Search Results (996)

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs—including volumetric models and vector representations—suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows. Full article

Background: Vitamin E has been studied for its role in reducing the growth of colorectal cancer (CRC). CRC is a worldwide health concern. A meta-analysis reported that CRC patients have a lower concentration of serum vitamin E, suggesting it to be a risk factor. Although rodent models are widely used in disease research, their application in studying vitamin E as a preventive or therapeutic agent in CRC is not well characterized. To address this gap, we conducted a scoping review to examine the available evidence, adhering to the PRISMA-ScR checklist. Methods: We searched PubMed, Google Scholar, Scopus, and Web of Science (WoS) for full-text English original articles published before May 2024, using Medical Subject Headings (MeSH) terms and free text. The following search string strategy was applied: (Vitamin E OR tocopherol$ OR tocotrienol$) AND (Colo$ cancer OR colo$ carcinoma) AND (Rodentia OR mouse OR Rodent$ OR mice OR murine OR rats OR guinea OR rabbit OR hamsters OR Animal model OR Animal testing OR animals) AND (neoplasm$ OR “tumor mass” OR tumor volume OR tumor weight OR tumor burden). Data were charted into five categories using a standardized, pretested form. The charted data were synthesized using descriptive and narrative methods. Conclusions: This study highlights that γ- and δ-tocopherols, as well as δ-tocotrienol and its metabolites, were reported to reduce tumor volume and formation in various rodent models. While these results are promising, this scoping review identifies a need for further research to address translational barriers such as dosing, bioavailability, and long-term safety before clinical application. Full article

This study presents a Design for Additive Manufacturing (DfAM)–driven redesign of an industrial robot vacuum gripper for Fused Deposition Modeling (FDM), focusing on the systematic transformation of a multi-part, machined aluminum assembly into a lightweight, support-minimized polymer component suitable for continuous industrial operation. Beyond a practical redesign, the work contributes a geometry-centered DfAM methodology that links internal channel topology, overhang control, and functional interfaces to manufacturability, vacuum performance, and cost efficiency. The development follows three iterative design revisions, progressing from a geometry-adapted baseline toward a fully DfAM-optimized solution. A key innovation is the introduction of support-free internal vacuum channels with triangular cross-sections, enabling complete elimination of soluble support material within enclosed cavities. This redesign reduces the internal vacuum volume by 44%, leading to faster vacuum response while maintaining functional suction performance. The optimized overhang angles, filleted load paths, and DfAM-compliant suction cup seats significantly reduce post-processing requirements and improve structural robustness. Experimental validation under industrial operating conditions confirms that the final design achieves reliable vacuum performance and mechanical durability. Compared to the original configuration, the optimized gripper demonstrates a substantial reduction in manufacturing complexity, with printing time reduced by approximately 50% and total part cost decreased by 26%, primarily due to eliminated tooling, reduced support material, and simplified post-processing. The presented results demonstrate that DfAM principles, when applied systematically at both global and internal geometry levels, can yield quantifiable functional and economic benefits. The findings provide transferable design guidelines for support-free internal channels and functional interfaces in FDM-manufactured vacuum components, offering practical reference points for researchers and practitioners developing end-use additive manufacturing solutions in industrial automation. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

Background: Triple-negative breast cancer (TNBC) remains primarily treated with chemotherapy due to the lack of effective therapeutic targets, but this approach carries significant systemic toxicity and a high risk of drug resistance. Curcumin (Cur), despite its multifaceted antitumor activity, faces limitations in clinical application due to poor water solubility and weak targeting properties. This study aims to develop a folate/mitochondria dual-targeted curcumin–copper chelate liposome (Cu-Cur DTLPs) formulation that enables copper accumulation within tumor cells and induces copper-mediated cell death, thereby providing an effective and relatively low-toxicity therapeutic strategy for triple-negative breast cancer. Methods: Curcumin–copper chelates (Cu-Cur) were first synthesized and characterized using mass spectrometry, NMR, and infrared spectroscopy. Subsequently, dual-targeted liposomes (Cu-Cur DTLPs) were prepared via the thin-film dispersion method, with systematic evaluation of particle size, zeta potential, encapsulation efficiency, and in vitro release profiles. In vitro cytotoxicity was assessed against 4T-1 and MDA-MB-231 cells using the MTT assay. In a 4T-1 tumor-bearing BALB/c mouse model, comprehensive evaluation of targeting efficiency, antitumor efficacy, and mechanisms of action was conducted via in vivo imaging, tumor volume monitoring, immunohistochemistry (detecting FDX1 and DLAT proteins), and TUNEL staining. Results: Cu-Cur DTLPs with a uniform particle size of approximately 104.4 nm were successfully synthesized. In vitro and in vivo studies demonstrated that compared to free curcumin and conventional liposomes, Cu-Cur DTLPs significantly enhanced drug accumulation in tumor tissues and exhibited effective tumor growth inhibition. Mechanistic studies confirmed that this formulation specifically accumulates copper ions within tumor cells, upregulates FDX1, promotes DLAT oligomerization, and induces mitochondrial dysfunction, thereby driving copper death. TUNEL staining ruled out apoptosis as the primary mechanism. Safety evaluation revealed no significant toxicity in major organs. Conclusions: The Cu-Cur DTLPs developed in this study effectively induce copper-mediated death in TNBC through a dual-targeted delivery system, significantly enhancing antitumor activity with favorable safety profiles. This establishes a highly promising novel nanotherapeutic strategy for TNBC treatment. Full article

This study presents a comprehensive digital workflow for the archaeological investigation and heritage enhancement of the Coëby megalithic necropolis (Brittany, France). Dating to the Middle Neolithic, between the 4th and 3rd millennia BC, this chronology is established through stratigraphy, material culture, and radiocarbon dating. Focusing on cairns TRED 8 and TRED 9, which are two excavation units, we combined field archaeology, photogrammetry, and topographic data with open-source 3D geometric modeling to reconstruct the monuments’ original volumes and test construction hypotheses. The methodology leveraged the free software Blender (version 3.0.1) and its Bagapie extension for the procedural simulation of lithic block distribution within the tumular masses, ensuring both metric accuracy and realistic texturing. Beyond static reconstruction, the research explores innovative dynamic and narrative visualization techniques. We employed the FILM model for smooth video interpolation of the construction sequences and utilized the Wan 2.1 AI model to generate immersive video scenes of Neolithic life based on archaeologically informed prompts. The entire process, from data acquisition to final visualization, was conducted using free and open-source tools, guaranteeing full methodological reproducibility and alignment with open science principles. Our results include detailed 3D reconstructions that elucidate the complex architectural sequences of the cairns, as well as dynamic visualizations that enhance the understanding of their construction logic. This study demonstrates the analytical potential of open-source 3D modelling and AI-based visualisation for megalithic archaeology. Full article

Bone defects in the maxillofacial region severely impair patient function and esthetics. Free autologous bone grafting remains the gold-standard treatment; however, surgical intervention at donor sites limits clinical applicability. Treatment using artificial materials also presents challenges, including insufficient bone regeneration and poor biocompatibility. Bio three-dimensional (3D) printing, which enables the fabrication of scaffold-free 3D constructs from cellular spheroids has emerged as a promising regenerative approach. This study investigated the osteogenic potential of scaffold-free constructs composed of human dental pulp stem cell (DPSC) spheroids in a rat mandibular defect model. DPSCs isolated from extracted human teeth were used to generate spheroids, which were assembled into 3D constructs using a Bio 3D printer. The spheroids exhibited higher mRNA expression of stem cells and early osteogenic markers than monolayer cultures. The constructs were transplanted into mandibular defects of immunodeficient rats, and bone regeneration was assessed eight weeks post-transplantation. Radiographic and micro-Computed Tomography analyses revealed significantly greater bone volume and mineral density in the 3D construct group. Histological and immunohistochemical examinations confirmed newly formed bone containing osteogenic cells derived from the transplanted DPSCs. These findings indicate that Bio 3D-printed, scaffold-free DPSC constructs promote mandibular bone regeneration and may provide a novel strategy for maxillofacial reconstruction. Full article

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Resistance against water penetration is one of the key indicators of concrete durability in humid and pressurized environments. An intelligent model based on the XGBoost machine-learning algorithm was developed to predict the water penetration depth, using 1512 independent experimental measurements. The influential variables included water pressure, pressure duration, thermal cycles, fiber content, curing, and compressive strength. The investigated concrete specimens and field-tested structures in this study were exposed to arid and hot climatic conditions, and the proposed model was developed within this environmental context. To accurately simulate the water transport behavior, a cylindrical-chamber test was employed, enabling non-destructive and in-situ evaluation of structures. Correlation analysis revealed that compressive strength had the strongest negative influence (r = −0.598), while free curing exhibited the strongest positive influence (r = +0.654) on penetration depth. After hyperparameter optimization, the XGBoost model achieved the best performance (R² = 0.956, RMSE = 1.08 mm, MAE = 0.81 mm). Feature importance analysis indicated that penetration volume, pressure, and curing were the most significant predictors. According to the partial dependence analysis, both pressure and duration exhibited an approximately linear increase in penetration depth, while a W/C ratio below 0.45 and curing markedly reduced permeability. Microstructural interpretation using MIP, XRD, and SEM tests supported the physical interpretation of the trends identified by the machine-learning model. The results demonstrate that machine-learning-models can serve as fast and accurate tools for assessing durability and predicting permeability under severe environmental conditions. Finally, the permeability of several real structures was evaluated using the machine-learning approach, showing excellent prediction accuracy. Full article

Microalgae are photosynthetic microorganisms capable of fixing CO₂ to produce O₂ and a wide variety of metabolites of interest. Attempts have been made to describe their growth dynamics using mathematical models; however, these models fail to fully represent the dynamics of this bioprocess. Therefore, achieving maximum biomass production in the shortest possible time represents a control challenge due to the nonlinear and time-varying dynamics. Some classic control strategies implemented for this bioprocess are totally or partially dependent on a mathematical model, resulting in controllers with low performance, implementation complexity, and limited robustness. This is where the Virtual Reference Feedback Tuning (VRFT) approach becomes relevant, as it is a model-free control strategy. VRFT is based on the iterative generation of a virtual reference with the aim of minimizing steady-state error, without requiring an explicit model of the bioprocess. Its implementation involves the collection of experimental data in open loop, the minimization of a cost function in closed loop, and the linearization of the system around a stable equilibrium point. This work presents the design and implementation of a VRFT-based control strategy applied to the closed cultivation of the microalga Scenedesmus obliquus UTEX 393 in three flat photobioreactors at laboratory scale. The variables controlled using this strategy were temperature, photosynthetically active light intensity, and level. The experimental results showed that the pre-established references were met. A steady-state temperature of 25 ± 0.625 °C, a PAR (Photosynthetically Active Radiation) light intensity of 100 ± 5 µmol·m⁻²·s⁻¹, and level control that ensured a constant volume of the culture medium were achieved. This suggests that VRFT is a viable control alternative for this type of bioprocess under nominal conditions. Full article

Headspace (HS) in anaerobic batch biodigesters is a critical design parameter that modulates pressure stability, gas–liquid equilibrium, and methanogenic productivity. This systematic review, guided by PRISMA 2020, analyzed 84 studies published between 2015 and 2025, of which 64 were included in the qualitative and quantitative synthesis. The interplay between headspace volume fraction ${\mathrm{V}}_{\mathrm{H}\mathrm{S}}/{\mathrm{V}}_{\mathrm{t}\mathrm{o}\mathrm{t}}$, operating pressure, and normalized methane yield was assessed, explicitly integrating safety and instrumentation requirements. In laboratory settings, maintaining a headspace volume fraction (HSVF) of 0.30–0.50 with continuous pressure monitoring P(t) and gas chromatography reduces volumetric uncertainty to below 5–8% and establishes reference yields of 300–430 NmL CH₄ g⁻¹ VS at 35 °C. At the pilot scale, operation at 3–4 bar absolute increases the CH₄ fraction by 10–20 percentage points relative to ~1 bar, while maintaining yields of 0.28–0.35 L CH₄ g COD⁻¹ and production rates of 0.8–1.5 Nm³ CH₄ m⁻³ d⁻¹ under OLRs of 4–30 kg COD m⁻³ d⁻¹, provided pH stabilizes at 7.2–7.6 and the free NH₃ fraction remains below inhibitory thresholds. At full scale, gas domes sized to buffer pressure peaks and equipped with continuous pressure and flow monitoring feed predictive models (AUC > 0.85) that reduce the incidence of foaming and unplanned shutdowns, while the integration of desulfurization and condensate management keep corrosion at acceptable levels. Rational sizing of HS is essential to standardize BMP tests, correctly interpret the physicochemical effects of HS on CO₂ solubility, and distinguish them from intrinsic methanogenesis. We recommend explicitly reporting standardized metrics (Nm³ CH₄ m⁻³ d⁻¹, NmL CH₄ g⁻¹ VS, L CH₄ g COD⁻¹), absolute or relative pressure, HSVF, and the analytical method as a basis for comparability and coupled thermodynamic modeling. While this review primarily focuses on batch (discontinuous) anaerobic digesters, insights from semi-continuous and continuous systems are cited for context where relevant to scale-up and headspace dynamics, without expanding the main scope beyond batch systems. Full article

The characteristics of shale oil reservoirs, such as low porosity, ultra-low permeability, and complex pore structure, are key factors affecting effective pore space and fluid migration. This study focuses on medium-to-high maturity mud shale in the Qing-1 Member of the Qingshankou Formation in the Gulong Sag. Using methods such as XRD, organic geochemical testing, and multi-scale pore characterization (FE-SEM, low-temperature CO₂–N₂ adsorption, high-pressure mercury intrusion, and CT scanning), the lithofacies and pore structure were comprehensively characterized, and their controlling factors were analyzed. The results indicate: (1) The mineral composition is dominated by felsic and clay minerals. Based on a three-level classification standard of “mineral composition–sedimentary structure–organic matter abundance”, seven subfacies were identified, with the dominant lithofacies being Felsic–Clayey Mixed Shale and Felsic-bearing Clay Shale. (2) The reservoir space consists of inorganic pores, organic pores, microfractures, and a small amount of other auxiliary pores, exhibiting “bimodal” pore size characteristics. Micro–mesopores dominate adsorption, while macropores/microfractures control free oil seepage; mesopores contribute the most to pore volume. (3) In terms of oil-bearing potential, Felsic–Clayey Mixed Shale shows prominent movable oil potential (average OSI: 133.08 mg/g; S₁ > 2 mg/g, OSI > 100 mg/g). (4) CT-based 3D stick-and-ball models indicate that Felsic–Clayey Mixed Shale has the best connectivity (connectivity rate: 30.63%), with throat radii mostly ranging from 1–15 μm and pore radii from 2–20 μm. (5) Pore development is synergistically controlled by total organic carbon (TOC, with an optimal range of approximately 1–2.5%), clay/felsic mineral ratio, and bedding/structural fractures. The formation of the pore system is the result of dynamic coupling of organic–inorganic interactions during diagenetic evolution: intergranular pores of clay minerals and microfractures jointly contribute to specific surface area and pore volume, while bedding fractures connect nanopore clusters to enhance seepage capacity. This study improves the integrated understanding of dominant lithofacies, pore structure, and oil-bearing potential in the Qing-1 Member of the Gulong Sag, providing a basis for sweet spot evaluation and development optimization. Full article

Background/Objectives: Continuous trauma-system monitoring is limited by the lack of scalable, low-cost tools. The Pavia Trauma Management Epidemiology (PaTraME) project uses routinely collected ICD-9-CM discharge data (SDO) and the Trauma Mortality Probability Model (TMPM) to derive Injury Severity Score (XISS) and probability of death (TMPM-POD), creating a cost-free surveillance framework for regional trauma networks. Methods: We conducted a retrospective study of all major-trauma admissions (XISS > 15) in Pavia Province from 2014 to 2021. Anonymized SDO records were linked with emergency department flows and mortality registries. XISS and TMPM-POD were computed for each case. Case volumes, severity distributions, hub-centralization, and mortality (in-hospital, 30-day, and 180-day) were analyzed using trend and regression models (p < 0.05). Conclusions: We identified 1959 major-trauma admissions. Volumes increased up to 2019, dropped during the COVID-19 pandemic, and partially recovered in 2021 (p < 0.001). Overall, 61.5% of patients were admitted to hub centers, with an upward trend (p < 0.001). Hubs treated more severe trauma (median XISS 17 vs. 16; TMPM-POD 0.06 vs. 0.05, both p < 0.001). In-hospital mortality remained stable (8.2–11.4%, p = 0.828). TMPM-POD showed strong agreement with observed in-hospital mortality (Lin’s concordance correlation coefficient 0.81), though calibration worsened at higher risk levels. PaTraME confirms TMPM-POD as a valid mortality predictor and demonstrates a reproducible administrative-data framework for trauma surveillance. Rising hub admissions and stable mortality despite increasing complexity suggest improved system performance. Stratification of XISS and TMPM-POD between hub and spoke centers highlights peripheral hospitals managing disproportionately severe cases, informing targeted resource allocation and supporting quality improvement via automated dashboards. Full article

Population aging has intensified the prevalence of falls among older adults, making automatic Fall Detection Systems (FDS) a key component of telemonitoring and remote care. Among wearable-based approaches, inertial sensors, particularly accelerometers, offer an effective and low-cost alternative for continuous monitoring. However, the impact of the selection of the sampling frequency on model performance remains insufficiently explored. This work seeks to determine the sampling rate that best balances accuracy, stability, and computational efficiency in wearable FDS. Five representative algorithms (CNN-LSTM, CNN, LSTM-BN, k-NN, and SVM) were trained and evaluated using the SisFall dataset at 10, 20, 50, and 100 Hz, followed by a multi-stage validation including the real-fall repositories FARSEEING and Free From Falls, as well as a seven-day continuous monitoring test under real-life conditions. The results show that deep learning architectures consistently outperform traditional classifiers, with the CNN-LSTM model at 20 Hz achieving the best balance of accuracy (98.9%), sensitivity (96.7%), and specificity (99.6%), while maintaining stable performance across all validations. The observed consistency indicates that intermediate frequencies, around 20 Hz and down to 10 Hz, provide sufficient temporal resolution to capture fall dynamics while reducing data volume, which translates into more efficient energy usage compared to higher sampling rates. Overall, these findings establish a solid empirical foundation for designing next-generation wearable fall-detection systems that are more autonomous, robust, and sustainable in long-term IoT-based monitoring environments. Full article