Search Results (8,243)

This paper utilizes panel data of Chinese A-share listed manufacturing firms from 2006 to 2022 and measures regional digital infrastructure by the number of internet broadband access ports per capita. It systematically examines the moderating role of digital infrastructure in the relationship between labor protection policies and firms’ labor productivity. The findings are as follows: (1) Digital infrastructure exhibits a positive moderating effect on the relationship between the Labor Contract Law and firms’ labor productivity. This conclusion remains generally robust across multiple robustness tests and endogeneity treatments, and the direction of the results remains consistent after applying an instrumental variable approach to alleviate endogeneity concerns. (2) The digital transformation channel exhibits a negative relationship, indicating that compliance pressure associated with the institutional reform generates a short-term “crowding-out effect” on firms’ digital investment; the human capital channel shows a positive relationship, indicating that digital infrastructure strengthens the institutional effect by improving the level of urban human capital. (3) The moderating effect is particularly pronounced in cities with strong digital industry foundations, abundant fiscal resources, and firms that have not received government digital subsidies. These results provide empirical support for optimizing the supporting environment of labor protection policies, accelerating digital infrastructure development, and enhancing enterprise adaptability to institutional changes. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

This study explores the impact of blockchain technology (BT) implementation on sustainable environmental performance (SEP). Further, the study explores the moderating role of environmental management accounting (EMA) on the BT–SEP relationship. The sample comprises 415 managers in an Egyptian industrial firm. Data were analyzed using Smart-PLS 4 software. The results revealed a positive and significant impact of BT on SEP. Moreover, EMA showed a significant moderating role as it strengthens the relationship between BT and SEP. These results hold significant implications for policymakers, investors, regulators and corporate executives, underlining the importance of BT implementation and EMA strategies and techniques in shaping SEP, particularly within developing markets such as Egypt. This study makes a distinguished added value to the accounting literature by highlighting the valuable consequences of BT, and EMA on SEP in a unique unexplored context. This study highlights the critical role that EMA plays in moderating the BT–SEP relationship, in contrast to early studies in the literature that focused on examining the direct impact of BT or EMA on SEP. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article

In this paper, we present a system built on Hyperledger Fabric (HLF) that leverages Confidential Computing (CC) technologies to strengthen data privacy guarantees beyond those achievable through application-level mechanisms alone. While HLF natively supports data confidentiality through Private Collections (PCs), which restrict data visibility to a subset of authorized network participants, these mechanisms do not protect data at the hardware level: a privileged or compromised hosting platform can access plaintext data in memory and on the filesystem irrespective of HLF access control policies. To address this limitation, we integrate CC into HLF by adopting Intel Software Guard Extensions (SGX) in conjunction with the Gramine framework. This integration enables the execution of HLF components—peer nodes, orderers, Chaincodes and client applications—within Trusted Execution Environments (TEEs). Furthermore, to securely grant access to selected data to a trusted third-party software (TPS) external to the blockchain network, we leverage the Remote Attestation (RA) feature provided by CC, as streamlined by Gramine and enforced on a per-request basis, ensuring that only verified enclaves (or “SGX enclaves”) with expected measurements may access private data. In addition, the Sealing mechanism is employed to persistently store cryptographic material required by HLF components on the filesystem while preserving both confidentiality and integrity. Together, PCs, RA, Sealing, and enclave-based execution establish a layered privacy guarantee: PCs enforce application-level data segregation among channel participants; RA provides measurement-based access control for an external TPS; Sealing ensures that cryptographic material and blockchain state remain encrypted on the filesystem; and enclave-based execution protects data in use through hardware-level memory encryption. The proposed system has been applied and experimentally validated in a logistics use case in the Port of Genoa: benchmarks against an experimental HLF deployment demonstrate an average 95th-percentile (p95) performance overhead of approximately 1.3× attributable to SGX memory encryption and Gramine-based enclave execution, whereas an elevated memory usage footprint (33–35 GB per organization) has been measured, mainly due to the Gramine environment: this remains an open direction for future work. Full article

Background: Draft surveys are widely used to estimate cargo mass during bulk vessel loading and unloading; however, conventional procedures depend on manual draft readings that are episodic, labor-intensive, and sensitive to environmental conditions. Existing camera-based automated approaches rely on draft mark recognition or explicit waterline detection, which remain vulnerable to illumination variability, hull fouling, and wave-induced disturbances. Methods: This paper proposes a computer vision framework deployed at the Port of Santos, Brazil, using fixed quay-side cameras and a private 4G network infrastructure for continuous image transmission. Unlike prior methods, the framework estimates emergent hull height by segmenting vessel hull contours from bow and stern viewpoints using customized YOLOv8 instance-segmentation models, without relying on draft marks or waterline detection. Pixel measurements are converted to metric units using a nearby bollard of known height as a local physical reference. Results: Field experiments monitor a Panamax bulk carrier over approximately 6.5 days, processing more than 34,000 images from each camera at an average rate of 3.7 images per minute. Both bow and stern segmentation models achieve mAP50-95 mask scores of 0.980 and 0.965, respectively, confirming precise and stable hull boundary delineation. Hull height decreases from 8.27 m to 4.64 m at the bow and from 7.98 m to 3.98 m at the stern over the loading period, with coherent and temporally stable trends across independent viewpoints. Conclusions: The proposed approach delivers repeatable and continuous hull-height estimates under real operational conditions, including variable lighting, background clutter, and partial occlusions, offering a practical and non-intrusive complement to traditional draft surveys for continuous vessel loading monitoring in modern ports. Full article

Robotic colorectal surgery has revolutionized minimally invasive techniques worldwide, offering a more stable operative platform, superior 3D visualization, and wristed instrumentation for challenging pelvic dissections. This narrative review describes the global evolution of robotic colorectal surgery, from the use of multi-port to single-port systems, modular platforms and endoluminal robotic devices. Using Hong Kong’s role as an innovation and research hub, this review demonstrates how integrated innovation, structured training, workflow efficiencies and digital policy frameworks can overcome barriers and inform international implementation of robotic colorectal surgery. Since 2005, The Chinese University of Hong Kong has been pioneering the use of robotic platforms in colorectal surgery, performing first-in-human trials of the da Vinci SP system, the locally developed Sentire C1000 and the EndoMaster EASE for robotic ESD. There is increasing evidence supporting the use of the robotic platform over laparoscopic colorectal surgery, with benefits including reduced open conversions, fewer intraoperative complications, shorter hospital stay, better long-term functional outcomes and improved oncologic outcomes. However, several challenges remain before robotics can be implemented widely on a global scale, such as higher initial capital costs, limited training access, surgeon credentialing and governance for AI-driven data analytics. Full article

This letter presents a Ku-band 2 × 2 patch array antenna that supports dual-polarization operation using a simple cooperative feed network. Depending on the selected input port of the proposed simple feed network, the 2 × 2 array antenna radiates either vertically or horizontally polarized waves. The proposed feed structure consists of two serially connected power dividers placed on the same geometrical plane, enabling dual-polarization without additional multilayer routing. The microstrip line-based feed network also enables a 180${}^{\circ }$ reversed placement of the radiating elements thereby improving the cross-polarization ratio of the proposed array antenna, achieving better than 30 dB across the operating band. The fabricated antenna, designed for a center frequency of 14.9 GHz with a 6.8% fractional bandwidth, demonstrates a realized gain higher than 10 dB for both polarization modes. Measurement results in terms of the input impedance bandwidth, isolation, gain, and cross-polarization ratio are in good agreement with simulation results. Full article

The maritime sector plays a critical role in global logistics systems, acting as a key link within international supply chains. Companies in this sector generate significant employment across the logistics and global value chain. However, it is noticeable that this sector still lacks innovative ideas related to the growth of the level of sustainable development. A wider adoption of green innovations could significantly improve environmental performance and reduce the ecological impact of maritime activities. A key factor that can stimulate the development of innovations in the maritime sector is green finance solutions. Dedicated financing for the greening of the maritime sector can catalyze innovation implementation processes both on ships and in ports. This article analyzes the opportunities for investments in greening activities using specific green finance instruments. This article presents the current situation of the maritime sector in terms of innovation and opportunities for project financing and increasing the value of companies, as well as key technological solutions that increase the level of sustainability in this sector. One of the key challenges is the limited intervention of governments and international organizations in accelerating maritime decarbonization. Maritime sector companies are slow to make progress towards sustainability—there is a lack of fundamental innovation and voluntary efforts to decarbonize. This has led to a situation where a large part of the innovations created are unprofitable today. The authors of this article suggested key investment directions—digitalization and robotization solutions, modernization of old ships and greening solutions for port companies. These actions would provide a short-term breakthrough, but it is necessary to consistently invest in new types of innovations based on scientific research. Full article

Despite being a biodiversity hotspot, the Republic of Seychelles faces a critical challenge with an estimated 90% of its food imported. This dependency exposes the country to global supply disruptions and climate-related risks, while pressure on protected ecosystems continues to rise. In response, the Erasmus+ Capacity Building Higher Education GreenTraINT project (Green Training INTernational Program for agriculture, livestock farming, and conservation), co-funded by the European Union (2024–2026), aims to strengthen local expertise in sustainable agriculture, livestock farming, and biodiversity conservation. Through a transnational partnership involving European and Seychellois universities and institutions, GreenTraINT is co-designing innovative higher education modules tailored to the island’s priorities in agriculture, livestock, and biodiversity conservation. This paper focuses on a detailed needs analysis conducted in early 2025 across a diverse group of 84 stakeholders, including students, educators, NGOs, and professionals. The findings reveal a strong demand for applied training in sustainable food systems and biodiversity conservation, blended teaching methods, and programs that bridge theory with hands-on skills. Inspired by other Erasmus+ projects such as NETCHEM and SPARKLE, GreenTraINT adopts a multi-stakeholder, needs-driven approach that aligns international academic expertise with local development goals. As a key milestone, a Summer School in 2026 will pilot the newly developed modules. In the long term, GreenTraINT seeks to leave a lasting legacy by integrating its curriculum into national education pathways, thereby contributing to food security and environmental resilience. With less than four years remaining to achieve the 2030 Agenda targets, the project positions higher education reform as a strategic accelerator for SDG implementation in small island developing states (SIDS). By linking curriculum innovation to measurable sustainability priorities, GreenTraINT helps narrow the SDG implementation gap in vulnerable island contexts. The project offers a model for international collaboration in higher education for sustainability in SIDS. Full article

Given the continuous expansion of global trade, coastal and estuarine environments have been increasingly modified by anthropogenic pressures associated with port development, particularly through inlet stabilization by jetties, which often causes unintended environmental changes. This study evaluates alterations in estuarine and coastal hydro-sedimentological dynamics resulting from the construction of jetties (1911–1915) in the Patos Lagoon estuary, Brazil. A calibrated and validated numerical model (TELEMAC-3D) was used to compare pre-jetties and present conditions. Results showed that the morphological changes induced by the jetties altered estuarine circulation and sediment retention mechanisms. The reduction in current velocities within the channel increased sediment trapping, decreasing sediment transport capacity towards the adjacent coast. In contrast, along the plume jet, flow acceleration enhanced offshore export of fine suspended sediments, shifting deposition from nearshore areas to deeper offshore zones. Under northeastern wind conditions, a higher potential for mud deposition near the western jetty was observed in the post-construction scenario, reflecting a change in local deposition trends. These human-induced modifications not only reorganize sediment pathways but also influence habitat distribution and deposition patterns, highlighting the importance of considering engineering structures in sustainable coastal and estuarine management strategies. Full article

With the rapid maturation of autonomous driving technology, the hybrid traffic of Internal Container Trucks (ICTs) and External Container Trucks (ECTs) has become a major trend in Automated Container Terminals (ACTs), imposing higher demands on the interaction efficiency between trucks and Yard Cranes (YCs). This paper proposes a comprehensive optimization strategy for the coordinated scheduling of ICTs, ECTs and YCs under hybrid traffic. First, a task combination strategy for ICTs is designed to improve ICT utilization by pairing delivery and retrieval tasks across yard blocks. Second, a Chebyshev-motion-based coordination strategy for YC gantry and trolley movements is developed to reduce travel time and optimize handover points. Third, a mixed-integer programming model is formulated to minimize total energy consumption. An Improved Hybrid Genetic Algorithm (IHGA) is then developed, incorporating chaotic initialization, simulated annealing-based mutation, and dual local search to enhance convergence and solution quality. Simulation results confirm that the proposed model and strategy effectively reduce the total energy consumption of task execution, and the designed algorithm outperforms comparative algorithms in both optimization capability and convergence speed. Overall, the research provides theoretical support for future automated terminal development and practical guidance for achieving efficient and sustainable port operations. Full article

Sturge–Weber Syndrome (SWS) is a rare neurovascular disorder caused by a somatic mosaic missense point mutation in GNAQ, resulting in a facial capillary malformation (port-wine birthmark) and abnormal blood vessels in the eye and brain. Symptoms include seizures, stroke-like episodes, and glaucoma. Acute seizures induced with low-dose kainate in a transgenic GNAQ R183Q mouse model of SWS assessed seizure susceptibility and the impact of seizures on cerebral vasculature. Mice expressing human GNAQ mutation experienced more severe seizures and greater seizure-induced mortality compared to littermate controls. Mutant mice had longer cortical microvessels, with larger diameters; the expression of tight junction proteins was reduced 2 days after seizures. Blood–brain barrier permeability was not different from controls after chronic gene expression, although vascular dilation persisted compared to controls. These data demonstrate increased seizure susceptibility in this somatic mosaic model of SWS, bidirectional interactions between seizure and vascular remodeling, and chronic persistence of vascular malformation. Full article

In South Korea, 99.7% of international freight is transported through ports. At ports handling massive cargo volumes, prolonged truck waiting times have become a significant social concern. To enhance port operational efficiency and ensure driver safety, systematic congestion management is required, which can be facilitated by predicting truck turnaround time (TAT) in advance. However, existing TAT prediction studies have focused on individual ports where data collection is feasible, limiting the applicability of these models to other ports. The objective of this study was to evaluate the transferability of TAT prediction models to different ports. For the analysis, digital tachograph data capturing the trajectories of heavy-duty trucks were employed. The results indicate that a long short-term memory-based model effectively captures the complex operational characteristics of ports and demonstrates high predictive accuracy at Busan New Port and Busan North Port. By applying transfer learning from the best-performing Busan New Port model, the predictive accuracy for Gunsan Port, a target port with limited data, was substantially improved. This study confirms the feasibility of applying transfer learning in ports with constrained data availability, demonstrating that practical TAT prediction models can be developed under realistic operational constraints. Full article