Search Results (6,842)

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Background/Objectives: Absent septum pellucidum (ASP) is a rare fetal midline brain finding that may occur in isolation or alongside broader central nervous system (CNS) malformations, genetic disorders, or septo-optic dysplasia (SOD). Accurate prenatal diagnosis and counseling remain challenging because apparently isolated ASP may be reclassified following fetal magnetic resonance imaging (MRI), postnatal neuroimaging, or specialist assessment. This structured narrative review aimed to synthesize current evidence on prenatal imaging findings, associated anomalies, genetic evaluation, and postnatal outcomes in fetuses with ASP. Methods: This structured narrative review used PRISMA-informed reporting. PubMed and Google Scholar were searched for full-text English-language studies published from 2014 through the updated search date (8 June 2026). Data on gestational age at diagnosis, imaging classification, associated anomalies, genetic testing, postnatal assessment, and neurodevelopmental, ophthalmological, and endocrine outcomes were extracted. Study methodological quality was appraised using Joanna Briggs Institute tools. Results: Seven studies comprising 342 fetal ASP cases were included. Of these, 94 cases (27.5%) were classified as isolated ASP prenatally, but only 57 remained isolated postnatally when follow-up data were available. SOD was confirmed after birth in 11 of 94 (11.7%) fetuses with prenatally isolated ASP. As definitions, imaging protocols, genetic testing strategies, and follow-up duration differed substantially across studies, these pooled values are descriptive observations rather than formal quantitative estimates. Conclusions: ASP is a heterogeneous prenatal finding. The prognosis is most favorable when ASP remains isolated following a detailed prenatal and postnatal evaluation. Multidisciplinary follow-up involving fetal medicine, neuroradiology, genetics, ophthalmology, endocrinology, and neurology is essential for risk stratification and counseling. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

Background: Insomnia and stress-related sleep disorders are increasingly recognized as systemic conditions linked to the microbiota–gut–brain axis (MGBA). With growing clinical interest in natural products that modulate the gut environment, this systematic review evaluates the efficacy and mechanisms of non-pharmacological interventions, specifically probiotics, prebiotics, dietary indices, and botanicals, in alleviating insomnia, restoring circadian rhythms, and modulating neurochemical markers. Methods: In strict accordance with PRISMA 2020 guidelines, we searched PubMed, ScienceDirect, Scopus, and The Cochrane Library for English language studies published from inception to March 31, 2026. Eligibility was restricted to studies with rigorously controlled designs, specifically randomized controlled trials (RCTs) and controlled in vivo animal studies. Interventions had to target the gut microbiota, with primary outcomes measuring sleep quality (subjective or objective) or sleep-related neurochemical markers. We excluded uncontrolled, single-arm, or observational designs; in vitro studies; non-original research; and studies involving subjects with severe medical or psychiatric comorbidities (e.g., cancer, ADHD, severe psychiatric disorders) to prevent confounding variables, though mild-to-moderate anxiety and depression were permitted. Risk of bias was assessed using the Cochrane RoB 2.0 and SYRCLE tools. Due to significant methodological heterogeneity, a narrative synthesis stratified by intervention and population was conducted. This review was not registered in PROSPERO. Results: A total of 56 studies (33 humans, 23 animals) met the inclusion criteria. Taxonomic nomenclature was updated to reflect 2020 reclassifications (e.g., Lactiplantibacillus plantarum). In human trials, interventions significantly improved subjective sleep metrics (PSQI, ISI). Recent additions demonstrated the efficacy of the Dietary Index for Gut Microbiota (DI-GM) and the improvement in N3 sleep latency by yeast mannan. Furthermore, whole-food patterns (e.g., the MIND diet) and Traditional Chinese Medicine (TCM) decoctions successfully enriched beneficial taxa, such as Bacteroides coprophilus, and increased short-chain fatty acid (SCFA) production. Animal models demonstrated that “psychobiotic” strains (Bifidobacterium breve, Lacticaseibacillus paracasei), prebiotics (GOS/PDX), and TCM formulas effectively restored GABA/5-HT profiles, lowered morning cortisol, and facilitated REM rebound in PCPA-induced models, while also consolidating non-rapid eye movement (NREM) sleep and downregulating clock genes (Per1/Per2). Conclusions: Psychobiotics, prebiotics, and botanicals represent a highly viable non-pharmacological strategy for treating insomnia. However, current evidence is constrained by a heavy reliance on subjective human questionnaires, short follow-up durations limiting insight into long-term stability, and a substantial translational gap between mechanistic rodent models and human clinical outcomes. Full article

