bluuebunny/crossref_metadata_2025 huggingface dataset:
Body Image and Other Mood Vulnerabilities in Adolescents with Polycystic Ovary Syndrome and Metabolic Alterations
Authors: Barbagallo Federica , Tiranini Lara , Placentino Chiara , Mariacci Giacomo , Piccinino Manuela , Cucinella Laura , Calogero Aldo E. , Nappi Rossella E.
Published: April 2024

Introduction: Psychological vulnerability is a relevant component of polycystic ovarian syndrome (PCOS), but it is still under-explored, especially during adolescence. The aim of this study was to describe a selection of psychometric characteristics in a clinical sample of Italian adolescents with PCOS. Moreover, we reported the associations of body image, eating attitudes, and mood with metabolic features. Methods: Our sample included 128 adolescent girls (age range: 14–19 years) with PCOS. Validated psychometric questionnaires were administered: State Anxiety Inventory (STAI), Beck Depression Inventory (BDI), Body Attitude Test (BAT), Bulimia Investigation Test (BITE), Eating Attitudes Test (EAT), and Perceived Stress Scale (PSS). Results: Anxiety was the most prevalent mood disorder (63.1% trait anxiety and 57% state anxiety). Our cohort also showed a high prevalence of depression (39.1%), body image dissatisfaction (49.2%), disordered eating (11.7%), and bulimic risk (41.4%). PCOS adolescents with obesity and insulin resistance (IR) had statistically significant higher body image distress compared to those with normal weight and without IR (p < 0.001). The Sobel test for mediation showed that body image dissatisfaction mediates the relationship between state anxiety and bulimic risk (Z = 3.42, p < 0.001) and between depression and bulimic risk (Z = 4.59, p < 0.001). Conclusions: A considerable number of patients with PCOS experience psychological disorders during adolescence. IR and obesity play a role in the distress associated with body image, further contributing to psychological vulnerability, especially in the bulimic domain. A comprehensive biopsychosocial approach in adolescents with PCOS represents the basis for effectively managing and preventing complications arising from both psychological and biological disorders in adulthood.
Galvanic Corrosion Behavior of the X80 Steel Welded Joint
Authors: Li Yadong , Sang Jiaxu , Yang Yunzhi , Fang Guoxin , Pang Jianjun , Liu Feng
Published: April 2024

Wire beam electrode techniques and classical electrochemical techniques were used to investigate the effect of the area ratio of each part of the welded joint on the galvanic corrosion behavior. The results showed that the order of the corrosion current density of the different regions in the simulated X80 steel welded joint was as follows: coarse-grained heat-affected zone > fine-grained heat-affected zone > intercritical heat-affected zone > base metal > weld metal. As the area ratio of weld metal increased, the galvanic potential shifted positively, the maximum anode galvanic current density increased and the main anode galvanic effect increased. On the other hand, as the area ratio of the base metal and the heat-affected zone decreased, the coupled potential shifted negatively and the maximum anode galvanic current density decreased. The galvanic corrosion intensity of the simulated X80 steel welded joint increased as the area ratio of the weld metal decreased and increased as the area ratio of the heat-affected zone and base metal increased. To enhance corrosion resistance, it is advisable to choose a shape with a larger groove to increase the WM area in the welded joint. Additionally, selecting a welding method with lower heat input and a higher energy density can help reduce the HAZ area in the welded joint.
Correction: Sai, R.; Abumousa, R.A. Impact of Iron Pyrite Nanoparticles Sizes in Photovoltaic Performance. Coatings 2023, 13, 167
Authors: Sai Refka , Abumousa Rasha A.
Published: April 2024

It has been brought to the authors attention that Figure 1 [...]
Influence of HNT-ZnO Nanofillers on the Performance of Epoxy Resin Composites for Marine Applications
Authors: Şomoghi Raluca , Mihai Sonia , Teodorescu George-Mihail , Vuluga Zina , Gabor Augusta Raluca , Nicolae Cristian-Andi , Trică Bogdan , Vătău Daniel Mihai Stănescu , Oancea Florin , Stănciulescu Cătălin Marian
Published: April 2024

