bluuebunny/crossref_metadata_2025 huggingface dataset:
Gas Box Exhaust Design Modification for Accidental Hazardous Gas Releases in Semiconductor Industry
Authors: Lim Keun-Young , Jung Seungho , Kim Sang-Ryung
Published: November 2024

Hazardous substances such as hydrogen and chlorine are used in semiconductor manufacturing. When these gasses are discharged, they are mixed with outside air and are connected to a treatment facility through a duct inside a gas box. This study investigated an optimal exhaust design to prevent fire explosions and toxic exposure by optimizing the exhaust volume when hazardous substances leak from the gas box of semiconductor manufacturing equipment. In this study, carbon monoxide was used for modeling. A 75 mm duct was used, and the tracer gas was released into the gas box at 15.4 LPM. The concentrations were measured at nine points inside and outside the gas box. According to the test results, in an experiment designed with 0% air intake, the internal leakage concentration was measured to be more than 25% of the LEL (lower explosive limit) for 10 min when leakage occurred due to stagnant flow, and the outside toxicity concentration was also measured to be more than 50% of the TWA (time-weighted average) value. When the air intake ratio was designed to be 100%, there was a point on the outside that exceeded 50% of the TWA, confirming that excessive air intake could also cause gas to leak outside. Finally, when the intake ratio was designed to be 50% in both directions, it was confirmed that the airflow was maintained smoothly, and the hazardous gasses were safely diluted and discharged through the duct. This study was conducted to improve the safety of workers in the field in the event of leakage of flammable and toxic gasses by testing the location and area of the air intake hole in the gas box exhaust port. Through this effort, the aim is to present specific standards for gas box design and to assist in establishing a legal framework or standardized guidelines.
Numerical Simulation of the Melting of Solid Particles in Thermal Convection with a Modified Immersed Boundary Method
Authors: Shi Yang , Shao Xueming , Xu Jian , Yu Zhaosheng
Published: November 2024

A new immersed boundary method is proposed for the numerical simulation of the melting of solid particles in its own liquid at a high temperature. The main feature of the new method is the use of the modified direct-forcing immersed boundary method for the solution of the flow field and the sharp-interface immersed boundary method for the temperature field. The accuracy of the proposed method is validated via three problems: the sedimentation of a non-melting particle, the melting of a fixed particle under mixed thermal convection, and the sedimentation of a melting particle. The method is then applied to the investigation of the effects of various parameters, the particle interactions and the particle shape on the particle melting time. A correlation for the melting time of a circular particle in forced thermal convection is established as a function of the Reynolds, Prandtl, and Stefan numbers. The melting time of a particle in mixed thermal convection first increases and then decreases, as the Grashof number increases. The effects of the particle interactions on the melting time are complicated due to the natural convection between two particles. The sufficiently strong natural convection can even render the downstream particle melt faster than the single particle. For the same particle area, the elliptic particle with the aspect ratio being around 1.4 melts most slowly.
Q-Learning-Incorporated Robust Relevance Vector Machine for Remaining Useful Life Prediction
Authors: Wang Xiuli , Li Zhongxin , Wang Xiuyi , Hu Xinyu
Published: November 2024

Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM faces the issue of poor robustness, which is mainly manifested as poor prediction accuracy and difficulty in fitting when the predicted data fluctuate greatly. This is due to weights and random errors following Gaussian distributions, which are highly sensitive to outliers. Also, the traditional model training process heavily relies on an additional feature extraction process, which suffers from the problem of effective data loss as well as the risk of overfitting. Thus, a robust regression framework against outliers is developed by incorporating t-distribution into the RVM. And a Q-learning (QL) algorithm is embedded into the constructed robust RVM model to replace the feature extraction process. In addition, this paper firstly predicts the degradation trend of RUL to enhance the accuracy and interpretability of RUL prediction. Finally, a comparative experiment on the performance degradation of capacitors in the traction system is designed, and the root mean square errors for the QL-RRVM, QL-RVM, RRVM, and RVM models are obtained as 0.751, 8.599, 38.316, and 41.892, respectively. The experimental results confirm the superiority of the proposed method.
Bridging the Gap: A Systematic Review and Meta-Analysis of Interventions to Address Barriers in Migrant Mental Health Care Access
Authors: Marchi Mattia , Laquatra Giuseppe , Yaaqovy Ahikam David , Pingani Luca , Ferrari Silvia , Galeazzi Gian Maria
Published: November 2024

Despite their higher risk of mental health conditions, migrants often face barriers to accessing mental health care. This systematic review aims to synthesize the evidence on interventions to improve mental health care access for migrants (protocol CRD42024556575). PubMed, Embase, PsycINFO, and CINAHL were searched for experimental and observational studies on this topic. A narrative summary and a meta-analysis of the study findings are presented, along with a GRADE quality assessment. Eighteen reports, accounting for 3285 migrants, were included in the final selection. There were six randomized controlled trials, nine non-randomized clinical trials, and three observational studies. The interventions consisted of psychoeducation (27.8%), digital tools (22.2%), outreach programs, counseling, peer support (each 11.1%), and miscellaneous approaches (16.7%). There was marked heterogeneity in the intervention structure and outcome measure across the studies. Nevertheless, all studies reported a positive effect of the interventions on mental health care access. Still limited by few studies providing data suitable for the pooled estimate, the meta-analyses found a significant effect in reducing stigma and improving mental health literacy. Overall, this review identified promising interventions for improving migrant mental health care access. Future research is needed to validate interventions that can be applied sustainably across different contexts and migrant populations.
Aromatics Alkylated with Olefins Utilizing Zeolites as Heterogeneous Catalysts: A Review
Authors: Al-Sultani Samaa H. , Al-Shathr Ali , Al-Zaidi Bashir Y.
Published: November 2024

The alkylation reaction of aromatic compounds gains considerable attention because of its wide application in bulk and fine chemical production. Aromatics alkylated with olefins is a well-known process, particularly for linear alkylbenzene, phenyloctanes, and heptyltoluene production. As octane boosters and precursors for various petrochemical and bulk chemical products, a wide range of alkylated compounds are in high demand. Numerous unique structures have been proposed in addition to the usual zeolites (Y and beta) utilized in alkylation procedures. The inevitable deactivation of industrial catalysts over time on stream, which is followed by a decrease in catalytic activity and product selectivity, is one of their disadvantages. Therefore, careful consideration of catalyst deactivation regarding the setup and functioning of the process of catalysis is necessary. Although a lot of work has been carried out to date to prevent coke and increase catalyst lifespan, deactivation of the catalyst is still unavoidable. Coke deposition can lead to catalyst deactivation in industrial catalytic processes by obstructing pores and/or covering acid sites. It is very desirable to regenerate inactive catalysts in order to remove the coke and restore catalytic activity at the same time. Depending on the kind of catalyst, the deactivation processes, and the regeneration settings, each regeneration approach has pros and cons. In this comprehensive study, the focus was on discussing the reaction mechanism of 1-octene isomerization and toluene alkylation as an example of isomerization and alkylation reactions that occur simultaneously, shedding light in detail on the catalysts used for this type of complex reaction, taking into account the challenges facing the catalyst deactivation and reactivation procedures.
Marital Adjustment, Spiritual Well-Being, and Locus of Control in Married Couples
Authors: Bedir Akpınarlı Beyzanur , Eryücel Sema
Published: November 2024

The aim of this study is to examine the relationship between marital adjustment, spiritual well-being, and locus of control in married couples. A total of 526 married individuals living in İzmir, Turkey, 283 (53.8%) women and 243 (46.2%) men between the ages of 18 and 65, participated in the research. The married individuals were evaluated individually rather than as couples. The of control scale (LCS) developed by Dağ, the marital adjustment test (MAT) developed by Locke and Wallace and adapted into Turkish by Tutarel Kışlak, the spiritual well-being scale (SWBS) developed by Ekşi and Kardaş, and a socio-demographic information form were applied to the participants. SPSS 22.0 was used for statistical analysis of the data obtained in the study. The findings were evaluated at a 5% significance level within a 95% confidence interval (p < 0.05). The findings of the study revealed that the marital adjustment of male participants was higher than that of females, and that as spiritual well-being increased, marital adjustment also increased. A positive relationship was found between transcendence, one of the sub-dimensions of spiritual well-being, and marital adjustment and internal locus of control. Similarly, a positive correlation was identified between harmony with nature and marital adjustment and internal locus of control, while a negative relationship was found between anomie and marital adjustment and internal locus of control. Moreover, negative and significant relationships were found between marital adjustment and external locus of control determinants such as belief in luck, meaninglessness of making an effort, belief in an unjust world, and anomie, one of the sub-dimensions of spiritual well-being. Additionally, a positive relationship was found between higher education and internal locus of control, and a positive relationship between marriages conducted between the ages of 17 and 20 and an external locus of control.
The Politics of Christianity in Shaping the Political Dynamics of Zambia
Authors: Zgambo Timalizge
Published: November 2024

Religion can often be very influential in the political system and political actors frequently take advantage of the leverage that it provides. In the Zambian case, Christianity in particular plays a crucial role in politics and policymaking, dating from the pre- to post-colonial era. Around 1880, Zambia, then Northern Rhodesia, became a British colony and, at the same time, Christianity was introduced within the context of the European culture. Later, 27 years after independence, Zambia was declared a Christian nation, and all Zambian political leaders have embraced Christianity as the nation’s identity. Thus, Christianity plays a critical function in Zambia’s political sphere. The main aim of this paper is to critically examine how Christianity seeks to direct the political agenda in Zambia’s national politics. It demonstrates the interplay between church and state relations linked to how the state seeks to govern the nation in a Godly manner and the implications on public policymaking in Zambia. This paper explores a multifaceted analysis of the existing literature and the ideas around the politics of the state and religion. It argues that (i) