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Tactile working memory limits the amount of information that can be processed through touch, with important implications for the design of haptic communication systems. Although visual and auditory working memory have been extensively investigated, tactile working memory, particularly for spatial and spatiotemporal sequences, remains less well understood. The present study examined tactile working memory capacity in two psychophysical experiments. Participants reproduced sequential vibrotactile stimuli delivered to the forearm via a 3 × 3 array of voice-coil actuators by entering responses through keypresses. Both experiments employed an adaptive 3-up/1-down staircase procedure, in which sequence length was adjusted according to response accuracy, and thresholds were estimated from reversal points. In Experiment 1 (Ordered Recall), participants reproduced both the spatial locations and the temporal order of stimulation, yielding a memory capacity threshold of approximately four items. In Experiment 2 (Unordered Recall), participants recalled only the set of stimulated locations without regard to order, resulting in a higher threshold of approximately five items. These results demonstrate that incorporating temporal sequencing demands into spatial recall substantially increases cognitive load and reduces effective tactile memory capacity. The findings clarify fundamental limits of tactile working memory and provide practical guidance for the development of haptic interfaces, wearable feedback systems, and sensory substitution technologies that must balance information complexity with human cognitive constraints. Full article

(This article belongs to the Section Wearables)

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29 pages, 847 KB

Open AccessArticle

Supply Chain Coordination with Guaranteed Auction Contracts

by Xinyu Geng and Jiaxin Wang

Mathematics 2026, 14(8), 1267; https://doi.org/10.3390/math14081267 (registering DOI) - 11 Apr 2026

Abstract

This paper investigates the problem of contract coordination in a two-tier multi-unit auction supply chain consisting of a seller and an auction house. We theoretically show that the conventional commission-based mechanism distorts the transmission of demand information from the demand side to the supply side, thereby preventing effective supply chain coordination. In contrast, guaranteed auction contracts can achieve coordination under both cooperative and non-cooperative game frameworks. Under the cooperative game setting, profits are allocated according to a Nash bargaining solution, in which each party receives its disagreement payoff and a bargaining-power-weighted share of the surplus, with risks and returns being allocated symmetrically. Under the non-cooperative game setting, the supply chain leader can appropriate a larger share of the total profit while bearing relatively lower risk. These results indicate that, as the supply chain leader, the auction house can select different cooperation modes under guaranteed auction contracts according to its bargaining position, but profit allocation should be benchmarked against the cooperative game outcome in order to enhance the long-term competitiveness and stability of the supply chain. Full article

17 pages, 2217 KB

Open AccessArticle

Beyond Conventional Methods: Rapid and Precise Quantification of Polyphenols in Vigna umbellata via Hyperspectral Imaging Enhanced by Multi-Scale Residual CNN

by Hao Liang, Xin Yang, Nan Wang, Xinyue Lu, Wenwu Zou, Aicun Zhou, Xiongwei Lou and Yufei Lin

Sensors 2026, 26(8), 2356; https://doi.org/10.3390/s26082356 (registering DOI) - 11 Apr 2026

Abstract

Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R²) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R² values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article

(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)

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24 pages, 9004 KB

Open AccessArticle

PbS-Decorated TiO₂ Nanotubes via SILAR for Enhanced Wear and Corrosion Protection in Technical Coatings

by Hafedh Dhiflaoui, Karim Choubani, Jabeur Ghozlani, Syrine Sassi, Wissem Zayani, Mohamed Aziz Hajjaji, Lotfi Khezami, Mohamed Salah, Mounir Gaidi, Mohamed Ben Rabha, Mohammed A. Almeshaal and Anouar Hajjaji

Crystals 2026, 16(4), 254; https://doi.org/10.3390/cryst16040254 (registering DOI) - 11 Apr 2026

Abstract

TiO₂ nanotubes were synthesized using the anodization method on Ti foils and decorated with PbS nanoparticles by the SILAR method at different cycle numbers (10, 15, 20, 25, and 30). These samples were characterized using SEM, TEM, XRD, and microhardness tests. Morphologically, the PbS nanoparticles were evenly dispersed on TiO₂ nanotubes in the shape of small spheres. With an increase in the number of cycles, the size and shape of the nanoparticles increased. This also affected the structure and crystallinity of the PbS NPs, as the crystallite size of PbS increased. The in-depth analysis of the tribological characteristics of the coatings conducted using the scratch test allowed us to evaluate the adhesion of the coatings, a crucial aspect in determining their effectiveness and durability. Furthermore, we found that the wear resistance of the coatings increased with the number of PbS cycles up to 15 cycles. However, for the samples with higher size distribution and crystallite size, such as those with more than 15 cycles, the microhardness continued to decrease. This indicates that the addition of PbS can improve the durability of TiO₂ coatings, making them a potential candidate for advanced surface coatings in demanding engineering applications. Electrochemical measurements were conducted to assess the corrosion resistance of the samples. The electrochemical impedance spectra (EIS) results revealed that the PbS/TiO₂ coatings with 15 deposition cycles exhibited the most effective corrosion resistance, with a dense and uniform distribution of PbS nanoparticles forming a compact barrier that effectively protects against corrosion. The charge transfer resistance (R_ct) and the absorption capacitance (Q_ab) values were higher for the 15-cycle sample (4.49 Ω·cm² and 0.9 Fsⁿ⁻¹cm⁻², respectively). Full article

(This article belongs to the Special Issue Crystaline TiO2 Nanotubes: From Production, Modification, to Innovative Applications)

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23 pages, 1801 KB

Open AccessArticle

Bridging Communication Studies and Employability: ESCO-Based Curriculum Mapping and Job-Vacancy Skill Signals

by Marina-Paola Ojan, Pablo Lara-Navarra and Sandra Sanz-Martos

Educ. Sci. 2026, 16(4), 606; https://doi.org/10.3390/educsci16040606 - 10 Apr 2026

Abstract

Universities are increasingly expected to bridge the gap between higher education, skills development, and graduate employability, yet evidence-based approaches to curriculum–labour market alignment remain limited in Communication Studies. This study examines which ESCO-mapped occupational profiles and transversal competencies are represented in official curricula of leading Spanish Communication programmes (RQ1), how demand for communication-related occupations evolved in Spain over 2018–2023 (RQ2), and where the most salient alignment gaps emerge to inform curriculum redesign (RQ3). We used an explanatory sequential mixed-methods design combining documentary analysis of programme verification reports and national disciplinary documentation, an ESCO-based mapping of curricular profiles, and labour-market intelligence from 2,701,503 job postings (2018–2023) mapped to ESCO to analyse demand dynamics, volatility, and skill patterns. Results show strong curricular convergence around a shared core of ESCO profiles (71.8% of identified codes shared across institutions) alongside institution-specific specialisations (28.2%). Labour demand fluctuated markedly across the period and exhibited heterogeneous volatility by occupation, and transversal competency patterns differed significantly across professional groupings, supporting segment-specific interpretations of alignment and mismatch. Overall, ESCO combined with job-posting analytics provides a replicable framework for continuous curriculum calibration and employability-oriented programme redesign, particularly for hybrid profiles that integrate technical, analytical, relational, and ethically grounded capabilities. Full article

(This article belongs to the Special Issue Bridging the Gap: Higher Education, Skills, and Graduate Employability)

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18 pages, 606 KB

Open AccessArticle

Information-Preserving Spiking for Accurate Time-Series Forecasting in Spiking Neural Networks

by Jiwoo Lee and Eun-Kyu Lee

Electronics 2026, 15(8), 1597; https://doi.org/10.3390/electronics15081597 - 10 Apr 2026

Abstract

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)

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12 pages, 3296 KB

Open AccessArticle

Cassette-Based Automated Production of 2-Deoxy-2-[¹⁸F]fluorocellobiose on the Trasis AllInOne with Undetectable [¹⁸F]FDG Contamination

by Falguni Basuli, Jianfeng Shi, Swati Shah, Jianhao Lai, Dima A. Hammoud and Rolf E. Swenson