Slaughterhouse wastewater introduces potentially toxic elements into aquatic ecosystems, yet bioaccumulation patterns in commercial fish species and associated human health risks remain underexplored in West Africa. This study quantified zinc (Zn), lead (Pb), iron (Fe), magnesium (Mg), chromium (Cr), and cadmium (Cd) in three ecologically distinct fish species—Oreochromis niloticus (Nile tilapia), Clarias gariepinus (African sharptooth catfish), and Mugil cephalus (Flathead grey mullet)—from two slaughterhouse-impacted rivers (Transamadi and Mgbuosimini) and a control site (Iwofe) in Rivers State, Nigeria. Metal concentrations were measured using atomic absorption spectrophotometry. Two-way ANOVA assessed species and location effects. Principal component analysis (PCA) was performed, with Mg used as a potential geogenic tracer, as its loading pattern was independent of Pb and Cd and consistent with the natural background. A Water Quality Index (WQI) classified Mgboshimini and Iwofe as having poor water quality (WQI > 75), while Transamadi had medium quality. Health risks were evaluated using estimated daily intake (EDI), target hazard quotients (THQ), and hazard indices (HI) following USEPA guidelines. Metal levels varied significantly by species and location (p < 0.001). Flathead grey mullet from Mgbuosimini had the highest Pb (1.50 ± 0.05 mg/kg) and Cd (0.41 ± 0.02 mg/kg), exceeding EU maximum levels for fish muscle (Pb 0.30 mg/kg, Cd 0.05 mg/kg) by 500% and 800%, respectively. PCA explained 77.5% of the variance, with Pb and Cd clustering as anthropogenic sources, while Mg loaded independently. THQ for Pb approached unity in Flathead grey mullet (0.88), and THQ for Cd reached 0.97. HI exceeded 1.0 in all species from Mgbuosimini, peaking at 2.07 in Flathead grey mullet. Uncertainty analysis (using ±SD) gave a HI range of 1.89–2.25 for this species, all above the safety threshold. Carcinogenic risk for Flathead grey mullet (3.97 × 10⁻⁴) approached the upper acceptable limit. Slaughterhouse effluent appears to elevate Pb and Cd burdens in fish, with detritivorous Flathead grey mullet posing the highest health risk. Exceedance of safety thresholds and HI > 1.0 indicate potential non-carcinogenic and carcinogenic risks. We recommend improved wastewater treatment and species-specific consumption advisories. Full article

Background and Aims: The “quantity” of bone can be evaluated by dual-energy X-ray absorptiometry (DXA) scans, but not its “quality. We aim to study the clinical relevance of urinary-N-terminal telopeptide (NTx) in a retrospective exploratory study. Patients and Methods: The medical records of patients with osteoporosis, osteopenia with or without fractures, and with available urinary NTx were retrospectively reviewed; those on anti-osteoporotic medication before the start of the study were excluded. In all NTx levels, bone-specific alkaline phosphatase (BSAP), parathormone, serum calcium, and vitamin D were measured. In all cases, a recent DXA scan and fracture risk assessment (FRAX) had been performed. Appropriate statistics were applied using SPSS. 15. Results: Included were 93 patients (17.2% males); thirty-one (33.33%) had osteoporosis, 56 (60.21%) osteopenia, whereas 36 (38.7%) had prior or existing fractures. Older participants had lower NTx levels, and females had higher NTx levels, albeit NS. A negative correlation was found between the T-score of the left hip and NTx levels (p = 0.015) but not of the right hip or lumbar spine. In multivariate analysis, NTx levels (p = 0.013) and FRAX (p = 0.001) were significantly associated with fractures. Patients with osteoporosis had higher NTx levels when compared to patients with osteopenia (p = 0.015). NTx at a cut-off value of 207.4 showed a sensitivity of 80.6% and a specificity of 56.1% for the diagnosis of previous fracture with an area under the curve (AUC) of 0.72 (95% CI: 0.61, 0.83). Conclusions: Elevated NTx levels were significantly associated with existing or prior fractures. Combining DXA scan and FRAX, with NTx testing, may provide a comprehensive approach to osteoporosis assessment and treatment. Further prospective studies are warranted to validate its clinical utility. Full article

The Directive (EU) 2024/3019 on urban wastewater treatment (WWT) imposes new, stringent targets for nutrients and pharmaceutical compounds, thereby requiring the implementation of tertiary and quaternary treatments and promoting water reuse. This study evaluated the ecotoxicological impacts of advanced wastewater treatments applied to the effluent from a WWTP after secondary treatment: ultrafiltration (UF), ultraviolet radiation (UV), ozonation (OZ), and non-thermal plasma (NTP). Ecotoxicity assays were conducted using Raphidocelis subcapitata (chronic tests) and Daphnia magna (acute and chronic tests), representing primary producers and consumers, respectively. For R. subcapitata, no significant growth inhibition was observed for most treatments, while growth was promoted due to the presence of nutrients, except for OZ, which produced inhibitory effects. In D. magna, acute toxicity was low for most treatments, except for OZ, which showed significant toxicity. An additional chronic exposure experiment was conducted for the NTP-treated effluent, inducing adverse effects on growth and reproduction of D. magna; in contrast, R. subcapitata showed no effects, demonstrating species-specific sensitivity and trophic-level-dependent responses. These findings demonstrate that although advanced oxidation technologies enhance water quality, they may cause sublethal and lethal ecotoxicity effects, highlighting the importance of ecotoxicological evaluations in risk assessment of quaternary treatments, framed by Directive (EU) 2024/3019. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article

Background: Patients with mild traumatic brain injury (mTBI) have a small but clinically relevant risk of intracranial injury (ICI), requiring timely detection. Computed tomography (CT) remains the diagnostic gold standard but is costly and exposes patients to ionising radiation. Combining blood-based biomarkers, glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), with clinical decision rules may allow safe exclusion of ICI without CT, reducing unnecessary imaging, radiation exposure, and resource use. Methods: A systematic review of clinical and economic studies in patients with mTBI was registered in PROSPERO (CRD420251051158). Searches were conducted in January 2025 and updated in May 2025 in MEDLINE, Embase, and the Cochrane Library. The aim was to assess the diagnostic accuracy and economic value of the combination of GFAP and UCH-L1 compared with CT scanning to rule out ICI in both adults and children with mTBI. Where available, studies directly comparing GFAP and UCH-L1 with S100β were also analysed descriptively. The quality of the clinical evidence was assessed with QUADAS-2 and GRADE. Meta-analyses used a bivariate random-effects model, with heterogeneity and sensitivity analyses explored. Results: Overall, 21 studies were considered in our review. Moderate- to high-quality evidence indicates that GFAP and UCH-L1, when used together with clinical assessment, have very high sensitivity and can reliably rule out ICI in adults with mTBI presenting within 12 h to the emergency department. Evidence for paediatric populations shows promise but remains very limited. Specificity is low, particularly in older adults, which limits the ability to reduce CT use in this high-risk group. Research on age-adjusted cut-offs is ongoing and may help to reduce the proportion of false positive tests without compromising sensitivity. Few studies directly compared GFAP and UCH-L1 with S100β, with slightly higher to equivalent sensitivity for GFAP and UCH-L1. Economic evaluations suggest possible cost savings and reduced CT utilisation, but these analyses rely on assumptions unsupported by robust data and are highly context-dependent. There is a lack of clarity in the included studies regarding whether existing clinical head rules were used to define the study populations (i.e., to determine which patients would be recommended for CT scanning) and, if so, which specific rules were applied. Conclusions: Evidence shows that GFAP and UCH-L1 can safely exclude ICI in adults with mTBI in whom a CT scan would otherwise be considered based on clinical assessment or decision rules. Nevertheless, real-world evidence and cost-effectiveness data are scarce. Further prospective studies, including paediatric and elderly populations, and integration with clinical decision rules will be informative to ensure optimal use in clinical practice. Full article

Background and Objectives: With the rising number of geriatric surgical patients, postoperative cognitive dysfunction (POCD) has become a major concern, linked to impairments in memory, attention, and executive function. POCD increases morbidity, prolongs hospitalization, and diminishes quality of life. This review examines the mechanisms underlying POCD, with emphasis on neuroinflammation, blood–brain barrier (BBB) disruption, and oxidative stress, and evaluates the impact of anesthetic techniques on cognitive outcomes in the elderly. Materials and Methods: This narrative review used a targeted literature search to identify relevant clinical, translational, and mechanistic evidence on POCD in older surgical patients. The evidence was synthesized qualitatively, with attention to heterogeneity in study populations, anesthetic techniques, cognitive assessment methods, and follow-up duration. Results: Neuroinflammation, BBB compromise, oxidative stress, perioperative stress responses, and patient vulnerability appear to contribute to POCD. Evidence comparing anesthetic techniques remains heterogeneous. Some studies suggest associations between general anesthesia, volatile agents, and early postoperative cognitive changes, whereas other comparative and randomized studies do not demonstrate consistent long-term cognitive differences between general, regional, neuraxial, volatile, and intravenous anesthetic approaches. Regional and neuraxial techniques may reduce anesthetic or opioid exposure in selected patients, but they should not be interpreted as definitively superior for POCD prevention. Adjunctive and multimodal strategies, including dexmedetomidine and non-opioid analgesics, show potential benefits, although evidence remains variable. Conclusions: Individualized anesthetic planning, early risk stratification, avoidance of excessive anesthetic depth, hemodynamic optimization, multimodal analgesia, and postoperative recovery strategies may help reduce modifiable contributors to POCD. Current evidence does not support a definitive hierarchy of anesthetic techniques for preventing POCD, and further high-quality studies are needed. Full article

Background: Dental ankylosis (DA) in growing patients leads to progressive infraocclusion and alveolar ridge deformities, compromising future implant rehabilitation. Decoronation has been proposed as a biologically driven alternative to extraction for preserving alveolar bone during growth. Objective: We aimed to evaluate the clinical outcomes of decoronation—alveolar ridge preservation, infraocclusion progression, implant site development, and the influence of treatment timing—in growing patients with ankylosed permanent anterior teeth. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. A comprehensive search of MEDLINE (EBSCO), EMBASE, Scopus, and Web of Science was performed (January 2006–May 2026), supplemented by grey literature screening. Eligible studies included clinical investigations reporting outcomes of decoronation in patients ≤18 years. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) and Joanna Briggs Institute (JBI) checklist. Certainty of evidence was evaluated using the GRADE framework. Lastly, an inter-rater agreement was quantified using Cohen’s kappa coefficient. Results: Five studies (two retrospective cohorts and three case series) comprising 140 decoronated teeth with follow-up periods ranging from 1 to 30 years were included. A total of 78 records were identified across four databases; five studies met the eligibility criteria after duplicate removal and screening. Inter-rater agreement at the full-text eligibility stage was good (κ = 0.70). The overall risk of bias was low to moderate, and the certainty of evidence was rated as low using the GRADE framework. Vertical alveolar bone preservation or gain was consistently observed, particularly when decoronation was performed during the prepubertal or pubertal growth phases. The largest cohort (n = 103) reported substantial vertical bone gain when intervention occurred at a mean age of 13.0 years in girls and 14.6 years in boys. Infraocclusion stabilisation or improvement was reported across all studies. In contrast, horizontal ridge reduction persisted, with the only quantitative study reporting a mean bucco-palatal loss of 1.67 ± 1.12 mm (p = 0.004). No included study directly assessed implant placement outcomes. Overall, the certainty of evidence was low due to observational study designs, heterogeneity in outcome assessment, and absence of controlled comparators. Conclusions: Decoronation appears to be a promising strategy for preserving vertical alveolar bone and stabilising infraocclusion in growing patients with ankylosed teeth, particularly when performed before or during the pubertal growth phase. Evidence showed considerable bone height preservation, though horizontal ridge reduction persisted across cases. However, the certainty of evidence remains low because available studies are observational, heterogeneous, and lack direct extraction comparators. Therefore, high-quality prospective studies with standardised outcome measures and controlled comparisons are required to establish definitive clinical protocols. Participants underwent decoronation during childhood or adolescence (≤18 years); reported follow-up periods of up to 30 years reflect monitoring that extended into adulthood. Clinical significance: For clinical decision-making, decoronation should be considered once ankylosis with progressive infraocclusion is confirmed during active growth, ideally before the pubertal spurt; the decision should be guided by growth stage rather than chronological age, and clinicians should anticipate likely horizontal ridge reduction by planning for possible augmentation at implant placement and coordinating multidisciplinary follow-up until skeletal maturity. Full article

Reliable carbon emission data are essential for the effective operation of emissions trading systems (ETS), especially as China’s ETS expands to include energy-intensive industries. This study proposes a hybrid, risk-based anomaly detection framework for high-frequency CO₂ emission data by cross-validating material-based emissions with flue gas-based monitoring data. Under normal operating conditions, the ratio of material-based to flue gas-based emissions is expected to remain within a relatively stable distribution. Potential high-risk periods can therefore be identified when this relationship is distorted or when local temporal patterns deviate from expected behavior. The framework combines Hartigan’s dip test with a window-based Random Forest (RF) classifier, which is suitable for continuous monitoring data that may exhibit temporal dependence. The framework was evaluated using 15-min CO₂ emission data from a cement production facility, with simulations of anomaly magnitude, duration, and mode. Results show that the dip test performs well for long-lasting or strong anomalies, whereas the RF model is more sensitive to subtle, short-term deviations. In the integrated framework, 94.7% of anomalous periods were detected by at least one method and flagged as potential data-quality risks, whereas normal periods were not flagged, supporting its use to prioritize verification efforts. Full article