Abstract: Background/Objectives: Large language models (LLMs) are increasingly applied to medical image interpretation; however, their diagnostic accuracy and reliability in musculoskeletal radiology remain uncertain. This study evaluates the diagnostic performance and confidence calibration of LLMs in detecting and classifying bone tumors on radiographs. Methods: This retrospective observational study analyzed a dataset of 257 radiographs with confirmed diagnoses obtained from Radiopaedia, including normal studies and a spectrum of benign and malignant bone tumors. Cases were selected to ensure representation across multiple tumor types. Three LLMs (ChatGPT 5.3, X-ray Interpreter GPT-4.1, and X-ray Interpreter Gemini) evaluated each image using a standardized prompt assessing abnormality detection, tumor detection, classification, and confidence. Outcomes included diagnostic accuracy, false positive abnormality rates, false negative rates, tumor hallucination rates, and confidence calibration. Results: Abnormality detection was high across models, with Gemini demonstrating the highest sensitivity (up to 100%). Tumor detection was strongest in lesions with characteristic features, including osteosarcoma and osteochondroma. False negative rates varied substantially, with GPT-4.1 demonstrating the highest rate (29.9%), followed by ChatGPT (24.8%) and Gemini (6.6%). Primary diagnostic accuracy was highest for osteosarcoma in GPT-4.1 (80%), while ChatGPT 5.3 performed best in benign lesions, including osteochondroma (84.6%) and non-ossifying fibroma (76.9%). Tumor subtype classification remained limited across all models and was poorest for Ewing sarcoma (0% in ChatGPT and GPT-4.1; 10.3% in Gemini). False positive abnormality rates were highest in GPT-4.1 (40.7%), followed by Gemini (25.9%) and ChatGPT (13.5%). Tumor hallucination occurred only in Gemini (12.3%). All models demonstrated confidence miscalibration, with higher confidence observed in incorrect predictions and in tumor-negative cases. Conclusions: LLMs demonstrate strong performance in detecting radiographic abnormalities but remain limited in tumor subtype classification, particularly for diagnostically challenging lesions such as Ewing sarcoma. Elevated false positive and false negative rates, along with systematic overconfidence—especially in GPT-4.1—highlight important limitations for clinical use. These findings support the role of LLMs as adjunctive tools rather than independent diagnostic systems.

Abstract: Background: Genetic variations such as single nucleotide polymorphisms (SNPs) as part of pharmacogenomics play an important role in the metabolism of drug and hence their active concentrations in blood plasma. Objectives: The aim of this study is to select candidate compounds from a TCM dataset that may be repurposed for arterial and venous thromboses management. This shall be achieved through development and evaluation of an ensemble deep learning model that taking into account the genetic variations in protein sequences. Methods: BIOSNAP dataset was supplemented with 321,657 drug–target pairs consisting of SNP variants of wild-type proteins. The application dataset consisted of a TCM dataset containing 35,553 ingredients. The control group was set as the pathogenic group, whilst the treatment group was set as the non-pathogenic group. Contrastive and non-contrastive deep cross-modal attention ensemble modelling was developed, evaluated and applied. Results: Contrastive regularisation effect improved the performance of the Contrastive Learning (CL) Ensemble over the Non-CL Ensemble model as well as the Dong et al. (May 2025) CL model in the test set (AUPR 0.919 vs. 0.894 vs. 0.813). Safflower yellow A, Paeoniflorin and Notoginsenoside R6 were associated with existing TCM and highly ranked for interaction with Factor Xa genetic variants. Highly interacting protein targets were identified. Conclusions: Ensemble modelling with contrastive learning resulted in performance improvements and can be useful for selecting TCM compounds for antithrombotic management. This is a step towards personalised drug selection and can simultaneously facilitate interpretation of the biological rationales for risk vs benefit evaluations during decision making.

Abstract: Background: The coexistence of Myasthenia Gravis (MG) and Rheumatoid Arthritis (RA) represents a rare but clinically challenging form of polyautoimmunity, raising interesting questions about shared immunopathogenic mechanisms and the safety of long-term immunomodulatory therapies. Methods: The article describes a case report of a 66-year-old female with a 12-year history of seropositive RA who subsequently developed seropositive MG during long-term exposure to hydroxychloroquine (HCQ) therapy. Following discontinuation of HCQ, methotrexate (MTX) therapy was initiated and stable control of both diseases was temporally obtained. Results: Three years later, the patient presented with upper gastrointestinal bleeding and severe microcytic anemia. Further evaluation revealed advanced liver fibrosis (F4) and severe gastropathy, consistent with Child–Pugh class A cirrhosis. Viral, alcoholic, and autoimmune causes of chronic liver disease were excluded. In the absence of alternative etiologies, this was considered possibly associated with MTX therapy, in the context of additional metabolic risk factors, including type 2 diabetes mellitus and increased body mass index. Conclusions: The complex interplay between polyautoimmunity and treatment-related toxicity is underscored in this article. Overlapping autoimmune diseases may arise on a shared immunological background, while therapeutic agents may contribute to disease expression or long-term complications. These findings highlight the need for individualized therapeutic strategies and vigilant monitoring, particularly in patients with coexisting metabolic risk factors.

Abstract: In the paper, a systematic treatment of sensitivity analysis of multivariable control systems from a perspective of the characteristic transfer functions (CTFs) method is given. The CTFs method (also called Characteristic Gain Loci method) allows one to associate with an N-dimensional multi-input multi-output (MIMO) system a set of N independent single-input single-output (SISO) characteristic systems and thereby to reduce the analysis and design of a MIMO system to analysis and design of N SISO systems. The formulas are derived determining the sensitivity functions of the CTFs and sensitivity vectors of the canonical basis axes to small variations of parameters of general type MIMO systems. The relations between the sensitivity functions of the open-loop and closed-loop MIMO systems are established. Two illustrative examples are considered. The first of them concerns the sensitivity of a two-dimensional not robust system with large degree of skewness of the canonical basis axes. In the second example, the sensitivity of the control system of a hexacopter (multirotor UAV with six rotors) to small degradations of the motors’ efficiency is analyzed.

Abstract: (1) Background: Retinol has consistently demonstrated efficacy in improving signs of skin aging. However, recent European Union regulations have limited its cosmetic concentration to 0.3%, creating the need for new formulations to be capable of maintaining high efficacy, safety, and tolerance. (2) Material and Methods: This clinical study aimed to evaluate and compare the rejuvenating effects and tolerance of a 0.5% retinol serum with a new equivalent technology, Retinduo®, which previously showed promising preclinical results. A single-center, prospective, randomized, controlled, double-blind, two-arm parallel study was conducted in 40 Caucasian women aged 38–60 years with moderate photoaging (Glogau II). 20 participants applied Retinduo® serum and 20 applied retinol 0.5%, following a progressive ap-plication protocol. Clinical and instrumental assessments measured hydration, firmness, elasticity, tone homogeneity, melanin levels, skin roughness, wrinkle parameters, and stratum corneum thickness. (3) Results: Both formulations signifi-cantly improved hydration, firmness, and elasticity from day 28 onward. Retinduo® showed a significant increase in viscoelasticity (R8) from day 56, while retinol 0.5% did not demonstrate significant changes in this parameter. Melanin reduction was observed with Retinduo® at days 28 and 56 and with retinol 0.5% just at day 28. Although a reduction in melanin was observed with both ingredients, the reduction was more significant with Retinduo® at 56 days. Both treatments reduced the thickness of the stratum corneum; however, with Retinduo®, a significant and more pronounced reduction was achieved after 3 months of treatment (30% (p=0.0001) vs. 12% (p=0.033). Retinduo® demonstrated significant wrinkle depth reduction at day 28 and in wrinkle amplitude (width and length of wrinkles) at the end of treatment, while 0.5% retinol showed a positive trend in this parameter. Both products exhibited excellent tolerance. (4) Conclusions: Overall, Retinduo® achieved comparable or slightly superior anti-aging effects while aligning with current European regulatory limits.

Abstract: In this study we develop a Land-Use Random Forest (LURF) model for the Campania Region (southern Italy) that combines 2022 daily PM10 observations from 13 quality-controlled ARPA Campania stations with a rich set of spatial predictors to produce daily concentration maps at 1000 m × 1000 m resolution, from which annual statistics (mean, percentiles, and exceedances) are derived through temporal aggregation. The predictor space includes resident population, land-cover and imperviousness indicators, road-network metrics derived from OpenStreetMap, meteorological fields from the ERA5 reanalysis, satellite aerosol optical depth (AOD) from MODIS Terra and Aqua—scaled by ERA5 boundary-layer height (AOD/pbl)—daily mean PM10 from a nested CHIMERE simulation, and a binary categorical predictor (IdDust) flagging days affected by Saharan dust transport events. The hyperparameters for the LURF model are selected via a nested inner grid search; generalisation performance is assessed through a spatially aware leave-location-out cross-validation (LLO-CV) scheme, which prevents optimistic bias arising from spatial autocorrelation among neighbouring stations. Under LLO-CV, the LURF achieves R2=0.54, RMSE =11.1 μg m−3, and MAE =8.0 μg m−3, against R2=−1.11, RMSE =23.6 μg m−3, and MAE =19.1 μg m−3 for the raw CHIMERE output evaluated on the same observations. The inclusion of IdDust as a categorical covariate allows the Random Forest to partition the training distribution between dusty and non-dusty regimes, improving the representation of episodic high-PM10 events and reducing systematic underestimation at the upper tail of the concentration distribution. CTM-derived PM10 and ERA5 boundary-layer and pressure fields emerge as the dominant predictors, collectively accounting for the majority of explained variability, while IdDust ranks among the physically interpretable secondary predictors. The 1000 m maps highlight marked urban–rural contrasts, resolving hotspots in the Naples metropolitan area and along major motorway corridors that remain unresolved at typical CTM grid spacings. By embedding physically based CTM output, satellite aerosol diagnostics, and dust-event classification within a flexible machine-learning framework, the proposed approach offers a low-cost, operationally tractable tool for high-resolution PM10 exposure assessment in regions characterised by complex terrain and heterogeneous emission sources.

Abstract: To explore the genetic diversity and adaptive evolutionary mechanism of Mastacembelus armatus in the Dongjiang and Ganjiang River Sources, whole-genome resequencing was performed on three populations of M. armatus from Xunwushui (XW) and Jiuqu River (DN) in the Dongjiang River Source, and Taojiang (XF) in the Ganjiang River Source. Population genetics methods were integrated to analyze their genetic structure, differentiation characteristics and selection signals. The results showed that a total of 209.05 Gbp of Clean Data was obtained from the three populations, with the Q30 base percentage reaching 94.42% and the average mapping rate to the reference genome being 97.85%, indicating high reliability of the sequencing data. A mean of 7,459,686 single nucleotide polymorphisms (SNPs) were detected, with a transition/transversion ratio of 1.52 and a heterozygous SNP ratio of 2.22%. The total number of genome-wide insertions and deletions (InDels) was 1,902,722±23,247. Gene Ontology (GO) functional annotation revealed a consistent variation pattern of core genes among the three populations. Phylogenetic tree, Admixture and principal component analysis (PCA) confirmed that the three populations belonged to a single evolutionary clade and shared a genetic origin from two ancestral populations (the lowest cross-validation error at K=2), while significant genetic differentiation was observed among populations: XW and DN populations had similar genetic backgrounds and closer genetic relationships, both biased towards the blue ancestral component, whereas XF population was inclined to the red ancestral component, with the DN population showing the highest degree of genetic admixture. Individuals within the XF population had more distant genetic relationships and the longest linkage disequilibrium (LD) decay distance, which was speculated to be associated with its small population size and low recombination rate; in contrast, the XW population had the shortest LD decay distance, corresponding to the characteristics of large population size and high recombination rate. Analysis of population genetic diversity indicated that XW and DN populations were classified as the high-diversity group (with more than 440,000 polymorphic markers, expected heterozygosity >0.31 and polymorphism information content (PIC) ≈0.25), while the XF population was the low-diversity group (with 342,646 polymorphic markers, expected heterozygosity of 0.2608 and PIC of 0.2073). Only the minor allele frequency (MAF) of the XF population (0.2829) was slightly higher than that of the other two populations. This study systematically elucidated the characteristics of genetic differentiation and diversity differences of M. armatus in the Dongjiang and Ganjiang River Sources, providing a genome-level scientific basis for the conservation of genetic resources, development of molecular markers and analysis of environmental adaptive mechanisms of this species.

Abstract: Artificial Intelligence (AI) is increasingly central to modern system engineering and service operations, enabling real-time decision-support in cyber-physical and data-intensive environments. This study develops an Extended Deep Neural Network–Logistic Regression (EDNN–LR) hybrid framework as a scalable AI solution for predictive optimisation within Industry 4.0 decision systems. The model integrates the nonlinear learning capability of deep neural networks with the interpretability and convergence stability of logistic regression, thereby enhancing transparency, robustness, and computational efficiency in engineering applications characterised by uncertainty and behavioural variability. The proposed framework is validated using a publicly available financial–cyber dataset comprising over 4.44 million records from CoinMarketCap (2013–2025), representing a dynamic cyber-physical decision environment analogous to complex industrial ecosystems. Implemented in MATLAB R2024a and TensorFlow 2.17, the model achieves rapid convergence by epoch 142 and 98 % classification accuracy (AUC = 0.846, MSE = 0.79, recall = 90.6 %) on selected high-liquidity assets. These results confirm the framework’s ability to model nonlinear dependencies and adapt to stochastic disturbances typical of service-oriented and engineering-operation contexts. Beyond predictive precision, the EDNN–LR framework provides explainable probabilistic outputs that can be directly incorporated into decision variables such as resource allocation, demand forecasting, and dynamic scheduling under real-time constraints. Its hybrid design reduces computational cost, enhances interpretability, and enables cross-domain adaptability—from financial risk management to logistics, supply-chain coordination, and energy-system optimisation. By bridging deep learning, system engineering, and behavioural decision analytics, this study contributes a generalised AI-driven architecture for intelligent and transparent decision-support across Industry 4.0 service and production ecosystems.

Abstract: Autumn-winter forage scarcity limits subtropical livestock systems. This study aimed to: (1) develop a segregating F₁ population derived from parents contrasting in autumn-winter biomass yield (WBY) in tetraploid Paspalum notatum; (2) estimate phenotypic and genetic variability for WBY across environments; (3) determine the relationship between WBY and spring-summer biomass yield (SBY); and (4) assess the feasibility of UAV-derived vegetation indices as non-destructive estimators of dry autumn-winter biomass yield (WBY) for future breeding. A population of 182 tetraploid F1 hybrids was evaluated at two sites in Corrientes Province, Argentina (2022-2024). WBY exhibited wide genotypic variability across locations and years (p < 0.001), with significant effects of genotype, location, and genotype × location interaction. Broad-sense heritability (H²) ranged from 0.41 to 0.64, reflecting sensitivity to thermal and moisture conditions of each environment. WBY showed a positive, moderate association with SBY (R² = 0.20 - 0.26), indicating that selection for cool-season yield does not compromise summer productivity. Among the indices evaluated, the Normalized Difference Red Edge Index (NDRE) was the most robust predictor of WBY (R² up to 0.67), though predictive accuracy varied with environmental conditions. Overall, the results demonstrate substantial and exploitable genetic variation for cool-season forage yield in P. notatum.

Abstract: While South Africa’s Constitution guarantees the right to healthcare for all who live in the country, health inequities exist based on migrant status. This paper examined how discrimination intersects with structural and institutional practices to produce unequal access to healthcare services for black foreign migrants in South Africa. Desk reviews of policy frameworks and relevant academic literature were used to analyze the existing disconnect in South Africa’s rights-based legal commitments and the lived realities of foreigners. Adopting a theoretical framework that integrates structural violence, intersectionality, and bureaucratic discretion, the findings are discussed by conceptualizing discrimination as a structural and interpersonal determinant of health. The study found that foreign nationals’ experiences regarding access to healthcare services are not incidental but embedded within complex socio-political dynamics of scarce resources, institutional practices, and institutional ambiguity. The consequences of these inequities involve delayed care-seeking and increased vulnerability to preventable diseases among black immigrants, with a broader public health risk. Drawing from the study, policy clarity is recommended, and the strengthening of mechanisms to ensure equitable access to healthcare in the country.

Abstract: Mozambique possesses significant natural gas resources. Yet, a vast majority of its population relies on solid biomass for cooking, resulting in detrimental effects on health, livelihoods, and productivity, as well as devastating environmental impacts. Domestic use of these resources could boost energy productivity, security and support sustainable development. We conducted a mixed-methods study involving interviews, descriptive statistics, and a multinomial logistic regression model. For this study, data was gathered from a random survey of 434 households in natural gas-rich peripheries within Northern Inhambane and Maputo City aiming to identify determinants of household energy choice for cooking. The results showed that as the income increases, the odds of choosing electricity, LPG, and biomass increase. Notably, in energy-rich peripheries, the odds of choosing biomass as an alternative fuel to natural gas are reduced by 96.2% when compared to non-energy-rich regions. The urban and more educated dwellers were more likely to switch to electricity and LPG. Energy infrastructure and system-related incidents were key reasons for switching away from natural gas to biomass. Based on these findings and given natural gas’s preference as a transition cooking fuel in energy-rich peripheries, the government should prioritize investment in energy systems, allocate more domestic gas, and promote its use. This effort aims to enhance access to clean cooking and raise public awareness of its health and environmental benefits.

Abstract: Short-term rider demand forecasting is a foundational operational capability for Mobility-on-Demand (MoD) systems, enabling proactive vehicle pre-positioning, dynamic pricing, and service-level optimization across ride-hailing, bike-sharing, carsharing, and demand-responsive transit platforms. Despite a rapidly growing body of literature, the field lacks a comprehensive and critically structured synthesis of methodological developments, input feature practices, evaluation standards, and unresolved research gaps. This paper presents a Systematic Literature Review (SLR) conducted in accordance with the PRISMA protocol, encompassing 291 peer-reviewed studies published between 2016 and 2025 across transportation engineering, intelligent transportation systems, and machine learning venues—the most comprehensive corpus assembled for this topic to date. The review identifies a clear five-generation methodological succession—from classical statistical and machine learning models through recurrent and convolutional deep learning architectures to Graph Neural Networks and transformer-based models—with signal decomposition methods and probabilistic architectures emerging as distinct 2023–2025 trends. Most significantly, we identify a seventeen-dimension research gap matrix that involves research gaps such as probabilistic demand forecasting, cross-city transfer, decision-focused predict-and-optimize frameworks, etc. Further, six concrete research directions grounded in these gaps are proposed, each accompanied by specific methodological proposals rather than general aspirational statements. The findings underscore the need for standardized benchmarking protocols, open dataset releases with documented preprocessing, and a fundamental reorientation of model evaluation from statistical accuracy metrics toward composite operational, probabilistic, and equity-aware performance objectives.

Abstract: The availability of publicly available genetic data has the potential to accelerate medication repurposing efforts. In this study by integrating GWAS summary statistics for medication use with phenome data, Mendelian randomization and genetic correlation analyses were performed to find significant medication-trait associations. Genetic predisposition to paracetamol use was associated with a cluster of affective traits and alcohol intake. Examination of eQTL data revealed genes at 17q21 regions have pleiotropic effects on both paracetamol use and the identified traits. A second signal was identified between levothyroxine use and diabetes. Subjects that were genetically predisposed to use levothyroxine were at higher risk to develop diabetes. Conditional analysis indicates the use of levothyroxine will likely lower the risk of diabetes by lowering body weight. Findings from eQTL analysis identified several genes at HLA regions that exert pleiotropic effect on both traits.Our findings suggest that paracetamol warrants further investigation for the treatment of affective disorders and alcoholism, while levothyroxine may offer protective benefits against diabetes via weight modulation.

Abstract: The Caribbean exhibits hyperendemic dengue transmission with near-universal adult seroprevalence in many territories, driven by sustained co-circulation of all four DENV serotypes and the domestic ecology of Aedes aegypti. Serosurveys report adult IgG rates as high as 93–100% in the French West Indies, Puerto Rico, and Jamaica, while children frequently acquire multitypic immunity before adolescence. High inapparent infection rates and population mobility complicate surveillance and mask true transmission intensity. These immunoepidemiological conditions elevate the risk of severe disease via antibody-dependent enhancement and challenge both acute diagnostics and vaccine policy. Effective control demands year-round integrated vector management, improved molecular and neutralization-based surveillance, pediatric-focused prevention strategies, and cautious deployment of balanced tetravalent vaccines informed by serotype-specific and genomic data.

Abstract: Cryopreservation integrates technologies that enable the long-term preservation of biological materials at extremely low temperatures. Since the serendipitous discovery by Audrey Smith and Christopher Polge that glycerol enhances the survival of frozen chicken sperm, the field has advanced rapidly and found applications across cell therapy, biobanking, and tissue engineering. This concise review provides an overview of cryopreservation principles and the current commercial landscape in cell freezing media, highlighting key players, challenges and recent developments in tissue and small-organ preservation.

Abstract: 15Cr14Co12Mo5Ni2, as a new type of low-carbon high-alloy aviation gear steel, has shown significant application potential in the transmission systems of aero engines due to its excellent high-temperature performance. In this paper, the aviation gear steel 15Cr14Co12Mo5Ni2 was treated by carburizing and quenching process. The microstructure distributions of the carburized and quenched aviation gear steel at different quenching temperature were analyzed by OM, SEM and EBSD. Subsequently, the axial tension-compressive fatigue tests (stress ratio R=-1) were carried out using a high-frequency fatigue testing machine after heat treatment at different quenching temperature (1020℃, 1050℃ and 1080℃), and the stress-number of cycles (S-N) curves were obtained by fitting the number of fatigue fracture cycles. The fracture morphologies were observed by SEM and the fracture mechanisms were analyzed. The research results show that the distribution of the microstructure and carbides exhibit gradient characteristics, and the carbide content decreases and the effective carburized layer depth decreases from 0.65mm to 0.45mm with increasing quenching temperature, also the main carbide types are M₂₃C₆ and M₇C₃. The fatigue life of 15Cr14Co12Mo5Ni2 gear steel decreases as the quenching temperature increases. Their fatigue strengths at a given fatigue life of 10⁶ cycles at 1020℃, 1050℃ and 1080℃ are 192 MPa, 183 MPa and 158 MPa, respectively. The cracks propagate outward from the core and the propagation rate accelerates with the increasing quenching temperature, eventually fracturing in the carburized layer. The fracture mechanism of 15Cr14Co12Mo5Ni2 gear steel at the quenching temperatures of 1020℃ was a mixed mode of intergranular and cleavage brittle fracture, while at 1050℃and 1080℃, it is mainly brittle fracture accompanied by local ductile fracture.

Abstract: This concept paper examines the growing integration of artificial intelligence (AI) into psychotherapeutic contexts, with particular attention to its implications for the mental health of adults and young adults. Against the backdrop of a global mental health crisis characterized by insufficient therapeutic resources, rising demand, and persistent stigma, AI-assisted interventions — including chatbot-based tools, machine learning-supported diagnostics, and algorithmically personalized treatment pathways — have emerged as a promising yet contested field. The paper introduces key concepts such as AI-assisted psychotherapy, digital mental health interventions (DMHIs), and conversational agents, and situates them within current psychological and clinical frameworks. Drawing on recent empirical studies, theoretical analyses, and ethical debates, it investigates the potential of AI to democratize access to mental health support while critically addressing concerns around therapeutic alliance, algorithmic bias, data privacy, and the irreducible dimensions of human connection in clinical care. The paper further examines the intersection of AI and established psychotherapeutic modalities, including Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), and supportive counseling. By identifying research gaps and unresolved tensions, this review advocates for an evidence-based, ethically grounded, and human-centered approach to AI integration in mental health — one that positions AI as a supplement to, rather than a substitute for, professional therapeutic relationships.

Abstract: This paper presents a theoretical framework aimed at examining the Riemann Hypothesis (RH) through the lens of a differential interaction operator Φ(s,δ) acting on the Hilbert space l²(N). By mapping the Dirichlet η-function to a trace-class operator, we analyze the resulting phase torque J(δ,t), which is governed by a hyperbolic sine bias. We propose a product criterion wherein the operator trace vanishes if and only if a zero exists at mirrored coordinates across the critical line. Furthermore, we explore how the Diophantine independence of prime logarithms, when amplified by the hyperbolic lever, may mathematically restrict the trace from vanishing off the critical line Re(s) = 1/2. Within the constraints of this oper ator construct, the analysis suggests a geometric mechanism consistent with the confinement of non-trivial zeros to the critical line.

Abstract: Agrivoltaics (Agri-PV) represents a promising solution to improve land-use efficiency by simultaneously allowing crop growth and photovoltaic (PV) energy generation, with additional benefits for crop production if properly engineered. However, when crystalline silicon (c-Si) PV modules are used for Agri-PV, even in semi-transparent configurations, shading occurs over crops, potentially reducing agricultural yields. Enhancing light diffusion is a key strategy to partially compensate for this effect, as diffuse light is more efficiently utilized by most plants. This study aims at engineering the transparent section of a semi-transparent c-Si PV module, assessing its optical, light-scattering, and efficiency-related properties for Agri-PV applications. The experimental work involved fabricating and testing various transparent stack configurations and mini-module prototypes to evaluate their suitability for Agri-PV integration. Optical characterization using a spectrophotometer revealed that certain stack configurations significantly enhance light diffusion, while maintaining good transmittance values for crops growth. To further analyze angular light scattering, a custom-built setup to measure the Bidirectional Transmittance Distribution Function (BTDF) was developed. The results showed that primarily anti-glare films (AG) and secondarily specific encapsulants (TPO) and flexible layers can effectively improve light distribution, helping to mitigate shading effects. Following AG application, Haze values exceeded 89%, indicating enhanced light diffusion capabilities. The impact of different stacks on module efficiency was also assessed through mini-modules testing. Findings indicate that enhanced light diffusion can be achieved with minimal efficiency losses. Specifically, the application of the AG resulted in a reduction of the Cell-To-Module efficiency ratio (CTM_η) of less than 1%. These results confirm that semi-transparent PV modules can be optimized for Agri-PV applications without significantly compromising energy output.

Abstract: Reliable Aboveground Biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy cover proxies) were extracted from normalised point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha⁻¹; R² = 0.994) and Villaseca (RMSE = 0.872 Mg ha⁻¹; R² = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha⁻¹; R² = 0.994). TreeQSM closely matched field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.