Search Results (397)

Autologous fat grafting is the main surgical technique for soft tissue reconstruction. However, its clinical use with more extended volumes is limited by repeated procedures due to the little possibility of banking tissue, donor-site morbidity and unpredictable graft resorption rates. To overcome these problems, adipose tissue engineering has focused on developing injectable scaffolds. Most of them are hydrogels that closely mimic the biological, structural and mechanical characteristics of native adipose tissue. This review provides an overview of current injectable scaffolds designed to restore soft tissue volume defects, emphasizing their translational potential and future directions. Natural injectable scaffolds exhibit excellent biocompatibility but degrade rapidly and lack mechanical strength. Synthetic injectable scaffolds provide tunable elasticity and degradation rates but require biofunctionalization to support cell adhesion and tissue integration. Adipose extracellular matrix-derived injectable scaffolds are fabricated by decellularization of adipose tissue. Accordingly, they combine bio-mimetic structure with intrinsic biological cues that stimulate host-driven adipogenesis and angiogenesis, thus representing a translatable “off-the-shelf” alternative to autologous fat grafting. However, despite this broad spectrum of available injectable scaffolds, the establishment of clinically reliable soft tissue substitutes capable of supporting large-volume and long-lasting soft tissue reconstruction still remains an open challenge. Full article

This paper examines how logistics performance conditions the relationship between trade openness, economic growth and per capita CO₂ emissions in Mediterranean economies. Using an unbalanced panel of 20 countries over the period 2007–2022, we combine static fixed-effects, dynamic panel generalized method of moments (GMM) estimators and Method-of-Moments Quantile Regression (MM-QR). CO₂ emissions per capita, the World Bank Logistics Performance Index (LPI), trade openness and GDP per capita are drawn from World Bank databases, and interaction terms between LPI and both income and trade openness are constructed to capture conditional effects. The results from fixed-effects and system GMM estimations show that logistics performance exerts a robust and statistically significant negative effect on emissions, whereas GDP per capita is a positive driver and trade openness tends to reduce emissions when logistics capacity is sufficiently strong. Negative and significant interaction terms between LPI and both income and openness indicate that logistics efficiency amplifies the environmental benefits of trade and growth. Quantile regressions reveal that these patterns are most pronounced in high-emission countries, where improvements in logistics performance and its interaction with trade and income generate larger marginal reductions in CO₂ emissions. Overall, the findings highlight the central role of logistics modernization and green trade facilitation in reconciling trade-led growth with decarbonization in the Mediterranean Basin. From a policy perspective, the evidence suggests that prioritizing green logistics and trade facilitation—particularly in high-emission Mediterranean economies—can yield the largest marginal reductions in CO₂ emissions. Full article

The Food Bank of Bogotá is a non-profit organization whose primary mission is to provide food aid to economically vulnerable people and others. One of its key operations is the distribution of food to over 600 beneficiaries. In this research, we present the design and implementation of a computer application that calculates the delivery schedule of the Food Bank vehicles. Firstly, the beneficiaries of the Food Bank are clustered into four delivery zones, and their orders are assigned to specific weeks of the month. Next, a variant of the Capacitated Periodic Vehicle Routing Problem (CPVRP) is solved with an open-source tool. Lastly, routes are assigned to days of the week depending on the traffic conditions. The numerical results showed significant improvements in terms of total time reduction with respect to the business-as-usual practice. This tool is essentially for the monthly planning of the distribution of routes. These routes eventually will need adjustments because of changes in the beneficiaries’ demand, traffic conditions, fleet availability, and so forth. At the time of writing, the model is being integrated with another application that records and tracks the orders in the Food Bank. The users of this application would handle the daily operation and will make manual adjustments if needed. Finally, we discuss the main limitations of the application, which lie primarily in the need to educate both the Food Bank staff and the beneficiaries’ management, who are accustomed to last-minute orders, very tight time windows, and reactive delivery schedules that are highly inefficient. Full article

Mega-event-led urban waterfront regeneration has played a key role in shaping public open spaces, particularly in newly developed areas within the Chinese context. However, public perceptions and their influence on the use of newly built open spaces created through mega-event-led regeneration have not been examined in existing research. To address this gap, this study establishes an integrated assessment framework to evaluate the quality of urban waterfront open spaces. A mixed methods approach was adopted, including direct observations and 770 online questionnaires collected between July and October 2024 at the South Bank of the Qiantang River (SBQR) in Hangzhou, China. Spatial analysis and Importance–Performance Analysis (IPA) were employed to determine priority improvement areas that should inform future waterfront regeneration strategies. The results indicate that inclusiveness emerged as the most important factor for enhancing waterfront open space quality, while spatial aesthetics ranked the lowest. Among the sub-sub factors, elements related to improving water accessibility, enhancing natural surveillance, providing artificial shelters and diverse seating options, introducing distinctive water features, and shaping collective memory through digital technologies are the key priorities for improvement in the future urban waterfront regeneration policies. Finally, the study highlights that the intangible legacies of the Asian Games and the adaptive reuse of informal built heritage have the potential to reshape a distinctive new city image and collective memory, even in the absence of tangible and formally recognised heritage buildings. Full article

The swift development of digital financial services has increased transaction volumes and heightened system performance requirements. Cardless cash deposit transactions at PT Bank XYZ have significantly increased since 2022. This growth necessitates an evaluation and improvement of the existing system architecture. This study proposes a microservices-based architecture deployed in a middleware environment to enhance performance, scalability, and availability. Key enhancements include asynchronous service processing, dual-layer authentication, and data caching using the Terracotta Server Array. The evaluation uses metrics such as CPU usage, RAM usage, latency, throughput, error rate, success rate, and recovery time. Both the monolithic and microservice architectures were assessed through stress testing. Tools used include Red Hat OpenShift Dashboard, NMon Visualizer, and Apache JMeter. Results indicate that the microservices architecture outperforms the monolithic architecture by delivering better resource efficiency, lower latency, higher throughput, and faster recovery times. Moreover, implementing dual-layer authentication enhances security without significantly increasing system complexity. The findings confirm the long-term viability of the microservices architecture for high-demand financial applications. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

This study proposes the Urban Landscape Emotion Analysis Framework (ULEAF) based on images of urban parks shared on social media. This framework integrates an emotion recognition module driven by a convolutional neural network (ConvNeXt Tiny) with a semantic extraction module supported by multimodal semantic matching models (CLIP and DeepSentiBank ANP lexicon). It constructs a systematic analysis pathway from semantic understanding to emotional perception, effectively overcoming the limitations of traditional research methods. Results indicate that positive emotion images predominantly correlate with nature, health, and openness, while negative emotion images are closely associated with the characteristics of decay, abandonment, and oppression, as well as loneliness and calmness, estrangement and disharmony, and gloom and bleakness. Findings reveal trends consistent with prior research, further validating the stable association between urban landscape visual features and emotional perception. The analytical framework developed in this study facilitates the systematic revelation of semantic characteristics and affective perception mechanisms in large-scale urban park imagery, providing scientific reference for optimizing urban park landscapes and implementing emotion-oriented design. Full article

The purpose of this research article is to assess the emissions performance of a marine genset that was retrofitted to combust biomethane in a dual-fuel mode. The retrofits are part of our research efforts to provide a green cold-ironing solution for vessels at berth or in anchorage, and to advocate for a greener electrification of the port sector. An experimental campaign is presented to test the emissions performance by substituting biomethane as an energy basis. Up to 60% biomethane energy substitution is tested under low, medium, and high engine loads. The engine load is controlled via a resistive load bank, and the respective emissions were captured using portable gas analyzers. The results reveal a poor utilization of the gaseous fuel, leading to low engine efficiencies, high CO, and unburnt hydrocarbons at low and intermediate engine loads. However, marine gensets are utilized at high engine loads. At these loads, the specific fuel consumption improves. As indicated in the open literature, biomethane leads to high CO, and unburnt hydrocarbons and the respective NO_x emissions drop compared to diesel-only cases. Full article

This paper presents a PRISMA-guided systematic literature review (2015–2025) of 20 empirical studies on digital transformation in retail banking, examining how artificial intelligence (AI) strengthens cybersecurity, enables FinTech collaboration through interoperable APIs and open-banking infrastructures, and embeds data-driven decision-making across core functions. We searched major databases, applied predefined eligibility criteria, appraised study quality, and coded outcomes related to digital adoption, operational resilience, and customer experience. The synthesis indicates that AI-enabled controls and API-mediated partnerships are consistently associated with higher digital-maturity indicators, conditional on robust model-risk governance and prudent third-party/outsourcing management. Benefits span improved customer experience, efficiency, and inclusion; however, legacy systems, regulatory fragmentation, cyber threats, and organizational resistance remain binding constraints. We propose a unified framework linking technology choices, regulatory design, and organizational outcomes, and distill actionable guidance for policymakers (e.g., interoperable standards, proportional AI governance, sector-wide cyber resilience) and bank managers (sequencing AI use cases, risk controls, and partnership models). Future research should assess emerging technologies—including quantum-safe security and central bank digital currencies (CBDCs)—and their implications for digital-banking stability and trust. Full article

This study examines the effectiveness of a fine-tuned generative AI system—trained with a domain question bank—for question generation and automated grading in programming education, and evaluates its instructional usability. Methodologically, we constructed an annotated question bank covering nine item types and, under a controlled environment, compared pre- and post-fine-tuning performance on question-type recognition and answer grading using Accuracy, Macro Precision, Macro Recall, and Macro F1. We also collected student questionnaires and open-ended feedback to analyze subjective user experience. Results indicate that the accuracy of question-type recognition improved from 0.6477 to 0.8409, while grading accuracy increased from 0.9474 to 0.9605. Students’ subjective perceptions aligned with these quantitative trends, reporting higher ratings for grading accuracy and question generation quality; overall interactive experience was moderately high, though system speed still requires improvement. These findings provide course-aligned empirical evidence that fine-tuning with domain data can jointly enhance the effectiveness and usability of both automatic question generation and automated grading. Full article

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare Logistic Regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N = 1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation-weighted F1-score f1_weighted. For the four classical models, class weighting in the loss (class_weight $\in \{\mathrm{None},‘\mathrm{balanced}’\}$) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted CrossEntropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1–6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC $=0.8800$). Under extreme imbalance, objective choice and operating point matter at least as much as architecture. Full article

Scale mites of the genus Geckobia (Pterygosomatidae) are highly specialized permanent parasites of geckos, but their diversity and ecology in arid environments remain poorly understood. We examined 1135 museum specimens of Ptyodactylus geckos collected from 1965 to 1991 across Israel and the West Bank’s Mediterranean–desert climate gradient to investigate environmental effects on Geckobia mite distributions and population structure. We analyzed prevalence, intensity, population structure, and seasonal patterns across three climate zones using standard parasitological methods and Köppen–Geiger climate classification. We describe two new species, Geckobia inermis sp. nov. and G. parva sp. nov., from Ptyodactylus puiseuxi and provide the first descriptions of previously unknown life stages: the male and nymphchrysalis of G. squameum and the imagochrysalis and larva of G. bochkovi. We report P. oudrii as a new host for G. synthesys and address taxonomic confusion regarding northern Israeli host populations following recent phylogenetic revisions of Ptyodactylus. Only 37 hosts were infected (3.26% prevalence), with a significant female bias in G. squameum populations. Most mites (94.6%) concentrated in the tympanum, where we documented a “double skin plug”, closing the ear opening and creating favorable microenvironments for mite survival. The results demonstrate climate as the primary factor structuring mite distributions: environmental filtering showed systematic prevalence decline from Mediterranean zones (4.3%) to desert-edge areas (1.1%), representing a 3.9-fold gradient that exceeded host species effects by 5.2-fold. Populations exhibited phenological plasticity, with Mediterranean mites peaking in winter versus spring activity in semi-arid zones. These findings reveal how climate constrains ectoparasite persistence in arid systems, with implications for understanding parasite responses to environmental change. Full article

As Morocco aspires to position itself as a regional hub for financial innovation in Africa, its Fintech sector presents a paradox: despite a robust digital infrastructure and growing institutional support, adoption remains limited. Systemic barriers—such as a persistent cash-based culture, low mobile money usage, and fragmented collaboration—continue to impede the sector’s growth. Against this backdrop, this study applies the Delphi research method to systematically identify and prioritize the most pressing challenges and strategic actions facing Morocco’s Fintech ecosystem. Drawing on the insights of 45 experts from finance, technology, academia, startups, and service-oriented organizations, the study follows a three-phase process: open-ended brainstorming, narrowing down, and final ranking. The process produced consensus around 12 key challenges and 12 strategic actions, including the need for an open banking framework, a unified national Fintech vision, regulatory sandboxes, and improved collaboration between incumbents and startups. These findings offer actionable insights to Moroccan policymakers and industry leaders and contribute to Fintech research in emerging economies. Full article

Antimicrobial resistance represents a mounting global health concern, primarily attributable to the widespread and indiscriminate use of antibiotics. This has led to the emergence of resistant strains and a gradual decline in the clinical efficacy of existing therapeutic agents. In this context, the design of new antimicrobials remains a significant challenge. This study evaluated, using in silico tools, the binding affinity of eight novel fluoroquinolone derivatives against the DNA gyrase of six bacterial species, using moxifloxacin as the reference compound. Target protein sequences were retrieved from the Protein Data Bank and GenBank and subsequently modeled using SwissModel, I-TASSER, and Phyre2. The generated structures were assessed with MolProbity, and those with the best scores were selected for molecular docking. Proteins were prepared using Chimera 1.18 and AutoDockTools 1.5.7. The active site was identified with Discovery Studio 2024. Ligands were built in ZINC, prepared using Open Babel v3.1.1.60, and docked with AutoDock Vina v1.2.3.57. Docking validation was performed with DockRMSD. Considering these results, four new molecules (A1, B1, C1, and D2) were designed to improve their pharmacokinetic properties by modifying the TPSA value of the original structures. However, the new docking assays revealed that these optimized compounds did not exhibit a significant increase in affinity toward the target enzyme. The findings suggest that compound C retains a favorable profile as a potential antimicrobial agent against resistant strains. Full article

Through binding to complementary mRNAs, microRNAs (miRNAs) mediate gene silencing. The stability and half-life of microRNAs are controlled by two isoforms of the RNA-binding protein Roquin. This study aimed at identifying the role of Roquin to miRNA-dependent regulation of the transcriptome in the post-ischemic heart. Both Roquin isoforms are highly conserved between rats and humans and constitutively expressed in cardiomyocytes. In both cell species, hypoxia induces a down-regulation of Roquin-1 and Roquin-2. An integrative miRNA-and-mRNA analysis (MMIA) identified miR-23b-5p as a potential interaction partner of Roquins. The open data bank TargetScan8.0 suggests that the transcription factor ZBTB20 is a potential target of miR-23b-5p. The level of expression of ZBTB20 correlated with the functional recovery of rat hearts after myocardial infarction. Moreover, the down-regulation of Roquin-2 in AC16 cells by siRNA under normoxic conditions was associated with an up-regulation of miR-23b-5p and a down-regulation of ZBTB20. Furthermore, in the case of hypoxia-dependent down-regulation of Roquin, the subsequent down-regulation of ZBTB20 was reversed with the help of an antagomir against miR-23b-5p. In conclusion, hypoxia-induced down-regulation of the two Roquin isoforms was associated with an increased stability of miR-23b-5p, a Roquin-2-dependent miRNA, which subsequently led to silencing of the transcription factor ZBTB20. Full article