Abstract: Minimal residual disease (MRD) refers to the persistence of low-level malignant cells or tumor-derived nucleic acids that remain after curative-intent therapy and are undetectable by conventional diagnostic methods. In oncology, MRD has emerged as a powerful biomarker with well-established prognostic value in hematologic malignancies and rapidly expanding relevance in solid tumors. Advances in sensitive detection technologies, including multiparameter flow cytometry, quantitative real-time polymerase chain reaction, next-generation sequencing, and digital polymerase chain reaction, have enabled the identification of residual diseases at the molecular level, often preceding clinical or radio-logical relapse. Beyond its conventional role as a binary indicator of treatment response or cure, MRD is increasingly recognized as a dynamic longitudinal biomarker that supports personalized disease management. Within this evolving paradigm, patient-informed MRD strategies that incorporate tumor-specific molecular profiling and serial monitoring, particularly through circulating tumor DNA, offer the potential to guide treatment adaptation, including escalation, de-escalation, maintenance optimization, and surveillance strategies across both hematologic and solid malignancies. In this review, we summarize the biological basis of MRD, current and emerging detection methodologies, and clinical applications across cancer types, with a focus on patient-informed approaches. We also discuss key limitations, including assay standardization, biological variability in solid tumors, and the lack of clearly defined actionability thresholds. Finally, we highlight future directions for integrating MRD with multi-omics and AI-driven analytical frame-works to enable adaptive, risk-informed cancer management and advanced precision oncology.

Abstract: The performance of ballasted railway tracks under cyclic loading is a critical issue in urban railway systems, where high traffic frequency and geometric constraints accelerate track degradation, which, in turn, leads to accumulation of plastic deformations that potentially reduce operation efficiency. This study presents a numerical framework for rail track performance assessment based on two complementary modeling approaches: a fully continuous Finite Difference Method (FDM) model, and a hybrid Discrete Element Method–Finite Difference Method (DEM–FDM) model. The continuous FDM simulations are employed to evaluate the global mechanical response of the track support system and to compute conventional stability indicators, including the factor of safety (FS). In parallel, the hybrid DEM–FDM simulations explicitly represent the ballast layer using DEM to capture inter-particle interactions, accumulation of permanent deformation, and particle fragmentation under cyclic loading, while rails, sleepers, sub-ballast, and subgrade are modeled using FDM to describe system-level load transfer. Ballast performance is assessed by linking safety factors obtained from the continuous models with mechanically derived permanent deformation and stress measures extracted from the hybrid simulations. This dual-modeling framework enables a systematic investigation of the influence of ballast layer thickness and material type on deformation accumulation, stress transmission, and granular degradation mechanisms. The results reveal distinct behavioral trends among different ballast materials, showing that increased ballast thickness generally improves track performance, while material-specific degradation mechanisms govern the evolution of permanent deformation under repeated loading. The proposed approach establishes a quantitative bridge between traditional stability-based design metrics and deformation-based performance indicators, providing a rational basis for performance-based evaluation, comparison, and optimization of ballast configurations, through a set of robust numerically derived relationships for railway track design abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Abstract: Background/Objectives: Abnormalities in the partial pressure of carbon dioxide (PCO₂) can occur during respiratory support and may contribute to adverse neonatal outcomes. This study aimed to assess the incidence of early hypocapnia and hypercapnia in mechanically ventilated preterm infants and their major associated outcomes. Methods: A single-center retrospective cohort study (2017–2024) was conducted in preterm infants < 32 weeks’ gestation who required >24 hours of invasive ventilation within the first 3 days of life. Perinatal-neonatal data were retrieved from the medical database. Admission blood gas values (arterial and capillary-venous) and the maximum and minimum PCO₂ in the first 72 hours were evaluated. Normocapnia was defined as PCO₂ 35–45 mmHg, hypocapnia 45 mmHg. Primary outcomes were the incidence of PCO₂ abnormalities; secondary outcomes included death or severe brain injury (SBI), SBI alone, and bronchopulmonary dysplasia (BPD) among survivors. Logistic regression identified independent predictors of the secondary outcomes. Results: Among the 134 infants evaluated, most experienced both hypercapnia and hypocapnia. Hypercapnia occurred in 81.3% of infants, and hypocapnia in 93.2%. Death or SBI was observed in 51.5%, and SBI alone in 42.5%. Gestational age < 28 weeks, air-leak syndromes, and pulmonary hemorrhage were independent predictors of death or SBI. Among survivors, hypercapnia and gestational age < 28 weeks independently predicted BPD. Infants with adverse outcomes had higher maximum PCO₂ values and greater PCO₂ variability, although these were not independent predictors of SBI or death. Conclusions: PCO₂ instability is highly prevalent in ventilated preterm infants necessitating personalized ventilation management. Extreme prematurity remains the strongest risk factor for adverse outcomes, while hypercapnia independently predicts BPD.

Abstract: This review explores a hypothetical and previously underexplored ecological pathway that may contribute to virus dispersal, including human pathogens, through passive transport involving free-living nematodes and migratory animals. Available data on nematode-associated viruses, nematode survival in diverse environments, and mechanisms of passive dispersal are synthesized to propose a conceptual framework for long-distance pathogen movement. Particular attention is given to the ecological interactions among nematodes, animals, and viruses, and to the potential role of these interactions in shaping pathogen distribution patterns under environmental and anthropogenic pressures. The article discusses a theoretical model of possible virus transfer across ecological niches and highlights key gaps requiring experimental validation. This study highlights a previously underestimated route of potential virus transmission, including human pathogens, through possible long-distance dispersal (500 km or more) by free-living nematodes and migratory birds. Data on the spread of viruses of nematodes of the genus Caenorhabditis spp., the survival of nematodes in various conditions, and their spread by various groups of animal carriers, including their ability to pass through the gastrointestinal tract of birds in a viable state, are analyzed. The role of a number of migratory bird species as biological carriers not only of free-living nematodes themselves over considerable distances, but also of viruses hypothetically associated with nematodes on/inside their bodies, is considered as a potential mechanism. This work raises questions about previously underestimated biological risk factors associated with this potential route of passive pathogen dispersal to new territories and ecological niches, especially in conditions of environmental stress, intensive animal husbandry, and global movement of wild animals. The article discusses a hypothetical scenario in which SARS-CoV-2 and other viruses could be passively dispersed through ecological interactions involving nematodes and migratory birds. Understanding the ecological dynamics of the interaction between birds, nematodes, and virusesmay contribute to ecological risk assessment and understanding of emerging pathogen dynamics. This manuscript presents a conceptual ecological hypothesis and should not be interpreted as evidence of confirmed transmission pathways.

Abstract: This paper introduces the AI-Enabled Language Continuum, a novel framework that unifies hearing enhancement, speech recognition, and generative writing through deep neural architectures, creating a seamless pipeline from raw audio input to coherent textual output. Traditional systems handle these tasks in isolation, leading to inefficiencies and error propagation; our approach leverages hierarchical transformers and neural audio codecs to process noisy speech signals progressively first restoring acoustic clarity, then transcribing with contextual awareness, and finally generating expressive prose. By modelling the language spectrum as a continuous flow, we employ multi-stage training with shared embeddings that capture phonetic, semantic, and creative elements, trained on diverse corpora including LibriSpeech for enhancement, CommonVoice for recognition, and instruction-tuned datasets for writing. Experimental results demonstrate superior performance: PESQ scores improve by 25% in noisy conditions compared to baselines like Deep Noise Suppression, word error rates drop to under 8% on adverse audio, and generated text achieves ROUGE scores exceeding 0.45 while maintaining factual fidelity to transcribed inputs. This continuum not only advances assistive technologies such as hearing aids and real-time transcription tools but also paves the way for multimodal AI agents capable of end-to-end language processing in resource-constrained environments. Our contributions include a scalable architecture for cross-domain collaboration and ablation studies validating stage-wise synergies, offering a blueprint for future integrated language systems.

Abstract: A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between different products, while ensuring consistent geometric alignment throughout the process. Geometrical deviations in these devices can adversely impact product quality along the entire vehicle assembly chain. This paper presents the development and implementation of two patented use cases: a cyber-physical inspection system, fully operational in serial production and a cyber-physical assembly system, tested successfully in the prototype phase. The first actively mitigates the effects of device deviations in real time, while the second enables on-demand configuration of flexible, advanced positioning devices via precision part matching, effectively preventing systematic deviations. Challenges and insights from both systems are discussed. Four previously introduced building blocks for automating quality control processes are validated and generalized for broad applicability across manufacturing processes and project phases via cross-system comparative analysis: integrated capture of process and product data, automated data analytics, automated decision-making, and autonomous process intervention. This work proposes a validated, scalable framework integrating design and implementation of cyber-physical systems to support zero-defect manufacturing.

Abstract: The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations of skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and successful treatment. However, achieving precise and efficient segmentation remains a major challenge, as existing methods often struggle to balance computational efficiency with the ability to capture complex lesion characteristics. To address this challenge, we propose a novel deep learning framework that integrates the PVT v2 backbone with two key modules: Spatial-Aware Feature Enhancement (SAFE) and Multiscale Dual Cross-attention Fusion (MDCF). The SAFE module refines multi-scale encoder features through a dual-branch architecture that bridges the feature discrepancy across network depths by combining fine-grained shallow-layer details with deep semantic information via adaptive offset prediction. The MDCF module establishes bidirectional cross-attention between decoder and encoder features, followed by multi-scale deformable convolutions that capture lesion boundaries and small fragments at heterogeneous receptive fields, thereby enriching semantic details while suppressing background responses. The proposed model was evaluated on two public benchmark datasets (ISIC 2016 and ISIC 2018), achieving Intersection over Union (IoU) scores of 87.33% and 83.67%, respectively, demonstrating superior performance compared to current state-of-the-art methods. These results indicate that our framework significantly enhances skin lesion image analysis and offers a promising tool for improving early detection of skin cancer.

Abstract: Background: Quality of Life (QoL) is a multidimensional construct influenced by physical, mental, and psycho-emotional factors. Holistic movement practices (HMPs) have shown potential benefits for well-being; however, empirical evidence supporting the effectiveness of developed protocols remains limited. This study aimed to investigate the effects of the Bio-gymnastic® method, a mind–body exercise protocol, on health-related QoL in adults and to explore possible differences by gender and age. Methods: A pilot longitudinal study was conducted involving 171 adults (female: 151, male: 20) who completed a 10-week Bio-gymnastic program consisting of one supervised session per week. The protocol integrated self-body awareness, postural control, breathing regulation, muscle activation–relaxation, and multisensory stimulation. Quality of life was assessed before and after the intervention using the Italian version of the SF-36 Health Survey. A two-way ANCOVA was applied to evaluate pre-post changes, accounting for the interaction effects of gender and age categories. Results: Significant improvements were observed across several SF-36 domains following the intervention. Notable gains were observed in physical functioning, role-physical, bodily pain, general health, vitality, and mental health, with medium to large effect sizes in the physical domains. Improvements were generally independent of gender and age, although an interaction effect between gender and age emerged for physical functioning in the oldest participants. Conclusions: The proposed method appears to be an effective, low-dose holistic exercise intervention, suggesting that it may enhance multiple dimensions of QoL in adults. These preliminary findings support the potential role of mind–body practices as accessible health-promotion strategies. Further randomised controlled studies with balanced samples and objective outcome measures are needed to confirm and extend these results.

Abstract: Hemorrhagic shock remains one of the leading causes of preventable death for both civilian and military trauma. Fluid resuscitation is the primary treatment but requires constant monitoring, particularly for volume non-responsive patients susceptible to fluid overload, pulmonary edema, and other life-threatening conditions. To overcome fluid non-responsiveness, vasoactive drugs or vasopressors can be necessary adjuvants to fluid therapy but require tedious titrations that can be difficult to manage during mass casualty situations. This study developed and evaluated automated closed-loop vasopressor controllers for hemorrhage scenarios. Ten physiological closed-loop controller (PCLC) configurations with different underlying functionalities were tuned to be either more aggressive or conservative to reach target mean arterial pressure. A hardware-in-loop test platform with fluid-pressure responsiveness derived from animal data tested each controller across three different starting pressure scenarios. The platform successfully differentiated controller designs based on performance metrics. While some configurations overshot target and others could not reach target pressure, strong-performing PCLCs consistently reached and maintained target quickly. Three candidate PCLCs outperformed the rest and will be evaluated across wider scenarios to develop a robust controller design. This work accelerates PCLC-driven vasopressor administration development, providing a necessary fluid resuscitation adjuvant for precise hemodynamic management in hemorrhagic trauma.

Abstract: Elucidating the pathophysiological mechanisms of mental disorders remains a critical challenge in psychiatric research. Recent studies have highlighted the potential involvement of cytoskeletal and molecular motor abnormalities in the development of mental disorders such as schizophrenia and autism spectrum disorder (ASD). This review synthesizes the latest findings on the relationship between cytoskeletal and molecular motor abnormalities and mental disorders. The cytoskeleton, composed of microtubules, actin filaments, and intermediate filaments, along with molecular motors such as kinesins, dyneins, and myosins, plays crucial roles in neurodevelopment, synapse formation, and neurotransmission. In schizophrenia, decreased expression of the microtubule-associated protein MAP2 and abnormalities in the DISC1 gene have been reported, potentially leading to dendritic morphological abnormalities and neurodevelopmental disorders. Additionally, abnormalities in molecular motors such as KIF17 and KIF1A have been implicated in synaptic plasticity disturbances. In ASD, Myosin Id has been identified as a risk gene, with its localization in dendritic spines recently elucidated. Furthermore, abnormalities in actin-related proteins such as SHANK3 and CYFIP1 have been shown to cause synaptic dysfunction. These findings suggest that mental disorders arise from complex pathologies involving multiple cytoskeletal and molecular motor-related protein abnormalities. Future research should focus on elucidating the functions of individual proteins and adopting a comprehensive approach that includes glial cells. Advances in this field may deepen our understanding of the pathophysiological mechanisms of mental disorders and potentially lead to the development of novel therapeutic strategies.

Abstract: Background: Cardiovascular diseases are the main cause of mortality and morbidity in Portugal, with coronary artery bypass grafting (CABG) being one of the most performed surgeries by cardiothoracic centers. After cardiac surgery, patients often experience a decrease in physical capacity, which results in an increased risk of mortality or hospitalization expenditures. The objective of this systematic review was to characterize changes in respiratory function in patients undergoing CABG. Methods: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Web of Science, Pubmed, SCOPUS, and Sport Discus were searched using a predefined research strategy to identify relevant original studies published until August 2025. To be included, studies must have assessed adult patients submitted to CABG who evaluated the respiratory function before and after cardiac surgery. Studies that reported other types of cardiac surgery were excluded. The Risk of Bias in Non-randomized Studies-of-Exposure and the Cochrane risk-of-bias tool for randomized trials were used to analyse the risk of bias of the selected studies. Results: After screening 1184 potential articles, six studies met the inclusion criteria. Studies included participants with CABG (n=324), with a mean age ranging between 54.05 ± 13.6 and 67 ± 10 years. Conclusions: All included studies reported significant postoperative reductions in respiratory function following CABG in forced vital capacity, forced expiratory volume in one second, maximal inspiratory pressure and maximal expiratory pressure. This systematic review highlighted the decline in pulmonary function following CABG, supporting the clinical importance of monitoring these impairments to enhance health-related quality of life.

Abstract:

This paper proposes a unified theoretical framework based on discrete space element dynamics. The core concept posits the existence of a conserved "spatial raw material" through which quantum virtual processes continuously generate new spatial elements, forming localized density gradients that manifest as spacetime curvature. This mechanism inherently excludes superlative effects, remains compatible with general relativity under covariance constraints, and provides a unified explanation for challenges such as dark matter, dark energy, and black hole singularities. The paper first elucidates the fundamental principle of "global covariant symmetry" and then offers an ultimate interpretation of symmetry breaking: symmetry is not "broken" but rather a local cost paid for global covariance. The core dynamics of this framework are systematically developed, with rigorous derivations of Newtonian gravitational limits, mass-energy equations, the principle of the constancy of the speed of light, the fundamental form of Maxwell's equations, and Newton's three laws from basic assumptions. Furthermore, by strictly defining k-body stable entanglement classes on discrete spacetime graphs, the symmetry group is proven to be SU(k), and the gauge group of the Standard Model—SU(3)×SU(2)×U(1)—is uniquely derived. Under the continuous limit, the Yang-Mills action, chiral fermions, Higgs field, and Einstein's gravity are obtained. The theory predicts all 28 independent parameters of the Standard Model—including gauge coupling constants, fermion mass spectra, CKM matrices, PMNS matrices, Higgs parameters, strong CP parameters, and neutrino mass squared differences—with deviations from experimental values generally below 10⁻⁴ to 10⁻⁸. These predictions constitute the "geometric periodic table" of physical constants, signifying that the 28 free parameters of the Standard Model are completely nullified. The article concludes with multiple quantitative predictions verifiable by future experiments, providing a self-consistent, comprehensive, and experimentally testable new pathway for the unification of quantum gravity and particle physics.

Abstract: Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modelling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&amp;S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multidimensional framework for evaluating vulnerability in historic buildings where physical fragility cannot be adequately captured through structural indicators alone. While existing approaches primarily prioritize load-bearing behaviour, they often overlook typological discontinuity, spatial fragmentation, and the erosion of architectural and cultural value. STVSM addresses this limitation through three weighted sub-indices: structural vulnerability (SV), typological degradation (TV), and heritage value (HV), each calibrated using expert-derived micro- and macro-level weighting coefficients. Field-based deterioration scores (0–1) are combined with these weights to generate SV, TV, and HV values, which are then integrated into a Conservation Priority Index (CPI). Although conceptually informed by building-scale seismic vulnerability literature, the model does not aim to simulate earthquake performance or replace numerical structural analysis. Instead, it operates as a comparative decision-support framework that incorporates seismic-informed deterioration patterns within a broader, conservation-oriented logic. The model is applied to twenty-five historic buildings across three heritage contexts: traditional houses in Cumalikizik, vernacular dwellings in Balıkesir–Karesi, and nineteenth-century Greek Orthodox churches in Bursa. The results demonstrate that integrating structural condition, typological integrity, and heritage value provides a transparent, repeatable, and scalable basis for conservation prioritization across diverse historic building stocks.

Abstract: Epidemiological Surveillance of Nursing Diagnoses (ESND) represents an emerging field within Community and Public Health Nursing, aiming to strengthen the visibility of nursing‑sensitive phenomena in health information systems. This study applied the Community Assessment, Intervention and Empowerment Model (MAIEC) to evaluate the empowerment level and diagnose the community process of a Primary Health Care Island Unit in the Autonomous Region of the Azores, Portugal, regarding the promotion of ESND. A descriptive, cross‑sectional design was used, combining documental analysis, a community empowerment assessment, and a structured questionnaire administered to 172 nurses. Results revealed substantial gaps in community leadership, including low levels of knowledge about ESND, the Local Health Diagnosis, and documentation in priority ICNP® foci. Community participation indicators showed limited clarity of the ESND process, low awareness of organizational structures and partnerships, and a lack of visible formal leadership. Community coping was characterized by minimal prior ESND experience and low training levels, although more than half of participants identified contextual strengths. Overall, the findings indicate a community with developmental potential but requiring targeted interventions to strengthen leadership, participation, and coping capacities. Enhancing training, communication, and organizational structures will be essential to support the sustainable implementation of ESND and reinforce the contribution of nursing to public health surveillance.

Abstract: Edible insects are increasingly recognized as a sustainable protein source, yet systematic evidence on the safety and efficiency of indigenous processing practices remains limited. This study combines ethnographic surveys with microbiological analysis to document traditional harvesting, processing, and preservation methods for edible insects across nine ecological zones in Tanzania. Findings reveal edible insect harvesting practices through wild harvesting and varied by insect type, habitat, and seasonal availability. Unexpectedly, 30.9% of respondents reported raw consumption of edible insects. A wide diversity of insect specific processing methods was observed, including dry toasting, frying, boiling, sun drying, and smoke drying, reflecting adaptations to insect morphology, perishability, and intended use. While thermal processing practices were generally effective in elimination of major pathogens (Salmonella spp., Escherichia coli and Listeria monocytogenes), preservation challenges related to drying efficiency, post-processing handling, and storage were evident. Although most samples complied with East African Standards (EAS 1186:2023), sun-dried and toasted products exhibited high total viable counts and yeast & mold levels. No formal training to handlers was recorded, and processing practices were primarily transmitted orally and experientially through storytelling and observation. These findings demonstrate the potential of indigenous knowledge as a foundation for safe insect food systems, while identifying priority interventions such as improved drying infra-structure, hygienic handling, and Hazard Analysis Critical Control Point (HACCP) aligned protocols to support commercialization and regional trade without eroding cultural integrity.

Abstract: Rising atmospheric CO₂ levels have increased the demand for robust, scalable adsorbents for practical CO₂ capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen-nitrogen (N−N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO₂ uptake. We highlight key synthetic strategies and representative systems, including porphyrin-azo networks, and discuss the relatively sparse experimental literature on alternative N−N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations and binding-energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO₂ adsorption. This review provides guidelines for designing POPs with enhanced CO₂ adsorption, offering an outlook and discussing challenges for future studies.

Abstract: Performance variability in MgO-based cements stems partly from poorly characterized dissolution kinetics of commercial lightly burned magnesia (LBM). Existing studies focus on high-purity materials under acidic conditions, but LBM dissolves also in alkaline condition where Mg(OH)₂ precipitation prevents reliable sampling at high pH. We validated pH monitoring against ICP-AES for tracking initial LBM dissolution kinetics across pH 2.0-11.0 and temperatures 25-85°C. Commercial LBM (32 ^m2/g, 7.5 wt% CaO) exhibited rates one to two orders of magnitude higher than synthetic magnesia (10⁻⁸ to 10⁻¹² mol/cm²·s). X-ray diffraction, electron microscopy with energy-dispersive spectroscopy, and BET analysis revealed enhanced reactivity from poor crystallinity, multiphase composition, and high surface area with textural porosity. Temperature effects peaked at 75°C before declining due to Mg(OH)₂ passivation. The validated method provides practical guidance for MBC quality control and performance optimization.

Abstract: A single channel Rayleigh Density Temperature Lidar (RDTL) with a receiver telescope of 85 cm diameter was installed at Poker Flat Research Range (PFRR), Chatanika, Alaska (65°N, 213°E) in November 1997. To increase the incoming signal count the receiver diameter was increased to 1 m in 2016. In order to prevent damage of the photomultiplier tube due to the high incoming signal counts, the RDTL receiver system was modified to a three-channel system. However, temperature calculations from the individual channel retrieval showed a mismatch between them and this created a problem in combining the signal counts from the three channels into one to achieve higher confidence in the data. In this study, a correction procedure has been developed and deployed to the signal counting statistics of the RDTL to eliminate instrumental biases and get 100% agreement in temperatures between the three channels.

Abstract: This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures.