bluuebunny/crossref_metadata_2025 huggingface dataset:
Can Employee Training Stabilise the Workforce of Frontline Workers in Construction Firms? An Empirical Analysis of Turnover Intentions
Authors: Yao Wenyan , Anuar Arshad Mohd , Yang Qinjie , Tan Jianping
Published: January 2025

The construction industry is a critical pillar of China’s economy, providing substantial employment opportunities for society. However, the high turnover rate among frontline construction workers has become a significant challenge to the development of the industry. This high mobility not only increases recruitment and training costs for companies but also seriously affects the quality, safety, and productivity of construction projects. This study aims to investigate the impact of employee training on the turnover intention of frontline employees in the construction industry, as well as to analyze the role of organizational identification and perceived supervisor support. Data were analyzed through a structured questionnaire survey of 533 frontline construction employees using the study’s Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that training is effective in reducing turnover intention among frontline employees and that this effect is partly achieved by increasing employees’ organizational identification. In addition, perceived supervisor support moderates the relationship between employee training and turnover intention, and the effect of employee training on reducing turnover intention is more significant when perceived supervisor support is high. This study further validates the applicability of social exchange theory in the context of China’s construction industry, reveals the key roles of employee training and organizational identification in reducing turnover intention, and highlights the important influence of perceived supervisor support as a means of enhancing the effectiveness of employee training, which provides a rationale for Chinese construction firms to optimize the training system and enhance supervisor support in order to improve employee retention intention.
Design and Shear Bearing Capacity Calculation of All-Welded Irregular Joints in Steel Traditional Chinese Buildings
Authors: Wu Zhanjing , Wang Xinwu , Bu Xin , Dong Jinshuang
Published: January 2025

Steel traditional Chinese buildings (STCBs) are constructed using modern materials, replicating the esthetics of ancient Chinese buildings, but their irregular joints differ significantly from those in conventional steel structures. To investigate the influence of beam section shape and axial compression ratio on the failure mode and shear resistance of all-welded irregular joints (WIJs) in STCBs, the size proportion relationships in the traditional Chinese modular construction system for such joints in existing practical projects are analyzed. Four exterior joint specimens were designed and fabricated for pseudo-static loading tests. The failure mode, hysteresis curve, and skeleton curve of the specimens were obtained. The test results indicate that the failure mode of the specimens involves shear deformation in the lower core area, with final failure due to crack formation in the weld at the junction between the column wall and the beam flange. As the axial compression ratio increases, the bearing capacity of the joint decreases. Based on the test results, the numerical model was established by using finite element software Abaqus2016, and parameter analysis was performed by varying the axial compression ratio of the column. After analyzing the force transfer mechanism of the core area in the WIJs of STCBs, a simplified calculation formula for the shear bearing capacity of the core area was derived based on the proportional relationship outlined in the construction manual from the Song Dynasty. The calculated results show good agreement with the experimental results, providing a basis for the structural design of WIJs in STCBs.
Impact of Safety Leadership and Employee Morale on Safety Performance: The Moderating Role of Harmonious Safety Passion
Authors: Slil Emhemed , Iyiola Kolawole , Alzubi Ahmad , Aljuhmani Hasan Yousef
Published: January 2025

Construction sites are among the most hazardous workplaces, making safety a critical concern in the sector. This study investigates the interplay between safety leadership, employee morale, harmonious safety passion, and safety performance through the lens of the social exchange theory. This research aims to assess how safety leadership impacts safety performance directly and indirectly, with employee morale acting as a mediator while harmonious safety passion moderates these relationships. A quantitative approach was employed, with data collected through structured questionnaires administered to construction workers in Istanbul and Ankara, Turkey. A total of 438 valid responses were analyzed using the Statistical Package for the Social Sciences (version 25) and Analysis of Moment Structures (version 24) for reliability, validity, and hypothesis testing. The findings confirm that safety leadership positively influences both safety performance and employee morale. Employees with high morale directly contribute to improved safety performance and act as a mediator between safety leadership and safety performance. Additionally, harmonious safety passion moderates the relationship between employee morale and safety performance, strengthening the link for workers with higher levels of passion. However, its moderating effect on the relationship between safety leadership and safety performance was not significant. These insights offer practical implications for designing safety training programs, leadership development initiatives, and policies aimed at improving safety outcomes in construction. Future research should explore longitudinal designs and diverse contexts to further validate these findings.
Numerical Modelling and Damage Assessment Criteria for FRP-Retrofitted RC Columns
Authors: Sousa Inês , Peres Rita , Couto Rita , Bento Rita , Castro José Miguel
Published: January 2025

The numerical modelling procedures and damage criteria of both existing and retrofitted reinforced concrete (RC) elements are crucial for reliable seismic assessment and the retrofitting of existing RC buildings. A widely used retrofitting technique involves the application of fiber-reinforced polymers (FRPs) to vulnerable RC elements, enhancing their flexural and shear capacities. However, the current version of EC8-3 does not explicitly provide guidelines for numerical modelling, nor does it offer specific information on the assessment of retrofitting elements. This study focuses on developing a concentrated plasticity modelling approach and defining the damage state criteria for retrofitted RC columns with FRPs, based on experimental data from the literature. A database comprising 99 FRP-retrofitted RC columns was compiled, and regression analysis procedures were used to calibrate the peak load rotation, ultimate rotation, and post-capping rotation capacities. The modelling approach was validated through comparison with existing formulations using OpenSees, and the results indicate its adequacy for the seismic assessment of retrofitted buildings. This research advances the seismic assessment of FRP-retrofitted RC elements by introducing a novel trilinear moment–rotation model and refined damage criteria, which provide higher predictive accuracy in comparison to existing proposals. It also addresses critical gaps in seismic assessment practices by proposing the peak load rotation as a more reliable SD limit state threshold for retrofitted columns.
Assessment of Service Quality in Urban Sports Facilities: A Comprehensive Evaluation Framework Applied to Shanghai, China
Authors: Yang Yusheng , Tang Shuoning
Published: January 2025

Urbanization in developing countries has led to increasing demands for high-quality public services, highlighting the need for an effective spatial allocation of resources. This study evaluated the spatial distribution of sports facilities in Shanghai’s central urban areas and Pudong New Area, focusing on accessibility, variegation, and coherence at municipal, district, neighborhood, and community levels. The findings reveal that Pudong New Area has higher-quality facilities compared to central areas, with a distinct “southwest-northeast” axis of high-quality sports facilities in Pudong. A multidimensional spatial evaluation framework was developed, offering practical insights for optimizing resource allocation, enhancing accessibility, and addressing supply-side gaps in developing countries.
Evaluating Cetuximab Regimens in Head and Neck Cancer: Insights from a Retrospective Cohort Study
Authors: Wang Chih-Chun , Yeh Shyh-An , Chen Wen-Hui , Li Hung-Ju , Yang Chuan-Chien , Huang Tse-Jen , Su Yu-Chieh
Published: January 2025

Background/Objectives: The aim of this study is to assess the effectiveness of cetuximab combination therapy in patients with recurrent or metastatic head and neck cancer treated at a hospital in Southern Taiwan. Methods: This study analyzed a retrospective cohort of 67 patients who were treated between January 2020 and May 2024 with two cetuximab regimens, cetuximab combined with cisplatin and 5-Fu, which were administered every four weeks during hospitalization (CPF4) and every two weeks as outpatient treatment (CPF2), respectively. The clinical outcomes, including overall survival and progression-free survival (PFS), were compared across the treatment regimens and age groups using Kaplan–Meier survival curves and Cox proportional hazard models. Results: The median overall survival was 11.1 months (95% confidence interval, 7.8–14.5), with CPF2 showing a potential PFS advantage in patients aged 46–60 years (p = 0.049). No significant differences in overall survival were observed between CPF2 and CPF4. CPF2, which was administered in an outpatient setting, was associated with improved convenience, reduced hospitalization, and potentially lower risks of hospital-acquired infections. Conclusions: CPF2 exhibits practical advantages and comparable effectiveness, making it the preferred treatment regimen for eligible patients. Further studies with larger populations and molecular stratifications are needed to confirm these findings and develop better treatment strategies.
Venous Thromboembolism in Patients with Neuroendocrine Neoplasms: A Systematic Review of Incidence, Types, and Clinical Outcomes
Authors: Massironi Sara , Gervaso Lorenzo , Fanizzi Fabrizio , Preatoni Paoletta , Dell’Anna Giuseppe , Fazio Nicola , Danese Silvio
Published: January 2025

Background: Neuroendocrine neoplasms (NENs) are a heterogeneous group of tumors with unique biological characteristics and complications, including thromboembolism. This systematic review evaluates the incidence, types, and clinical outcomes of venous thromboembolic events (VTEs) in NEN patients. Methods: A systematic search of PubMed, Scopus, and Embase was conducted to identify studies on TEs in NENs. Eligible studies included case reports, case series, and retrospective cohort studies reporting VTEs, including deep vein thrombosis (DVT), pulmonary embolism (PE), and visceral vein thrombosis (VVT). Data were extracted on tumor site, functionality, differentiation grade, and VTE type. Results: In total, 33 studies were included, comprising 26 case reports, 2 case series, and 5 retrospective cohort studies. VTE prevalence ranged from 7.5% to 33% across studies. The most common VTEs were DVT, PE, and portal vein thrombosis (PVT). A meta-analysis revealed a pooled VTE prevalence of 11.1% (95% CI: 9.07–13.53%). Pancreatic NENs exhibited the highest thrombotic burden, particularly in poorly differentiated and advanced-stage tumors. Functioning tumors, including glucagonomas and ACTH-secreting NENs, were strongly associated with VTEs, potentially related to their systemic effects on coagulation and inflammation. Conclusions: Venous thromboembolism is a significant complication in NEN patients, especially in advanced or poorly differentiated tumors. Early detection and targeted management are critical for improving outcomes. Further investigations are required to clarify the mechanisms underlying thromboembolism in NENs and to develop optimized prophylactic and therapeutic strategies tailored to this patient population.
CT-Derived Quantitative Image Features Predict Neoadjuvant Treatment Response in Adenocarcinoma of the Gastroesophageal Junction with High Accuracy
Authors: Graf Markus , Ziegelmayer Sebastian , Reischl Stefan , Teumer Yannick , Gassert Florian T. , Marka Alexander W. , Raffler Philipp , Bachmann Jeannine , Makowski Marcus , Reim Daniel , Lohöfer Fabian , Burian Egon , Braren Rickmer
Published: January 2025

Background: The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ). Methods: A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients. Results: Baseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups. Conclusions: Posttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.
Natural Killer Cell-Secreted IFN-γ and TNF-α Mediated Differentiation in Lung Stem-like Tumors, Leading to the Susceptibility of the Tumors to Chemotherapeutic Drugs
Authors: Kaur Kawaljit , Celis Angie Perez , Jewett Anahid
Published: January 2025

We demonstrate that natural killer (NK) cells induce a higher cytotoxicity against lung cancer stem-like cells (hA549) compared to differentiated lung cancer cell lines (H292). The supernatants from split-anergized NK cells (IL-2 and anti-CD16 mAb-treated NK cells) induced differentiation in hA549. Differentiated lung cancer cell line (H292) and NK cells differentiated hA549 expressed reduced NK cell-mediated cytotoxicity but expressed higher sensitivity to chemotherapeutic drugs. This finding validated our previous reports demonstrating that the levels of tumor killing by NK cells and by chemotherapeutic drugs correlate directly and indirectly, respectively, with the stage and levels of tumor differentiation. We also demonstrate the role of IFN-γ and TNF-α in inducing tumor differentiation. NK cells’ supernatants or IFN-γ and TNF-α-induced tumor differentiation was blocked when we used antibodies against IFN-γ and TNF-α. Therefore, IFN-γ and TNF-α released from NK cells play a significant role in differentiating tumors, resulting in increased susceptibility of tumors to chemotherapeutic drugs. We also observed the different effects of MHC-class I antibodies in CSCs vs. differentiated tumors. Treatment with anti-MHC-class I decreased NK cell-mediated cytotoxicity in hA549 tumors, whereas it increased NK cell-mediated cytotoxicity when differentiated tumors were treated with antibodies against MHC-class I.
Frontal Plane Knee Kinematics and Kinetics During Gait in Children and Youth with Achondroplasia—Correspondence with Static X-Ray Images and Relevance to Symptoms
Authors: Hösl Matthias , Thamm Antonia , Afifi Faik Kamel , Nader Sean
Published: January 2025

Background: Frontal knee malalignments are hallmarks of Achondroplasia (ACH), along with disproportional short stature. Typically, X-rays are used to assess them, but 3D gait analysis (3DGA) may additionally be used to evaluate dynamic knee function. The research questions were as follows: (1) What is the relationship between X-rays and 3DGA in ACH? (2) Do children with ACH have abnormal frontal knee kinematics and kinetics? (3) Are there aspects of 3DGA that relate to knee symptoms? Methods: A total of 62 knees of 31 children with ACH (age: 11.1 ± 4.3 years, 34 symptomatic knees) underwent 3DGA and X-ray as part of their standard clinical care. X-rays were analyzed for mechanical tibiofemoral angle (mTFA). Relationships between X-rays and 3DGA were determined. Sixty-two knees of 31 age-matched typically developing (TD) children served as references for 3DGA. Frontal knee kinematics (including thrust RoM) and adduction moments (KAMs) were compared. Multiple regression was performed for measurements associated with KAM, and ANOVA was used to compare TD and ACH knees with and without pain. Results: There was a high correlation between static frontal knee angles and mTFA (r = 0.93, p < 0.001, mean difference = −2.9°). ACH knees with a regular mTFA also showed significantly increased KAM. Multiple regression analysis showed that mTFA was the most relevant predictor of KAM (R2 = 0.41–0.75). Symptomatic knees (n = 34/62) experienced significantly more knee RoM in early stance than asymptomatic knees. Conclusions: Three-dimensional gait analysis may be an objective screening method for dynamic knee alignment and stability and may complement radiography in monitoring ACH. Symptoms may depend on knee thrust, while the impact of altered KAM needs further study.
Unpacking the Mood States of Children and Youth in Saskatchewan, Canada, in the Context of the COVID-19 Pandemic: Insights from the “See Us, Hear Us 2.0” Study
Authors: Shakurun Nahin , Hinz Tamara , Adeyinka Daniel A. , Muhajarine Nazeem
Published: January 2025

Background/Objectives: The COVID-19 pandemic created a growing need for insights into the mental health of children and youth and their use of coping mechanisms during this period. We assessed mood symptoms and related factors among children and youth in Saskatchewan. We examined if coping abilities mediated the relationship between risk factors and mood states. Methods: “See Us, Hear Us 2.0”, a cross-sectional study of 563 child–parent dyads, provided the data. The dependent variable, current mood state, was measured by the CoRonavIruS health Impact Survey (CRISIS) scale. Independent variables included sociodemographics, behaviors, household conditions, and coping ability. Multiple linear regression and mediation analyses were conducted, ensuring sample representativeness with sampling weights and addressing missing data through multiple imputations. Results: The participants reported mood symptoms (“moderate” to “extreme”) ranging from 23% to 38% on the CRISIS scale. Factors such as older children, hybrid learning, disrupted activities, and increased screen time worsened moods. The ethnic minority groups (BIPOC) living in mid-sized cities/towns experienced more negative moods compared to Whites residing in cities. Coping ability mediated the relationship between extracurricular activities and mood states. Conclusions: Our results underscore the importance of tailored interventions, recognizing the diverse needs of specific age groups, gender identities, and ethnicities and addressing the adverse effects of the pandemic-related disruptions on the mental health and well-being of school children in Saskatchewan. Our study also suggests prioritizing the diverse needs of children and youth during the planning and implementation of mental health services in the province.
Twelve-Month Outcomes and Optical Coherence Tomography (OCT) Biomarkers After Intravitreal Dexamethasone Implantation in Pseudophakic Eyes with Post-Vitrectomy Cystoid Macular Edema (CME)—Refractory to Medical Therapy
Authors: Pignatelli Francesco , Niro Alfredo , Addabbo Giuseppe , Viggiano Pasquale , Boscia Giacomo , Grassi Maria Oliva , Boscia Francesco , Iaculli Cristiana , Clima Giulia Maria Emilia , Barone Antonio , Giancipoli Ermete
Published: January 2025

Background: In this study, we evaluated the incidence of cystoid macular edema (CME) after pars plana vitrectomy (PPV) for different retinal pathologies and assessed the role of optical coherence tomography (OCT) biomarkers in guiding treatment decisions in post-surgical CME patients who were refractory to medical therapy over a follow-up period of 12 months. Methods: Medical records of consecutive pseudophakic patients, who underwent PPV for different retinal pathologies, were retrospectively evaluated in this single-center, uncontrolled study. The incidence of post-PPV CME was assessed. Eyes with post-PPV CME in the first 2 months after surgery, with available clinical and OCT data for 12 months after surgery, were included in the evaluation. The mean best-corrected visual acuity (BCVA; logMAR), mean central macular thickness (CMT; μm) change, and response to different treatments [medical therapy and intravitreal dexamethasone (DEX) implant] were evaluated 1, 3, 6, 9, and 12 months after PPV. The impact of OCT biomarkers on the exposure to DEX implants was assessed. Adverse events, potentially related to the treatment, were investigated as well. Results: Of the 346 pseudophakic patients (352 eyes) who participated in this study, 54 (54 eyes) developed CME within the first 2 months after PPV (incidence of 15.3%). Among them, 48 patients were deemed eligible for the 12-month analysis. Preoperative mean BCVA (1.44 ± 0.99 logMAR) significantly improved to 0.32 ± 0.37 logMAR after 12 months (p < 0.001). The mean baseline CMT of 347 (±123.5) μm significantly decreased to 290 μm (±80.4; p = 0.003) by the end of the follow-up. Twenty-five eyes (52%) required one or more DEX implants for CME, due to being refractory to topical therapy. Significant correlations were found between the mean CMT values at various time points. Additionally, patients who required DEX implants at months 3 and 9 were more likely to present intraretinal fluid (IRF), disorganization of inner retinal layers (DRIL), disorganization of outer retinal layers (DROL), and hyper-reflective foci (HRF) at 1-month OCT. Five patients experienced a slight increase in intraocular pressure (IOP), which was successfully managed with topical medication. Conclusions: Topical therapy alone can be a valuable option for post-PPV CME in approximately 50% of patients. Significant visual recovery and macular thickness reduction at 12 months demonstrated that DEX implants can be a safe and effective second-line treatment for pseudophakic patients with post-PPV CME and who are refractory to medical therapy. Early post-surgical OCT biomarkers may indicate a more severe CME that might benefit from the steroid implant.
Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review
Authors: Zerunian Marta , Polidori Tiziano , Palmeri Federica , Nardacci Stefano , Del Gaudio Antonella , Masci Benedetta , Tremamunno Giuseppe , Polici Michela , De Santis Domenico , Pucciarelli Francesco , Laghi Andrea , Caruso Damiano
Published: January 2025

Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models.
Regional Epidemiological Study on the Dental Status of the First Permanent Molar in Romanian Schoolchildren
Authors: Beresescu Liana , Beresescu Gabriela Felicia , Esian Daniela , Vlasa Alexandru , Benedek Csilla , Sabau Raluca , Stoica Alexandra Mihaela
Published: January 2025

Background/Objectives: Dental caries remains a significant public health challenge in Romania, with recent studies reporting a prevalence of 40% in children’s permanent teeth, with 90% of cases untreated. This study aimed to evaluate the dental status of the first permanent molars in children aged 11–12 years. Methods: This cross-sectional study was conducted over 12 months at the Integrated Center for Dental Medicine in Târgu Mureș and two private clinics in Transylvania. A total of 516 children, aged 11–12 years, were examined using the ICDAS II classification. Data on carious lesions and dental treatments performed were collected. Results: Of the 2064 first permanent molars examined, 57.99% had carious lesions, fillings, or extractions, while 41.28% were free from caries. Among the affected molars, 41.71% had untreated caries, 9.30% were filled, and 6.25% were sealed. Boys showed a significantly higher prevalence of advanced lesions (ICDAS 4–6) compared to girls. Caries predominantly affected the pits and fissures (87.46%). Conclusions: This study reveals a high prevalence of carious lesions in first permanent molars and a low rate of treatment. The findings emphasize the need for improved oral health education, increased access to dental care, and the development of national strategies to prevent and treat dental caries in children.
Development of a Solar-Powered Edge Processing Perimeter Alert System with AI and LoRa/LoRaWAN Integration for Drone Detection and Enhanced Security
Authors: Mejia-Herrera Mateo , Botero-Valencia Juan , Ortega José , Hernández-García Ruber
Published: January 2025

Edge processing is a trend in developing new technologies that leverage Artificial Intelligence (AI) without transmitting large volumes of data to centralized processing services. This technique is particularly relevant for security applications where there is a need to reduce the probability of intrusion or data breaches and to decentralize alert systems. Although drone detection has received great research attention, the ability to identify helicopters expands the spectrum of aerial threats that can be detected. In this work, we present the development of a perimeter alert system that integrates AI and multiple sensors processed at the edge. The proposed system can be integrated into a LoRa or LoRaWAN network powered by solar energy. The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. It is complemented by two PIR motion sensors and a microwave sensor with a range of up to 11 m. Additionally, the DC magnetic field is measured to identify possible sensor movements or changes caused by large bodies, and a configurable RGB light signal visually indicates motion or sound detection. The monitoring system communicates with a second MCU integrated with a LoRa or LoRaWAN communication module, enabling information transmission over distances of up to several kilometers. The system is powered by a LiPo battery, which is recharged using solar energy. The perimeter alert system offers numerous advantages, including edge processing for enhanced data privacy and reduced latency, integrating multiple sensors for increased accuracy, and a decentralized approach to improving security. Its compatibility with LoRa or LoRaWAN networks enables long-range communication, while solar-powered operation reduces environmental impact. These features position the perimeter alert system as a versatile and powerful solution for various applications, including border control, private property protection, and critical infrastructure monitoring. The evaluation results show notable progress in the acoustic detection of helicopters and drones under controlled conditions. Finally, all the original data presented in the study are openly available in an OSF repository.
Invariant Representation Learning in Multimedia Recommendation with Modality Alignment and Model Fusion
Authors: Hu Xinghang , Zhang Haiteng
Published: January 2025

Multimedia recommendation systems aim to accurately predict user preferences from multimodal data. However, existing methods may learn a recommendation model from spurious features, i.e., appearing to be related to an outcome but actually having no causal relationship with the outcome, leading to poor generalization ability. While previous approaches have adopted invariant learning to address this issue, they simply concatenate multimodal data without proper alignment, resulting in information loss or redundancy. To overcome these challenges, we propose a framework called M3-InvRL, designed to enhance recommendation system performance through common and modality-specific representation learning, invariant learning, and model merging. Specifically, our approach begins by learning modality-specific representations along with a common representation for each modality. To achieve this, we introduce a novel contrastive loss that aligns representations and imposes mutual information constraints to extract modality-specific features, thereby preventing generalization issues within the same representation space. Next, we generate invariant masks based on the identification of heterogeneous environments to learn invariant representations. Finally, we integrate both invariant-specific and shared invariant representations for each modality to train models and fuse them in the output space, reducing uncertainty and enhancing generalization performance. Experiments on real-world datasets demonstrate the effectiveness of our approach.
The Group-Algebraic Formalism of Quantum Probability and Its Applications in Quantum Statistical Mechanics
Authors: Gu Yan , Wang Jiao
Published: January 2025

We show that the theory of quantum statistical mechanics is a special model in the framework of the quantum probability theory developed by mathematicians, by extending the characteristic function in the classical probability theory to the quantum probability theory. As dynamical variables of a quantum system must respect certain commutation relations, we take the group generated by a Lie algebra constructed with these commutation relations as the bridge, so that the classical characteristic function defined in a Euclidean space is transformed to a normalized, non-negative definite function defined in this group. Indeed, on the quantum side, this group-theoretical characteristic function is equivalent to the density matrix; hence, it can be adopted to represent the state of a quantum ensemble. It is also found that this new representation may have significant advantages in applications. As two examples, we show its effectiveness and convenience in solving the quantum-optical master equation for a harmonic oscillator coupled with its thermal environment, and in simulating the quantum cat map, a paradigmatic model for quantum chaos. Other related issues are reviewed and discussed as well.
Multi-Level Network Topology and Time Series Multi-Scenario Optimization Planning Method for Hybrid AC/DC Distribution Systems in Data Centers
Authors: Chen Bing , Zhang Yongjun , Liang Handong
Published: January 2025

With the rapid development of the Internet, cloud computing, big data, artificial intelligence, and other information technologies, data centers have become a crucial part of modern society’s infrastructure, which puts forward very high requirements for the safety and reliability of power supply. Most of the servers, networks, and other equipment in data centers are DC-driven loads, which can significantly enhance resource utilization efficiency by efficiently accessing the DC power supply through voltage source converter-based high-voltage direct current transmission and distribution technology. For this reason, this paper first proposes a multi-level network topology design method for AC/DC distribution systems in the context of data centers. Based on the analysis of the adaptability of AC/DC distribution systems in data center access, the design and analysis of its multi-level network topology is carried out at the physical level for the construction of hybrid AC/DC distribution systems in data center. On this basis, a time series multi-scenario planning model of AC/DC distribution system with distributed generation in data center is established, the configuration strategy of AC/DC distribution system is investigated, and a time series multi-scenario optimization planning method for hybrid AC/DC distribution systems in data centers is proposed. Finally, the validity of the proposed method is verified by simulation examples.
Dual-Policy Attribute-Based Searchable Encryption with Secure Keyword Update for Vehicular Social Networks
Authors: Wan Qianxue , Liu Muhua , Wang Lin , Wang Feng , Zhang Mingchuan
Published: January 2025

Cloud-to-Vehicle (C2V) integration serves as a fundamental infrastructure to provide robust computing and storage support for Vehicular Social Networks (VSNs). However, the proliferation of sensitive personal data within VSNs poses significant challenges in achieving secure and efficient data sharing while maintaining data usability and precise retrieval capabilities. Although existing searchable attribute-based encryption schemes offer the secure retrieval of encrypted data and fine-grained access control mechanisms, these schemes still exhibit limitations in terms of bilateral access control, dynamic index updates, and search result verification. This study presents a Dual-Policy Attribute-based Searchable Encryption (DP-ABSE) scheme with dynamic keyword update functionality for VSNs. The scheme implements a fine-grained decoupling mechanism that decomposes data attributes into two distinct components: immutable attribute names and mutable attribute values. This decomposition transfers the attribute verification process from data owners to the encrypted files themselves, enabling data attribute-level granularity in access control. Through the integration of an identity-based authentication mechanism derived from the data owner’s unique identifier and bilinear pairing verification, it achieves secure updates of the specified keywords index while preserving both the anonymity of the non-updated data and the confidentiality of the message content. The encryption process employs an offline/online two-phase design, allowing data owners to pre-compute ciphertext pools for efficient real-time encryption. Subsequently, the decryption process introduces an outsourcing local-phase mechanism, leveraging key encapsulation technology for secure attribute computation outsourcing, thereby reducing the terminal computational load. To enhance security at the terminal decryption stage, the scheme incorporates a security verification module based on retrieval keyword and ciphertext correlation validation, preventing replacement attacks and ensuring data integrity. Security analysis under standard assumptions confirms the theoretical soundness of the proposed solution, and extensive performance evaluations showcase its effectiveness.
Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction
Authors: Sun Mulin , Fu Yuhao , Sun Mingyao , Huang Run , Teng Yun
Published: January 2025

Under the background of energy conservation and emission reduction, how to rationally and scientifically utilize the non-renewable resources of northeastern farmland is particularly important. In this study, the carbon emission coefficient method is used to select six major carbon sources with energy consumption, including energy consumption in the process of fertilizer production, agricultural machinery use, irrigation, and agricultural waste treatment, to measure the spatial and temporal carbon emissions from the utilization of farmland resources in Northeast China during 2012–2021. A gray prediction model is constructed to predict the carbon emissions from the utilization of farmland resources in the next 10 years, and the logarithmic mean Divisia index model is used to analyze the effects of the various influencing factors on the carbon emissions from farmland utilization. The results show the following: (1) Between 2012 and 2021, carbon emissions from farmland use in Northeast China show a fluctuating development trend of rising and then falling, and the distribution of carbon emissions within the region is characterized by a decreasing trend of “high-middle-low” from the north to the south. (2) Carbon emissions from farmland use in the next 10 years will maintain a gently decreasing trend. (3) The industrial structure of farmland, the level of economic development and the level of urbanization play a contributing role in carbon emissions. The industrial structure of farmland, the level of economic development, and the level of urbanization contribute to carbon emissions from the use of farmland resources. (4) The efficiency of farmland use, the regional industrial structure, and the size of the labor force inhibit the carbon emissions from the use of farmland. This study provides a scientific basis and strategic recommendations for optimizing the use of farmland resources, adjusting the structure of energy use, and realizing the balanced development of land and energy resources under the goal of energy conservation and emission reduction in Northeast China.
Coordinated Optimization of Multi-Regional Integrated Energy Service Providers with Flexible Reserve Resources
Authors: Wang Xueting , Zhong Hao , Zou Xianqiu , Wang Qiujie , Li Lanfang
Published: January 2025

Aiming at solving the problem of new energy and load uncertainty leading to a steep increase in the demand for flexible reserve resources by integrated energy service providers (IESPs), a coordinated and optimized scheduling method for multi-region integrated energy service providers considering flexible reserve resources is proposed. First, for the uncertainty of new energy and load, Latin hypercube sampling is used to generate scenarios, and the scenarios are reduced by a K-means clustering algorithm. Second, based on the interaction relationship between the active distribution network (ADN) and multi-region IESPs, a mixed game model of the ADN and IESP alliance is established. ADN guides IESPs to optimize their operation by setting prices for electricity and reserves, and IESPs fully tap their own flexible reserve resources according to the prices set by ADN and achieve power interoperability through the interaction of IESPs in multiple regions to synergistically cope with the uncertainties of new energy and load. Finally, the example results show that the model proposed in this paper is able to realize the allocation of flexibility resources in a wider range, reduce the reserve pressure on the superior grid, and improve the profitability of IESPs.
Research on the Factors Influencing the Thermoplastic Rheological Properties of Wood
Authors: Wang Yujie , Qi Yiyang , Zhao Zhongyuan
Published: January 2025

The rheology of wood thermoplastics is a crucial factor in enhancing wood utilization efficiency, significantly impacting the various applications of wood. The rheological properties of these thermoplastics are influenced by several variables, including moisture content, temperature, and fiber morphology. This study aims to investigate the rheological characteristics of wood under differing moisture levels (from absolute drying to water-impregnated states), thicknesses (ranging from 3 to 15 mm), compression methods, and compression conditions through a series of compression tests. The results show that moisture content and thickness substantially affect the rheological properties of wood thermoplastics, whereas thermal compression conditions exert a comparatively minor influence. Additionally, analysis of the content of the three primary elements in wood, alongside microscopic morphological examination, reveals that increased fiber length and higher length–diameter ratio are associated with enhanced rheological properties of wood thermoplastics. Notably, the influence of cellulose content (ranging from 40%–50%) on these rheological characteristics appears to be limited.
The Impact of Grape Maceration on Quality and Biogenic Amine Formation in Slovak Tokaj Wines: Examination of Microbial, Chemical and Sensory Properties
Authors: Semjon Boris , Bartkovský Martin , Očenáš Peter , Regecová Ivana , Megyesy Eftimová Zuzana , Výrostková Jana , Mesarčová Lýdia , Kováčová Mariana , Várady Matúš , Šuľáková Lucia , Marcinčák Slavomír
Published: January 2025

Grape maceration is essential in modulating the quality of Tokaj wines. The aim of this research was to analyse the effect of two maceration practices—one utilizing the application of a Saccharomyces cerevisiae culture and the other being maceration without a yeast culture in experimental Lipovina (Vitis vinifera L.) white wine from the Slovak part of the Tokaj region. Multiple factor analysis was used for the examination of wine quality regarding the formation of biogenic amines (BAs), and chemical, microbial and sensory variables were analysed over three months of wine production. The formation of bioactive components in wine was affected by maceration and maceration with the addition of a yeast culture, which resulted in a significantly increased antioxidant activity, as well as total phenolic and flavonoid content (p < 0.001). The wine samples that underwent maceration scored significantly lower in their appearance, aroma and taste (p < 0.05), which could be caused by a higher phenolic content and specific taste. The dynamics of BA formation in wine, evaluated using multiple factor analysis, highlighted that the maceration and maceration with an added yeast culture significantly affected these processes (p < 0.05). Microbiological examination promoted stable yeast counts during the maceration process, ensuring their longer presence during fermentation. Future research should aim to optimize maceration time in order to enhance the sensory quality of macerated wines without increasing the BA concentration, especially that of histamine.
Empowering Global Supply Chains Through Blockchain-Based Platforms: New Evidence from the Coffee Industry
Authors: Agnola Tommaso , Ambrosini Luca , Beretta Edoardo , Gremlich Giuliano
Published: January 2025

Global supply chains, especially in commodity trading, are plagued by fragmentation, lack of transparency, and trust deficits among participants. These issues lead to inefficiencies, increased costs, and an over-reliance on intermediaries. The present Communication describes a blockchain-based platform that leverages Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to address these challenges in supply chain management. Developed in collaboration with coffee industry stakeholders, our approach proposes a platform with an integrated marketplace for seller discovery, enables precise order definition with detailed terms and conditions, and actively guides both buyers and sellers throughout the shipping process, managing financial guarantees and ensuring a secure transaction flow. The platform is compatible with both traditional banking infrastructure and modern crypto-based systems, enabling seamless financial transactions. In cases where disputes arise, we empower users to easily collect all communications and documents to present to legal authorities, expediting the resolution process. The platform is implemented using the Internet Computer Protocol (ICP) for secure, on-chain storage and application hosting, and is integrated with the Ethereum blockchain to leverage its extensive decentralized finance (DeFi) ecosystem, significant liquidity, and robust stablecoin infrastructure, thereby facilitating secure financial transactions. Moreover, we introduce an SSI-based authentication and authorization framework that spans across the entire platform, including both the Ethereum Virtual Machine (EVM) and Internet Computer Protocol (ICP), enabling unified role-based access control through verifiable credentials. A value-added of the present Communication, the framework is demonstrated by means of a detailed case study in the coffee industry, highlighting the technical challenges addressed during implementation. While quantitative efficiency metrics will be established through upcoming real-world testing with industry partners, the platform’s design aims to streamline operations by reducing intermediary dependencies and automating key processes. Finally, the Communication provides insights into its adaptability to other industries facing comparable supply chain challenges, presenting an approach focused on enhancing trust and reducing reliance on intermediaries.
Anomaly Detection in Spatiotemporal Data from Fiber Optic Distributed Temperature Sensing for Outdoor Fire Monitoring
Authors: Bian Haitao , Luo Xiaohan , Zhu Zhichao , Zang Xiaowei , Tian Yu
Published: January 2025

Outdoor fire detection faces significant challenges due to complex and variable environmental conditions. Fiber Optic Distributed Temperature Sensing (FO-DTS), recognized for its high sensitivity and broad monitoring range, provides significant advantages in detecting outdoor fires. However, prediction models trained in laboratory settings often yield false and missed alarms when deployed in complex outdoor settings, due to environmental interferences. To address this issue, this study developed a fixed-power fire source simulation device to establish a reliable small-scale experimental platform incorporating various environmental influences for generating anomalous temperature data. We employed deep learning autoencoders (AEs) to integrate spatiotemporal data, aiming to minimize the impact of outdoor conditions on detection performance. This research focused on analyzing how environmental temperature changes and rapid fluctuations affected detection capabilities, evaluating metrics such as detection accuracy and delay. Results showed that, compared to AE and VAE models handling spatial or temporal data, the CNN-AE demonstrated superior anomaly detection performance and strong robustness when applied to spatiotemporal data. Furthermore, the findings emphasize that environmental factors such as extreme temperatures and rapid temperature fluctuations can affect detection outcomes, increasing the likelihood of false alarms. This research underscores the potential of utilizing FO-DTS spatiotemporal data with CNN-AE for outdoor fire detection in complex scenarios and provides suggestions for mitigating environmental interference in practical applications.