Abstract: Background/Objectives: Intraoperative neuromonitoring (IONM) has improved safety in thyroid and parathyroid surgery, yet intermittent IONM (I-IONM) may miss traction injuries developing between stimulations. We evaluated the feasibility and clinical utility of a trend-based intermittent monitoring mode (NIM Vital NerveTrend®) that records closely spaced stimulations and plots amplitude and latency over time. Methods: We conducted a prospective observational study at a high-volume endocrine surgery unit (January–September 2025). Forty-four consecutive patients undergoing thyroidectomy and/or parathyroidectomy with NerveTrend® were enrolled. EMG responses were categorized as Green (amplitude >50% of baseline and latency < 110%), Yellow (amplitude < 50% or latency >110%), Red (amplitude < 50% and latency >110%), and Loss of Signal (LOS: amplitude < 100 µV). Primary outcomes included LOS prevalence and the association between stimulation frequency and the appearance of Yellow trends. Ethical approval: AVEN protocol 486/2024/OSS/AOUPR; informed consent obtained. Results: Of 71 nerves at risk (NAR), 55 had a valid baseline and were analyzed; LOS occurred in 3/55 NAR (5.5%). The mean number of stimulations per NAR was 4.5 (range 1–9). Cases with both Green and Yellow points had a significantly higher mean number of stimulations than cases with only Green points (5.1 vs. 3.8; Student's t-test p = 0.0059). One Red measurement occurred in a case that progressed to LOS. Conclusions: NerveTrend® provided near real-time functional feedback while maintaining the simplicity of I-IONM. Increased stimulation frequency was associated with early Yellow trend alerts, potentially signaling traction stress and enabling timely surgical adjustments. Larger multicenter studies and protocol standardization are warranted.

Abstract: Paracetamol (PAR) was selected as an emerging micropollutant model to evaluate the effectiveness of the photo-Fenton process using natural Bandjéli ore (BO) as a heterogeneous source of iron. An aliquot of 1 ml of the activated product was introduced into 200 mL of an aqueous solution of paracetamol at a defined concentration. The tests were conducted in a double-jacketed glass photoreactor (0.2 L), continuously stirred and equipped with two UVA PL-L lamps (36 W, λ = 365 nm), with the temperature maintained at 20°C and pH around 2.4. The photo-Fenton process was applied with different initial concentrations of paracetamol (10–50 mg/L), different H₂O₂/PAR initial molar ratio (10:1 and 5:1), and different ferric ion concentrations (2.84-4.73 mg/L). Under these conditions, complete elimination of paracetamol was achieved in less than 3 h for iron contents below 5 mg/L, in compliance with the discharge standards applicable in France and Togo. Inhibition tests with propan-2-ol highlighted the predominant role of hydroxyl radicals and the secondary involvement of superoxide radicals in the subsequent stages. Taken together, these results demonstrate that the Bandjéli ore is an effective, sustainable, and economically advantageous alternative to commercial iron salts for the implementation of the photo-Fenton process in the decontamination of water polluted by persistent organic micropollutants.

Abstract: Legal judgment prediction (LJP) increasingly relies on large language models whose full fine-tuning is memory-intensive and susceptible to catastrophic forgetting. We present LawLLM-DS, a two-stage Low-Rank Adaptation (LoRA) framework that first performs legal knowledge pre-tuning with an aggressive learning rate and subsequently refines judgment relations with conservative updates, using dedicated LoRA adapters, 4-bit quantization, and targeted modification of seven Transformer projection matrices to keep only 0.21% of parameters trainable. From a structural perspective, the twenty annotated legal elements form a symmetric label co-occurrence graph that exhibits both cluster-level regularities and asymmetric sparsity patterns, and LawLLM-DS implicitly captures these graph-informed dependencies while remaining compatible with downstream GNN-based representations. Experiments on 5,096 manually annotated divorce cases show that LawLLM-DS lifts macro F1 to 0.8893 and achieves an accuracy of 0.8786, outperforming single-stage LoRA and BERT baselines under the same data regime. Ablation studies further verify the contributions of stage-wise learning rates, adapter placement, and low-rank settings. These findings demonstrate that curriculum-style, parameter-efficient adaptation provides a practical path toward lightweight yet structure-aware LJP systems for judicial decision support.

Abstract: The Overseer model is widely used in New Zealand for estimating nitrate (NO₃⁻) leaching losses in agricultural systems. This study evaluated the accuracy of the Over-seer model in simulating nitrate (NO₃⁻) leaching through a two-year lysimeter experi-ment conducted at Woodhaven Gardens, New Zealand, under beetroot and pak choi cultivation. Seven distinct nitrogen (N) fertiliser treatments were applied to assess model performance. In Year 1, Overseer overestimated NO₃⁻ leaching by an average of 45.2 kg N/ha (15.7%), due to underestimated crop uptake. Similarly, overestimations were observed in Year 2, with overprediction rates reaching up to 63.5%. Sensitivity analysis highlighted soil texture, impeded layer depth and crop residue incorporation as key drivers of leaching variability, underscoring the need for improved model cali-bration. Overseer performed reasonably well under lysimeter conditions, with a strong linear relationship (Pearson’s correlation coefficient r = 0.89, P < 0.0001) between measured and predicted values and explaining 77% of the variance (R²=0.77) in the observed data. The model predicted a baseline leaching loss of 39.4 kg N/ha/year even when measured losses were zero. Overseer demonstrates moderate reliability in simulating NO₃⁻ leaching under vegetable cropping systems but exhibits notable limi-tations in handling crop-specific N dynamics, soil hydrology, and fertiliser timing.

Abstract: Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the Urban Heat Island effect and enhancing stormwater man-agement, alongside benefits for public health and biodiversity. Effective GI imple-mentation remains challenging, particularly in dense, rapidly urbanized Mid Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project. The project aims to bridge the theo-ry-practice gap, enabling pilot implementations in multiple Italian Mid Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, traf-ficked urban area projected to face "extremely strong heat stress" by 2050. Design in-terventions included spatial optimization, strategic species replacement, creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area, a measurable reduction in the UTCI index, a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces.

Abstract: Sulfites are the most extensively used additive in oenology to prevent oxidation and microbiological spoilage. However, their potential adverse health effects have increased the demand for low sulfite wines. Strategies are required to ensure microbiological stability while preserving the quality of the wine. This study evaluated strategies for reducing or replacing added sulfites using chitosan and low doses of lysozyme in Tannat winemaking, measuring their effects on microbial diversity, physicochemical parameters, and sensory attributes. Treatments were vinified by triplicate: reduced sulfites (RS: 30 mg/L), chitosan (C: 100 mg/L), reduced sulfites with chitosan (RS+C: 30 mg/L + 100 mg/L), reduced sulfites with lysozyme (RS+L: 30 mg/L + 5 mg/L), and a tra-ditional winemaking (TW: 125 mg/L sulfites). Sulfur dioxide lowered lactic acid bacteria counts, whereas chitosan and lysozyme treatments maintained higher populations. Metagenomic analyses showed decreased bacterial diversity under sulfur dioxide, while chitosan promoted a more complex microbiota. Lysozyme selectively reduced lactic acid bacteria, mainly affecting Oenococcus spp. Lower sulfite decreased phenolic concentra-tions possible due to reduced protection against oxidation, leading to color differences among treatments. The results indicate that strategies to reduce or replace sulfites in-fluence microbial dynamics, acidity, phenolic structure, and color, highlighting the importance of careful process management to maintain wine quality. Keywords: reduced sulfur dioxide, wine microbial community, Tannat wine composition.

Abstract: Soil salinity is a major constraint to strawberry (Fragaria × ananassa) cultivation, adversely affecting plant growth, yield, and fruit quality. Salinity stress triggers complex physiolog-ical and biochemical responses, including osmotic adjustment, antioxidant defense, ion homeostasis, and shifts in metabolite accumulation. Genotype-specific variability in tol-erance highlights the potential for breeding salt-resilient cultivars. This review summa-rizes current knowledge on strawberry responses to salinity, emphasizing on the impacts on growth, photosynthesis, water relations, and fruit quality, as well as the underlying mechanisms of tolerance. In addition, it reviews biologically based approaches, including biostimulants, small signaling molecules, and plant–microbe interactions, that help alle-viate salinity stress and strengthen plant resilience. By integrating these physiological in-sights with advances in biological and breeding-based approaches, the review provides a comprehensive framework for improving strawberry performance under saline conditions and guiding future cultivation and genetic improvement strategies.

Abstract: Goldbach’s strong conjecture, asserting that every even integer greater than two can be expressed as the sum of two prime numbers, remains one of the oldest unresolved problems in mathematics. Despite overwhelming numerical verification and powerful partial results, a complete analytic proof has remained elusive. At the same time, extensive computations have revealed a striking empirical phenomenon known as Goldbach’s comet: the rapidly growing number of Goldbach representations as a function of the even integer E, forming a characteristic comet-like structure when plotted.This review article provides a comprehensive synthesis of classical analytic number theory, modern distributional results on primes, and recent structural insights in order to explain the existence, shape, and persistence of Goldbach’s comet. We introduce and develop a unified framework based on three complementary quantities: the dominance ratio Ω(E), measuring the growth of available prime density relative to local obstructions; the density field λ, encoding the smooth asymptotic behavior of primes; and the obstruction constant Κ, bounding the maximal effect of local gaps and covariance.We show that Ω(E) diverges, reflecting a fundamental scale separation between global prime density and local irregularities, and that λ-weighted obstructions remain bounded while density grows without bound. This framework explains why no gap-based or covariance-based mechanism can suppress Goldbach representations and why the number of representations necessarily increases. We argue that Goldbach’s conjecture is thereby reduced to a single, well-identified uniform realization problem within existing analytic methods.Rather than claiming a final proof, this article aims to clarify the conceptual structure underlying Goldbach’s conjecture, explain Goldbach’s comet as a necessary consequence of prime density dominance, and position the conjecture within a sharply defined analytic frontier. The result is a coherent, literature-grounded explanation of why Goldbach’s conjecture must be true and what precise technical step remains to complete its proof.

Abstract: We propose SuperSegmentation, a unified, fully-convolutional architecture for semantic keypoint correspondence in dynamic urban scenes. The model extends SuperPoint’s self-supervised interest point detector–descriptor backbone with a DeepLab-style Atrous Spatial Pyramid Pooling head for semantic segmentation and a lightweight sub-pixel regression branch. Using Cityscapes camera intrinsics and extrinsics to construct geometry-aware homographies, SuperSegmentation jointly predicts keypoints, descriptors, semantic labels(e.g., static vs. dynamic classes), and sub-pixel offsets from a shared encoder. Our experiments are conducted on Cityscapes, where a backbone pretrained on MS-COCO with strong random homographies over approximately planar images is fine-tuned with deliberately attenuated synthetic warps, as we found that reusing the aggressive COCO-style homographies on Cityscapes produced unrealistically large distortions. Within this controlled setting, we observe that adding semantic masking and sub-pixel refinement consistently improves stability on static structures and suppresses keypoints on dynamic or ambiguous regions.

Abstract: Hardware-in-the-Loop (HIL) simulation has become an indispensable tool for rapid and cost-effective development and validation of power electronic systems. This paper presents a detailed experimental characterization and validation of a PLECS-based HIL model for a Dual Active Bridge (DAB) DC-DC converter controlled using Single Phase Shift (SPS) modulation. An extensive experimental investigation is conducted to characterize the converter's performance across a wide range of operating conditions. The primary objective of this work is to validate and fine-tune the PLECS-based HIL model of a single DAB converter, laying the foundation for building more complex models, such as configurations with multiple converters connected in series or parallel. A DAB prototype has been characterized by varying the PWM phase shift angle between the input-output full-bridges over a range of equivalent input-output voltage levels. The power flow and efficiency were also analyzed at different voltage gains (M = 0.6, 1, and 1.4). In addition, the influence of key parameters like switching frequency and leakage inductance on the converter’s power flow and efficiency was experimentally evaluated. The experimental efficiency trends and power characteristics across the operating points provide valuable insight into the optimal modulation range and loss mechanisms of the DAB converter under SPS control. The HIL model is thoroughly tested against the experimental hardware prototype by comparing key metrics, including transferred power and system efficiency. The results demonstrate a high degree of accuracy between the HIL model and the physical system across all tested operating conditions. This work provides a validated, high-fidelity HIL model and a comprehensive dataset that confirms the effectiveness of the PLECS platform for the development and optimization of DAB converters, thereby reducing design time and mitigating risks in subsequent prototyping stages.

Abstract: Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO2, and O2, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow.

Abstract:

Deep Eutectic Solvents (DESs) and in essence naturally available DESs (NADESs) are considered to be green solvents due to their low vapor pressure, non-flammability, thermal stability, good solvent power and low oxicity. These properties make them attractive as safer and more environmentally acceptable solvent options. Green Chemistry promotes the use of renewable and biocompatible compounds such as amino acids, lipids and acids of natural origin to yield more sustainable DESs, which yields their application in several industrial processes. Driven by the current requisite for sustainable progress, along with overcoming dependence on fossil-based resources, the current work details important findings pertaining to the design of sustainable NADESs from the perspective of green chemistry to exhibit suitable physico-chemical properties and a low toxicological profile. Biodegradation studies using OECD 301D closed bottle test (CBT) were performed to observe the biodegradability of 15 selected NADESs. Toxicity controls were run along with the CBT run to observe the behavior of these NADESs in the environment. In this framework, the present paper investigates the development of safer NADESs. The results obtained suggest that our synthesized NADESs, have high biodegradability and low toxicity towards microalgae. Although a conventional threat to the environment would seem out of reach, it must be hypothesized that such compounds might act as enhancers of eutrophication phenomena.

Abstract: The rapid development of the low-altitude economy is driving significant societal and industrial transformation. Unmanned aerial vehicles (UAVs), as key enablers of this emerging domain, offer substantial benefits in many applications. However, their unauthorized or malicious use poses serious security, safety, and privacy risks, underscoring the critical need for reliable UAV detection technologies. Among existing approaches, such as radar, acoustic, and vision-based methods, radio frequency (RF)-based UAV detection has gained prominence due to its long detection range, robustness to lighting and weather conditions, and capability to identify RF-emitting UAVs even when visually obscured. Nevertheless, conventional RF-based approaches often suffer from limited feature representation and poor generalization. In the past few years, convolutional neural networks (CNNs) have become the mainstream solution for RF signal recognition. However, most real-valued CNNs (RV-CNNs) process only the magnitude component of RF signals, discarding the phase information that carries valuable discriminative characteristics, which may degrade recognition performance. To address this limitation, this paper proposes a complex-valued CNN (CV-CNN) for UAV RF signal recognition, which exploits the full complex-domain structure of RF signals to enhance recognition accuracy and robustness. The proposed CV-CNN accounts for both the magnitude and phase components of RF signals from UAVs, thereby enabling true complex-valued convolutional operations without loss of phase information. The effectiveness of this approach is validated on the DroneRFa dataset, which encompasses RF signals from 25 distinct UAV categories. The impact of model hyperparameters, including network depth, convolutional kernel size, and dropout strategy on recognition performance is investigated through a series of ablation experiments. Comparisons are also conducted between the performance of CV-CNN with identical parameters and RV-CNN, both in noise-free and noisy conditions. The experimental results demonstrate that the CV-CNN exhibits superior robustness and interference resistance in comparison to its real-valued counterpart, maintaining high recognition accuracy even under low signal-to-noise ratio (SNR) conditions.

Abstract: Complex or difficult cesareans are associated with significant short- and long-term complications. The complications rate increases with increasing number of cesareans, and the incidence of cesarean section is increasing. To accurately identify women at high risk of surgical difficulty during a cesarean, ultrasound, in addition to clinical assessment, can be used to evaluate many risk factors including placenta previa, placenta accreta spectrum (PAS) disorders, fibroids, severe pelvic adhesions and membranous fetal vessels. Ultrasound is a well-established practice in obstetrical care as ultrasound is easily available, accessible, easy to perform, and well acceptable to the women. However, there are few studies on the role of preoperative ultrasound in the management of complex or difficult cesareans beyond the risk assessment of PAS. Currently, preoperative ultrasound is performed in selected cases only. The aim of this review article is to discuss the benefits and the use of ultrasound assessment before different types of complex or difficult cesareans. Whether ultrasound assessment should be performed before all cesarean sections will also be discussed.

Abstract: This paper introduces a novel Magnetorheological (MR) damper integrating a ball-screw mechanism (SMRB damper), designed to unify translational and rotational motion for enhanced automotive suspension performance. While shear-mode rotary MR dampers offer excellent responsiveness and stability, prior designs face persistent issues such as high off-state torque, structural complexity, or limited damping force. The proposed damper aims to overcome these limitations. Its design and operating principle are presented, followed by the development of a mathematical model based on the Bingham-plastic formulation and finite element analysis. To maximize damping capability, the key structural parameters are optimized using an Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, a prototype is fabricated based on the optimized results, and experimental tests validate its performance against simulation predictions, demonstrating its improved potential for vibration control applications.

Abstract: Tropical forests are critical for global climate, biodiversity conservation, and supporting local livelihoods, yet they remain highly vulnerable to human-induced pressures (deforestation and degradation) and climate change impacts (diseases, fires, and drought). The overarching aim of this review is to assess how artificial intelligence (AI) and machine learning (ML) are transforming remote sensing-based monitoring of tropical forests, with a focus on their potential to enhance the detection and estimation of forest change and support tropical forest-related climate policy frameworks. The strengths of this review lie in its comprehensive synthesis of technical, institutional, and governance dimensions, achieved by systematically analyzing evidence from operational forest monitoring platforms and peer-reviewed literature (2010–2025). Using structured search and qualitative analysis, the review evaluates advances in AI/ML applications, identifies technical and institutional barriers, highlights emerging solutions, and provides practical, policy-relevant recommendations. This review identifies critical gaps and proposes a roadmap for scaling AI/ML for tropical forest monitoring. It finds that AI/ML tools, particularly supervised and unsupervised classifiers, deep learning models, time-series analytics, and multi-sensor data-fusion approaches, have become central to advancing remote sensing— enhancing accuracy, automation, and scalability for monitoring deforestation, forest degradation, biomass change, and forest dynamics. However, effective adoption of these technologies still faces persistent barriers—such as limited access to high-quality training data, reliance on proprietary platforms, technical capacity gaps, and unresolved ethical and governance challenges. The review concludes that overcoming these barriers through open training datasets, platform-agnostic infrastructures, capacity building, and inclusive governance is essential for scaling robust, transparent, and locally owned AI-enabled forest monitoring systems. Advances in AI/ML in remote sensing will support climate mitigation, biodiversity conservation, and equitable decision-making in tropical forest countries.

Abstract: The Kelvin formulation of the second law of thermodynamics permits the following generalization: If the efficiency of a heat engine approaches unity, then the rejected work vanishes. This generalization allows deriving the behavior of a Carnot cycle near absolute zero of temperature. Also, the unattainability of absolute zero can be shown. In turn, these results allow deriving the behavior of the entropy near absolute zero, as has already been shown previously. The point of view is the phenomenological, macroscopic, and non-statistical one of classical thermodynamics.

Abstract: The objective of this paper is to rigorously define the Kadec-Klee property for modular spaces endowed with a sequential convergence structure, and to demonstrate that this property leads to the normal structure in such spaces. Consequently, we establish that the Kadec-Klee property defined herein implies the corresponding fixed point property for these spaces. These results are new in the modular space setting. Furthermore, given that the examined class of spaces encompasses Banach spaces, modular function spaces, and various other types, our theory offers a comprehensive, unified framework for exploring the interconnections between the Kadec-Klee property, normal structure, and the fixed point property.

Abstract:

Self-congruency refers to the coherence between an individual’s emotional experience and their enacted behavior. Because discrepancies between internal states and outward actions (i.e., self-congruency) are linked to vulnerability in mental health, identifying physiological signatures associated with self-congruency may offer novel biomarkers for psychological well-being. Therefore, this study investigated whether temporal covariance between cardiac and neural activity reflects individual differences in self-congruency. Thirty-eight healthy adults underwent resting-state functional magnetic resonance imaging to quantify neural dynamics variability, while cardiac activity was recorded using photoplethysmography to derive heart rate variability (HRV) measures. Self-congruency was assessed using a graphic rating scale in which participants adjusted the spatial overlap between two circles representing their emotional experience and enacted behavior. Temporal coupling between cardiac HRV and regional BOLD activity was quantified using cross-covariance analysis across biologically plausible temporal shifts. At the group level, covariance predominantly reflected brain-to-heart influence, particularly within regions central to the neurovisceral integration model such as the ventromedial prefrontal and anterior cingulate cortices. In contrast, individuals with higher self-congruency displayed stronger heart-to-brain-directed interplay, especially within regions implicated in emotion regulation and empathy, including the right rostral middle frontal gyrus and supramarginal gyrus. These findings indicate that although top-down regulation characterizes global heart-brain dynamics, greater alignment between emotional experience and enacted behavior is associated with increased bottom-up cardiac influence on neural activity. Given the relevance of both heart-brain communication and self-congruency for mental health, these results suggest a potential physiological-psychological biomarker axis with implications for prevention strategies.

Abstract: Planning a vacation is not easy and choosing a destination is itself a difficult task. But with modern machine learning technology we can predict user preferences and recommend suitable destinations for vacations. This research aims to analyze public preferences between two popular vacation destinations named mountains and beaches, using ma- chine learning techniques. By considering demographic factors like age, gender, income, education and lifestyle choices, this study explores the influences on vacation destination preferences. A unique dataset containing over 52,000 instances is used to predict whether individuals prefer mountains or beaches, employing algorithms like Decision Tree, Random Forest, Gradient Boosting, Deep Learning, and Ensemble Methods. The study concludes that Deep Learning models achieved the highest accuracy of 99.81%, followed by Gradient Booster at 98.85%. The results suggest that machine learning can enhance personalized travel recommendations and contribute to more efficient tourism marketing.