Abstract: Against the backdrop of escalating global climate risks, this study systematically investigates the effects, boundary conditions, and interactive mechanisms of two core policy instruments—green finance and environmental regulation—on agricultural supply chain resilience. Using panel data of China’s Shanghai and Shenzhen A-share listed agribusiness firms from 2010 to 2025 and a two-way fixed effects model, we find: First, climate risk serves as a critical external pressure driving resilience building, yet its positive impact is conditional on a “capacity threshold”—only significant for firms with high resilience and large scale. Second, the two policy instruments exhibit heterogeneous structural thresholds: green finance demonstrates a “supporting-the-weak” effect, enhancing resilience primarily in small and medium-sized enterprises (SMEs) with low resilience, but is constrained by an “institutional–technological” double threshold. In contrast, environmental regulation displays a “scale bias”, with its statistically significant positive effect limited to large firms. Third, climate risk negatively moderates the effectiveness of green finance: under high-risk conditions, firms tend to divert green funds toward short-term relief, eroding long-term resilience investment, and this “policy failure” risk is particularly pronounced among SMEs. Fourth, mechanism tests rule out the traditional mediation channel of alleviating financing constraints; moreover, the two policy instruments have not yet formed significant synergistic effects under the current institutional framework. This study extends the application boundaries of the resource-based view and dynamic capabilities theory in high-risk contexts, provides micro-level empirical evidence on policy instrument implementation biases in heterogeneous market structures, and offers theoretical support and practical references for developing climate-smart agricultural supply chain policies.

Abstract: We derive a penalty strength criterion for ridge regression using stochastic complexity, which is a refined variant of the minimum description length principle. Since stochastic complexity doesn’t typically account for the effect of regularisation on complexity, despite its ability to simplify models, we are required to make a slight modification to the un- derlying coding scheme. Our scheme makes use of a weighted ensemble of regularised model fits rather than a mixture of maximum likelihood estimates. Under this modification, regularisation is interpreted as reducing model complexity by constraining flexibility. In the case of ridge regression, the complexity penalty term that we derive can be expressed analytically as the log determinant of the residual operator. We demonstrate the effect of this complexity penalty by fitting a linear readout to a reservoir computer.

Abstract: We study the effects of weak magnetic fields (around 2 mT) on the growth of Staphylococcus aureus (S. aureus) in the presence of a few sweeteners (monosaccharides, disaccharides, sugar alcohols, and consumer-grade sweeteners). Bacterial growth rates were compared in various magnetic fields at room temperature. Bacterial growth was estimated using optical absorbance measurements at various wavelengths, and pH values were manually estimated using pH strips. Absorbance was measured at 492 nm and 630 nm, which are wavelengths comparable to the size of a cell of S. aureus after division. This comparability plays a vital role in the scale of measured absorbance values. The results imply that bacterial growth may be reduced due to acidic byproducts formed by metabolizing sugars or sugar alcohols, as an increasingly acidic solution is less ideal for bacterial growth. Magnetic fields were also found to have a minor effect on pH estimates. These results reveal potential effects on microorganisms in the presence of sugars and sugar alcohols in addition to weak magnetic fields, demonstrating the contribution of various environmental conditions with increasing prevalence in the modern day.

Abstract: Background: An estimated 28,900 deaths around the world in 2021 were attributed to unintentional CO poisoning. Following inhalation, CO binds to hemoglobin with an affinity 220–240 times greater than that of oxygen to form carboxyhemoglobin (COHb). While the constituents of CO exposure are known to determine CO uptake in the blood, much less is understood regarding individual variability of the response to a given CO stimulus. Thus, the purpose of this paper was to explore the relationship between hemoglobin mass (HbM, a proxy for blood hemoglobin content) and the magnitude of the ensuing carboxyhemoglobinemia. Methods: This is a theoretical work based solely on considerations and published data. Discussion: Currently considered the gold standard for HbM assessment, the CO-rebreathing technique relies on the dilution principle i.e. the lower the HbM values the higher the ΔCOHb following a standardized CO bolus administration or an outdoors exposure. Accordingly, previously published prediction equations with HbM and ΔCOHb as the predictor and outcome variables, respectively, are reviewed with particular reference to the (confounding) factor of pulmonary ventilation. As far as treatment to CO poisoning is concerned, dynamic exercise emerges as a supplement to oxygen therapy to facilitate CO removal from human body. Screening procedures aiming to identify individuals susceptible to CO poisoning should henceforth include HbM assessments.

Abstract: Benefiting from tunable emission from ultraviolet to near-infrared windows, long luminescence lifetimes, and exceptional photostability, rare-earth-doped nanomaterials overcome the limitations of conventional dyes and quantum dots, enabling deep-tissue, high-resolution, and low-background imaging. As multifunctional fluorescent probes, rare-earth-doped nanomaterials are driving the development of next-generation biomedical imaging. This review summarizes recent advances in the structural design of rare earth-doped nanomaterials, surface engineering for biocompatibility, and targeting strategies for improved performance, and highlights their integration into advanced imaging modalities, including NIR-I/II fluorescence, FLIM, PAI, super-resolution STED, multimodal FL/MRI/CT, X-ray-excited luminescence, and persistent luminescence. Meanwhile, mechanistic insights, material innovations, and comparative advantages are discussed. Furthermore, challenges related to quantum yield, scalable synthesis, imaging resolution, and clinical translation are considered, while future directions—centered on multifunctional probe design, NIR-II imaging, and AI-assisted data analysis—are proposed, offering a versatile platform for precise multimodal imaging with significant potential to advance early diagnosis, personalized therapy, and clinical applications.

Abstract: Hysteresis is normally unavoidable in hydrogels under complex external loading conditions due to the intermolecular friction, which usually leads to fatigue. Here, we develop a sarcomere-inspired double-network hydrogel made from polyacrylamide, alginate and phytic acid, whose hysteresis can be precisely modulated by preloading. Particularly, due to the synergy of micellization, fibrillation and micro-lubrication, the as-prepared hydrogel displays an ultra-low hysteresis (≤ 0.02 %) after it experiences a pre-tensile process at a specific amplitude and strain rate, or even possesses negative hysteresis in the case of low tensile amplitudes or high strain rates. Interestingly, smart responses of the developed hydrogel to cyclic tensile loadingare similar to the mechanical behaviors of sarcomeres in vivo. Likewise, the derived hydrogel with ultra-low hysteresis performs reliably even at temperatures as low as -20 ℃. The ultra-low hysteresis presented by the biomimetic hydrogel with ultra-low hysteresis makes it suitable for many engineering fields like electrical sensing with superior reliability (the corresponding electrical signal (ΔR/R0) is stable even after 1000 stretching-unstretching cycles). Moreover, the design strategy of hydrogels with programmable hysteresis provides an innovative methodology for the future development of smart high-performance hydrogels.

Abstract: Technology plays a vital role in the way teacher’s work, communicate, and share their knowledge particularly after COVID pandemic. Thus, it is a matter of great importance both from theoretical and practical point of view to understand the factors that govern knowledge sharing through technologies. This study integrates Teo’s composite model of Technology Acceptance(TA) and Knowledge Sharing(KS) construct of Van den Hooff & Van Weenen, to empirically examine the relationship between technology acceptance and knowledge sharing among teachers. A study used a descriptive-correlational cross-sectional research approach. 225 participants responded to the survey. The study used Technology Acceptance Questionnaire, Teo( 2011) with 20 items(α=0.84) and Knowledge Sharing Questionnaire by Van den Hooff and Van Weenen ( 2004), with 12 items(α=0.96). One- sample t-test was used to find out the degree to which Technology Acceptance and Knowledge Sharing is practised by teachers. Pearson’s correlation was used to identify if there is any positive relationship between Technology Acceptance components and Knowledge Sharing elements. Structural equation modelling (SEM) was used to study whether any of the factors of Technology Acceptance can significantly predict the two types of Knowledge Sharing.Perceived Usefulness (PU) and Facilitating Conditions (FC) emerged as the most influential factors of Technology Acceptance in driving teachers’ Knowledge Sharing.

Abstract: Coronary heart disease (CHD) is a leading cause of morbidity and mortality, driven by metabolic remodeling, vascular inflammation, and perivascular adipose tissue (PVAT) dysfunction. We integrated bulk transcriptomic datasets to develop a machine learning–based diagnostic model, evaluated 113 algorithms, and identified a seven-gene signature (PYGL, PTGS2, PFKFB3, MMP9, CYP1B1, CXCR1, ABCB1) with robust predictive performance. Single-cell RNA sequencing of coronary PVAT revealed substantial cellular heterogeneity and prioritized PFKFB3 as a hub linking glycolytic activity to NF-κB regulon activity. Macrophage-centered communication via SPP1, MIF, and other pathways was enhanced in disease conditions. Virtual knockout of PFKFB3 induced transcriptional changes enriched in immune activation, phagocytosis, and oxidative stress, while molecular dynamics simulations indicated stable salidroside binding within PFKFB3. Together, these analyses provide a multi-layered framework connecting glycolytic remodeling, inflammatory transcriptional activity, and intercellular signaling in CHD. The findings support PFKFB3 as a potential biomarker and mechanistic hub, and suggest that salidroside may modulate its activity. This study offers an integrative computational foundation for future experimental validation and mechanistic exploration of PVAT dysfunction in CHD.

Abstract: NHEJ is one of the preferred DNA-damage repairing mechanisms for human cells, due to its rapid onset and activity throughout the cell cycle. It is, nevertheless, error prone and highly relevant in numerous oncological malignancies. In the recent years, it gained a lot of attention as a target either for cancer treatment or synergetic strategies, as pharmacological- and radio-sensitization approaches. This study evaluated the genotoxicity and radiosensitization potential of three DNA-PK inhibitors (PIK-75, M3814, and CC-115) in breast cancer by comparing MDA-MB-231 (NHEJ reliant) and MCF-7 (NHEJ and HR reliant) cell lines. Cells were preincubated with the DNA-PK inhibitors, using four different concentrations either in monotherapy or in combination with a DNA damage-inducer (doxorubicin or [64Cu]CuCl2). Viability was measured by MTS assays at 24, 48, and 72 h, while the DNA damage by γH2AX and flow cytometry. DNA-PK blockade preferentially sensitized NHEJ-reliant breast cancer cells to doxorubicin and 64Cu effect. Combination treatments generally reduced viability relative to inhibitor’s monotherapy, with clearer concentration dependence and stronger effects in MDA-MB-231. In the case of [64Cu]CuCl2 alone, MDA-MB-231 viability was reduced to 70–75% at 24 h, whereas several inhibitor combinations reduced it further; MCF-7 showed weaker or inconsistent potentiation. Delayed cytotoxicity was most obvious at 72 h, showing persistence of unrepaired DNA damage after DNA-PK inhibition. Overall, the results highlight the potential of exploiting repair-pathway dependence to improve targeted radiotherapy in breast cancer.

Abstract: The introduction of sustainable practices into waste management can have favorable environment impact, increase resource value, and economic gains. Hydrothermal technologies have strong potential for the production of up-cycled ingredients from biowaste (amino acids, sugars, phenols, pharmacologically-active compounds, etc.), enabling additionally high energy recovery (50-80%) from biowaste with net-negative carbon emission. This review discusses the use of subcritical and supercritical water technologies for sustainable valorization of biowaste and conversion of biomass into high-value chemicals and biofuels. The potential for the extraction/generation of bioactive compounds from plant and animal waste is presented, emphasizing the efficiency, compound stability, and bioactivity of fractions obtained. The possibilities of simultaneous extraction of added-value compounds and hydrolysis of feedstock biopolymers by said technologies are elaborated. The review further addresses the production of biofuels through hydrothermal carbonization for solid fuels, hydrothermal waste liquefaction for liquid fuels, and supercritical water gasification for gaseous fuels. The paper highlights the environmental and economic advantages of technologies based on sub- and supercritical water, over conventional chemical and fermentative routes, emphasizing their contribution to circular bioeconomy by converting biowaste into value-added products and sustainable energy sources.

Abstract: Cycling is one of the most popular sports and recreational activities. Millions of new people start to integrate bicycling into their daily routines every year. Fitness and activity trackers are the most powerful motivation tools for cycling novices and serious cycling enthusiasts. For this purpose, we present LaserFit, a laser-based direct force power meter for fitness and activity tracking during cycling. We developed embedded hardware to collect the torque and the wheel rotation data, which is produced by a laser-based position sensing system mounted on the rear wheel to precisely record the power output produced by the rider during cycling. The sensor data transmits to a smartphone via Bluetooth/ANT+ for data acquisition and analysis. Our device can be produced at low costs and deliver a level of accuracy similar to that obtained with the most expensive systems available on the market. To evaluate the accuracy of our system extensive experiments were conducted. The results of the present study suggest that the LaserFit power meter provides a strong relationship (r = 0.97) across a range of trials in laboratory and field conditions when compared with the SRM power meter. The LaserFit is therefore considered a valid alternative for training and performance measurement during continuous cycling.

Abstract: Antibody–drug conjugates (ADCs) are a pivotal technology for precision cancer therapy, harnessing the synergistic effects of antibody targeting and toxin delivery. However, traditional ADCs encounter limitations in efficacy that stem from tumor resistance, heterogeneity, and intense target competition. Dual-payload ADCs (DP-ADCs) represent a promising solution to these challenges, as they leverage dual mechanisms of action that mitigate acquired drug resistance and enhance adaptability to tumor heterogeneity. The complex structure of DP-ADCs presents substantial quality control hurdles. In this manuscript, we review the current payload selection and conjugation strategies of DP-ADCs and examine recent advances in quality control research. Specifically, we analyze the analytical challenges related to the quantification of free toxins, the determination of the total antibody content, and the characterization of the drug-to-antibody ratio and its distribution. Ultimately, the aim of this work is to provide valuable guidance for future DP-ADC quality control analyses to facilitate their clinical translation and application.

Abstract: Lignin-cellulose mixtures (LCMs) generated as intermediates in wood biorefineries are commonly separated into lignin and cellulose. However, using ultrasound (US) to pro-cess these mixtures could create novel, valuable materials not possible with conven-tional methods. This study looked at how lignin affects the US modification of these mixtures. Crude and partially delignified LCMs were successfully prepared using aqueous solutions of EtOH, THF and dilute NaOH and then subjected to short, high-power US treatment. The resulting materials were characterised using FT-IR spectroscopy, particle size analysis, water retention value analysis, SEM and XRD. Sonication rapidly reduced the mean particle size, generating cellulose nanofibril-like structures in all samples according to SEM. The response depended strongly on lignin content, with samples containing lower amounts of lignin exhibiting substantially higher hydration capacity and stronger US responsiveness. At the molecular level, lig-nin removal exposes cellulose surfaces and enhances hydrophilic interface formation, increasing water uptake and suspension stability. Thus, results show that lignin limits accessible hydrophilic cellulose surface area rather than preventing fragmentation by sonication. US is therefore a chemical-lean strategy to tune the physicochemical prop-erties of partly delignified LCMs and expand the product portfolio of integrated wood biorefineries towards novel advanced lignocellulosic materials.

Abstract: This study analyzes the relationship between digital access and knowledge-economy practices among young Ecuadorian adults, with emphasis on their implications for sustainable digital inclusion and knowledge-based development. The study is based on the premise that access to the Internet, devices, and technological tools does not necessarily ensure critical, productive, collaborative, or knowledge-generating uses of information. A quantitative, non-experimental, cross-sectional, and descriptive-correlational design was applied to a sample of 441 young Ecuadorian adults aged 18 to 30. Data were collected through an online questionnaire and analyzed using descriptive statistics, reliability analysis, KMO and Bartlett indicators, the Mann–Whitney U test, effect sizes, and Spearman’s rho with 95% confidence intervals. The instrument showed very high internal consistency for knowledge-economy practices (α=.978; ω=.978) and digital access and technological resources (α=.963; ω=.964). The KMO values were also adequate for both variables (.963 and .902, respectively), and Bartlett’s tests were statistically significant (p<.001). The results showed that digital access received more favorable ratings than knowledge-economy practices. A very strong, positive, and statistically significant association was found between digital access and knowledge economy practices (ρ=.822, 95% CI [.765, .870], p<.001). Information management and collaboration was the dimension most strongly associated with digital access (ρ=.820, 95% CI [.764, .868], p<.001). Women reported higher scores than men in knowledge-economy practices, although the effect size was small (r=.158; rrb=.184). These findings suggest that digital access is a necessary but insufficient condition for sustainable digital inclusion. The study contributes empirical evidence from a developing-country context and highlights the need for educational and public-policy strategies that transform connectivity into critical learning, collaboration, innovation, and knowledge creation.

Abstract: This prospective case series evaluated the short-term outcomes following percutaneous cementoplasty as the sole palliative treatment for appendicular osteosarcoma in 10 dogs. Synthetic self-hardening calcium-phosphate bone substitute was injected into the osseous defect under fluoroscopic guidance after curettage of the bone tumor. Clinician assessment included a numerical rating score for lameness, offloading, and ease of lifting the contra-lateral limb as well as the 4A-VET post-operative pain scale. Owner assessment was obtained using three descriptive questionnaires, the Helsinki Chronic Pain Index (HCPI), the Canine Brief Pain Inventory (CBPI) and the Canine Symptom Assessment Scale (CSAS). Measures were recorded preoperatively and at 2-, 4-, 8-, and 12-weeks following surgery. Early improvement in the 4A-Vet score was noted at the 2-, 4-, 8-, and 12-week time points for all major pain and function metrics. Similarly, the CBPI pain severity and interference scores demonstrated early postoperative improvement during the 2- and 4-week time points with partial attenuation by 8- and 12-weeks. Panting, difficulty sleeping, whining/moaning, and lack of appetite were significantly reduced when assessed via the CSAS. Cementoplasty as a monotherapy, affording early pain relief and improved structural integrity, supports its role as a limb-sparing option for dogs unable to undergo amputation.

Abstract: The accelerating accumulation of atmospheric carbon dioxide (CO₂) from fossil fuel combustion represents one of the foremost environmental challenges of the twenty-first century. This paper presents the design, theoretical basis, and experimental framework of a novel artificial photosynthesis system capable of capturing CO₂ from combustion flue gases and converting it into oxygen (O₂) and energy-rich compounds, directly mimicking the biochemical process performed by trees. The proposed system integrates a sodium carbonate (Na₂CO₃) absorption tower for CO₂ capture, a thermal desorption unit for solvent regeneration, and a cobalt oxide-catalyzed photosynthetic reactor for CO₂-to-O₂ conversion. System performance is quantified using non-dispersive infrared (NDIR) sensors for CO₂ measurement and electrochemical oxygen sensors for O₂ detection. Stoichiometric analysis indicates that 1 kg of captured CO₂ yields approximately 0.73 kg of O₂, and national-scale deployment projections suggest energy savings of approximately $200 billion per year by 2030 alongside a potential reduction of 302,600 million metric tons of CO₂ emissions. Comparative analysis with existing decarbonization approaches—including carbon capture and storage (CCS), hydrogen production, and enhanced oil recovery (EOR)—demonstrates that artificial photosynthesis offers a fundamentally superior outcome by permanently transforming CO₂ into life-sustaining O₂ rather than merely sequestering or displacing it. This work establishes a laboratory-scale proof of concept and a systematic experimental roadmap for scaling the technology to industrial application.

Abstract: The growing demand for high-efficiency, high-power-density converters in data centers, electric vehicle chargers, and renewable energy systems has accelerated the adoption of wide bandgap (WBG) devices. Gallium nitride (GaN) transistors offer superior switching speed, lower losses, and higher power density compared with Silicon (Si) devices. Accurate characterization of GaN switching dynamics is essential due to parasitic effects and transient phenomena affecting performance and reliability. The Double Pulse Test (DPT) is widely used to quantify critical parameters, including switching energy losses, dynamic RDS(on) and transient voltage and current waveforms. This paper reviews DPT techniques for GaN devices, focusing on measurement methodologies, parasitic mitigation, and reliability considerations, providing practical guidance for optimizing high-frequency GaN-based power converters.

Abstract: Expanding renewable energy capacity in sub-Saharan transmission systems is a cornerstone of sustainable development, yet weak grid infrastructure and the absence of flexible storage remain principal barriers to reliable and low-carbon energy access. This paper addresses the economic and environmental dimensions of that challenge by proposing a hierarchical multi-objective framework for the optimal siting and sizing of Battery Energy Storage Systems (BESS), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs the NSGA-II evolutionary algorithm to simultaneously maximize daily price-arbitrage revenue—the economic sustainability indicator—and minimize active power losses—the environmental efficiency indicator. For each candidate design, the lower level solves a multi-period DC Optimal Power Flow (DC-OPF) via CasADi/IPOPT, with thermal branch constraints embedded as hard linear inequalities through the Power Transfer Distribution Factor (PTDF) matrix, and voltage-corrected loss estimates recovered via a vectorized Extended DC Power Flow (EDCPF) model. Over 500 NSGA-II generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving maximum daily revenue of approximately € 10,033 at a marginal loss increment of 6.7×10⁻³ MWh. The Pareto front provides Mali system planners with a quantitative tool for balancing private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is both technically tractable and economically viable in the resource-constrained context of sub-Saharan sustainable energy transitions.

Abstract: This study evaluates the feasibility of using asphalt concrete as an impermeable core material for rockfill dams under tropical conditions. Laboratory testing and numerical modeling were conducted to assess the hydraulic and mechanical performance of asphalt concrete mixtures produced with locally available aggregates in Thailand. Asphalt mixtures were designed using the Marshall method with asphalt binder contents of 6% and 7% and target air void contents between 1-4%. Laboratory testing included permeability testing, Marshall stability testing, and triaxial compression tests to determine hydraulic conductivity, shear strength parameters, and deformation characteristics. Results show that asphalt concrete mixtures with air void contents below 1% exhibit extremely low permeability, with hydraulic conductivity on the order of 10⁻¹¹–10⁻¹² m/s, satisfying requirements for impervious dam cores. Triaxial compression tests yielded cohesion values between 97-572 kPa and friction angles ranging from 31° to 52°, indicating adequate shear resistance. Numerical simulations performed using GeoStudio compared rockfill dams with asphalt concrete cores and conventional clay cores. The results demonstrate that a 0.5‑m‑thick asphalt concrete core provides comparable seepage control and slope stability while requiring significantly smaller material volume. The findings suggest that asphalt concrete cores represent a technically feasible and economically advantageous alternative to clay cores, particularly in regions where suitable clay materials are limited.

Abstract: Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology.