Abstract: Background: Ketamine is used as an adjunct analgesic for cancer pain refractory to opioids, but evidence remains limited to small series, predominantly from high-income settings. Few studies include both adults and children, and data from the Middle East are sparse. Methods: We conducted a retrospective study of all patients who received intravenous ketamine for pain at King Hussein Cancer Center (KHCC), Jordan, between February 2018 and April 2026. Pain scores, medications, and vital signs were extracted from the structured EHR. Unstructured variables (pain mechanism, adverse events) were extracted using a schema-constrained large language model pipeline (GPT-4.1-mini) and validated in a 10% random sample by two investigators (AA, EK). The primary outcome was the paired within-patient change in peak pain (maximum NRS in 72 hours pre-ketamine vs maximum NRS in 24 hours post), tested by the Wilcoxon signed-rank test. Results: 433 patients received IV ketamine (183 [42%] paediatric; 72% deceased at follow-up). Among 141 patients with paired peak data, the median paired reduction in peak pain at 24 hours was 1 point (IQR 0 to 5; mean 2.5 ± 3.8; p < 0.001; matched-pairs rank-biserial r = 0.58). Among 107 patients with baseline peak pain >=3, 67 (63%) achieved a >=2-point reduction. Planned sensitivity analyses using peak-to-nadir and mean-to-mean metrics confirmed the direction of the effect. Adverse events were documented in 25 patients (5.8%); retrospective ascertainment likely underestimates true incidence. Conclusions: In the largest single-centre series to date, adjunctive IV ketamine was associated with reduced peak cancer pain in adults and children. The observational design precludes causal inference. These data support ketamine’s feasibility as a palliative analgesic adjunct across age groups in a middle-income setting. The bimodal response pattern (63% responders alongside non-responders) is a hypothesis-generating finding for future trial enrichment. A placebo-controlled randomised trial stratified by pain mechanism is a logical next step where equipoise can be established.

Abstract: As one of the major auxiliary businesses of airlines, air cargo is profoundly reshaping their profit models and development patterns. Combining the industrial characteristics of high liquidity and high risk in the air transportation industry, this paper constructs a multi-dimensional evaluation index system covering financial and logistics operation dimensions, selects the panel data of China Eastern Airlines from 2012 to 2024 as the research sample, and applies the entropy weight-TOPSIS combined method to conduct an empirical analysis of financial performance evaluation for verifying the feasibility of the adopted method. On this basis, taking the cargo indicators reflecting logistics capability and its operational strategy as independent variables, and the comprehensive index of operational performance formed by the coupling of key financial indicators and logistics operation indicators as the dependent variable, multiple groups of experiments are designed to systematically explore the influence mechanism of changes in logistics capability and its operational strategy on airlines' operational performance. Meanwhile, scenario experiments on the variation of weight coefficients are conducted to observe its sensitivity. The research results show that changes in logistics capability and its operational strategy have a significant impact on airlines' operational performance. This study not only reveals that different degrees of attention to enterprise operation elements will exert a profound influence on the overall corporate performance, but also highlights the value of air cargo, making up for the deficiencies in existing studies combining airline performance evaluation with cargo logistics. Against the backdrop of growing global economic uncertainty, this study can provide decision-making references and theoretical support for airlines' business operations and sustainable development strategic planning.

Abstract: Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated the influence of reference evapotranspiration (ET₀) on ETa estimation using Random Forest (RF), Bagging, Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), NDVI, and soil moisture (SM) were used as predictors under two scenarios: with and without ET₀. Variable importance and model interpretability were evaluated using Permutation Feature Importance (PFI) and SHAP analyses. Results showed that ET₀ was the dominant predictor when included in the models, exhibiting the highest importance and contribution values across all approaches. When ET₀ was excluded, the explan-atory structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Although model accuracy generally decreased without ET₀, RF and Bagging maintained stable performance (R² ≈ 0.97), whereas LSBoost, GAM, and MLR exhibited more pronounced reductions in predictive accuracy. The analyses further revealed that SM consistently enhanced model performance by reducing prediction errors and improving robustness, particularly in the absence of ET₀. Overall, the findings demonstrate that both model selection and the physical representativeness of input variables are critical for reliable ETa estimation. Moreover, ET₀ drives an atmosphere-controlled evaporative demand regime, whereas Rs becomes the primary controlling factor when ET₀ is unavailable.

Abstract: Intuitive judgments feature high subjective confidence despite inability to articulate evidential basis—a dissociation between confidence and accessible justification that challenges existing accounts of rapid decision-making (19,33,39). We propose intuition constitutes a metacognitive phenotype arising from preconscious integration of weak interoceptive and contextual signals within subcortical-cortical circuits centered on the amygdala-insula-vmPFC axis (8,10,37,38).We present empirical validation using heartbeat detection data (N=234) from an openly available dataset (MacCormack et al., 2024; OSF: osf.io/z7c2a) (7,24). Results demonstrate significant dissociation between objective performance (M=0.597, SD=0.124) and subjective confidence (M=0.522, SD=0.168), with systematic underconfidence (M=+0.075, t(233)=+6.65, p<.001, d=0.43). Critically, 67.9% exhibited performance exceeding confidence, confirming impaired metacognitive access despite intact discrimination (11,12). Weak performance-confidence correlation (r=.326, R²=10.6%) and independence from conscious interoceptive awareness (MAIA: r=−.062, p=.346; BAQ: r=−.098, p=.134) support subcortical preconscious mechanisms (10,37,38). Robustness analyses—quality filtering (d=0.45 after outlier removal), latent awareness factor extraction (r=-.072, p=.27), TOST equivalence testing (p<.05), Bayesian analysis (BF₀₁≈4.2), and null simulation (p<.000001)—confirm findings are not artifacts of measurement choice, outliers, or sampling noise.Convergent evidence from OpenNeuro intracranial EEG (ds003374) (20) reveals amygdala dynamics consistent with transient integration: block-level decoding at chance (40%, p=.76) but time-resolved analysis shows early gamma-driven peak (4–8s post-stimulus), indicating phase-specific processing incompatible with sustained categorical representations. HCP-derived fusiform face area maps (27,51,52) demonstrate categorical information can be focally organized, constraining 'diffuse storage' alternatives.We introduce an operational three-component dissociation protocol and modular five-test empirical roadmap yielding discriminating predictions: temporal precedence (subcortical→cortical), interoceptive timing effects (systole→threat bias), content-specific integration versus nonspecific gain modulation, lesion double-dissociations, and pharmacological specificity. The framework is falsifiable: null results in any of five critical tests would necessitate revision. While neural mechanisms remain inferential (no causal manipulation, lesion data, or purpose-built fMRI), the demonstrated behavioral phenotype, robust independence from awareness, convergent neural dynamics, and pre-specified falsification criteria establish a testable foundation for mechanistic investigation.

Abstract: Synchronous with technological progress in Artificial Intelligence (AI), demands on underlying computing architectures are increasing rapidly. Neuromorphic computing (NMC) offers novel approaches to energy-efficient processing of time-dependent information and enables low-latency, sensor-proximal interaction between environment, humans, and machines. Yet its relevance for industry and AI development extends beyond dedicated neuromorphic platforms: core neuromorphic principles, including event-driven processing, temporal coding, and co-located memory and computation, are increasingly being absorbed into mainstream AI hardware and software architectures. Rather than emerging as a unified standalone paradigm in the near term, NMC appears poised to reshape AI systems through gradual, principle-level integration and hybridization with classical approaches. This essay, the first in a four-part series, introduces the technical foundations of NMC and develops the above argument through a structured comparison of biological information processing, classical artificial neural networks (ANNs), and spiking neural networks (SNNs). Using visual information processing as a concrete illustrative example, we highlight the distinct operating principles of each paradigm and begin to contextualize their respective limitations and potentials. We discuss the hypothesis that the broader impact of NMC will unfold through hybridization rather than replacement and consider implications for industrial actors and technology transfer. The series aims to make the potential of NMC accessible and tangible for product development and process innovation. It is offered as a contribution to an ongoing cross-disciplinary dialogue, with particular attention to the European research and innovation ecosystem and the strategic opportunity it represents for technology sovereignty and industrial competitiveness.

Abstract: The study proposes framework that integrates Quantum Key Distribution (QKD) with Advanced Encryption Standard (AES-256) to enhance the security of distributed healthcare analytics. The proposed model enables end to end encryption by allowing decentralised training across multiple healthcare institutions. To improve the model’s adaptability across diverse clinical environments both manual and automated feature selection techniques are incorporated under federated learning settings. The framework further integrates access control mechanisms within the federated learning update pipeline to address multi layered security requirements in healthcare systems. The evaluation of the framework has been measured through simulations conducted on the University of Southern Queensland (UniSQ) High Performance Computing (HPC) cluster. The implementations are mostly based on the python libraries for AES encryption, key generation, alongside federated logistic regression, and mutual information based feature selection. Experimental results demonstrate that the proposed hybrid framework achieves strong data confidentiality while maintaining model accuracy. The system effectively mitigates data breach risks with minimal computational overhead. Overall, the proposed framework provides a scalable and practical solution for quantum resilient healthcare analytics. This work establishes a foundation for future research in quantum safe federated learning techniques for healthcare systems.

Abstract: Background/Objectives: Fibrous dysplasia (FD) is a benign fibro-osseous disorder charac-terized by the replacement of normal bone with fibrous tissue and immature woven bone, most commonly involving the craniofacial skeleton. It typically presents as an intraosse-ous lesion in children and young adults. Gingival presentation without prominent in-traosseous expansion is exceedingly rare. This report describes an unusual case of FD presenting predominantly as an exophytic gingival mass in the anterior maxilla of a mid-dle-aged patient. Methods: A middle-aged patient presented with a slowly enlarging gingival mass extend-ing from the right canine to the left central incisor region. After being lost to follow-up for approximately 4.5 years, the patient returned with increased swelling, pain, spacing of the anterior teeth, and functional impairment affecting mastication and speech. Clinical, ra-diographic, surgical, and histopathologic findings were evaluated. Surgical management included excision of the lesion, extraction of non-restorable teeth, and bone grafting under general anesthesia. Results: Radiographic examination demonstrated minimal osseous involvement without a clearly defined intraosseous expansile lesion. Histopathologic analysis revealed irregular curvilinear trabeculae of woven bone within a fibrous stroma containing bland spin-dle-cell proliferation, consistent with FD. At the six-month follow-up, the patient remained asymptomatic without complications and was undergoing prosthetic rehabilitation with plans for future implant placement. Conclusions: FD may rarely present as a predominantly gingival lesion with minimal ra-diographic evidence of bone involvement, posing a diagnostic challenge. Recognition of this atypical presentation is important to avoid misdiagnosis and to facilitate appropriate management through comprehensive clinicoradiologic and histopathologic correlation.

Abstract: Language comprehension theories can model prediction, processing cost, and interpretive outcome, but they do not yet formalize access: the point at which linguistic structure becomes retrievable to a bounded observer. This paper develops Observer-Dependent Entropy Retrieval in Linguistic Comprehension, the linguistic-domain formulation of ODER, as a bounded-access theory of semantic timing, reanalysis, and collapse. The framework separates availability, prediction, retrieval, and comprehension as distinct operations. Meaning becomes available when linguistic structure is retrieved and stabilized under finite attention, working memory, contextual familiarity, and interference constraints, not when a signal is merely present or statistically predictable. The framework constructs an observable linguistic retrieval manifold, defined by task-defined retrievable structure S_obs(τ) over observer-indexed retrieval time, and imposes reconstructibility: a law of access must be identifiable from the observable retrieval trajectory. S_obs(τ) does not denote total linguistic meaning, but the structure made retrievable under a specified task, observer, and measurement operator. Reconstructibility restricts admissible access laws to first-order, observable-only dynamics. Bounded access further forces gap form, where retrieval depends on the remaining stabilizable structure, S_max−S_obs(τ). Within the spectrally admissible smooth-access regime, the hyperbolic tangent is selected as the conditional extremal baseline, not as a universal empirical fit. Linguistic ODER derives a ranked measurement family for observer-indexed timing, retrieval rate divergence, temporal localization, reanalysis pressure, ambiguity interference, compression thresholds, and aggregation loss. Controlled linguistic and synthetic trace testbeds instantiate these dynamics under known structural conditions and identify the boundary of the constant rate smooth-access baseline. Prior constant-rate convergence results are treated here as benchmarked evidence for a smooth-access subset, not as a ceiling on the theory’s scope. ERP and reading-time analyses provide natural measurement surfaces for the theory’s access-layer timing predictions, while full saturation-specific validation of the tanh-regulated smooth-access law remains a separate empirical target. The framework supplies language processing with a formal access variable and a disciplined program for studying when meaning becomes available to bounded observers.

Abstract: Fallen pedestrians—those lying prone, supine, or crouching on roadways—represent a critical and largely unaddressed vulnerability in contemporary Advanced Driver Assistance Systems (ADAS). Standard pedestrian detection systems achieve only 21.4% true positive rate (TPR) at night for non-upright subjects, compared with 98.2% for upright pedestrians, creating a 76.8 percentage-point detection gap with direct fatality consequences. This article synthesises three complementary peer-reviewed contributions into a unified closed-loop safety architecture: (1) real-time multi-modal detection via the Advanced Fallen Object Detection System (AFODS); (2) physics-grounded post-collision kinematic reconstruction; and (3) injury-risk quantification translating detection latency into Head Injury Criterion (HIC) and AIS-grade fatality probability. The integrated framework, which forms the technical basis of Japanese Patent Application No. 2025-167440 (PCT deadline: October 3, 2026), demonstrates that fatal head injury probability is reducible from 66.2% (no detection baseline at 50 km/h) to 0.7% under worst-case AFODS detection. A five-stage empirical validation roadmap is presented, culminating in regulatory conformance assessment to ISO 26262, ISO 21448 (SOTIF), and Euro NCAP 2026 Post-Crash Safety protocols. The article identifies critical open challenges and defines the trajectory toward prototype deployment, real-world forensic validation, and commercialisation.

Abstract: Background: Functional near-infrared spectroscopy (fNIRS) during cognitive tasks contains slow haemodynamic oscillations from neurovascular, superficial systemic and cardiorespiratory sources. We investigated whether output-only modal analysis can provide dynamic systems descriptors during a cognitive task in type 2 diabetes with cognitive impairment versus healthy controls. New method: Total haemoglobin (HbT) from our previously published Mini-Cog exercise-intervention dataset in older adults with type 2 diabetes and healthy controls was re-analysed using a harmonized modal phenotyping framework. Trigger-bounded INIT/LAST segments were processed by three estimators – multiverse Koopman dynamic mode decomposition (DMD), residual-validated Koopman DMD, and numerical algorithms for subspace state-space system identification/operational modal analysis (N4SID/OMA). Brain modes were classified using spatial, haemodynamic and short-separation evidence, cardiorespiratory modes were labelled by physiological bands and evaluated with internal automatic multiscale peak detection support. Results: The DMD-family estimators revealed a reproducible INIT to LAST increase in brain modal frequency and spatial structure across all groups. Multiverse DMD showed false discovery rate (FDR)-significant effects for all four primary brain metrics in all groups with Hedges’ (d_z=0.61-2.00), and residual-validated DMD reproduced the pattern. N4SID was conservative, yielding one sensitivity supported primary cell. Mixed models showed no FDR-significant univariate Group x Phase interaction. External fNIRS–ECG–respiration validation showed N4SID/OMA was most accurate for cardiac rate with mean absolute error 2.04–2.38 beats/min and (r=0.98) whereas respiration from prefrontal HbT was unreliable. Conclusions: Output only modal phenotyping provides a transferable, claim tiered approach for fNIRS dynamics. The data support a shared within-task brain-state transition, not a diabetes- or exercise-specific intervention effect.

Abstract: The progressive failure of pancreatic beta-cells under chronic glucolipotoxicity drives the pathogenesis of type 2 diabetes mellitus (T2DM). This metabolic stress overwhelms the folding capacity of the endoplasmic reticulum (ER), hyperactivates the unfolded protein response (UPR), engages terminal pro-apoptotic signaling (CHOP), and promotes beta-cell dedifferentiation. In this systematic review and meta-analysis, registered with PROSPERO (CRD420261370436), we evaluated the preclinical efficacy of the low-molecular-weight chemical chaperones tauroursodeoxycholic acid (TUDCA) and 4-phenylbutyrate (4-PBA) in preserving beta-cell exocytotic identity and mitigating ER stress. Following PRISMA 2020 guidelines, a systematic search of PubMed, Scopus, and Web of Science (January 2016–May 2026) identified four eligible experimental studies. Preclinical models (INS-1 and βTC-6 cell lines, Wistar rats, and C57BL/6 mice) exposed to high-fat or high-fat/high-fructose diets, cholesterol loading, or protein restriction followed by high-fat feeding showed impaired or dysregulated glucose-stimulated insulin secretion (GSIS) and upregulated ER stress markers. Co-administration of TUDCA or 4-PBA consistently reversed these defects, restoring the GSIS stimulation index and reducing pro-apoptotic markers. A hierarchical Bayesian random-effects meta-analysis estimated a robust pooled restoration ratio of 1.87 (95% credible interval [CrI]: 1.39 to 2.46), with the entire credible mass above the null (posterior probability of benefit > 0.99) and modest between-study heterogeneity. In conclusion, TUDCA and 4-PBA act as structural ER scaffolds that prevent terminal UPR activation and preserve the beta-cell exocytotic machinery, positioning them as candidate disease-modifying agents that merit confirmatory clinical evaluation.

Abstract: Severe windthrow events can create highly favorable conditions for bark beetle outbreaks by generating large quantities of weakened breeding material. This study investigated the population response of the Mediterranean pine engraver, Orthotomicus erosus Wollaston, following a microburst windstorm that struck Marjan Forest Park, an urban Mediterranean Aleppo pine (Pinus halepensis Mill.) forest in Split, Croatia, in July 2025. Approximately 2,000 m3 of damaged timber remained in the forest until February 2026, enabling assessment of colonization dynamics and outbreak development. Field inspections, laboratory analyses, and pheromone trap monitoring were conducted between July 2025 and April 2026. Colonization of windthrown material began within two weeks after the disturbance, and infestation rates reached 94% of examined logs by December 2025. Most infested logs (86%) already contained emergence holes, indicating successful completion of beetle development. Pheromone traps captured a total of 131,588 individuals, with more than 88% recorded during spring 2026 and a pronounced peak occurring on 8 April 2026. The appearance of newly attacked standing trees in spring 2026 confirmed rapid population expansion from fallen material to living hosts. Results demonstrate the exceptional outbreak potential of O. erosus following extreme climatic disturbances and emphasize the importance of rapid sanitation measures and continuous monitoring in Mediterranean pine forests under climate change conditions. Delayed salvage logging after severe windthrow can rapidly trigger outbreak development of O. erosus.

Abstract: The main objective of this study is to obtain the average parameters of gas-discharge plasma when controlling the steady position of the bow shock wave (BSW) using the combined action of a gas discharge initiated by a current from an external source and a magnetic field near the frontal surface of the model. The studies were carried out using both experimental and numerical methods in xenon and air. A comparison of the numerical and experimental dependences of the relative distance of the steady BSW from the model on the discharge power showed good agreement. Based on the conducted flow modeling, taking into account the dependence of the adiabatic index on the degree of ionization and the degree of nonequilibrium, and using the Burm's theory, gas-discharge plasma characteristics were obtained, such as the degree of ionization and the degree of nonequilibrium, the electron density and the electron temperature in the absence and presence of a magnetic field. By this way an integrated experimental-computational system was formed in which the measured characteristics of the discharge and BSW, as well as the numerically obtained averaged plasma parameters in the impact zone, are combined with the Burm's theory to clarify the thermodynamic state of the medium and determine the corresponding characteristics of the gas-discharge plasma. The obtained results can be used to develop control systems for high-speed flows that take into account the influence of plasma parameters and the electric and magnetic fields.

Abstract: Background/Objectives: Finerenone improves cardiovascular and renal outcomes in diabetic kidney disease (DKD), but hyperkalemia remains a key safety concern. Patients with elevated baseline potassium levels (≥ 4.9 mEq/L) are largely excluded from clinical trials, and real-world data in this population are scarce. Methods: In this retrospective multicenter cohort study derived from the FINE-TURK cohort, adults with DKD who initiated finerenone with baseline serum potassium ≥ 4.9 mEq/L were included. The primary outcome was clinically significant hyperkalemia (CSH) (≥ 5.5 mEq/L) within three months. Multivariable logistic regression analyses were used to identify associated factors, with multiple sensitivity analyses performed. Results: 166 patients were included, of whom 47 (28.3%) had baseline potassium levels between 5.1 to 5.5 mEq/L. Of the 166 patients, 35 (21.1%) developed CSH, and 10 (6%) patients had follow-up potassium ≥ 6.0 mEq/L. 126 (76.8%) patients required no intervention, 24 (14.6%) were initiated on potassium binders, and finerenone was discontinued only in 12 (7.3%) patients. Baseline eGFR, baseline urinary albumin, loop diuretic use and 20mg finerenone dose were associated with CSH. In contrast, baseline serum potassium was not associated with CSH. Conclusions: In patients with DKD and elevated baseline potassium levels, finerenone initiation was associated with manageable rates of hyperkalemia. Our findings support the cautious use of finerenone in selected patients under close monitoring, as well as highlight the need for a multidimensional approach to hyperkalemia risk assessment.

Abstract: Artificial intelligence increasingly mediates diagnosis, prognosis, triage, prescribing and escalation of care, yet its ethical evaluation remains fragmented. We mapped scientific, bioethical and normative literature on patient autonomy, algorithmic justice, professional and institutional responsibility, and human dignity, using an author-curated corpus with traceable expansion. Of 119 full-text records, 108 were retained after deduplication and screening. The synthesis identified a recurrent imbalance: governance, fairness and accountability were more operationalized than relational autonomy and dignity. Two exploratory mini-meta-analytic lanes were feasible. Mortality-related surveillance effects were favorable under a conventional random-effects model but non-confirmatory with modified Hartung-Knapp inference. Downstream clinical-action effects suggested increased clinician activation, with substantial heterogeneity and no direct equivalence to patient benefit. Artificial intelligence should therefore be evaluated as a sociotechnical intervention whose legitimacy depends on contestability, equitable performance, accountable institutions and preservation of meaningful human deliberation.

Abstract: The rapid growth of electric mobility, renewable energy storage, and portable electronics has sharply increased global lithium demand, highlighting the environmental and socio economic drawbacks of conventional extraction methods such as hard rock mining and brine evaporation. These processes are land intensive, slow, water consumptive, and carbon intensive, underscoring the need for next generation materials that enable selective, circular and sustainable lithium recovery. Zeolite based adsorbents have emerged as strong candidates, due to their crystalline frameworks, tunable pore architectures, ion exchange functionality, and exceptional thermal and chemical stability. This review covers recent advances in natural and synthetic zeolites, and zeolite-based composites for lithium capture, with emphasis on guiding design principles governing Li⁺ adsorption capacity and selectivity, transport behavior, and adsorption mechanisms across diverse feedstocks such as brines, geothermal fluids, seawaters, and battery recycling leachates. Lastly, we discuss current challenges and emerging opportunities that will guide future research aimed at advancing zeolite-based adsorbents toward sustainable, next-generation lithium recovery technologies.

Abstract: Dynamic risk prediction is an important statistical technique for detecting temporal changes in risk and provides quantitative support for early risk identification in clinical decision-making, industrial process monitoring, and financial anomaly detection. This study proposes a Temporal Convolutional Network Deep Cox Mixtures model (TCN-DCM) for longitudinal survival data by integrating a Temporal Convolutional Network, which learns temporal patterns from longitudinal covariates, with a Deep Cox Mixture framework that relaxes the conventional proportional hazards assumption. Simulation studies were conducted to compare the proposed model with existing deep learning-based methods, including Recurrent Deep Survival Machines and Dynamic-DeepHit, as well as the traditional joint model. The results showed that, when the proportional hazards assumption held, TCN-DCM outperformed the existing deep learning-based models. When the proportional hazards assumption was violated, TCN-DCM achieved predictive performance comparable to that of Recurrent Deep Survival Machines and yielded superior results for some evaluation metrics. The proposed model was further applied to a primary biliary cholangitis dataset, where it achieved the best overall predictive performance and illustrated dynamic individualized survival risk prediction. These findings indicate that TCN-DCM provides a flexible and broadly applicable approach for dynamic risk prediction in longitudinal survival analysis.

Abstract:

Abies koreana Wilson (Korean fir) and Abies nephrolepis (Trautv. Ex Maxim.) Maxim. (Khingan fir) are ecologically and economically significant coniferous species in East Asia. However, morphological similarities and hybridization complicate species identification, affecting conservation, forestry management, and commercial activities. Here, we developed a species-specific DNA marker using single nucleotide polymorphisms (SNPs) within the mitochondrial nad5 intron 1 region to discriminate between A. koreana and A. nephrolepis. In particular, PCR amplification and sequencing analyses of candidate drought and heat stress-responsive genes as well as mitochondrial nad5 intron 1 and nad5 intron 4 revealed a species-specific T-to-G substitution in nad5 intron 1. Allele-specific primers were designed, and competitive allele-specific labeled light emission technology was employed for SNP detection. The primers were validated using real-time PCR, achieving high specificity and reliability. Overall, the molecular diagnostic tool offers a practical solution for accurate species identification, aiding conservation efforts and sustainable resource management.

Abstract: This article examines the effects on internal auditing on consequence management within the KwaZulu-Natal Provincial Government (KZNPG), focusing on provincial departments and municipalities. This study is motivated by ongoing concerns about governance failures and accountability in the public sector. Despite a robust legislative framework and skilled personnel, high levels of irregular expenditure persist, particularly in key departments and local government institutions, prompting an exploration of the factors undermining effective consequence management. The study employs a qualitative methodology, analyzing internal audit processes and external audit findings from the Auditor General South Africa (AGSA), alongside existing literature on public sector auditing and accountability. Key findings reveal a significant gap between audit identification of governance failures and the subsequent enforcement of corrective actions. Systematic weaknesses, such as inadequate political will and ineffective enforcement mechanisms, consistently hinder the translation of audit recommendations into substantive changes in practice. In conclusion, the study underscores that while internal auditing has the potential to enhance accountability, its effectiveness is contingent upon political support and a conducive organizational culture. Recommendations include establishing stronger frameworks for accountability, reinforcing internal audit independence and fostering a culture of transparency within provincial departments and municipalities. The implication for the KZNPG are significant; implementing these recommendations could facilitate more effective consequence management, ultimately enhancing public trust and service delivery. By addressing the identified weaknesses, the government can mitigate irregular expenditures and improve governance outcomes in the region.

Abstract: Noise linked to industrialization has emerged as a pervasive yet often underestimated en-vironmental stressor affecting both ecological systems and human health. A large body of literature and peer-reviewed work links prolonged noise exposure to biodiversity disrup-tion, cardiovascular disease, sleep disturbance, psychological distress, impaired cognitive performance, and premature mortality. Despite these impacts, comparatively little research has sought to estimate the economic magnitude of the burden of environmental noise within a planetary health framework. This evaluation presents an economic assessment of noise-attributable mortality, using published epidemiological evidence and established economic valuation methods. A hazard-ratio-based framework was applied to estimate mortality attributable to chronic environmental noise exposure among populations ex-posed to 60- and 70-dB noise levels. Both tangible costs, representing forgone economic productivity, and intangible costs, representing societal welfare losses estimated through the Value of a Statistical Life framework, were examined. Under baseline assumptions, en-vironmental noise was associated with approximately 27,692 annual noise-attributable deaths and an estimated annual economic burden of US$361.19 billion, including US$7.28 billion in tangible costs and US$353.91 billion in intangible costs. Sensitivity analyses produced estimates ranging from roughly US$39.22 billion to US$1.20 trillion annually. The results highlight that environmental noise warrants consideration beyond its tradi-tional characterization as a nuisance as a consequential planetary health stressor with implication for biodiversity, public health, economic productivity, and societal well-being. Policies aimed at reducing environmental noise exposure, including technological, regu-latory, and urban-planning interventions, may subsequently produce substantial pub-lic-health and economic benefits while contributing to healthier, more sustainable and productive communities.