Epoxy resin was conjugated with halloysite nanotubes (HNT) and different types of ZnO nanoparticles (commercial ZnO and modified ZnO-ODTES) to obtain HNT-ZnO/epoxy resin composites. These ZnO nanoparticles (ZnO NPs) were utilized with the intention to enhance the interfacial bonding between the epoxy resin and the reinforcing agent (HNT). The properties of resulted epoxy resin composites were characterized by various methods such as FTIR-ATR, TGA, DSC, TEM-EDX, and Nanoindentation analyses. The thermal properties of the epoxy resin composites were enhanced to a greater extent by the addition of HNT-ZnO nanofillers. DSC testing proved that the modification in the glass transition temperature can be due to the physical bonding between the epoxy resin and filler (HNT and/or ZnO). It was seen that the epoxy resin modified with HNT and ZnO-ODTES has the highest resistance to scratching by having a good elastic recovery as well as high values for surface hardness (~187.6 MPa) and reduced modulus (2980 MPa). These findings can pave the way for the developing of ZnO-based marine coatings with improved properties.
Fruit and Vegetables Blockchain-Based Traceability Platform
Authors: Morais Ricardo , Rosado da Cruz António Miguel , Cruz Estrela Ferreira
Published: April 2024

Fresh food is difficult to preserve, especially because its characteristics can change, and its nutritional value may decrease. Therefore, from the consumer’s point of view, it would be very useful if, when buying fresh fruit or vegetables, they could know where it has been cultivated, when it was harvested and everything that has happened from its harvest until it reached the supermarket shelf. In other words, the consumer would like to have information about the traceability of the fruit or vegetables they intend to buy. This article presents a blockchain-based platform that allows institutions, consumers and business partners to track, back and forward, quality and sustainability information about all types of fresh fruits and vegetables.
Correction: Sritananuwat et al. Effectiveness and Safety of Boesenbergia rotunda Extract on 3T3-L1 Preadipocytes and Its Use in Capsaicin-Loaded Body-Firming Formulation: In Vitro Biological Study and In Vivo Human Study. Cosmetics 2024, 11, 24
Authors: Sritananuwat Phaijit , Samseethong Tipada , Jitsaeng Kusuma , Duangjit Sureewan , Opanasopit Praneet , Rangsimawong Worranan
Published: April 2024

In the original publication [...]
Toward Direct Exploration of the Few-Femtosecond Dynamics of Electronic Coherence and Correlation in Quantum Materials Using Time- and Angle-Resolved Photoemission Spectroscopy
Authors: Rossnagel Kai , Bauer Michael
Published: April 2024

Over the last two decades, time- and angle-resolved photoemission spectroscopy (trARPES) has become a mature and established experimental technique for the study of ultrafast electronic and structural dynamics in materials. To date, most trARPES investigations have focused on the investigation of processes occurring on time scales of ≳30 fs, in particular, relaxation and thermalization, and have therefore been blind to the initial sub-10 fs dynamics related to electronic coherence and correlation effects. In this article, we illustrate how current trARPES setups reach their limits when it comes to addressing such extraordinarily short time scales and present an experimental configuration that provides the time, energy, and momentum resolutions required to monitor few-femtosecond dynamics on the relevant energy and momentum scales. We discuss the potential capabilities of such an experiment to study the electronic response of materials in the strong-field interaction regime at PHz frequencies and finally review a theoretical concept that may in the future even overcome the competing resolution limitations of trARPES experiments, as imposed by the time–bandwidth product of the probing laser pulse. Our roadmap for ultrafast trARPES indicates a path to break new experimental ground in quantum nonequilibrium electronic dynamics, from which new possibilities for ultrafast control of optical and electronic signals in quantum materials can be explored.
Crystal Plasticity Finite Element Modeling of the Influences of Ultrafine-Grained Austenite on the Mechanical Response of a Medium-Mn Steel
Authors: Shen Pengfei , Liu Yang , Zhang Xiang
Published: April 2024

Medium manganese (medium-Mn) steel, one of the third-generation advanced high-strength steels (AHSS), delivers impressive mechanical properties such as high yield strength, ultimate tensile strength, and uniform elongation. One notable feature of medium-Mn steels is the presence of ultrafine-grained (UFG) austenite, achieved through phase transformation from the parent martensite phase during intercritical annealing. While, in general, UFG is considered a strengthening mechanism, the impact of UFG austenites in medium-Mn steel has not been fully studied. In this manuscript, we advance our previous work on crystal plasticity simulation based on the Taylor model to consider fully resolved high-fidelity microstructures and systematically study the influence of the UFG austenites. The original microstructure with UFG is reconstructed from a set of serial electron backscatter diffraction (EBSD) scans, where the exact grain morphology, orientation, and phase composition are preserved. This microstructure was further analyzed to identify the UFG austenites and recover them to their parent martensite before the intercritical annealing. These two high-fidelity microstructures are used for a comparative study using dislocation density-based crystal plasticity finite modeling to understand the impact of UFG austenites on both the local and overall mechanical responses.
Luminescence and Electron–Hole-Trapping Centers in α-Ca2P2O7−Mn
Authors: Nurakhmetov Turlybek N. , Alibay Temirulan T. , Zhangylyssov Keleshek B. , Nurpeissov Aibek S. , Pazylbek Sapargali , Griesiute Diana , Zarkov Aleksej , Kareiva Aivaras
Published: April 2024

The mechanisms of formation of induced intrinsic and impurity radiative states, which consist of intrinsic and impurity electron–hole-trapping center states in irradiated Ca2P2O7−Mn and Ca2P2O7  phosphates, were investigated using thermoactivation and vacuum-ultraviolet spectroscopy methods. These centers are excited at photon energies of 4.0 eV and 4.5 eV, which are within the matrix’s transparency region. New radiative-induced states at 3.06 eV and 2.92 eV are demonstrated to be generated upon the excitation of anions by photons with energies of 5.0 and 5.64 eV. This process is due to charge transfer from the ion to the impurities, specifically Mn2+(O2−−Mn2+) and the neighboring ion O 2−−(P2O7)4−. Furthermore, upon the excitation of matrix anions with photon energies exceeding the band gap (8.0–8.25 eV), electron-trapping by impurities such as Mn2+ and (P2O7)4− ions results.
Research on Fe Removal, Regeneration Process, and Mechanical Properties of Mg Alloy AM50A
Authors: Chen Zhao , Zhou Changfa , Liu Wenbo , Chen Sanxing , Gao Cong , Jia Shaowei , Yu Xiaowen , Zhou Wang , Luo Bolin , Zhang Qingshuang
Published: April 2024

In recent years, the widespread application of Mg alloy casting and Mg alloy products has generated a large amount of Mg alloy waste. This experiment used a single factor experimental analysis method to study the optimal process for removing Fe from Mg alloy AM50A waste, and developed an efficient Fe removal and regeneration process for Mg alloy AM50A. It was found that the optimal refining temperature for removing Fe ions was 670 °C, the optimal refining (RJ-2) agent mass ratio was 1.5%, and the optimal refining time was 40 min. Regenerated J40-1.5-AM50A Mg alloy was prepared using the best refining process, and its composition and mechanical properties were tested and analyzed. The experimental results show that the composition of the regenerated J40-1.5-AM50A Mg alloy prepared by this method is consistent with AM50A, with an Fe removal rate of 96.2%. The mechanical properties were improved compared to the original AM50A sample, with a maximum tensile strength increase of 1.611 KN and a tensile strength increase of 26.333 MPa. The elongation after fracture is 2.25 times that of the original sample. Research has shown that the RJ-2 refining agent can provide mechanical properties of magnesium alloys during the refining process. By analyzing the composition, XRD, SEM, and EDS of AM50A (Fe) and J40-1.5-AM50A, it was found that the refining process accelerates the removal of Fe in the form of Fe deposition.
The Mosquitoes of Morelos, Mexico: DNA Barcodes, Distribution, Ecology and the Resurrection of the Name Culiseta dugesi Dyar and Knab (Diptera: Culicidae)
Authors: Ortega-Morales Aldo I. , Hernández-Triana Luis M. , Garza-Hernández Javier A. , Ramírez-Huicochea Carlos M. , Martínez-Gaona Andrés J. , Quijano-Barraza J. Manuel , González-Acosta Cassandra , Correa-Morales Fabián
Published: April 2024

To update the record of mosquito diversity in the state of Morelos, Mexico, mosquito collections were conducted in both physiographical regions of Morelos: the Neo-volcanic Axis and the South Mountain Range. Immature stages were collected from aquatic habitats, while adult mosquitoes were collected using CDC light traps, Shannon traps and approaching/landing on the collecting personnel. All specimens were identified using taxonomic keys and the mitochondrial cytochrome C oxidase subunit 1 (COI). Following Mexico State, this is the second study of mosquito taxonomy of a complete Mexican state using COI barcoding. In Morelos, a total of 58 species of mosquito occurs, of which we report 12 for first time (Aedes vexans, Ae. euplocamus, Ae. vargasi, Psorophora cilipes, Ps. lineata, Culex restrictor, Cx. interrogator, Cx. nigripalpus, Cx. tarsalis, Cx. sandrae, Cx. lactator and Toxorhynchites moctezuma). Morelos now ranks sixth in mosquito diversity among Mexican states. Phylogenetic trees were constructed for 32 species, including Ae. shannoni, which is a new record for GenBank databases. Additionally, we report COI barcode sequences of Culiseta dugesi Dyar and Knab, which is resurrected from the synonymy of Cs. particeps (Adams). Fourth-instar larvae and adult females of Cs. dugesi were redescribed. This brings the number of mosquito species in Mexico to 248. Ecological and distributional notes for the new records, the medical importance of mosquito species in Morelos, and an identification key for larvae and females of the known Culiseta species occurring in Mexico are also included.
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series
Authors: Riccio Christian , Martone Angelo , Zazzaro Gaetano , Pavone Luigi
Published: April 2024

We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven epilepsy analyses. Methods: Using the sliding window technique with a two-second window length and a one-second time slip, we extract multiple features from the preprocessed EEG time series of 20 patients from the Freiburg Seizure Prediction Database. In addition, we assign a class label to each instance to specify its corresponding seizure phase. All these operations are made through a software application we developed, which is named Training Builder. Results: The 20 tagged training datasets each contain 1080 univariate and bivariate features, and are openly and publicly available. Conclusions: The datasets support the training of data-driven models for seizure detection, prediction, and clustering, based on features engineering.
Peroral Pancreatoscopy-Guided Lithotripsy Compared with Extracorporeal Shock Wave Lithotripsy in the Management of Pancreatic Duct Stones in Chronic Pancreatitis: A Multicenter Retrospective Cohort Study
Authors: Iwata Keisuke , Iwashita Takuji , Mukai Tsuyoshi , Iwasa Yuhei , Okuno Mitsuru , Yoshida Kensaku , Maruta Akinori , Uemura Shinya , Yasuda Ichiro , Shimizu Masahito
Published: April 2024

Background: Extracorporeal shock wave lithotripsy (ESWL) is a common treatment for pancreatic stones in chronic pancreatitis. In contrast, peroral pancreatoscopy-guided lithotripsy (POPS-L) remains underexplored, with limited comparative studies to ESWL. This study compared the treatment outcomes of disposable POPS-L tools and ESWL for pancreatic stones. Methods: A retrospective analysis was conducted on 66 patients who had undergone pancreatic stone treatment at three institutions between 2006 and 2022. The treatment outcomes of POPS-L and ESWL were compared. Results: This study included 19 and 47 patients who had undergone POPS-L and ESWL, respectively. In a comparison between POPS-L and ESWL, the stone clearance rates were 78.9% vs. 70.2% (p = 0.55), while the procedure-related complication rates were 21% vs. 6.3% (p = 0.09). The median total session counts were 1 vs. 5 (p < 0.01). The cumulative stone recurrence rates were comparable in both groups. Multivariate analysis revealed no significant factors influencing the stone clearance rates, and the choice between POPS-L and ESWL did not affect the stone clearance rates. Conclusions: POPS-L and ESWL exhibited comparable treatment outcomes in terms of stone clearance, complications, and recurrence rates. Furthermore, POPS-L is advantageous due to the need for fewer sessions to achieve pancreatic stone clearance.
Reliability and Agreement Assessment of Sarcopenia Diagnosis through Comparison of Bioelectrical Impedance Analysis and Dual-Energy X-ray Absorptiometry
Authors: Lee Jung Hun , Kim Hee Jin , Han Sanghun , Park Seong Jun , Sim Myongheon , Lee Kang Hyun
Published: April 2024

A unified diagnostic criterion has yet to be established for sarcopenia. Therefore, we analyzed the reliability and validity of sarcopenia diagnosis using bioelectrical impedance analysis (BIA) compared with the gold standard, dual-energy X-ray absorptiometry (DEXA), and evaluated the predictive accuracy of BIA for diagnosis. The clinical trial, involving a total of 239 participants, was conducted between December 2018 and September 2019 on healthy volunteers without significant medical histories. The participants underwent health assessments, followed by sequential DEXA and BIA measurements. In both the low and normal appendicular skeletal muscle (ASM) groups, there were significant differences in the right arm, left arm, right leg, left leg, ASM, and ASM index (ASMI) between DEXA and BIA across all age groups (p < 0.05). BIA tended to overestimate compared to DEXA, but ASMI values for males and females were consistent with the criteria for sarcopenia. Bland–Altman analysis showed that each segment in both the low and normal ASM groups fell within the limits of agreement (LOA). The diagnosis of sarcopenia using BIA was significantly different from that using DEXA. However, it exhibited a significantly high correlation, fell within the LOA, and demonstrated high predictive accuracy. BIA can be considered an effective tool for diagnosing sarcopenia.
The Influence of Personality Traits on Postpartum Depression: A Systematic Review Based on the NEO-FFI Scale
Authors: Neda-Stepan Oana , Giurgi-Oncu Cătălina , Sălcudean Andreea , Bernad Elena , Bernad Brenda-Cristiana , Enătescu Virgil Radu
Published: April 2024

Postpartum depression (PPD) is a significant global health concern with profound implications for mothers, families, and societies. This systematic review aims to synthesize current research findings to understand better how personality traits, as assessed by the NEO Five-Factor Inventory (NEO-FFI), contribute to the development and progression of PPD. Conducted in January 2024, this review searched major databases like PubMed, PsycINFO, and Scopus up to December 2023, focusing on the NEO-FFI’s role in evaluating PPD. Following PRISMA guidelines, studies were selected based on strict criteria, including the exclusive use of NEO-FFI for personality assessment and a focus on postpartum women. A total of seven studies were included after a rigorous two-step screening process, and their data were qualitatively synthesized. The review covered a total of 4172 participants, with a prevalence of clinically significant postpartum depression symptoms ranging from 10.6% to 51.7%. Notably, Neuroticism emerged as a significant predictor of PPD, with odds ratios ranging from 1.07 (95% CI: 0.96–1.20) in some studies to as high as 1.87 (95% CI: 1.53–2.27) in others. In contrast, traits like Extraversion and Conscientiousness generally showed protective effects, with lower scores associated with reduced PPD risk. For instance, Extraversion scores correlated negatively with PPD risk (Beta = −0.171) in one study. However, the impact of other traits such as Openness and Agreeableness on PPD risk was less clear, with some studies indicating negligible effects. The review highlights Neuroticism as a consistent and significant predictor of PPD risk, with varying impacts from other personality traits. The findings suggest potential pathways for targeted interventions in maternal mental health care, emphasizing the need for comprehensive personality evaluations in prenatal and postnatal settings.
Resilience Development of Swiss Adolescents: A Convergent Mixed-Methods Approach
Authors: Kassis Wassilis , Graf Ulrike , Rietz Christian , Widmer Franziska
Published: April 2024

Introduction: We applied a convergent mixed-methods research design, focusing on data from Swiss students to identify patterns of resilience development in high school. Method: The study consisted of an online longitudinal survey conducted in two waves, in autumn 2019 (n = 377 grade seven) and spring 2021 (n = 257 grade eight). By combining latent transition analysis (LTA), a person-oriented quantitative method, and qualitative content analysis, we detected four resilience patterns. Results: The longitudinal survey revealed a decrease in the “blooming” pattern (students who displayed high levels of individual and social support indicators as well as satisfaction with their grades and academic success measures) over time and, on the other hand, an increase in the “challenged” pattern, suggesting larger numbers of students with low levels of social support and academic success. Additionally, qualitative interviews were conducted with four students from the sample. These interviews provided insights into the stressors; coping experiences, skills, processes, and resources; and outcomes related to resilience. Discussion: The analysis revealed key factors contributing to resilience, including empowering experiences, supportive individuals, self-help as a prioritized resource, and a positive school environment. Merging the data has elicited various claims such as improving both home and school environments, along with focusing on elaborating their interplay, is the most efficacious approach to bolstering resilience.
Views on Gender Differences in the Physics Classroom
Authors: Musters Natascha , Aarts Rian , Van Amelsvoort Marije , Swerts Marc
Published: April 2024

Concerns about the differences between boys and girls in educational achievement, school careers and educational choices have existed since the last century. Despite ongoing research, we still do not have a complete picture of gender-based differences in education. In particular, there is little comparative research on how teachers and students experience and deal with gender differences in their classrooms. Therefore, this study focuses on teacher and student perspectives on gender differences in the physics classroom of Dutch upper secondary education. The data were collected through questionnaires distributed among physics teachers (N = 72) and students (N = 212). The questionnaires for students and teachers were designed to reveal their perceptions of gender differences in the classroom, focusing on student learning characteristics and teacher–student interactions. Gender differences are reported to a larger extent by teachers than by students, especially in the area of students’ learning characteristics (e.g., boys showing more talent and interest in physics, girls showing more effort and self-regulation) and some in teacher–student interactions (e.g., girls asking teachers more questions). We conclude that concerns about differences between boys and girls are still present and need further research. More work is needed to fully understand the implications of these differences, which are expected to have an important impact on classroom interventions and guidelines for teachers to use in their classrooms.
The Relevance of Visibility in Cultivating Teacher Leaders’ Professional Identity
Authors: Chamo Nurit
Published: April 2024

In the realm of education, discussions surrounding the concept of “visibility” typically concentrate on students and marginalized populations, often overlooking highly positioned individuals, such as leading teachers. This research delves into the fundamental importance of visibility in the transformative process of teacher professional development as leaders. It investigates its character among teacher leaders and the experience of being seen and scrutinizes its central role in shaping a cohesive professional identity. Over a two-year period, data were gathered from 42 leading teachers in elementary and high schools using a phenomenological approach. The findings reveal a paradox: despite the visibility of teachers in their communities, expressions of a lack of professional visibility persist in their continuing professional development (CPD). However, visibility is crucial for research participants, serving as a catalyst for shaping their professional identity. This identity formation involves four key processes: recognizing one’s developmental journey, reflecting on professional experiences within a timeframe, fostering introspection, and reconstructing identity based on reflection outcomes. This iterative process signifies continuous evolution and analysis.
Pedagogical Translanguaging as a Socially Just Strategy for Multilingual Students in Occupational Therapy
Authors: Ramafikeng Matumo C.
Published: April 2024

Multilingual speakers’ languaging practices are undervalued and problematised in formal teaching and learning spaces in higher education. The environment has legitimised monolingualism as the only acceptable practice, hence students often lack the confidence to recruit their full linguistic repertoires. In the third and fourth years of their Occupational Therapy studies, many African students faced challenges due to socio-historic-political factors that put them at risk of failure. These challenges were addressed in academic support tutorial spaces using pedagogical translanguaging. The aim of the paper is to demonstrate the use of translanguaging as a socially just strategy, its affordances, and its challenges. This paper utilises data from a case study within a larger project aiming to describe the use of translanguaging in multilingual teaching and learning settings at a historically White university in South Africa. The case study comprised of eight African students. Linguistic ethnography and Moment analysis were employed. Pedagogical translanguaging and humour were used to create a space conducive to collaborative learning and co-construction of knowledge that granted epistemic access to occupational therapy discourse. Respect and dignity were fundamental in fostering cohesion, improving confidence, enacting speaking rights, and creating a sense of belonging among students who often felt alienated.
A Hybrid Scheme for TX I/Q Imbalance Self-Calibration in a Direct-Conversion Transceiver
Authors: Wang Ruhao , Gao Peng , Liu Jiarui , Wang Zhiyu , Wang Chenge , Yu Faxin
Published: April 2024

A generic transmitter (TX) I/Q imbalance self-calibration method, which was designed based on a hybrid analog and digital structure, is proposed in this paper. The whole calibration scheme was implemented using low-complexity digital–analog circuits based on a zero-force feedback loop. In order to eliminate the negative effect of local oscillator (LO) harmonics on the calibration, we used a variable-delay line (VDL) in the analog domain instead of the digital phase compensator. The prototype chip was fabricated within a 0.2∼5.0 GHz direct-conversion transmitter in a 65 nm CMOS process, and measurements found an image rejection ratio (IRR) of 65 dBc.
Smart Water ATM with Arduino Integration, RFID Authentication, and Dynamic Dispensing for Enhanced Hydration Practices
Authors: Koh Yit Yan , Kok Chiang Liang , Ibraahim Navas , Lim Chin Guan
Published: April 2024

This proposal outlines the development of a comprehensive solution to address hydration challenges through the creation of a Smart Water ATM with Arduino integration, RFID authentication, and dynamic dispensing capabilities. Traditional water dispensers often fall short in monitoring water intake and promoting optimal hydration practices. In response, our project aims to revolutionize hydration practices by integrating Arduino Mega and Uno boards into a Smart Water ATM with a Bottle Dispenser. This innovative system is designed to dispense specific water quantities based on user preferences, encourage the use of personal water bottles, display water temperature, and operate independently without the need for a water line connection. Prior to prototype fabrication, an extensive literature review and survey were conducted to understand existing water dispenser shortcomings and gather public perspectives in Singapore. The Smart Water ATM addresses identified issues by incorporating sensors to monitor water levels, dispense specific quantities, and measure water temperatures. The prototype fabrication involves designing a stainless-steel housing, 3D printing the Smart Water dispenser unit, and installing tanks, tubing, and electronic components. To enhance user interaction, the Smart Water ATM requires RFID authentication through Identity Cards, tracking daily water consumption. An LCD screen displays the dispensed water volume over the ATM’s lifespan, motivating users to be mindful of their water consumption and minimize wastage. Adjustments will be made for deployment in public spaces, such as train stations, where access to permanent water dispensers is limited. This proposal presents an innovative solution to promote enhanced hydration practices, encouraging users to adopt mindful water consumption habits.
Improving Real-Time Performance of Micro-ROS with Priority-Driven Chain-Aware Scheduling
Authors: Wang Zilong , Liu Songran , Ji Dong , Yi Wang
Published: April 2024

Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS.
Optimizing Network Service Continuity with Quality-Driven Resource Migration
Authors: Chen Chaofan , Song Yubo , Jiang Yu , Zhang Mingming
Published: April 2024

Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service functionality in the nodes, which increases the risk of service interruptions. Moreover, the migration decision-making process has not adequately accounted for the alignment between tasks and node resources, thereby amplifying the risk of system overload. To address these shortcomings, we introduce a Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates the migration process at an opportune moment, determined through an analysis of service quality. Subsequently, it employs a method combining Pareto optimality and the simulated annealing algorithm to identify the node most suitable for migration. This approach not only guarantees seamless service continuity but also ensures optimal resource distribution and load balancing. The experiments demonstrate that, in comparison with conventional migration strategies, QD-RMS achieves superior service quality and an approximate 20% increase in maximum task capacity. This substantiates the strategic superiority and technological advancement of the proposed strategy.
FATE: A Flexible FPGA-Based Automatic Test Equipment for Digital ICs
Authors: Zhang Jin , Liu Zhenghui , Hu Xiao , Liu Peixin , Hu Zhiling , Kuang Lidan
Published: April 2024

The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility.
Artificial Intelligence in Social Media Forensics: A Comprehensive Survey and Analysis
Authors: Bokolo Biodoumoye George , Liu Qingzhong
Published: April 2024

Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.
Explaining Misinformation Detection Using Large Language Models
Authors: Pendyala Vishnu S. , Hall Christopher E.
Published: April 2024

Large language models (LLMs) are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task. The methodology is generic and nothing specific is assumed for any of the LLMs, so the conclusions apply generally. Primarily, when it comes to misinformation detection, the experiments show that the LLMs are limited by the data on which they are trained.
Reconfigurable Low-Power CMOS Amplifier Stages for Broadband Impedance Spectroscopy
Authors: Pérez-Bailón Jorge , Calvo-López Belén , Medrano Nicolás
Published: April 2024

In this paper, a fully differential amplifier is proposed in a 1.8 V-0.18 μm CMOS (Complementary Metal-Oxide-Semiconductor) technology, which can accommodate both voltage (V-mode) and current (C-mode) inputs. Post-layout simulation results show a fixed gain amplifier exhibiting a 26 dB (V-mode)/89 dBΩ (C-mode) gain and a programmable gain amplifier featuring a 6–26 dB gain, overall yielding a 26.8–46.4 dB dB (V-mode)/89.6–109.2 dBΩ (C-mode) programmable gain range, with a 100 MHz bandwidth and a power and area consumption of 360.5 µW and 0.0177 mm2, respectively. This amplifier has been designed considering the constraints and specifications (including low voltage, low power, reduced noise and high common mode rejection ratio) for its use in an analogue Lock-in-based Frequency Response Analyser-Impedance Spectroscopy (FRA-IS) device. The proposed design introduces a novel fully differential open-loop structure based on a transconductance–transimpedance (TC-TI) topology for high performance applications with a broad programmable bandwidth. To compare this work, different figures of merit (FoMs) are introduced as well as a comparison table with other simulated and experimental results, reporting an overall better performance in terms of gain, frequency and power-area consumption.
A Dynamic Network with Transformer for Image Denoising
Authors: Song Mingjian , Wang Wenbo , Zhao Yue
Published: April 2024

Deep convolutional neural networks (CNNs) can achieve good performance in image denoising due to their superiority in the extraction of structural information. However, they may ignore the relationships between pixels to limit effects for image denoising. Transformer, focusing on pixel to pixel relationships can effectively solve this problem. This article aims to make a CNN and Transformer complement each other in image denoising. In this study, we propose a dynamic network with Transformer for image denoising (DTNet), with a residual block (RB), a multi-head self-attention block (MSAB), and a multidimensional dynamic enhancement block (MDEB). Firstly, the RB not only utilizes a CNN but also lays the foundation for the combination with Transformer. Then, the MSAB adds positional encoding and applies multi-head self-attention, which enables the preservation of sequential positional information while employing the Transformer to obtain global information. Finally, the MDEB uses dimension enhancement and dynamic convolution to improve the adaptive ability. The experiments show that our DTNet is superior to some existing methods for image denoising.
CFIEE: An Open-Source Critical Metadata Extraction Tool for RISC-V Hardware-Based CFI Schemes
Authors: Li Wenxin , Wang Weike , Li Senyang
Published: April 2024

Control flow critical metadata play a key role in hardware-based control flow integrity (CFI) mechanisms that effectively monitor and secure program control flow based on pre-extracted metadata. The existing control flow analysis tools exhibit some deficiencies, including inadequate compatibility with the RISC-V architecture, a steep learning curve, limited automation capabilities, and restricted data output formats. CFIEE is an open-source tool with a graphical interface for the automated extraction of control flow critical metadata. The tool possesses the capability to analyze RISC-V binary executables, transforming the binary into an intermediate representation (IR) in the form of the disassembled code, and extracting the critical metadata required for studying hardware-based CFI mechanism through a designed control flow transfer relationship analysis algorithm. The extracted metadata include program basic blocks and their corresponding hash values, control flow graphs, function call relationships, distribution of forward transfer instructions, etc. We selected 15 embedded system programs with processor adaptation for functional verification. The results demonstrate the CFIEE’s capability to automatically analyze programs within the supported RISC-V instruction set and generate comprehensive and precise metadata files. This tool can significantly enhance the efficiency of control flow metadata extraction and furnish configurable metadata for the hardware-based security mechanisms.
Research on 3D Visualization of Drone Scenes Based on Neural Radiance Fields
Authors: Jin Pengfei , Yu Zhuoyuan
Published: April 2024

Neural Radiance Fields (NeRFs), as an innovative method employing neural networks for the implicit representation of 3D scenes, have been able to synthesize images from arbitrary viewpoints and successfully apply them to the visualization of objects and room-level scenes (<50 m2). However, due to the capacity limitations of neural networks, the rendering of drone-captured scenes (>10,000 m2) often appears blurry and lacks detail. Merely increasing the model’s capacity or the number of sample points can significantly raise training costs. Existing space contraction methods, designed for forward-facing trajectory or the 360° object-centric trajectory, are not suitable for the unique trajectories of drone footage. Furthermore, anomalies and cloud fog artifacts, resulting from complex lighting conditions and sparse data acquisition, can significantly degrade the quality of rendering. To address these challenges, we propose a framework specifically designed for drone-captured scenes. Within this framework, while using a feature grid and multi-layer perceptron (MLP) to jointly represent 3D scenes, we introduce a Space Boundary Compression method and a Ground-Optimized Sampling strategy to streamline spatial structure and enhance sampling performance. Moreover, we propose an anti-aliasing neural rendering model based on Cluster Sampling and Integrated Hash Encoding to optimize distant details and incorporate an L1 norm penalty for outliers, as well as entropy regularization loss to reduce fluffy artifacts. To verify the effectiveness of the algorithm, experiments were conducted on four drone-captured scenes. The results show that, with only a single GPU and less than two hours of training time, photorealistic visualization can be achieved, significantly improving upon the performance of the existing NeRF approaches.
Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism
Authors: Yang Chengyu , Zhou Han , Chen Ximing , Huang Jiejun
Published: April 2024

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window.
Influencing Factors and Mechanisms of Corporate Social Responsibility Reputation under Green and Low-Carbon Transition: Evidence from Chinese Listed Companies
Authors: Fu Shuke , Tian Mengxia , Ge Yingchen , Yao Tingting , Tian Jiali
Published: April 2024

Amid China’s pursuit of a green and low-carbon transition, corporate social responsibility (CSR) is facing new challenges. Our research delves into the influencing factors and mechanisms for CSR reputation under green and low-carbon transition and provides practical enlightenment for enterprises to achieve sustainable development. This paper constructs a comprehensive index system of CSR from five dimensions (innovation, coordination, sustainability, openness, and sharing), and CSR reputation of China’s A-share listed companies is comprehensively estimated by using an entropy method and data from 2013 to 2021. Then, from the perspective of external supervision and internal governance, we discuss the influence factors of CSR reputation, with an emphasis on the impact of public environmental concerns. Finally, the realization mechanism of CSR is further revealed. It is found that public environmental concern and the expansion of the enterprise scale boost the enhancement of CSR reputation. However, a higher proportion of female managers tends to hinder CSR reputation. Furthermore, public environmental concern plays a more prominent role in improving CSR reputation of non-state-owned and eastern enterprises. Additionally, public environmental concern significantly enhances CSR reputation through green technology innovation and executive environmental awareness. This research provides valuable insights for improving CSR reputation and optimizing regulatory compliance and governance practices.