Molecules 2026, 31(8), 1260; https://doi.org/10.3390/molecules31081260 - 10 Apr 2026

Abstract

The global rise in the incidence and severity of invasive fungal infections, particularly among immunocompromised and immunodeficient patients, has created an urgent need for rapid and accurate diagnostic techniques. Therefore, fungal-specific positron emission tomography imaging agents are increasingly in demand, as they offer the potential for early-stage detection of fungal infections. Recently, 2-deoxy-2-[¹⁸F]fluorocellobiose ([¹⁸F]FCB), a fluorine-18-labeled analog of cellobiose that is selectively metabolized by fungal pathogens possessing cellulose-degrading mechanisms (cellulolytic), was developed for the targeted imaging of Aspergillus infections. However, the final [¹⁸F]FCB contained less than 2% unreacted 2-deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG), which can potentially interfere with image interpretation. Accordingly, this study aims to eliminate residual [¹⁸F]FDG from the final product by enzymatically converting it to [¹⁸F]FDG-6-phosphate through hexokinase-mediated phosphorylation. A Trasis AllInOne (Trasis AIO) module was used to automate the radiolabeling procedure. The reagent vials contain [¹⁸F]FDG, glucose-1-phosphate, cellobiose phosphorylase, adenosine triphosphate (ATP), and hexokinase. A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 45–50% (n = 3, decay-corrected) in a 40 min synthesis time, with a radiochemical purity of >99% (no detectable [¹⁸F]FDG). This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [¹⁸F]FCB for clinical PET imaging. Full article

(This article belongs to the Special Issue Advance in Radiochemistry, 2nd Edition)

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25 pages, 2314 KB

Open AccessArticle

Optimization Design of Interfacial Integrity for Composite Plugging Barriers in Carbon Sequestration Wells

by Zhiheng Shen, Yumei Li, Xinrui Li, Haoyuan Zheng, Yan Xi and Liwei Yu

Processes 2026, 14(8), 1222; https://doi.org/10.3390/pr14081222 - 10 Apr 2026

Abstract

The cement plug-casing interface is critical for long-term wellbore integrity in well abandonment to prevent fluid channeling. However, traditional cement easily debonds under long-term in situ stress and fluid exposure, causing seal failure and safety risks. To address this issue and overcome the limitations of conventional cement, a three-dimensional finite element model was established based on stress-seepage coupling theory. A systematic comparative analysis of the interface debonding mechanisms for three materials—cement, resin, and alloy—and their different combination sequences was conducted. The entire process of interface damage was quantified. The effects of material combination, formation elastic modulus, and injection rate on sealing performance were analyzed. Results show that the stiffness gradient dominates the failure mode, and the “cement–resin–alloy” configuration best suppresses damage propagation, reducing failure height by about 30%. Additionally, interface integrity is sensitive to formation constraints and operational parameters: the interface failure height decreases as the formation elastic modulus increases, and increases as the injection rate rises. The findings of this study can provide a theoretical basis and engineering reference for the optimal design of composite plugging barriers in demanding operational conditions, such as those encountered in carbon sequestration wells. Full article

(This article belongs to the Special Issue Advances and Innovative Solutions in CCUS Engineering: Carbon Capture, Utilization, and Storage)

21 pages, 1732 KB

Open AccessFeature PaperArticle

Modification Effects of High-Pressure Homogenization and Decolorization on Microalgae-Fortified 3D-Printed Foods

by Dalne Sinclair, Armin Mirzapour-Kouhdasht, Juan A. Velasquez, Da Chen, Senay Simsek and Jen-Yi Huang

Processes 2026, 14(8), 1221; https://doi.org/10.3390/pr14081221 - 10 Apr 2026

Abstract

The global transition towards sustainable food systems has intensified the search for alternative protein sources that can meet human nutritional demands with reduced environmental impacts. Although microalgae are rich in protein, their applications in food remain limited due to thick cell walls and intense green color. The aim of this study is to modify Chlorella vulgaris by high-pressure homogenization (HPH) and decolorization to improve its processability for extrusion-based 3D printing. Microalgal biomass was pretreated by HPH at different pressures (10,000, 15,000, 20,000 psi) for one to three passes, followed by pigment removal using ethanol of different concentrations (70, 85, 100%). Microscopic imaging shows that HPH effectively disrupted microalgal cell walls and caused cell disintegration, resulting in increased foaming stability (22–28%) but lower solubility (up to 24%), with other functional properties largely preserved. Ethanol treatments markedly decolored microalgae and increased their water-holding capacity (10–45%) and solubility (6–11%). The formulation of HPH-treated decolorized microalgae with soy protein isolate and xanthan gum increased the viscosity (66–179%) and elasticity (78–235%) of printing inks. The resulting 3D prints show higher hardness (47–128%), springiness (up to 155%) and chewiness (47–408%). The information obtained from this study provides guidance for modifying the functional and rheological properties of microalgae and contributes to advancing the formulation and manufacturing of microalgae-based foods. Full article

(This article belongs to the Special Issue Innovative Bioreactor Design and Advanced Optimization Strategies for Biorefineries and Bioprocessing)

19 pages, 459 KB

Open AccessArticle

Domestic Structural Transformation in a Critical Mineral Economy: A Multisectoral Assessment of Indonesia’s Nickel Downstreaming Strategy

by Abimanyu Hendi Asyono, Palupi Lindiasari Samputra and Hary Djatmiko

Economies 2026, 14(4), 133; https://doi.org/10.3390/economies14040133 - 10 Apr 2026

Abstract

Critical minerals are central to industrial strategies in the Global South, but evidence on how such policies reshape domestic production is limited. This paper maps Indonesia’s nickel ecosystem before and after the 2014 export ban using input–output multipliers and labor intensity from the 2010, 2016, and 2020 input–output tables. We provide a descriptive account of nickel’s evolving economic trajectory during the downstreaming push. Three patterns stand out. Forward linkages declined from 16 to 8 and backward linkages moved from 75 to 73, suggesting a narrower structure with greater specialization in higher value, more capital-intensive activities. Output multipliers rose most in sectors that support the electric vehicle supply chain, including professional and technical services, machinery, fabricated metals, transport equipment, energy, and finance. In contrast, the labor multiplier fell from about 6514 to 3366 jobs per IDR 1 trillion of final demand, implying a higher value added alongside lower employment intensity. Overall, downstreaming appears to work through structural concentration and growth in complementary sectors rather than broad-based diversification. Complementary policies in skills, regional development, and energy infrastructure are therefore critical for inclusive industrial transformation. Full article

(This article belongs to the Section Macroeconomics, Monetary Economics, and Financial Markets)

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50 pages, 1663 KB

Open AccessReview

Advances in Similar Day Methods for Short-Term Load Forecasting for Power Systems

by Monica Borunda, Luis Conde-López, Gerardo Ruiz-Chavarría, Guadalupe Lopez Lopez, Victor M. Alvarado and Edgardo de Jesús Carrera Avendaño

Forecasting 2026, 8(2), 32; https://doi.org/10.3390/forecast8020032 - 10 Apr 2026

Abstract

Short-term load forecasting is essential for the reliable, secure, efficient, and economic operation of modern power systems and electricity markets. Among many forecasting strategies, the similar day (SD) approach for short-term load forecasting was among the earliest used to assess power demand and remains one of the most intuitive and widely adopted techniques worldwide. However, over time, increasing system complexity, richer datasets, and advances in computational intelligence have led to the evolution of SD methodologies beyond heuristic-based rule formulations. This work presents a study of the relevant literature on short-term load forecasting using SD methods reported between 2000 and 2025. This study analyzes how similarity is defined, how forecasts are generated, and how both stages interact within the complete forecasting process in the reviewed literature. Based on these criteria, a unified taxonomy is proposed to classify SD methods into conventional, intelligent, and hybrid formulations. This study provides insight into the methodologies, their performance, and the systems in which they have been tested. The results show that SD-based approaches remain competitive for short-term forecasting and that incorporating artificial intelligence techniques can further enhance their accuracy. Full article

(This article belongs to the Topic Short-Term Load Forecasting—2nd Edition)

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16 pages, 1605 KB

Open AccessArticle

Green Enzyme Innovation: Improved Laundry Detergent Protease Production Through Solid-State Fermentation

by José Juan Buenrostro-Figueroa, Sergio Huerta-Ochoa, Cristóbal Noé Aguilar, María Isabel Reyes-Arreozola, Francisco José Fernández and Lilia Arely Prado-Barragán

Fermentation 2026, 12(4), 194; https://doi.org/10.3390/fermentation12040194 - 10 Apr 2026

Abstract

The increasing demand for environmentally sustainable and efficient laundry detergents has prompted the exploration of innovative biotechnological solutions. This study aims to integrate solid fermentation and by-product valorization for high-quality proteases suitable for laundry detergents. Of 486 strains isolated from fruit by-products, 9 were selected for their proteolytic activity, but only 3 showed proteolytic activity in the presence of detergent components. Strain M17, identified as Yarrowia lipolytica (Yl), proved to be the most effective in producing proteolytic extracts with activity similar to that found in commercial detergents. The produced proteases were incorporated into laundry detergent formulations, and their enzyme activity was compared with that of commercial laundry detergents. The results showed that the proteolytic extracts have enzyme activity similar to that of commercial laundry detergents. Culture media were developed to enhance protease production using fruit by-products. The highest activity (43.71 U (g dm)⁻¹) was achieved at C/N = 20.04, while the best productivity (1.37 U (g dm·h)⁻¹) at pH 7.0 and 30 °C was observed. The results demonstrate that culture media based on fruits and vegetable by-products enhance protease yield and activity. This approach not only reduces waste but also adds value to natural resources through an environmentally friendly process. This study underscores the potential of combining solid-state fermentation with by-products. Using Yl in combination with fruit and vegetable by-products is a practical, eco-friendly method for producing high-quality proteases for laundry detergents. This green enzyme innovation offers significant promise for advancing the detergent proteolytic enzymes and promoting sustainable practices in by-product management. Full article

(This article belongs to the Special Issue Fungal Biotechnology: Exploring the Potential of Solid State Fermentation for Sustainable Production)

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19 pages, 17345 KB

Open AccessArticle

Influence of CeO₂ Addition on Microstructure and Wear Behavior of Plasma Spray-Welded Stellite6/WC Composite Coatings

by Meiqiao Wu, Zhengbing Meng, Yajie Cui, Rongxin Lan, Jiangbo Deng, Dinghua Feng and Zixun He

Metals 2026, 16(4), 417; https://doi.org/10.3390/met16040417 - 10 Apr 2026

Abstract

This study systematically investigates the influence of CeO₂ content (0–0.6 wt.%) on the microstructure and mechanical properties of Stellite6/WC composite coatings fabricated by plasma spray welding. The phase composition and microstructure of the coatings were characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM), while microhardness and tribological performance were evaluated using a semi-automatic Vickers microhardness tester and a ball-on-disk tribometer. The results indicate that the coating with 0.4 wt.% CeO₂ exhibits the optimal combination of mechanical and tribological properties, achieving a maximum microhardness of 1107.62 HV_0.3—a 50.5% improvement over the unmodified coating—and a minimum wear mass loss of 1.4 mg, corresponding to a 78.1% reduction compared to the CeO₂-free counterpart. These findings demonstrate that appropriate CeO₂ addition significantly enhances both the microhardness and wear resistance of Stellite6/WC coatings, offering an effective strategy to mitigate surface degradation and extend the service life of 45 steel substrates under demanding operating conditions. Full article

(This article belongs to the Special Issue Mechanical Properties and Corrosion Behavior of Metals After Surface Modification, 2nd Edition)

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21 pages, 2958 KB

Open AccessReview

Therapeutic Potential of Peptides in Cancer Treatment: Focus on Peptide and Aptamer-Decorated Exosomes

by Prakash Gangadaran, Aswini Suresh Kumar, Kasinathan Kumaran, Kruthika Prakash, Sanjana Dhayalan, Ramya Lakshmi Rajendran, Vasanth Kanth Thasma Loganathbabu, Janani Balaji, Radhika Baskaran, Raksa Arun, Vanshikaa Karthikeyan, Sreyee Biswas, Chae Moon Hong, Kandasamy Nagarajan ArulJothi and Byeong-Cheol Ahn

Cancers 2026, 18(8), 1214; https://doi.org/10.3390/cancers18081214 - 10 Apr 2026

Abstract

Traditional cancer therapies such as surgery, chemotherapy, and antibody-based treatments often face significant barriers, including systemic toxicity, a lack of selectivity, and the emergence of drug resistance. These issues demand innovative and targeted solutions. Peptide-based therapeutics have gained prominence for their ability to disrupt cancer pathways and facilitate targeted drug delivery, offering structural flexibility, precise targeting, and low immunogenicity with minimal effects on healthy tissues. Concurrently, aptamers, which are structured nucleic acid molecules capable of high-affinity molecular recognition, are being developed as both direct therapeutic agents and as targeting ligands for the improved delivery of anticancer drugs. Combining peptide and aptamer technologies with engineered exosomes provides a modular drug delivery system that enhances targeting specificity, stability, and the ability to cross complex biological barriers such as the blood–brain barrier. The emergence of peptide-decorated, aptamer-decorated exosomes represents a new frontier in precision oncology, promising highly selective, biocompatible, and tunable cancer therapies. Further advances are required to overcome challenges in pharmacokinetics, scalable production, and regulatory compliance, but ongoing bioengineering and nanotechnology research continues to accelerate the translation of these innovative strategies toward improved cancer diagnostics and treatment outcomes. This review discusses the synergistic integration of peptides and aptamers with exosome-based delivery systems, highlighting their current applications and future possibilities. Full article

(This article belongs to the Special Issue Smart Nanotechnology for Drug Delivery in Cancer Therapy)

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15 pages, 1233 KB

Open AccessArticle

Sensor-Based Analysis of the Influence of Score Status and Playing Position on the Most Demanding Passages in Elite Women’s Football

by Baris Karakoc, Alper Asci and Paweł Chmura

Sensors 2026, 26(8), 2349; https://doi.org/10.3390/s26082349 - 10 Apr 2026

Abstract

This study aimed to investigate how score status and playing position affect the most demanding passages (MDPs) in elite women’s football. Data from ten matches from eighteen outfield players of the Turkish Women’s National Team were collected during UEFA Nations League fixtures in the 2024–2025 seasons. Players were monitored using wearable GPS sensors, and all locomotor variables were segmented into one-minute windows to identify peak demands. The analysed variables included total distance (TD), high-speed running (HSR), sprint distance (SD), high-acceleration distance (HIAccD), high-deceleration distance (HIDecD), high metabolic power distance (HMPD), and player load (PL). Generalised Estimating Equations (GEE) were used to assess the effects of score status and playing position. Wingers (WG) showed the highest TD, HSR, and HMPD values, while centre backs covered less TD and HSR than WG. Full-backs and forwards (FW) also recorded lower TD, although FW exceeded WG in sprinting (p = 0.045, d values = 0.66 [moderate effect]). Score status influenced MDPs, with TD decreasing when the match was tied and further declining when the team was behind; similar reductions occurred in HSR, HIAccD, HIDecD, and HMPD. In conclusion, both score status and position significantly shaped peak locomotor and mechanical demands. These findings may inform individualised training, recovery programmes, and score-dependent tactical planning in elite women’s football. Full article

(This article belongs to the Collection Sensor Technology for Sports Science)

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