times.Our analysis highlights the 2031 opposition as the most favorable case: a 56-day transfer with , only marginally exceeding the New Horizons record, and , challenging but potentially addressable with aerocapture or braking tug concepts. A 33-day extreme trajectory is also geometrically possible in 2031, though requiring departure energies ( ) and arrival speeds ( ) well beyond current or near-term propulsion systems.Earlier opportunities in 2027 and 2029, while closer in time, impose even higher energetic barriers (departure velocities ~19 km/s, arrival ~17.5–20 km/s), underscoring the counterintuitive reality that shorter Earth–Mars distances do not guarantee lower transfer energy.This study therefore proposes a new methodological framework: using early asteroid orbital predictions as trajectory templates to identify both feasible and aspirational rapid-transit missions. By linking NEO orbital geometry with Lambert-based transfer analysis, we establish practical benchmarks for propulsion and capture technologies, demonstrating that 2031 provides a near-term achievable baseline, while also defining the aspirational frontier of one-month Mars missions.

Abstract: The widespread adoption of Instagram has transformed entrepreneurial strategies by enabling individuals to engage in personal branding while simultaneously promoting business survival. This study explores how Instagram-enabled personal branding supports entrepreneurial resilience in competitive digital markets. Drawing on psychological branding and digital entrepreneurship literature, the research examines how visual storytelling, authentic self-presentation, and value-aligned messaging contribute to audience trust, engagement, and long-term business sustainability. Findings from recent empirical studies indicate that entrepreneurs who strategically integrate personal identity with brand messaging are better equipped to adapt to market challenges, enhance visibility, and sustain follower loyalty. The study highlights the critical role of social media platforms in shaping both individual and business success, offering practical insights for entrepreneurs seeking to leverage Instagram as a tool for survival and growth.

Abstract: In many hospitality workplaces, customer deviant behaviour is no longer an occasional frustration but a routine part of frontline employees' day, steadily draining their energy, dignity and desire to deliver great service. Yet in sub-Saharan Africa, especially in Ghana, there is still limited evidence showing how this behaviour undermines employees' motivation and what can realistically be done inside organisations to buffer its impact. This study explored how mistreatment from customers affects the service motivation of frontline employees in Ghanaian hotels and examines whether supportive supervisors can act as a protective buffer. Using a quantitative cross-sectional survey of 508 frontline staff in licensed hotels in the Kumasi Metropolis, the study applies Partial Least Squares Structural Equation Modelling (PLS-SEM) to test these relationships. The findings reveal that when customers display deviant behaviour, employees feel less motivated to offer high-level service. On the other hand, strong supervisor emotional support uplifts service motivation and partially mediates the harm caused by deviant customers. These results show that everyday supervisory support (listening, empathizing, and standing up for staff) can make a tangible difference to how motivated employees feel after difficult customer encounters. The study therefore offers practical guidance for hotel managers who want to safeguard employees and sustain high service standards in demanding customer environments.

Abstract: We perform Density Functional Theory calculations to determine adsorption energies of small gas molecules ($\mathrm{H}_2$, $\mathrm{N}_2$, NO, and CO) on defective, vacancy-laden, black phosphorene. Different configurations of single and double vacancies in the monolayer structure are considered, together with several possible adsorption sites onto them. The van der Waals interaction is considered for the exchange-correlation functional. This research aims to provide fundamental insights into how atomic vacancies can be engineered to tune phosphorene's surface reactivity, offering a broader understanding of its multifaceted applications.

Abstract: The increasing consumption of packaged foods hascontributed significantly to the rise of lifestyle-related health con-ditions such as obesity, diabetes, and hypertension, particularly inurban populations. Although nutritional information is mandatedon food labels, many consumers find these details difficult to interpret, limiting their ability to make informed dietarydecisions. This paper presents the design and implementationof an Explainable Artificial Intelligence (XAI)–based system fornutritional assessment and health optimization of packaged foodproducts. The proposed framework evaluates food items using key nutritional attributes, including sugar, sodium, saturatedfat, and calorie content, to compute a comprehensive healthscore. A linear regression model is employed due to its inherentinterpretability, enabling the system to explicitly explain the con-tribution of each nutrient to the final assessment. In addition tohealth scoring, the framework recommends healthier alternativeproducts by comparing nutritional profiles within a structuredfood database. By integrating transparency, interpretability, andactionable insights, the proposed approach enhances user trustand supports informed food choices, thereby contributing tohealthier lifestyles and aligning with Sustainable Development Goal 11.

Abstract: Endogenous heat shock cognate 73 kDa protein (HSC70) has an important role in early embryonic development. We assessed the effects of exogenous HSC70 on bovine embryo development and the expression of genes associated with apoptosis. Expression analyses of HSPA1A, HSPA8, BCL-2, and BAX genes were performed in bovine embryos in vivo on day 7 of development. The expression of HSPA1A and HSPA8 was associated with apop-totic gene (BCL-2 and BAX) expression in cultured bovine embryos in vitro that were sup-plemented with various concentrations (500 ng/mL or 1000 ng/ mL) of HSC70. The results indicated that the control group of in vitro embryos exhibited higher expression of the HSPA8, BAX, and BCL-2 genes compared with in vivo embryos (p ≤ 0.001). In vitro-produced embryos supplemented with 500 or 1000 ng/mL of HSC70 exhibited high-er expression of HSPA1A, HSPA8, BCL-2, and BAX genes compared with the control group (p ≤ 0.01). Embryos supplemented with 1000 ng/mL showed higher expression of the HSPA8 gene compared with the control group and the group supplemented with 500 ng/mL. However, embryos supplemented with 500 ng/mL exhibited more favorable char-acteristics (i.e., development stage and quality) compared with the control and 1000 ng/mL-treated groups. In conclusion, supplementation of bovine embryo culture media with 500 ng/mL recombinant HSC70 protein increased the expression of the HSPA1A and BCL-2 anti-apoptotic genes, resulting in an increase of the number of blastocysts produced in vitro.