Abstract:

Formaldehyde (HCHO), a prevalent indoor air pollutant released from furniture and building materials, poses significant health risks due to its carcinogenic nature. In this study, a binary cuprous oxide–titanium dioxide (Cu₂O–TiO₂) composite photocatalyst was synthesized via a hydrothermal method to enable efficient visible-light-driven degradation of gaseous formaldehyde at ambient temperature. The structural, mor-phological, and optical properties of the as-prepared catalysts were characterized us-ing XRD, SEM, TEM, EDX, and UV-Vis spectroscopy. While pristine Cu₂O exhibited a formaldehyde degradation efficiency of approximately 68% under white light illumi-nation, the incorporation of TiO₂ markedly enhanced the photocatalytic performance. Among the different mass ratios tested, the Cu₂O–TiO₂ (1:1) composite demonstrated the highest activity, achieving 83% degradation of formaldehyde within 240 minutes under white light. Enhanced performance is attributed to the formation of a hetero-junction that reduces the effective bandgap, promotes charge separation, and sup-presses electron–hole recombination. Additionally, the generation of carbon dioxide and water as end products confirmed complete mineralization. The catalyst also showed good reusability, retaining over 81% efficiency after five cycles. This work presents a cost-effective, stable, and visible-light-active Cu₂O–TiO₂ heterojunction photocatalyst with strong potential for indoor air purification applications.

Abstract:

Disordered eating in young adults is shaped by sociocultural pressures and may be modulated by genetic variation. We examined sex differences in eating-pathology, psychosocial correlates, at two candidate loci Hypocretin and Neuropeptide S (HCRTR1 rs10914456; NPSR1 rs324981). A total of 550 individuals visiting various nutrition clinics were initially approached for participation in the study. Of these, 460 consented to take part ,after exclusions, 360 completed SCOFF; 200 scoring >2 proceeded to EAT-26 and comprised the analytic sample (100 males, 100 females). Psychosocial factors (media influence, academic pressure, peer pressure, isolation/loneliness, and K-pop self-comparison) were assessed by a structured questionnaire. EAT-26 total and subscales were compared by sex (t-tests). Genotypes were contrasted by sex using χ² tests; allele frequencies were derived from genotype counts and ORs with CI were computed. Females showed higher EAT-26 total scores than males (29.7±1.9 vs 23.2±1.3; t(198)=2.82, p<0.005); 68% of females and 76% of males scored ≥20. Anorexia subscale scores were greater in females (t(198)=3.713, p<0.0003), as well as binge-eating scores (t(198)=1.722, p<0.05); bulimia indices did not differ by sex (p>0.05). Body dissatisfaction was common (87%) without sex difference (p>0.05).Significant sex associations were observed for media influence (χ²=67.94, p<0.05), academic pressure (χ²=45.6, p<0.0001), K-pop self-comparison (χ²=112.12, p<0.0001), peer pressure (χ²=46.37, p<0.05),and isolation/loneliness (χ²=28.72, p<0.0001).Genotyping data revealed marked sex-dependent associations at both loci. For HCRTR1 rs10914456, female cases showed a significantly higher frequency of the risk (TT) genotype, conferring 4.86-fold greater odds of carrying T-allele relative to males (OR = 4.86, 95% CI: 1.46–16.17, p = 0.001). In contrast, for NPSR1 rs324981, males exhibited a pronounced T-allele–driven risk pattern, being T-carriers (AT+TT) relative to females (OR = 4.11, 95% CI 1.23–13.68, p = 0.022).Within females specifically, the AA genotype was significantly overrepresented compared with T-carrying genotypes (AA vs AT+TT: OR = 3.25, 95% CI: 1.59–6.66, p = 0.0013).Collectively, these results highlight a female-specific recessive risk pattern at HCRTR1 and a male-specific dominant T-allele effect at NPSR1, underscoring robust sex-differentiated genetic susceptibility to disordered eating. Overall females exhibited severe eating-pathology and heightened psychosocial sensitivity than males, while genetic risk showed locus-specific sex patterns. Integrating psychosocial screening with genetic profiling may lead to early intervention.

Abstract: Background/Objectives: Celiac disease is an immune-mediated enteropathy with heterogeneous gastrointestinal and extraintestinal manifestations. Psychiatric symptoms, arthralgia, thyroid comorbidity, anemia, and abnormal liver tests can obscure recognition in primary care. Methods: We report a de-identified reflective case from routine family medicine practice, structured in accordance with CARE case-report principles. Results: A woman in her early sixties with hypothyroidism and glaucoma presented with new low mood, anhedonia, somnolence, generalized anxiety, increased alcohol intake, poor appetite, weight loss, abdominal bloating, diarrhea, flatulence, and polyarthralgia. Investigations showed mild anemia, markedly elevated ferritin and liver enzymes, uncontrolled hypothyroidism, and strongly positive tissue transglutaminase IgA (>250 kIU/L; reference 0.0-14.9). Radiographs showed mild osteoarthritis and osteopenia without erosive arthropathy. CT abdomen/pelvis excluded malignancy but showed severe diffuse hepatic steatosis and mild pancreatic atrophy. The patient declined gastroenterology referral and confirmatory endoscopy with duodenal biopsy. A working diagnosis of probable celiac disease was made; she commenced a gluten-free diet, received alcohol-cessation advice, had levothyroxine adjusted, and was followed in the community. Most symptoms improved within six months. Conclusions: Celiac serology should be considered in adults with anxiety or depressive symptoms accompanied by gastrointestinal symptoms, weight loss, arthralgia, autoimmune thyroid disease, or unexplained liver-test abnormalities. The case also highlights diagnostic uncertainty, and follow-up needs when biopsy is declined.

Abstract: This study employed the 5-meter Accuracy Digital Elevation Model (DEM) developed by the Geospatial Information Authority of Japan to analyze the spatial distribution of Yayoi-period archaeological sites using a Geographic Information System (GIS)–based approach. Unlike conventional prefecture-level classifications, this method enables higher spatial precision and more intuitive visual interpretation. The analysis provides new insights into the long-standing debate over the location of Yamatai (Yamataikoku) approximately 1,800 years ago and significantly increases the likelihood that it was located in northern Kyushu. The results also reveal clear regional specialization within northern Kyushu. The areas around present-day Asakura City and Ogori City appear to have functioned primarily as military centers, whereas the Yoshinogari site—one of the largest Yayoi settlements in Japan—shows strong specialization in agriculture, especially large-scale wet-rice cultivation. The area corresponding to present-day Fukuoka City likely served as a major urban center combining both military and agricultural functions. In addition, the study suggests that “Jimmu’s Eastern Expedition” may preserve certain historical elements rather than being entirely mythical. By introducing GIS-based methods and the supplemental use of generative AI, this study represents both a pilot project and an attempt to advance the digital transformation (DX) of ancient historical studies in Japan.

Abstract: Chronic migraine affects 1–2% of the global population and is the leading cause of neurological disability among women under 50 years of age. The advent of calcitonin gene–related peptide (CGRP)-targeting monoclonal antibodies and small-molecule receptor antagonists has constituted the first disease-specific preventive paradigm; nonetheless, real-world registries demonstrate that 30–50% of treated patients fail to revert to an episodic phenotype, with medication-overuse headache further complicating clinical management. The therapeutic ceiling observed with single-target CGRP pharmacology implies that chronification is governed by mechanisms operating upstream of, in parallel with, and beyond the trigeminovascular neuropeptide loop. The present narrative review synthesises converging evidence from 2020 to 2026 and advances a multi-stratum model in which chronic migraine is conceptualised as an emergent systems failure. Within the trigeminocervical complex, the alarmin high-mobility group box 1 (HMGB1) is proposed to function as an upstream catalyst of the Toll-like receptor 4 (TLR4)–NF-κB–CGRP signalling axis; murine nitroglycerin models indicate that HMGB1 silencing attenuates neuroinflammation and central sensitisation. Clinical data obtained from patients with medication-overuse headache reveal elevated circulating concentrations of lipopolysaccharide, HMGB1, and hypoxia-inducible factor 1-alpha, consistent with intestinal-barrier compromise driving sustained systemic neuroinflammation. Preclinical findings from 2026 document sex-specific perturbations of the gut microbiota and faecal metabolome, together with augmented allodynia in female chronic-migraine models; complementary work demonstrates that sleep restriction and caffeine synergistically reduce the trigeminovascular activation threshold in a sex-dependent manner. Functional neuroimaging implicates sustained decoupling of the salience, default-mode, and central-executive networks as the putative neural substrate of interictal cognitive morbidity. A complementary computational account, grounded in the Free Energy Principle, conceptualises chronification as the consolidation of pathologically rigid prior beliefs—a hypothesis amenable to falsification via task-based contingent-negative-variation, mismatch-negativity, and Hierarchical Gaussian Filter modelling of probabilistic-learning paradigms. It is concluded that progress in chronic-migraine research requires a transition from single-target optimisation toward multi-stratum intervention, anchored in a longitudinal transitional cohort with integrated neuroimaging, electrophysiological, microbial, and ecological-momentary endpoints.

Abstract: Continual learning (CL), also referred to as lifelong learning, aims to develop intelligent systems capable of learning continuously from sequential data while retaining previously acquired knowledge. As AI systems are increasingly deployed in dynamic real-world environments, CL has become essential for enabling long-term adaptation without catastrophic forgetting. This review provides a structured overview of major CL paradigms, including task-incremental, domain-incremental, class-incremental, online, multimodal, and federated CL. We examine the theoretical foundations of CL, particularly the stability-plasticity dilemma, catastrophic forgetting, transfer dynamics, and representation learning. In addition, we analyze major methodological categories, including regularization-based, replay-based, architecture-based, optimization-based, representation-learning, and parameter-efficient approaches. Recent developments involving transformers, prompt learning, foundation models, and multimodal adaptation are also discussed as emerging directions in modern CL research. Furthermore, this review highlights important issues related to benchmark fragmentation, evaluation inconsistency, memory constraints, computational efficiency, scalability, and privacy-aware learning. We also summarize key application domains, including computer vision, natural language processing, robotics, healthcare, and medical imaging. Finally, we identify open research challenges and future directions toward scalable, reliable, and deployment-oriented lifelong learning systems capable of operating effectively in continuously evolving environments.

Abstract:

A hypothesis was proposed that continuous dual-circuit mixing can be achieved by equipping a feed mixer-distributor with two leveling–mixing finger shafts, which, after lifting the feed mass to a certain height, collect it in the central part of the hopper and divide it into two flows directed toward the end walls of the hopper. In this case, continuous dual-circuit mixing is performed during each rotation of the leveling–mixing shaft. A structural and technological scheme, engineering documentation, and an experimental prototype of the feed mixer-distributor were developed. The machine consists of a 3.0 m³ hopper, two horizontal augers, two leveling–mixing finger shafts, a loading conveyor, and a drive mechanism. Theoretical investigations were conducted, and analytical expressions were obtained to determine the circumferential velocity of the fingers of the leveling–mixing device, which should ensure the movement of the feed mixture without scattering and provide the release of the feed mass from the finger surface at a finger rotation angle of 30°. Calculations based on the obtained analytical expressions showed that the critical circumferential velocity of the fingers was 0.8 m/s, while the rotational speed of the finger shaft was 19 min^-1. An analytical expression was also obtained to determine the velocity of feed mixture movement along the finger surface. Based on the calculations, the optimal value of this velocity was found to be 0.7 m/s. This value corresponds to the rational velocity of feed mixture transportation toward the end walls of the hopper. Laboratory experiments were carried out using the feed mixer-distributor at a leveling–mixing finger shaft rotational speed of n = 20 min^-1. The optimal mixing time required to achieve the target mixture uniformity was 5.5 min, which is 15.4% lower than that of existing machines. Comparative experiments also showed that incorporating the leveling–mixing device into the feed mixer-distributor reduced the power consumption of the mixing process by 34%.

Abstract: Microbiology laboratories generate extensive experimental outputs that are often in-sufficiently translated into applied innovation and technology development. This study presents a Routine-to-Research-to-Innovation (R2R) framework integrating routine labor-atory workflows with bioactivity validation, formulation development, and intellectual property (IP) mapping. Lactic acid bacteria isolated from Thai fermented foods demon-strated strong bacteriocin activity and storage stability, while secondary metabolites de-rived from Streptomyces and Brevibacillus exhibited antibacterial, antioxidant, and an-ti-inflammatory activities with prototype formulation potential. Red palm oil-based sys-tems enriched with microbial bioactives also showed favorable physicochemical stability under accelerated conditions. Patent landscape analysis (Thailand, 2020–2025) demon-strated translational alignment between laboratory outputs and existing innovation do-mains, supporting the potential application of the R2R framework in translational micro-biology, technology transfer, and early-stage innovation development.

Abstract: Introduction- Undergoing drastic metabolic and behavioral transformations, carcinogenesis is a cumulative process which leads to excessive proliferation in an unusual manner, camouflage to escape surveillance by the immune system. Aim-The aim of this review is to provide an in-depth exploration of immunology of cancer, highlighting the mechanisms and various aspects immune system, how it interacts with cancer cells and the challenges coming in its way due to tumor cell immune evasion. Method-A comprehensive literature survey and search was made across major electronic databases, which include PubMed, Scopus, and Web of Science, covering publications up to June 2025. The search strategy employed combinations of keywords and medical terms relevant to tumors. Conclusion-Representing one of the most significant advances in the field of oncology the evolving field of cancer immunotherapy offers promising treatment options thereby harnessing the body’s immune system to target cancer cells. Justification-it is not our intention to revisit many of the issues relating to tumor immunology, which have already been covered in detail previously in the literature. Rather this article focuses on the aspects that by compiling desperate foundational knowledge and parallel newer advances in this rapidly evolving field, the review offers a holistic framework of worth to our researchers, clinicians, and students working in the area of cancer immunology and oncology.

Abstract: While aspect-based sentiment analysis (ABSA) has gained significant progress in the identification of explicit opinion targets, the more challenging case, implicit aspects, has not been sufficiently studied. Implicit aspect extraction is particularly challenging as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than just say. In this paper, we propose a four-component hybrid solution for explicit and implicit aspect extraction that formulates aspect extraction as a controlled text generation task. The solution combines: (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive regeneration passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty is not in the individual components themselves, but in the principled orchestration of these components and the gating logic for when each stage is activated. Extensive experiments are conducted on eight benchmark ABSA datasets in both English and Arabic including SemEval 2014, 2015, 2016, ACOS and M-ABSA for English and SemEval 2016, HAAD, and M-ABSA for Arabic. The proposed solution consistently outperforms strong baseline methods and recent state-of-the-art models on English datasets with F1-scores of 0.8533, 0.713, 0.7859, 0.793 and 0.664 respectively, and F1-scores of 0.7336, 0.4765 and 0.7601 on Arabic datasets respectively. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing and structured knowledge for aspect extraction, and the potential of the proposed approach for implicit aspect identification in particular for morphologically rich and low-resource languages such as Arabic.

Abstract: It is shown that the existence of a maximal proper acceleration implies a bound for the acceleration in FICS.

Abstract: The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDS), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Although extensive research has been conducted on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to give a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial of Service (DoS) and Distributed DoS (DDoS) are the most common types of attacks in the literature, while Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation.

Abstract: The embryological basis for the lamination of the retroperitoneal fascia has long remained an anatomical paradox. Classical peritoneal fusion theories cannot account for either the highly organized multilaminar architecture of the mature fascia or the striking temporal lag between early visceral fixation (gestational weeks 9–18) and the abrupt, synchronized emergence of definitive fascial laminae around week 20. Integrating recent advances in fetal biomechanics, we propose that this developmental lag reflects a system-level mechanical transition driven by the geometric constraints of scaling and the evolutionary demands of obligate bipedalism.

Abstract: Coastal biodiversity conservation is challenged by fragmented datasets and the limited integration of environmental conditions into marine spatial planning (MSP). This study develops an operationalized adaptive Marine Spatial Planning (MSP) to support biodiversity conservation by linking remote sensing data, IoT-based water quality measurements, and spatial optimization within Spatial Decision Support System (SDSS). The Tidung Islands are used as a case study, where benthic habitats are mapped from 3 m PlanetScope imagery. Water quality observations are processed into the Nemerow Pollution Index (NPI) and subsequently interpolated through an ensemble approach that combines inverse distance weighting, random forest, and gradient boosting. A key innovation of this study is the incorporation of the Nemerow Pollution Index (NPI) as a dynamic environmental cost layer within Marxan-based conservation prioritization. These data were incorporated alongside anthropogenic pressures to evaluate multiple conservation scenarios The ensemble interpolation demonstrated strong predictive performance (R²=0.76;MAE=0.0306), enabling reliable spatial representation of environmental conditions. The results show that integrating environmental quality into MSP significantly improves spatial efficiency, reduces fragmentation, and enhances ecological representation compared to conventional approaches based on static variables. Moderate conservation targets (≈30%) produced the most optimal solutions (~2,200 cost; ~11 km boundary), while more ambitious targets resulted in fragmented and inefficient spatial configurations. The proposed framework offers a transferable approach for data-limited coastal regions, contributing to the advancement of adaptive biodiversity conservation strategies.

Abstract: Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in identifying stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction–driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture.

Abstract: Long‑acting injectable (LAI) formulations have transformed adherence in several psychiatric conditions, yet no depot antidepressant currently exists. People with major depressive disorder (MDD) remain at risk of accidental overdose from prescribed oral medications, particularly during periods of cognitive impairment or crisis. Artificial intelligence (AI)–driven molecular modelling now enables the design of antidepressant compounds optimised for slow‑release, water‑based depot systems that avoid the fibromas and granulomatous reactions associated with oil‑based injectables. This study outlines an AI‑enabled workflow for generating a novel antidepressant molecule with favourable receptor‑binding properties, low toxicity, and compatibility with biodegradable, aqueous depot carriers. The resulting formulation has the potential to reduce overdose risk, improve adherence, and decrease the burden of frequent GP prescribing.

Abstract: Since its emergence in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected a wide range of animal species, including wildlife. Although SARS-CoV-2 infection has been widely reported in wildlife, particularly in white-tailed deer (WTD; Odocoileus virginianus) across the United States, data on viral circulation in New England wildlife remain limited. Here, we evaluated SARS-CoV-2 infection and serological evidence of previous exposure in free-ranging wildlife from the northeastern United States. We examined samples from 1,646 animals representing 28 wildlife species, collected through wildlife rehabilitation centers, clinics, and hunter harvests in New England and Virginia between 2022 and 2025. SARS-CoV-2 RNA was detected in three WTD from Massachusetts and Vermont. Phylogeographic analysis of Vermont WTD sequences indicated a human SARS-CoV-2 lineage as the most likely source, consistent with a single human-to-deer spillover event followed by subsequent circulation within deer. Serological screening using ELISA detected SARS-CoV-2 antibodies in 12 individ-uals from three species, including Eastern cottontail (Sylvilagus floridanus), Eastern coyote (Canis latrans), and raccoon (Procyon lotor), although neutralizing antibodies were found in only one Eastern cottontail. Overall, these findings reveal ongoing but limited SARS-CoV-2 circulation in northeastern wildlife and highlight the importance of continued surveillance to detect spillover events, monitor viral evolution, and evaluate potential risks posed by wildlife.

Abstract: Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi‑arid regions, yet long‑term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi‑source precipitation datasets; CHIRPS, IMERG, and ERA5‑Land, against long‑term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single‑peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year‑to‑year variability. CHIRPS performs best overall, with monthly NSE values around 0.77 and KGE between 0.79 and 0.88, along with a consistent dry bias of 5–13%—a predictable error that can be corrected operationally. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals (NSE = −1.93 to −2.21). ERA5‑Land performs worst across nearly all metrics, with monthly NSE near or below zero, annual NSE dropping to −15.34, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro‑climatic monitoring in White Nile State, while IMERG and ERA5‑Land may still be useful in more specialized or time‑specific applications.

Abstract: Autonomous Haulage Systems (AHS) are becoming increasingly popular in recent years as mining operations seek to improve productivity and remove workers from hazardous environments. The integration of this technology in a systematic manner implies not only change management in operations, but also deeper perspective into mine planning implications. Currently, existing literature describes AHS and their implementation guidelines with focus on operational safety and autonomous system architecture, without systematically addressing required planning-level adaptations. This study aims to identify how mine planning frameworks must evolve to accommodate autonomy in open-pit metal mining operations. A systematic review is conducted using the PRISMA methodology with emphasis on identifying the principal aspects of AHS that must be considered in mine planning strategies. Findings reveal major shifts in workforce dynamics, communication infrastructure, and haul road geometry, alongside ongoing debates regarding optimal road width and load channelization. The study highlights the need for (i) holistic approaches to haul road and mine design, that are aware of technology, geotechnical, and mineral aspects with a data driven perspective (ii) human-systems integration and new needs in human-autonomous collaboration, and (iii) empirical validation of workforce transition strategies for more effective and safe deployment.

Abstract: Mitochondria regulate cellular energetics, redox balance, apoptosis, and inflammatory signaling in oral, airway, and systemic tissues. Hypoxia is a powerful modulator of mitochondrial function, with effects ranging from adaptive hormesis to overt injury. Cyclic altitude training, most delivered as intermittent hypoxic exposure or intermittent hypoxia training (IHT), has been proposed as a strategy to improve mitochondrial efficiency and exercise performance. By contrast, obstructive sleep apnea (OSA) exposes patients to uncontrolled chronic intermittent hypoxia (CIH), a pattern increasingly linked to endothelial dysfunction, ceramide-mediated mitochondrial dysfunction, insulin resistance, systemic inflammation, oral dysbiosis, and periodontitis. This narrative review covers intermittent hypoxia, mitochondrial biogenesis, hypoxia-inducible factor signaling, OSA, periodontitis, oral microbiome shifts, nitric oxide biology, and smoke-related mitochondrial injury. Appropriately dosed IHT can increase mitochondrial biogenesis, improve mitochondrial morphology, and augment oxidative capacity through pathways involving PGC-1alpha, hypoxia-inducible signaling, mitochondrial dynamics, and reactive oxygen species-dependent hormesis. In contrast, CIH in OSA promotes oxidative stress, sympathetic activation, endothelial injury, and inflammatory signaling and is associated with worse periodontal status and altered salivary microbiome profiles. Controlled IHT and OSA-related CIH, therefore, represent opposite ends of a hypoxia continuum, and mitochondrial health connects sleep-disordered breathing, periodontal inflammation, environmental exposures, and systemic cardiometabolic risk within a single conceptual frame. Sphingolipid signaling—particularly hypoxia- and toxicant-driven ceramide accumulation—connects CIH, inhaled environmental exposures, mitochondrial fragmentation, and the development of insulin resistance.