Abstract: Background: Generative artificial intelligence is entering higher education faster than many universities have been able to govern it, particularly in African contexts where policy ambition, institutional capacity, digital infrastructure, and pedagogical practice do not always advance at the same pace. Purpose: This study examines how generative artificial intelligence integration is publicly documented, governed, and framed at two universities in Rwanda: the African Leadership University and the Adventist University of Central Africa. Design: Guided by an integrated framework combining institutional readiness, Diffusion of Innovations, and a rights-based governance lens, the study adopts an interpretivist comparative multiple-case design based on document analysis and secondary analysis. The corpus comprises publicly retrievable institutional webpages, policy documents, academic regulations, handbooks, e-learning materials, research manuals, national policy texts, and recent peer-reviewed scholarship published or available between 2021 and April 2026. Findings: Public evidence indicates visible AI engagement at both universities, but in materially different forms. ALU appears more innovation-signalling, foregrounding AI research, student bootcamps, and academic-support programming. AUCA appears more governance-dense, with stronger public visibility of academic regulations, academic-integrity language, ICT and online-learning policies, plagiarism infrastructure, and AI-and-big-data institutional positioning. However, neither institution publicly presents a fully specified generative AI acceptable-use regime aligned with Rwanda’s evolving national and sectoral AI governance expectations. The findings therefore suggest that visible experimentation is advancing faster than visible rule specificity. Originality/value: The study contributes rare comparative African evidence on university AI governance and introduces a useful analytical distinction between innovation signalling and governance readiness. Practical implications: The central challenge is no longer whether universities will adopt AI, but whether they can align policy clarity, academic-integrity architecture, digital capacity, and educational purpose in institutionally credible ways. The study also identifies concrete priorities for later primary research on implementation, stakeholder interpretation, and assessment design.

Abstract: Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. A simple and powerful approach that lets the model auto-pilot, but weakens the need for physical characteristic understanding. Over more than a decade of our research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and Principal Component Analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest, ResNet50, and YOLOv10. The results confirmed the effectiveness of the PTC, which consistently improves classification accuracy by at least 7% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behaviour across all data forms, demonstrating its strong resilience and reliability.

Abstract: Background and Clinical Significance: Glioblastoma is the most common and aggressive primary malignant brain tumour in adults. Maximal safe surgical resection remains the cornerstone of treatment; however, tumour vascularisation may increase the risk of in-traoperative bleeding and complicate surgical management. Preoperative endovascular embolisation is commonly used for highly vascular intracranial tumours such as men-ingiomas, whereas its role in glioblastoma remains poorly defined. A focused literature review using the search string (((preoperative) AND (endovascular)) AND (embolization)) AND (glioblastoma) identified only two relevant publications, highlighting the scarcity of available evidence. In this context, we report a case series of three patients with intra-cranial lesions suspected to be high-grade gliomas who underwent preoperative angi-ographic evaluation and, when feasible, endovascular embolisation prior to surgical resection. Case Presentation: Three patients presenting with large intracranial lesions suggestive of high-grade glioma underwent preoperative digital subtraction angi-ography to assess tumour vascular supply (histological analysis confirmed the diagnosis of glioblastoma). In a 61-years-old woman with a right frontal tumour, selective catheteri-sation of a frontal branch of the right anterior cerebral artery enabled embolisation with coils, achieving partial tumour devascularisation before surgery. A second patient, a 53-year-old man with a large left temporo-fronto-insular mass extending to the corpus callosum, underwent embolisation of tumour feeders arising from the anterior choroidal artery using N-butyl cyanoacrylate and Lipiodol prior to resection. In a third case, a 77-year-old man with a left temporo-parietal lesion underwent preoperative angiography that demonstrated tumour capillary blush but no catheterisable feeding arteries, and embolisation was therefore not feasible. All patients subsequently underwent surgical resection without perioperative complications or new neurological deficits. Conclusions: Preoperative angiographic evaluation may help characterise tumour vascular supply in selected glioblastoma cases. When identifiable arterial feeders are present, endovascular embolisation may represent a feasible adjunct to facilitate surgical management. Further studies are required to better define the indications, safety profile, and potential benefits of this approach.

Abstract: Background: Liver abscesses represent an atypical yet potentially life-threatening complication of bacterial, fungal, protozoal, and helminthic infections. Frequently, the clinical findings associated with liver abscesses are nonspecific, necessitating a reliance on imaging for diagnosis. It is uncommon for a liver abscess to radiographically resemble a malignant liver tumor such as hepatocellular carcinoma (HCC). Here, we present the case of a 45-year-old male who was initially diagnosed with HCC (BCLC C) but was subse-quently found to have a liver abscess following biopsy. Case Presentation: A male patient, 45, presented with stiffness and pain in the right upper abdomen. He complained of nausea and vomiting since 10 days before admission as well. All supportive imaging suggested a diagnosis of HCC. A liver abscess was detected during a biopsy. A liver ultrasound-guided FNAB showcased chronic, suppurative in-flammation with negative acid-fast bacilli on Ziehl-Neelsen staining. The patient sub-sequently developed a complication of middle hepatic artery bleeding and underwent immediate embolization. Discussion: In fact, a liver abscess can be the initial manifestation of HCC. Patients tend to have a poorer prognosis because the diagnosis of a liver abscess often delays the discovery of the underlying HCC. Radiographically, liver abscesses range from well-circumscribed cystic lesions with an enhancing rim to heterogeneously enhancing mass-like lesions, which are sometimes indistinguishable from liver neoplasms. However, it is so scarce that a liver abscess may radiographically mimic HCC. Conclusion: Assessing liver abscess is somewhat complicated since the symptoms vary a lot. Therefore, a correct and exact diagnosis entail a combination of more comprehensive clinical and supporting examinations.

Abstract: Introduction Liver transplantation is currently an increasingly popular treatment method for patients with liver failure, both in adults and children. Antibody-mediated rejection (AMR), which is a very rare and poorly understood phenomenon, can lead to deterioration of graft function. The aim of the study was to analyze the clinical and histopathological manifestation of AMR in pediatric patients. Material and methods Sixty-two liver core biopsies from forty-two pediatric patients were included in this retrospective study. AMR was diagnosed in 7 children (in 10 biopsies), 35 demonstrated features of acute T-cell mediated rejection (TCMR) in 52 biopsies. C4d binding assay was performed in all biopsies using the immunohistochemical (IHC) method. The specimens were re-evaluated for signs of acute and chronic rejection, bilirubinostasis, and steatosis. Fibrosis was evaluated using a 6-grade Ishak scale. The Banff classification was used to assess TCMR activity. Evaluation of AMR was performed according to a newly developed original histopathological grading. Relationship between histopathological grading and morphological, as well as laboratory parameters was determined in each group depending on type of rejection. Statistical analysis was performed according to standard indications. Results The median age of patients (months) at the time of biopsy was 47.6 (15.03 – 98.83) and the median time from transplantation (months) was 0.9 (0.3 – 7.6). Results of the study brought evidence that histopathological lesions were the least specific manifestation suggesting AMR. Positive result of C4d staining with or without associated morphological abnormalities statistically increases the likelihood of AMR diagnosis. No statistically significant correlation was found between the type of rejection and laboratory tests. Conclusions: Diagnosis of AMR in transplanted liver is complicated and need to be complex. However, the proposed histopathological grading may be a helpful method for selecting patients who should be assessed for Donor-specific antibodies (DSAs) or in whom AMR should be suspected when DSA cannot be determined.

Abstract: Continuous monitoring of microclimatic variables is essential for precision viticulture and data-driven decision support systems. However, agricultural sensor networks are frequently affected by missing data due to hardware failures, communication issues, or maintenance interruptions. In this work, we propose a spatio-temporal graph-based autoencoder for reconstructing missing temperature and relative humidity time series collected from a five-node vineyard sensor network over a two-year period. The model combines a GRU-D-based temporal encoder with a GraphSAGE spatial module, enabling the joint exploitation of temporal dynamics and inter-node spatial correlations. Experimental results on real-world data show that the proposed approach achieves accurate reconstruction under realistic missing-data conditions. For moderate corruption levels (p=0.3), the model attains reconstruction losses of 0.003 for temperature and 0.005 for humidity using short temporal windows (L=36∼3h), corresponding to MAE values below 0.03∘C and 0.1%, respectively. Even at higher corruption levels (p=0.7), performance remains stable, with losses below 0.008 and 0.011, and MAE values within 0.05∘C and 0.17%. The results highlight a trade-off between temporal context and reconstruction stability: shorter windows yield better performance under moderate corruption, while longer windows (L=144∼12h) improve robustness under extreme data loss (p=0.9), reducing temperature reconstruction loss from 0.027 to 0.021 and MAE from 0.133∘ to 0.226∘. Additionally, temperature is consistently reconstructed more accurately than humidity, reflecting its smoother dynamics and stronger spatial coherence.

Abstract:

Methane production from Brosimum alicastrum seed coat was evaluated using a logistic model through three alkaline concentrations (0.19 M, 0.26 M, and 0.28 M) and three enzymatic activity levels (3000 U mL^-1, 5000 U mL^-1, and 7000 U mL^-1) as pretreatments. Laccase was produced through submerged fermentation using T. hirsuta Bm-2 fungi, while NaOH served as the alkaline agent. Enzymatic pretreatment resulted in the highest specific CH₄ yield (427.43±2.28 mL CH₄/g VS_added), surpassing both alkaline pretreatment (235.61 ± 9.19 mL CH₄/g VS_added) and the control (102.54 ± 5.55 mL CH₄/g VS_added). Kinetic analysis of CH₄ production indicated that cumulative CH₄ production reached its stationary phase within 30 days of digestion. Moreover, enzymatic pretreatment exhibited the highest CH₄ formation rate (0.15–0.17 h^-1), except for the control, which had a slightly higher rate (0.21 h^-1). The kinetic analysis revealed that the enzymatic pretreatment significantly improved the hydrolysis stage of Ramon's seed coat, promoting higher cumulative CH₄ production and leading to an increased specific CH₄ yield.

Abstract: We introduce PROOFGRID, a challenging benchmark suite for evaluating the reasoning competence of language models through machine-checkable proofs rather than final answers alone. PROOFGRID spans 15 tasks organized around proof writing, proof checking, proof masking, and proof gap-filling. The tasks are expressed in deliberately minimal formal notation, most notably NDL, a stripped-down language for natural deduction that fits in a short prompt and supports precise, auditable checking. As a result, correctness judgments are mechanical, reproducible, and fine-grained rather than dependent on human or LLM judges. PROOFGRID discriminates model capabilities with high resolution and covers a carefully calibrated difficulty spectrum, from foundational reasoning tests to structurally rich challenge tasks that no model currently solves, while avoiding reliance on domain knowledge, problems that can be easily outsourced to solvers, or long-context artifacts. The paper develops a detailed comparative framework for reasoning benchmarks and uses it to survey and systematize a broad and previously fragmented literature. This analysis clarifies how PROOFGRID differs from existing benchmarks not only in difficulty, but in representational choices, verification guarantees, reasoning depth, and vulnerability to shortcut-based success. A key methodological contribution of the paper is an instrumented proof-checking pipeline that tolerates minor surface-level deviations while explicitly identifying the first substantive failure in a stretch of reasoning. This significantly sharpens measurement resolution, separating high-level proof planning from low-level execution noise, and reveals that current frontier models often possess surprisingly strong proof-strategy capabilities even when their formal proof execution is brittle. Using this pipeline, we conduct an extensive evaluation and analysis of a broad set of open and proprietary models released from late 2024 until now. Our results demonstrate strikingly rapid advances as well as sharp remaining limitations. Frontier systems now perform strongly on several foundational tasks, yet difficult PROOFGRID tasks (especially those requiring global combinatorial reasoning or low-level proof synthesis) remain far from solved. Beyond task-level accuracy, we identify widespread epistemic instability, e.g., models often generate proofs containing local logical errors that they can nevertheless recognize and reject when those same inferences are presented in isolation. We formalize this phenomenon via an Epistemic Stability Index (ESI), a quantitative measure of cross-context coherence between model outputs for different but related tasks (such as generated proofs and corresponding entailment judgments). Finally, we complement raw accuracies with 2PL Item Response Theory (IRT) analyses, Wright maps, and a new normalized measure of task discrimination based on Fisher information and latent model abilities estimated from their responses. Taken together, these results position PROOFGRID not merely as a reasoning benchmark, but as a diagnostic framework for studying the emerging structure, limits, and internal coherence of reasoning in contemporary language models.

Abstract: Objective: To identify demographic, clinical, and behavioural determinants of COVID-19 vaccination and antiviral uptake in Australia using the Capability, Opportunity, Motivation - Behaviour (COM-B) framework with psychometric validation and LASSO-enhanced variable selection. Methods: Cross-sectional analysis of the 2024 KAB BREATHE survey (n=5,177) of Australian adults, intentionally enriched for risk-stacked (more than 1 chronic condition). Primary outcomes included 2023/2024 COVID-19 booster receipt, future vaccine intentions, vaccine/antiviral beliefs and antiviral uptake. Predictors included demographics, chronic conditions, and domain-specific leave-one-out (LOO) COM-B scores standardised to mean=0, SD=1. COM-B domains were assessed using Cronbach’s alpha. Univariate and multivariable logistic regression models were complemented by LASSO penalised logistic regression with 10-fold cross-validation. Results: Mean age was 51.5 years (SD 16.5); 61.4% were female; 70.3% were risk-stacked. Booster uptake declined sharply from 50.8% (2023) to 19.1% (2024). Cronbach’s alpha showed poor internal consistency for Capability (α=0.006) and Opportunity (α=−0.383) but acceptable for full Motivation (α=0.78). In adjusted models, age (aOR 1.02–1.03 per year), medically associated risk factors (aOR 1.66–3.51), and tertiary education (aOR 1.34–1.79) consistently predicted higher uptake and intention. Renting (aOR 0.59–0.78) and current employment (likely inversely associated with age) (aOR 0.73–0.83) were associated with lower uptake across all vaccine outcomes. Adding LOO COM-B scores substantially improved model fit (e.g., 2024 booster AUC 0.73→0.83); Motivation per SD was the strongest predictor (aOR 2.44–4.94 for vaccine outcomes, 1.52–2.49 for antivirals). LASSO models achieved CV-AUCs of 0.78–0.87. Among COVID-positive respondents (n=2,576), only 15.2% received antiviral treatment. Conclusions: Age, clinical risk, and socioeconomic factors, particularly housing tenure and employment status are key drivers of COVID-19 preventive behaviours (either positively or negatively). The COM-B framework, when corrected for circular prediction and validated via Cronbach’s alpha and LASSO, provides substantial explanatory value. Targeted interventions should address structural barriers faced by renters and younger, employed individuals while leveraging high motivation among older adults and clinically vulnerable groups. Implications for Public Health: These findings support a shift from knowledge-based campaigns towards equity-focused, multi-level public health strategies that address structural barriers to COVID-19 vaccination and antiviral access in Australia.

Abstract: A neural model for the formation of visual engrams is proposed, operating according to a non-Hebbian principle — specifically, through the enhancement of inhibitory synapses, up to and including the formation of veto synapses. The model relies on two hypothetical mechanisms: (1) rapid, repetitive reactivation ("ripple-reverberation") and (2) high-frequency synchronization enabling the activation of inhibitory synapses, which consequently become veto synapses. Through such learning, "neural locks" for familiar patterns are formed in memory. This model constitutes a component of a more general top-down model of visual recognition described previously (Levashov & Safiulina, 2025). The problem of processing activity patterns in living neural networks is discussed, as these patterns are not holistic but rather manifest as a mosaic of activated and non-activated neurons.

Abstract: Westudy coupled geometric flows involving the metric, dilaton, and flux fields arising from worldsheet β-functions in string theory. Extending the Ricci flow formalism, we derive parabolic evolution equations governing these fields and prove short-time exis tence and uniqueness for SU(3)-structure compactifications. We establish monotonicity properties of flow functionals analogous to Perelman’s entropy and identify conditions for moduli stabilization in type II backgrounds. Our results unify Ricci-type flow techniques with flux compactifications and suggest new mathematical tools for analyzing dynamical string backgrounds and quantum gravity.

Abstract: Background and Objectives: Minimally invasive cervical spine surgery (MIS-CSS) re-lies heavily on intraoperative fluoroscopic imaging, raising concerns about radiation exposure to patients and surgical staff. Unlike lumbar MIS, cervical-specific radiation exposure has not been systematically reviewed, despite distinct anatomical considera-tions including proximity to the thyroid gland and lens of the eye. This review aims to quantify intraoperative radiation exposure during MIS cervical spine procedures and evaluate available dose-reduction strategies. Materials and Methods: A systematic literature search was conducted across Pub-Med/MEDLINE, Scopus, and Google Scholar in April 2026 following PRISMA 2020 guidelines. Studies reporting original quantitative radiation data during minimally invasive cervical spine procedures in adult patients (≥10 patients) were included. Quality was assessed using the MINORS tool and JBI checklist. Results: Seven studies encompassing 380 patients were included. Procedures com-prised ACDF (four studies), minimally invasive posterior cervical laminoforaminotomy (two studies), and CT-navigated cervical instrumentation (one study). Patient effective doses during ACDF ranged from 0.015 to 1.3 mSv, with thyroid doses of 0.194–0.290 mGy. Standalone ACDF reduced patient dose by 36–58% compared to plated ACDF (p < 0.001). Navigation-assisted posterior cervical foraminotomy achieved a median fluoroscopy time of 10 seconds with negligible staff exposure. Surgeon per-procedure exposure during cervical discectomy (chest 0.122 µSv, lens 3.1 µSv, hands 7.1 µSv) was approximately half that of lumbar discectomy. Conclusions: Radiation doses during individual MIS cervical procedures are generally within occupational safety limits; however, cumulative exposure warrants attention in high-volume surgeons. Standalone implant designs and intraoperative navigation represent effective, complementary dose-reduction strategies. Standardized prospec-tive research is needed to establish cervical-specific radiation safety benchmarks.

Abstract: Maximal symmetry of the conformal FLRW frame \( \bar{g}=a(t)^{-2}g \) only admits constant curvature solutions, where constant matter density \( \bar{\rho}_{\textrm{m}}=R_{0}^{-2} \) constrains cosmology to a hyperbolic de Sitter solution \( a(t)=\textrm{sinh}(2t/R_{0})^{\frac{1}{2}} \), including reparametrizations like the \( \Lambda\textrm{CDM} \) solution \( \hat{a}=a^{4/3} \). Equipartition of the nonlocal gravitational field energy at the horizon shows equilibrium of Ricci (matter) and Weyl(BAO) densities of \( 12R_{0}^{-2} \) each, or \( \bar{H}_{0}=\sqrt{24\bar{\rho}_{\textrm{m}}}\approx72.1\;\textrm{km/s/Mpc} \). The BAO half predicts a concordance \( \hat{H}_{0}=\frac{4}{3}\sqrt{12\bar{\rho}_{\textrm{m}}}\approx68.0\;\textrm{km/s/Mpc} \). Adaptation of the Stefan-Boltzmann law to symmetries and time-dilation at the horizon in \( \bar{g} \) relates the nonlocal field energy associated with \( \bar{\rho}_{\textrm{m}} \) to a constant CMB temperature \( \bar{T}_{\textrm{CMB}}=2.725\pm0.009\:\textrm{K} \), within FIRAS confidence limits. It also predicts a baryon/photon density ratio \( \frac{12^{4}}{23}-1=900.56.. \), within Planck 2018 confidence limits. The existence of \( \bar{g} \) by itself seems to make a strong case for a stationary universe, where one expects to find mature galaxies at high redshifts.

Abstract: Travel-associated Legionnaires’ disease (TALD) investigations in hotels generate extensive environmental monitoring data. However, the occupational implications for workers who operate, maintain, clean, or inspect the same systems are rarely assessed. We developed a hybrid framework integrating a semi-quantitative environmental hazard model with deterministic Quantitative Microbial Risk Assessment (QMRA). In the first model, culture concentration bands were combined with physicochemical deviation indicators (temperature, free residual chlorine, and pH) to derive point-level hazard (Hi) and zone-level hazard (Hz). In the second model, a job-based presence matrix was combined with zone-specific serogroup-based severity using a simplified World Health Organization (WHO)-style 3×3 likelihood–severity approach. L. pneumophila (≥50 CFU/L) was detected in 29.94% of water samples and was significantly associated with low chlorine (<0.2 mg/L; RR 2.90) and hot water temperature <50 °C (RR 3.00). To enhance precision, QMRA was applied to estimate the daily inhaled dose (d) for 15 worker groups, indicating variability in modeled biological exposure across occupational categories. These findings suggest that occupational risk is shaped by the combined effect of pathogen concentration and exposure time. Under the hazard model, the highest zone-level hazard estimate was observed in kitchens and food and beverage (F&B) areas (Hz = 2.607), followed by machinery rooms (Hz= 2.022) and guest rooms (Hz= 1.874). These findings support the integration of worker protection into water safety management, particularly in areas and groups overlooked in routine investigations.

Abstract: Hydrogen (H2) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H2-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet about 25% of demand, although at costs higher than power grid (PG) electricity, while keeping emissions near zero. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H2 costs decline, and its share rises from 19% to 35%. These findings suggest that H2 can support long-term sustainability, resilience, and energy security.

Abstract: Floating belt conveyor routes, consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from the water. The paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. The analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half of their diameter. On the realized experimental device the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; on the contrary, if the immersion is greater than the radius of the float, the buoyancy force decreases.

Abstract: The vibroacoustic simulation of geared drivetrains has become increasingly important as electrified powertrains expose tonal gear noise and high-frequency structure-borne excitation more clearly than conventional internal-combustion vehicles. In this context, software choice is no longer a secondary implementation detail but a central engineering decision, because different platforms emphasize different parts of the excitation–transfer–radiation chain. This review therefore examines gearbox and geared-drivetrain NVH simulation from a software-specific perspective rather than a purely phenomenon-based one. The article critically compares dedicated gearbox CAE tools, general multibody dynamics platforms, integrated multiphysics and structural–acoustic finite-element environments, and early-stage 1D system simulation tools. The comparison covers major software ecosystems including KISSsoft/KISSsys, Romax Suite, SMT MASTA/DRIVA, MSC Adams, AVL EXCITE, RecurDyn/DriveTrain, Siemens Simcenter 3D Motion / Transmission Builder / Acoustics, SIMULIA Simpack, Ansys Motion with Mechanical/Acoustics and Motor-CAD, COMSOL Multiphysics, GT-SUITE, and Simcenter Amesim, while also considering relevant recent module extensions and workflow updates. The review shows that the current software landscape is structured around four main methodological layers: dedicated gearbox analysis tools that are strongest in gear-contact modeling and microgeometry iteration; high-fidelity multibody platforms that are strongest in system-level dynamic response and transmission-path representation; integrated structural–acoustic environments that provide the deepest access to housing vibration and radiated-noise prediction; and 1D or multidomain system tools that are most efficient for early concept evaluation and architecture-level trade-off studies. Recent developments since 2023 indicate a clear shift toward tighter support for electrified drivetrain NVH, measured manufacturing deviations, optimization workflows, and faster acoustic prediction, including reduced-order or embedded acoustic methods. At the same time, major gaps remain. Open literature still contains relatively few independent studies that validate the full chain from tooth contact and transmission error through dynamic transfer paths to housing vibration and radiated sound within a single commercial workflow. Likewise, interoperability for measured flank topography, wear-driven NVH evolution, and fully validated electro-magnetic–mechanical–acoustic simulation remains limited and uneven across platforms. For this reason, the review argues that current software ecosystems are best understood not as universally proven end-to-end solutions, but as partially overlapping toolchains with different strengths, evidence levels, and practical compromises.

Abstract: To address the challenge of separating fine-grained apatite from layered silicate gangue minerals (chlorite and biotite) in medium-low grade collophanite ores, this study systematically investigated the effect of carboxymethyl cellulose sodium (CMC-Na) as a selective depressant on flotation behavior of different particle size fractions and its underlying mechanism. Pure mineral and artificial mixed ore flotation experiments demonstrated that at pH 9 and collector dosage of 5 kg/t, CMC-Na enabled selective separation of apatite from gangue minerals, with optimal dosage showing significant particle size effects: for the -0.5+0.074 mm fraction, effective separation was achieved with collector alone; for the -0.074+0.023 mm fraction, the optimal CMC-Na dosage was 10~100 mg/L, yielding 87% apatite recovery for pure minerals and 41.8% recovery with 23.7% P₂O₅ grade for mixed ores; for the -0.023 mm fine fraction, the optimal dosage was 30~300 mg/L, achieving 24.8% recovery and 13.2% grade. Mechanism studies revealed that CMC-Na significantly enhanced the hydrophilicity of chlorite and biotite, enlarging their surface property differences with apatite. FTIR and XPS analyses indicated that CMC-Na adsorbed on biotite via ion exchange with interlayer K⁺ and coordination with octahedral Fe²⁺/Mg²⁺, and on chlorite through chemical coordination with octahedral Mg²⁺, whereas only weak physical adsorption occurred on apatite surface Ca²⁺. The adsorption strength followed the order: biotite > chlorite > apatite. This study provides an effective reagent scheme and theoretical basis for flotation separation of fine-grained phosphate ores.

Abstract: The preparation of high-performance radiation detector materials such as cadmium zinc telluride (CZT) relies on rigorous and efficient quality control to ensure the consistency of device performance. Traditional manual evaluation based on wafer-by-wafer inspection is time-consuming and makes it difficult to assess the downstream product yield at the ingot level in advance. This paper proposes a machine-learning-based prediction framework for CZT ingots, in which the product-level yield of test wafers from the same ingot is predicted using the double-sided electrical performance and spectral characterization data of a limited number of evaluation wafers. To address the limited number of ingot samples and the significant internal variability among wafers, statistical aggregate features, A/B-side difference features, threshold-ratio features, and intra-ingot Bootstrap resampling were combined, and multiple regression methods, including linear models, Random Forest, XGBoost, and neural networks, were systematically evaluated. The results show that the XGBoost model achieved the best overall performance, with the lowest mean squared error of 0.0352, a mean absolute error of 0.1448, and a Pearson correlation coefficient of 0.3187 on the test set. Furthermore, after combining model prediction with empirical rules, the true yield of test wafers for the top 22% candidate ingots increased from 61.50% to 63.59%. These results indicate that the proposed method can effectively support early ingot screening and processing-priority decisions. This study demonstrates the application potential of data-driven methods in early-stage quality evaluation of CZT crystals and provides a reference framework for yield prediction in similar multi-wafer crystalline materials.

Abstract: Agricultural waste accumulation offers potential for sustainable soil management in climate-resilient farming systems, but it also poses ongoing environmental challenges. This study examines the effects of vermicomposting, which turns agricultural waste into nutrient-rich organic fertilizer using Eisenia fetida, on crop productivity and soil fertility. Treatments were compared using a randomized experimental design that included many combinations of organic waste and a control. Crop growth and yield indices were examined in addition to soil physicochemical characteristics such as pH, organic carbon, total nitrogen, available phosphorus, and exchangeable potassium. Comparing vermicompost treatments to the control, the soil's nutritional content and structural quality significantly increased (p < 0.05). Mixed organic waste substrate trials outperformed single substrate trials, suggesting synergistic interactions that enhance microbial activity and nutrient cycling. Vermicompost application improved soil fertility indicators and increased crop growth and production. These findings show that vermicomposting is an effective waste valorization technique that supports the circular economy and sustainable agriculture. The study demonstrates how it can reduce environmental pollutants while enhancing soil health, agricultural yield, and fertilizer use efficiency. All factors considered, vermicomposting is a scalable and environmentally friendly way to increase the climate resilience of agricultural systems. More research should be done on long-term field performance, economic viability, and substrate combination optimization under different agroecological conditions.