Search Results (6,566)

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article

Background: Midaortic Syndrome (MAS) is a rare vascular condition characterized by segmental narrowing of the thoracic and abdominal aorta, often involving ostial narrowing of the renal or visceral arteries. While open surgical repair has been the standard treatment, it carries significant morbidity, especially in high-risk patients. Endovascular techniques, including the Chimney approach, provide a minimally invasive alternative to preserve and reestablish both aortic and branch vessel perfusion. This study evaluates the feasibility, safety, and early and midterm outcomes of the Chimney technique used in a cohort of patients with MAS. Methods: Between 2019 and 2025, 9 patients with MAS and branch vessel involvement underwent endovascular repair using the Chimney technique at Brüderklinikum Julia Lanz Hospital in the Mannheim Teaching Hospital of Heidelberg University. Pre-procedural planning was based on computed tomography angiography. Technical success, peri-procedural complications, changes in blood pressure, renal function, and target-vessel stent patency were monitored. Patients were followed over a median of 3 years (range, 0.08–6 years). Results: Nine patients (mean age 77.2 ± 8.7 years; 66.6% female) underwent endovascular repair for midaortic syndrome. All patients were unfit for open surgery. Comorbidities included hypertension (100%), coronary artery disease (100%), and chronic kidney disease (77.7%). Technical success and target-vessel patency were 100%, with no intraoperative deaths, impairment of renal function, or 30-day mortality. One patient (11.1%) developed an access-site hematoma, which was managed conservatively. Median hospital stay was 6 days. During a median 3-year follow-up (range 1 month–6 years), all chimney stents remained patent, patients experienced durable symptom relief, blood pressure improvement, and freedom from reintervention. Conclusions: The Chimney technique offers a safe and effective endovascular option for high-risk patients with Midaortic Syndrome, achieving high technical success, preserved branch-vessel patency, and improvement of symptoms. Larger studies with longer follow-up are warranted to confirm durability and optimize patient selection for this technique. Full article

The integration of operational technology (OT) and information technology (IT) within the Industrial Internet of Things (IIoT) has posed prominent security challenges for resource-constrained devices. Existing authentication architectures often suffer from critical vulnerabilities: one is their reliance on centralized trusted third parties, which creates single points of failure; the other is their use of static credentials like biometrics, which pose severe privacy risks if compromised. To address these limitations, this paper proposes DLR-Auth, which combines chaotic synchronization of semiconductor superlattice physically unclonable functions (SSL-PUFs) with Shamir’s secret sharing (SSS) to enable decentralized registration and revocable templates. Notably, DLR-Auth is a two-party authentication framework that removes the need for a separate online registration authority that operates directly between a user device ($U{D}_i$) and a server (S). In our setting, the server S still acts as the central relying party and hardware authority embedding the matched SSL-PUF module. The protocol also includes an efficient multi-access mechanism optimized for high-frequency interactions. Formal security analysis with the Real-or-Random (ROR) model proves the semantic security of the session key, while performance evaluations demonstrate that DLR-Auth has significant advantages in computational and communication efficiency. DLR-Auth thus offers a robust, scalable, lightweight solution for next-generation secure IIoT systems. Full article

In the face of escalating global challenges, including climate change, food insecurity, freshwater scarcity, soil degradation, and rapid urbanization, soilless farming systems, such as hydroponics, aquaponics, and bioponics, have emerged as innovative and sustainable farming solutions. Combined with precision agriculture technologies, these systems enable real-time optimization of inputs through smart sensors, automation, and predictive modeling, significantly reducing resource consumption while improving crop yields. This review provides a unique contribution by integrating and comparing the three major soilless systems within a single framework and by highlighting, for the first time, their potential synergies with precision agriculture. It critically examines soilless cultivation systems and their relationship with precision agriculture, assessing the agronomic, environmental, and economic benefits as well as the main challenges, including high initial costs, high energy consumption, the complexity of managing biological inputs, the lack of standardized protocols, and limited accessibility for small-scale producers. The review highlights the need to integrate renewable energy sources, develop biodegradable substrates, apply life cycle assessment methodologies, and implement adequate training and regulatory frameworks to promote wider adoption and sustainability. Full article

This study validates an automated seizure detection algorithm for multi-channel neonatal EEG, adapting a previously published method to a dataset with fewer electrodes. The Python-based implementation, SDApy, was applied to EEG recordings from 23 neonates to classify seizure and non-seizure epochs using a support vector machine trained on an independent dataset. The algorithm employs time- and frequency-domain features and maintains high generalization across different recording setups, achieving robust performance despite using only nine electrodes instead of nineteen. Evaluation metrics, including F1 scores and precision—recall curves, confirmed strong agreement between algorithm predictions and expert annotations for most patients. SDApy’s open-source implementation enhances accessibility compared with earlier MatLab versions, offering a transparent and cost-effective approach to clinical EEG analysis. The pipeline can operate with labels from a single expert, supports data pre-labeling for deep learning, and integrates well into neonatal intensive care unit monitoring workflows. Overall, SDApy demonstrates reliable adaptation to reduced-channel EEG and shows potential for real-time seizure detection, personalized threshold optimization, and integration into multimodal neurophysiological monitoring systems. Full article

Electronic devices are now ubiquitous across both professional and personal domains, often containing sensitive information that should remain undisclosed to untrustworthy third parties. Consequently, there is an increased demand for effective security measures to prevent the leakage of confidential data. While some devices utilize mathematically secure algorithms to safeguard sensitive information, there remains a vulnerability to informational leaks through Side-Channel Attacks (SCAs) targeting hardware platforms. Non-profiled SCAs, including Correlation Power Analysis (CPA), are particularly practical since they require access only to the target device. In this study, we propose and investigate the use of Deep Learning (DL) techniques to enhance the effectiveness of non-profiled SCAs through an optimized Deep Learning Power Analysis (DLPA) algorithm. Optimized DLPA attacks are implemented using Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) models, and are applied to the PRIDE SBox-4 block across conventional CMOS-style circuits and secure Sense Amplifier-Based Logic (SABL) Dual Precharge Logic (DPL) structure circuits. Both FinFET and TFET device technologies are evaluated. The experimental results show that the optimized DLPA approach consistently outperforms traditional CPA attacks. The optimized DLPA method succeeds even against TFET-based SABL-DPL circuits, which are resistant to conventional techniques. These findings demonstrate the increased threat posed by DL-based SCAs and highlight the need for evaluating hardware security against advanced machine learning-based methods. Full article

Amidst the paradigm shift in park city development from quantitative metrics to spatial performance, urban complex parks—a novel green space type developed privately yet fulfilling public functions—present an innovative approach to park provision in high-density urban areas. However, systematic empirical evidence on their social value remains scarce. This study characterizes urban complex parks as a new form of green public space that provides key ecosystem services and proposes a three-dimensional evaluation framework integrating “usage vitality, place attractiveness, and user satisfaction.” Analyzing 19 park-equipped complexes among 75 cases in Shanghai using LBS data and online reviews through controlled linear regression and comparative analysis, our results indicate complexes with parks were associated with significantly outperforming others in place attractiveness and user satisfaction. Key findings include associations with a 413.7 m increase in average OD distance, a 3.4–4.0% higher city-level visitor share, and 5.24 percentage points greater median positive review rate. Crucially, spatial location outweighs green ratio and size in determining social value. Ground-level parks, through superior spatial integration, function as effective “social-ecological interfaces,” significantly outperforming rooftop parks in attracting long-distance visitors, stabilizing foot traffic (≈3% lower fluctuation), and enhancing per-store visitation. This demonstrates that green space quality (experiential quality and spatial configuration) matters more than quantity. Our findings suggest that urban complex parks can create social value through perceivable naturalness and restorative environments, providing an empirical basis for optimizing park city implementation in high-density contexts and highlighting the need to reconcile broad attractiveness with equitable local access. Full article

Platinum is known as the most efficient catalyst for oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER). However, Pt catalysts still encounter high loading demands, poor atom utilization, and uncontrolled nanoparticle aggregation, which severely restrict their practical use. To address these issues, we designed a Pt-W_1.33C hybrid catalyst with strong interfacial coupling between Pt nanoparticles and the vacancy-rich i-MXene, W_1.33C matrix. This robust Pt-W_1.33C interaction effectively restricts Pt overgrowth, producing uniformly dispersed nanoparticles with an average physical size of 3.1 nm. The results show that the modulated electronic structure facilitates electron transfer from W_1.33C to neighboring Pt sites, which reduces the energy barriers of chemical reactions and enhances the intrinsic electrochemical catalytic activity of the hybridized catalysts. As a result, the Pt-W_1.33C catalyst with low Pt loading achieves an ORR overpotential of 320 mV at 0.1 mA cm⁻², an HER overpotential of 36 mV at 10 mA cm⁻², and Tafel slopes of 66 and 27.8 mV dec⁻¹ for ORR and HER, respectively. The enhanced ORR and HER performance of Pt-W_1.33C can be attributed to the synergistic interplay between Pt and W_1.33C, including the disordered stacking of W_1.33C, high conductivity of W_1.33C, high catalytic activity of Pt, and strong Pt-W_1.33C interfacial coupling, which, together, optimize electronic interaction and active-site accessibility in the hybrid catalyst. Full article

Simultaneous Wireless Information and Power Transfer (SWIPT) integrated with non-orthogonal multiple access (NOMA) offers a promising solution for energy-efficient Internet of Things (IoT) applications in the context of increasingly scarce spectrum resources. This paper addresses the energy efficiency (EE) maximization problem in a downlink cooperative SWIPT-NOMA network, where user cooperation is employed to mitigate the near-far effect and enhance network performance. We formulate the EE optimization problem for a multi-user scenario by jointly optimizing the transmission time, the power allocation ratio, and the transmission power of the near user in the cooperative SWIPT-NOMA network, and we propose a cooperative SWIPT-NOMA energy efficiency allocation algorithm. Firstly, the fractional programming problem for EE maximization is transformed into a more tractable form using the Dinkelbach method. Subsequently, the resource allocation variables are iteratively updated via variable substitution, successive convex approximation, and the Lagrangian dual method until the algorithm converges. Extensive simulations are conducted to evaluate the performance of the proposed algorithm under various conditions and to compare it with existing schemes. The proposed algorithm enhances network energy efficiency while ensuring user throughput, providing a more efficient resource allocation solution for wireless communication networks. Full article

The development of cost-effective non-noble metal catalysts for the partial oxidation of methane (POM) remains a key strategy for producing hydrogen-rich syngas while mitigating greenhouse gas emissions. In this study, cobalt-supported alumina (Co/Al₂O₃) catalysts were prepared using 5 wt.% of Co and calcined at 600, 700, and 800 °C. Subsequently, Co/Al₂O₃ catalysts were promoted with 10 wt.% Mg, Si, Ti, and Zr at the optimized calcination temperature. The catalysts were systematically characterized by FT-IR, XRD, N₂ physisorption, H₂-TPR, and XPS analyses. Catalytic activity tests for POM of CH₄ were conducted at 600 °C (CH₄/O₂ = 2 and GHSV = 14,400 mL g⁻¹ h⁻¹). Catalysts calcined at 700 °C (5Co/Al_700) exhibited the highest activity among unpromoted samples, with CH₄ conversion of 43.9% and H₂ yield of 41.8%. The superior performance was attributed to its high surface area and the abundance of reducible Co³⁺ species, generating a greater number of Co⁰ active sites. XPS results confirmed the structural stability of γ-Al₂O₃ and preserved Co–Al interactions across calcination temperatures, while promoters mainly modulated Co dispersion and redox accessibility. Among the promoted catalysts, the activity order followed: 5Co/10ZrAl > 5Co/10MgAl> unpromoted-5Co/Al_700 > 5Co/10SiAl > 5Co/10TiAl. Si and Ti promoted catalysts acquired less concentration of active sites and less activity as well. The concentration of reducible species as well as initial activity towards POM are comparable over Zr and Mg-promoted catalysts. However, earlier one has a higher edge of reducibility and sustained constant activity over time in a stream study. The Zr-promoted catalyst exhibited superior reducibility and remarkable stability, achieving 47.3% CH₄ conversion and 44.4% H₂ yield sustained over 300 min time-on-stream. TEM analysis of spent 5Co/10ZrAl indicated that Zr promotion suppressed graphitic carbon formation. Full article

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

This study investigates bit error rate (BER)-constrained power allocation for uplink non-orthogonal multiple access (NOMA) systems in which a base station employs one-bit analog-to-digital converters. Although one-bit quantization significantly reduces hardware costs and receiver power consumption, it also introduces severe nonlinear distortions that degrade detection performance. To address this challenge, a pairwise error probability expression is first derived for the one-bit quantized uplink NOMA model, from which an analytical upper bound on the BER is obtained. Based on this characterization, a fairness-driven max–min power allocation strategy is formulated to minimize the BER of the worst-performing user. A closed-form solution for the optimal power allocation is obtained under binary phase-shift keying (BPSK) signaling. Simulation results verify the tightness of the analytical BER bound and demonstrate that the proposed power allocation scheme provides noticeable BER improvements that compensate for the performance degradation caused by one-bit quantization. Full article

As urban architecture continues to emphasize integration with natural environments, the concept of waterfront buildings and blue–green spaces has been widely applied in the site selection of large urban structures. While existing research has extensively explored architectural types such as waterfront landscapes and sports venues, systematic studies on waterfront concert halls, as an important category of cultural architecture, remain limited. Specifically, the interaction mechanisms between such halls and their aquatic environments, as well as their impact on users’ psychological satisfaction, have not been thoroughly investigated. This study takes waterfront concert halls as representative cultural buildings and examines 1267 users from 12 typical waterfront concert halls across eight cities in China. A theoretical model was constructed with water visibility, water accessibility, water interactivity, and water integration as independent variables, biophilia and a sense of nature’s presence as parallel mediators, and user satisfaction as the dependent variable. Data were analyzed using covariance based structural equation modeling CB-SEM. The findings reveal that (1) water visibility, water accessibility, and water integration positively influence user satisfaction; (2) biophilia mediates the relationship between water visibility, water accessibility, water interactivity, water integration, and user satisfaction; (3) a sense of nature’s presence also mediates the relationship between these water-related variables and user satisfaction. This study empirically demonstrates the dual pathway psychological mechanism through which water elements influence user satisfaction, providing a new perspective for the design of waterfront cultural architecture. The research suggests that architects can enhance users’ biophilic instincts and sense of nature’s presence through specific design strategies, such as strengthening water visibility, optimizing waterfront circulation, and enriching water interaction experiences. These findings offer theoretical support for shifting contemporary architectural practice from physical space creation to environmental well-being promotion, while also establishing a practical foundation for developing human-centered evaluation systems for built environments. Full article

Background/Aim: Olfactory ensheathing cells (OECs) are widely studied for neural repair, yet OB- and OM-derived primary cultures differ in accessibility and cellular composition. This study aimed to establish donor-matched canine OB- and OM-derived primary cultures using harmonized isolation conditions and to quantify source-dependent differences in culture composition and proliferative activity. Materials and Methods: Olfactory bulbs (OBs) and olfactory mucosa (OM) were collected post-mortem from client-owned dogs (n = 10). Primary cultures were established under identical enzymatic dissociation and culture conditions. Culture composition was quantified by immunocytochemistry using p75^NTR (OEC marker) and fibronectin (fibroblast-associated marker), with an epithelial fraction assessed morphologically in OM. Proliferation was assessed by Ki-67 labeling using the Muse^® Ki-67 kit (n = 5 donors/group). Results: Both tissues yielded viable primary cultures. OB-derived cultures had a higher OEC fraction than OM-derived cultures (60.7 ± 6.4% vs. 39.0 ± 6.2%), whereas OM cultures consistently included an epithelial component (27.0 ± 6.6%). Ki-67 labeling was higher in OB-derived cultures than OM-derived cultures (30.2 ± 6.2% vs. 13.0 ± 2.5%; Welch’s t-test p = 0.0018). Conclusions: Canine OB and OM generate source-distinct primary cultures under standardized conditions: OB-derived cultures are OEC-enriched and more proliferative in vitro, while OM-derived cultures are more heterogeneous. These findings inform future optimization of OM-based protocols and motivate functional assays to test regenerative efficacy. Full article

Diagnosis of rickettsial infections is challenging due to nonspecific clinical symptoms and limitations of current diagnostic methods. Molecular assays allow early detection but are limited by cost and technical demands, whereas conventional serological tests often exhibit cross-reactivity and low sensitivity during the early stages of infection. This study aimed to develop and evaluate a recombinant-antigen sandwich ELISA for improved antibody detection against Rickettsia spp. Three Rickettsia akari proteins, rGroEL, rDnaK, and rA8GP63 (uncharacterized protein), were produced and validated for immunogenicity. The assay was evaluated using 94 patient serum samples, including those with positive, negative, and unknown clinical course. The optimized ELISA demonstrated high reproducibility, with IgG sensitivity of 89.47–95.39% and specificity of 90%. IgM detection, also assessed, showed lower sensitivity (42.11–82.89%) but maintained strong specificity (83.33%). The diagnostic performance was comparable to that of a commercial indirect immunofluorescence assay, with no cross-reactivity detected in sera from patients with unrelated infections. rDnaK and rA8GP63 represent newly explored diagnostic candidates. These findings highlight the potential of this recombinant protein-based ELISA as an accessible, sensitive and specific diagnostic tool, with a meaningful clinical impact for improving the early and accurate detection of rickettsial infections. Full article