Abstract: The increasing complexity of automotive software driven by ADAS and autonomous driving has intensified the need for time-deterministic network validation beyond CAN/LIN, while HIL integration remains constrained by limited ECU prototypes and labor-intensive manual configuration. This paper presents a network-oriented virtual verification environment that couples Renode-based virtual ECUs (vECUs) with FMU-based Interaction Layer (FIL) Nodes automatically generated from DBC specifications. The vECUs provide instruction-accurate execution of unmodified target binaries without physical hardware, while the generated FIL Nodes encapsulate communication behavior as model-based FMUs to maintain continuity from MIL to SIL without manual signal mapping. The proposed framework constructs virtual CAN networks from communication-definition files, reproduces periodic and event-triggered traffic patterns, and supports synchronized multi-ECU co-simulation under master-controlled time stepping. Experimental results show that the vHIL environment reproduces physical ECU timing behavior with a maximum relative error of 0.086%–0.171% across all evaluated step sizes (10 μs to 1000 μs), confirming binary-level timing fidelity. Replacing vECUs with FIL Nodes for network communication processing significantly reduces wall-clock execution time in multi-node configurations, demonstrating improved scalability without sacrificing determinism. These results demonstrate that the proposed methodology effectively reduces early-stage integration bottlenecks while preserving timing fidelity for automotive networked ECU validation.

Abstract: In oil production, the formation of oil emulsions due to reservoir water breakthrough is widely observed. Since the viscosity of these emulsions, which are considered polydisperse systems, can increase sharply depending on the degree of water cut, they create considerable difficulties in well-gathering systems and also increase hydraulic losses. The rheological properties of oil emulsions depend on the phase ratio, flow velocity, degree of dispersion, and numerous other parameters. There is no generalized model for the rheological description and determination of the properties of oil emulsions, which belong to anomalous and rheologically complex systems. Therefore, a diagnostic method for determining the viscosity of stable emulsions, taking into account the effect of increasing water content, is of great importance. In this article, the existing empirical expressions currently used for diagnosing the rheological properties of oil emulsions are examined. It has been determined that their application in oilfield practice is associated with certain difficulties and, in most cases, they are not considered suitable for solving engineering problems. In the article, a mathematical model has been developed, tested, and shown to provide good results for determining and predicting the viscosity of structurally stable oil emulsions depending on the degree of water cut.

Abstract: Among the micropollutants of medium-depth waters in isolated inhabited areas, the inorganic ones deserve special attention: nitrate anion (NO₃⁻) and phosphate anions (HxPO4^⁻(3⁻x)). The individual removal of these anions from water is widely studied, with different methods being found: chemical, ion exchange or biological. This paper presents a membrane method for the simultaneous removal of nitrate anion and phosphate anions from dilute synthetic aqueous solutions. The developed method is nanofiltration using composite membranes made of cellulose acetate (CA) and silver nanoparticles (Agnp). The composite membranes were made by phase inversion of the dimethylformamide (DMF) solution containing the two components (CA–Agnp) on a polypropylene (PP) capillary fiber using deionized waster as a coagulant. The DMF solution of CA containing Agnp was obtained by dissolving black-and-white cinematographic films (exposed and unexposed to light). CA–Agnp–PP composite membranes were tested for the simultaneous removal of nitrate anion and phosphate anions from aqueous solution by nanofiltration at pressures ranging from 5 to 25 bars. A removal of over 98% of phosphate anions and more than 95% of nitrate anion was achieved. Fluxes of 10 L·m^⁻2·h^⁻1 were obtained for the working pressure of 15 atm, depending on the pH, flow rate and concentration of the feed water (feed solution). Variable parameters studied were also the concentration of CA and Agnp.

Abstract: Islanded and weakly interconnected power systems face increasing operational challenges as the penetration of renewable energy grows, particularly in environments with limited flexibility and reserve support. This study investigates a residential energy community in Cyprus using real time prosumer data and proposes a hierarchical decentralized Energy Management System (EMS). The EMS was initially implemented as a rule-based Minimum Viable Prototype (MVP), enabling practical validation of system operation and data integrity under realistic conditions. Building on this foundation, the final EMS adopts a three-layer control architecture in which rule-based household storage operation and peer-to-peer energy trading are complemented by a community-level Model Predictive Control (MPC) strategy for shared battery management. The MPC leverages short-term net-energy forecasts to proactively schedule charging and discharging of the community battery with the objective of reducing grid energy imports and improving local renewable energy utilization, while respecting battery operational limits and regulatory constraints. Simulation results based on measured data demonstrate consistent improvements over the rule-based baseline, achieving up to 23.4% reduction in electricity cost, approximately 6–7% reduction in grid energy imports, and a 1–2% increase in community self-sufficiency. Although the reduction in imported energy is moderate, its cumulative impact over long-term operation leads to significant economic benefits under time-varying tariffs. These results demonstrate that even when MPC is applied exclusively at the community battery layer, coordinated system-level energy management can deliver stable and economically meaningful improvements under realistic operating conditions.

Abstract: As enterprises continuously rely on data to effectively drive and power business processes in a digital economy, privacy concerns have emerged as a major source of deep concern among consumers and privacy advocates in a digital society. Several scholars have shared their opinions and perspectives in related articles on this issue since privacy is a fundamental and constitutional right in many countries that must be protected at all times. While several practitioners and industry experts have proffered various privacy-preserving measures to help empower users to make informed decisions that relate to the use of their personal information, others have proposed various privacy preserving measures and mechanisms to help protect the use of personal information in a digital society to create the necessary confidence and trust amongst members of the public. With the current advances in artificial intelligence, social media platforms, and automation, the issue has emerged as a major source of concern among policymakers, privacy advocates, and industry experts. The purpose of the e-Delphi study was to gain consensus from the opinions of industry experts on best practice measures and various effective privacy-preserving measures to help enhance users’ privacy and allay privacy concerns in a digital society. The study adopted the Restricted Access/Limited Control (RALC) theory of privacy to provide a theoretical framework for the research study. The study included three rounds of questioning using the Delphi method. The findings from the study revealed that effective privacy-preserving measures, such as Data minimization, Privacy-By-Design, Privacy Labels and icons, Data Ownership and control, Third-party App Permission, Mandatory Data/Privacy Breach Notice, Frequent Policy Updates, End-to-End Encryption, User-Friendly Privacy Control features, and Informed Consent, provided an effective way to allay the fears of consumers in a digital age.

Abstract: Rising global temperatures pose a serious threat to wild small mammal’s population persistence. In this study, we investigated the molecular mechanisms underlying heat-induced testicular impairment in Brandt’s vole (Lasiopodomys brandtii), a dominant small mammal species of the Eurasian temperate steppe. Adult males were subjected to short term heat exposure at 37°C, 39°C, and 41°C. Heat stress at temperatures ≥39°C significantly reduced the testicular index and caused histopathological damage. Integrated transcriptomic and data-independent acquisition proteomic analyses revealed significant enrichment of pathways related to endoplasmic reticulum protein processing and barrier function. Further molecular validation demonstrated robust activation of the unfolded protein response, indicated by increased expression of ATF4, ATF6B, phosphorylated eIF2α, and XBP1. Together, these results identify endoplasmic reticulum stress as a key mediator of heat-induced testicular injury and highlight that 39°C represents a critical reproductive threshold for Brandt’s voles, even following short-term exposure.

Abstract: This article presents the design and deployment of ClimaBogotá v1.2, a climate prediction system tailored for high-altitude urban micro-zones in Bogotá, Colombia. The system combines low-cost IoT sensing, machine learning modeling, and cloud-based orchestration to enable scalable and affordable meteorological forecasting. Its architecture comprises Raspberry Pi-based weather stations, a Random Forest model trained on engineered temporal features, and an n8n-driven automation pipeline for real-time inference and dissemination via Telegram, PostgreSQL, and Grafana. With a Mean Absolute Error of 2.59°C and an R² of 0.6286 on a 30-minute forecast horizon, the system demonstrates both predictive reliability and operational feasibility using free-tier cloud resources. Unlike traditional weather systems, ClimaBogotá emphasizes modularity, adaptability, and cost-efficiency, offering a replicable framework for decentralized climate monitoring in data-scarce urban environments. Temporal misalignment between sensor nodes was identified as the primary constraint, informing future enhancements toward distributed learning strategies.

Abstract: Background/Objectives: Advanced-stage hepatocellular carcinoma is characterized by a very poor prognosis; thus, highly effective medication is still needed. Often overexpressed heat shock protein 90 is a promising target due to its pivotal role in carcinogenesis. Methods: Antiproliferative effects of novel synthesized inhibitor 246TMP-3SF5 on HepG2 and HuH-7 were analyzed through crystal violet staining. Apoptosis was assessed by measurement of subG1 peak, caspase-3 activity and cleavage of PARP. Ferroptosis was evaluated through reactive oxygen species, glutathione and malondialdehyde levels. Scratch assays were used to assess cancer cell migration and in-ovo models to analyze effects of 246TMP-3SF5 on angiogenesis and microtumors. Molecular docking and molecular dynamics simulation into heat shock protein 90 were carried out using Autodock Vina and Gromacs respectively. Results: Profound dose- and time-dependent antiproliferative effects of 246TMP-3SF5 against HCC cell lines were observed, revealing low micromolecular IC50 values. A significant increase in subG1 peak, key effector caspase-3 activity as well as cleavage of PARP strongly suggested apoptosis playing a crucial role in the antiproliferative effects. Additionally, HuH-7 cells revealed an elevation of reactive oxygen species and both cell lines showed significant glutathione depletion concomitant with malondialdehyde concentration increased upon treatment. The observed effect could be partially reversed applying ferrostatin-1, suggesting ferroptosis as an additionally relevant mode of action. Changes in the cell cycle as well as impaired tumor cell migration were observed. Upon treatment angiogenesis was impaired and mass of microtumors was significantly reduced. Molecular docking revealed interaction with catalytic site of heat shock protein 90. Conclusions: 246TMP-3SF5 is a promising novel inhibitor meriting further research as potential treatment against hepatocellular carcinoma.

Abstract: Across the post-Soviet space, the disappearance and commercialisation of traditional public realms have shifted everyday social life toward privately owned, publicly used environments. This study examines whether and how contemporary shopping malls function as quasi-public spaces and “third places” in Almaty, Kazakhstan’s largest city. Drawing on Oldenburg’s third-place theory and debates on the privatisation of the public realm, a mixed-methods design combined a visitor questionnaire (n = 412) across five malls, 40 hours of behavioural mapping, and 30 semi-structured interviews with visitors and mall managers. Results indicate that, beyond consumption, malls host substantial non-commercial sociability: 56.4% of respondents reported visiting primarily for dining, leisure, or meeting others rather than shopping, and the majority agreed that malls feel comfortable and broadly accessible. At the same time, only a minority unambiguously regarded the mall as a “public space”, and perceptions of inclusiveness varied significantly with age, income, and mode of arrival. Malls thus operate as conditional and negotiated third places whose publicness is real but managed. The paper argues that recognising malls as de facto social infrastructure has direct implications for socially sustainable urban planning in rapidly transforming Central Asian cities, and outlines design and policy measures to strengthen genuine inclusiveness.

Abstract:

Forest ecosystems in Northern Ghana's Guinea Savannah landscape face mounting pressures from illegal logging, charcoal production, agricultural expansion, bushfires, and climate variability, threatening biodiversity, carbon stocks, and the parkland mosaic of shea, dawadawa, neem, and baobab that sustains local livelihoods. The Risk Management Framework (RMF) offers a structured approach to anticipate, assess, and mitigate such environmental risks, yet its operational integration into forest governance in Sub-Saharan Africa remains weak. This study examined the awareness, understanding, and applied knowledge of the RMF among forestry stakeholders in Northern Ghana and analysed the socio-demographic and institutional factors shaping engagement with risk-based environmental governance. Using an explanatory sequential mixed-methods design, a structured survey was administered to 160 stakeholders across five districts (West Mamprusi, Mamprugu Moagduri, North Gonja, Sagnarigu, and Tamale Metropolitan), complemented by five focus group discussions with Community Resource Management Area (CREMA) groups and seven key informant interviews with officers from the Forestry Commission, Environmental Protection Agency, and Ministry of Food and Agriculture. Data were analysed using descriptive statistics, multiple linear regression, a validated three-item Knowledge Scoring Index (Cronbach's α = 0.78), and thematic analysis. Results show that while overall awareness of RMF was high (94%), applied knowledge was substantially weaker, particularly regarding the institution responsible for RMF implementation (mean = 0.32). Education, occupation, and composite knowledge score significantly predicted RMF knowledge, while gender and community-leader status did not. Qualitative findings revealed three structural patterns: symbolic risk governance, a community-leader bottleneck in information transmission, and an awareness–understanding divergence in which stakeholders interpret formal RMF terminology through indigenous and CREMA-based practices. The findings demonstrate that human knowledge systems mediate forest ecosystem outcomes and underscore the need for institutional clarification, targeted capacity-building, and a phased digital tools roadmap, including mobile-based reporting platforms, satellite-derived monitoring dashboards, and integration of indigenous early warning indicators, to strengthen forest sustainability, biodiversity conservation, and climate resilience in dryland Sub-Saharan Africa.

Abstract: Artificial Intelligence (AI) based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative research demonstrates that AI-driven systems can significantly improve academic performance, teacher productivity, and learner motivation, offering a scalable and equitable solution for the future of education in both traditional and digital environments.

Abstract: Background: Uhthoff’s phenomenon (UP) is a transient, fully reversible exacerbation of pre-existing neurological deficits in multiple sclerosis (MS) patients, triggered by minor elevations in core body temperature. In clinical practice, UP is a frequent source of distress, often misidentified as an acute inflammatory disease relapse. Objectives: This narrative review provides a critical analysis of UP, addressing the methodological heterogeneity in its epidemiological estimates, its clinical presentation, and its differential diagnosis from true relapses. Methods: Mechanistically, we synthesize traditional concepts of temperature-dependent conduction block with modern insights into neuroenergetic failure, mitochondrial dysfunction, inflammatory mediators, and autonomic dysregulation. Results: Furthermore, this work delineates current management strategies, establishing a clear distinction between robust evidence-based interventions and expert-informed practical guidance for patient education, physical rehabilitation planning, targeted active/passive cooling, and pharmacological approaches. Conclusion: Characterized as a pseudo-relapse, UP occurs independently of novel focal neuroinflammation. Although it does not inflict permanent structural damage to axons, the transient neuroenergetic crises and accompanying psychological burdens constitute a genuine threat to patient quality of life and functional autonomy, requiring systematic, interdisciplinary care.

Abstract: Medical multimodal foundation models (MMFMs) have become a central element of medical artificial intelligence, supporting progress in clinical workflows like diagnosis, report generation, and multimodal reasoning. However, existing surveys face issues with quick aging and brief coverage of model types. This paper offers a fine-grained review of MMFMs covering January 2023 to July 2025, filling these needs through a structured framework. We analyze key technical features—including model size, dataset size, and architectural designs—across three main model categories: Universal MMFMs (Uni-MMFMs) with wide use, Modality-specific MMFMs (MS-MMFMs) with single-modality specialization, and Organ-specific MMFMs (OS-MMFMs) with organ-specific tuning. We chart development paths and highlight major challenges: data scarcity and privacy constraints, insufficient cross-modal alignment, limited clinical interpretability, and poor generalization in real-world scenarios. We also propose future directions including data-level growth (multi-source integration, synthetic generation), architecture-level updates (unified image-text frameworks), user-centric features (interpretability, ethical compliance), and developer-focused improvements (continuous learning, multimodal conflict resolution). This survey summarizes the current state of MMFMs and provides a guide for building reliable, interpretable, and useful multimodal medical AI systems.

Abstract: Background/Objectives: Oxidative stress and low-grade chronic inflammation are implicated in the early pathogenesis of non-communicable diseases (NCDs). Malondialdehyde (MDA), a stable biomarker of lipid peroxidation, may reflect subclinical oxidative stress in apparently healthy young adults. This study aimed to: (1) establish a population-specific reference interval for plasma MDA in healthy Thai undergraduate students; and (2) evaluate associations between plasma MDA levels and lifestyle risk factors. Methods: This cross-sectional study enrolled 141 Thai college students aged 18–27 years. Plasma MDA was measured using the thiobarbituric acid reactive substances (TBARS) assay. A reference interval was determined non-parametrically (P2.5–P97.5) following IFCC recommendations in a healthy reference subgroup (n = 94). The 75th percentile (P75) was used as an exploratory cutoff for elevated MDA. Binary logistic regression identified independent lifestyle predictors of elevated MDA. Results: The plasma MDA reference interval was 1.16–9.23 µmol/L, with P75 = 6.07 µmol/L. Using this cutoff, 24.8% of participants had elevated MDA. High sugar intake (>24 g/day) was the only independent predictor of elevated MDA after multivariate adjustment (adjusted OR = 3.42; 95% CI: 1.08–10.87; p = 0.036). Conclusions: To our knowledge, this study established the first population-specific plasma MDA reference interval for Thai young adults using the TBARS method. High sugar intake was identified as a key modifiable lifestyle predictor of elevated lipid peroxidation, even in apparently healthy individuals. These findings support the use of plasma MDA as a practical, cost-effective screening biomarker for early oxidative stress surveillance and NCD prevention in young populations.

Abstract: Despite ethical constraints and strict oversight by local Institutional Animal Care and Use Committees, animal models still permit molecular investigations that would never be acceptable to humans. Nevertheless, experimental outcomes depend on species, strain, sex, age, hormonal status, diet, exposure to hypoxia, toxins, radiation, external stimuli, stress, and even housing conditions. Further complications stem from the heterogeneous cellular composition of tissues and from the procedures required to isolate and eventually immortalize specific cell subtypes. Moreover, most diseases are multi-factorial and associated with altered structure or/and expression of several genes. A major problem with genetically engineered animals is that together with the targeted gene numerous other genes are mutated or/and regulated owing to their interlinkage in functional pathways. However, animal models have the important advantage of allowing the investigator to control most of the regulating factors and produce biological replicates, while every human is a dynamic unique. This review examines the challenges, accuracy and limitations of the mouse, rat and rabbit models we used to decipher the transcriptomic alterations associated with several neurological disorders. Links to publicly accessible databases presenting the experimental protocols and expression profiles are provided for readers interested in reanalyzing our data and comparing with their own results.

Abstract: The monitoring of mental health states using electroencephalogram (EEG) signals has gained increasing attention due to its non-invasive nature for psychological disorders. Large Language Models (LLMs) and Explainable Artificial Intelligence (XAI) have been utilized in advancing the intelligence and interpretability of EEG analysis. However, existing methods face critical bottlenecks, including the fundamental modal gap, high computational costs, and poor global consistency. The limitation of rigid classification tasks without supporting clinical reasoning and natural language interaction. In this study, we propose a collaborative explainable AI framework for EEG mental health monitoring with constrained question-and-answer (QA) tuned LLM alignment, which builds a smooth transformation path from raw EEG signals to evidence, and constructs a structured QA dataset for the instruction fine-tuning of LLMs. The central objective of this work is not simply to maximize EEG classification accuracy, but to develop an evidence-grounded alignment and explanation framework that connects EEG-derived physiological evidence with QA-based LLM reasoning. Furthermore, this work designs a transparent collaborative XAI mechanism that embeds interpretable EEG feature information as prior knowledge directly into the QA generation process of the LLM, and develops a multi-level interpretable pipeline combining attention heatmap analysis and decision tree surrogate modeling to achieve precise alignment between LLM internal reasoning and EEG neurophysiological patterns. The proposed framework addresses the limitations of traditional rigid EEG classification tasks, promotes the XAI paradigm shift from high-cost post-hoc explanations to transparent embedded explanations, and enables robust clinical reasoning and natural language interaction based on EEG signals. Experimental results on a benchmark EEG mental state dataset demonstrate that the proposed framework stably captures neurophysiological characteristics corresponding to different mental states, and effectively improves the classification performance, decision transparency and clinical credibility of EEG-based mental health monitoring systems. In this setting, classification performance is treated as one evaluation aspect, while the primary contribution lies in constrained evidence-grounded alignment and QA-based LLM explainability. This advancement provides an initial feasibility study of real-time, scalable, and trustworthy intelligent EEG-based mental health analysis.

Abstract:

Glutamatergic neuron-to-glioma signaling mediated by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) has emerged as an important mechanism in glioma progression. We analyzed the expression of the AMPAR subunit genes GRIA1, GRIA2, GRIA3, and GRIA4 in lower-grade glioma (LGG). Expression of GRIA1–GRIA4 was highest in IDH-mutant/1p19q-codeleted tumors and lowest in IDH-wildtype tumors across both The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts. High expression of each GRIA gene was associated with longer overall survival (OS). Transcriptome-wide analyses identified positive correlations between an AMPAR score and genes involved in synaptic organization, neuronal connectivity, and neurotransmission. Co-expression analyses demonstrated coordinated expression between GRIA1-GRIA4 and genes encoding AMPAR auxiliary proteins. Gene Ontology (GO) enrichment revealed overrepresentation of synaptic signaling, trans-synaptic communication, and synapse organization. Although the AMPAR score was associated with favorable survival in univariate analyses, it did not retain independent prognostic significance after adjustment for key clinicomolecular variables. Elevated expression of AMPAR subunit genes in LGG was associated with favorable molecular subtypes, prolonged survival, and a synaptic transcriptional program. These findings suggest that GRIA1–GRIA4 expression may serve as a marker of a neuron-like, synaptically enriched biological state in LGG.

Abstract: Anthropogenic prehistory may be divided into two subsequent phases, the first one from the Last Common Ancestor (LCA) between humans and great apes till the emergence of the genus Homo, and the second phase onwards from then. Until hominins appeared, LCA had lived predominantly on trees, while Homo finally lived only on the ground. After LCA and before Homo, the intermediate bimodal transition period from 7 to 2 Myr BP was coined by the emergence of systematic bipedal gait, breaking the previously uniform quadrupedal locomotion symmetry. By contrast to versions of the common savannah hypothesis, this paper suggests an alternative fictitious scenario of periodic migration between alternatingly inhabitable arboreal refuges. Possibly caused by regional climate change, yet tree-climbing hominins were additionally forced to genetically develop speedy and efficient bipedal locomotion for survival during their temporary but extended regular excursions across open territory. Increasingly upright locomotion resulted in offspring’s early weaning, and in turn in the emergence of childhood with enhanced lethal risks for toddlers. Related selective pressure caused transformations of reproductive traits from gradual sexual selection in apes to undulating sexual conflicts in hominins. Between LCA and Homo, consistent with fossil evidence, the evolutionary bimodal transition phase, with forelimbs for climbing and hind limbs for running, did not necessarily require enlarged brains for advanced mental capabilities, nor specific communication or new forms of social cooperation such as those successively found in Homo. Assumingly, broken spatial and temporal environmental symmetry had induced related symmetry breaking of hominin behaviour, their anatomic structures and reproduction habits.

Abstract: Background/Objectives: Although cow’s milk allergy is the most common food allergy in infants, its diagnosis remains challenging. Conventional allergy tests, including skin prick testing and serum-specific IgE, can support the diagnosis in cases with a clear clinical history; however, neither positive nor negative results are sufficient for a definitive diagnosis without confirmation via an oral food challenge or reintroduction. The basophil activation test has emerged as a promising adjunctive tool in this context. Methods. We searched in the electronic databases PubMed and Google Scholar combinations of the following keywords: “basophil activation test”, “BAT”, “cow’s milk allergy”, “CMA”. Results. While its widespread use is currently limited by the lack of standardized protocols, the need for fresh blood samples, and restricted availability outside specialized centers, the basophil activation test has the potential to improve diagnostic accuracy and support clinical decision-making in IgE-mediated cow’s milk allergy. Importantly, basophil activation test may also reduce the need for oral food challenge by providing a safe, functional assessment of allergen reactivity, thereby minimizing patient risk and the burden of invasive testing. Conclusion. Further studies and validation are needed before the basophil activation test can be implemented as a routine diagnostic tool.

Abstract: Pre-storage fruit injuries are a major yet often underestimated cause of postharvest losses in apples, particularly during prolonged cold storage. This study evaluated the impact of minor pre-storage injuries on fruit deterioration and assessed the effectiveness of ammonium bicarbonate (2%) and clove bud essential oil (0.2%) as eco-friendly postharvest treatments compared with the fungicide fludioxonil (Scholar). Fresh red and yellow apples were classified as either intact fruit or fruit bearing minor injuries affecting less than 2% of the surface area (approximately 2 mm lesions). Treatments were applied by spraying before storage at 5°C for six months, followed by 10 days of shelf life at 20°C. Minor injuries significantly increased postharvest decay, weight loss, and quality deterioration in both cultivars. Injured yellow apples exhibited decay incidence of 17–24% and disease severity of 10.5–17.8%, whereas the more susceptible red apples showed decay incidence of 44.6–60% and disease severity of up to 50%. In contrast, non-injured fruit maintained better physicochemical quality and generally exhibited less than 5% decay incidence. Responses to storage and treatments differed between cultivars. Ammonium bicarbonate effectively reduced decay and helped maintain firmness in yellow apples, providing protection comparable to that of fludioxonil, whereas Scholar was the most effective treatment for reducing decay in red apples. Clove essential oil reduced disease development in non-injured fruit but showed limited effects on firmness preservation. The residual activity of fludioxonil remained detectable after prolonged storage, as demonstrated by inoculation assays performed after storage. The results demonstrate that fruit integrity at harvest is a critical determinant of successful long-term storage and that the benefits of postharvest treatments are substantially reduced when fruit are mechanically injured. Preventive treatment of uninjured fruit with ammonium bicarbonate or Scholar can significantly reduce postharvest losses, while careful handling and sorting to eliminate injured fruit remain essential components of sustainable apple storage management.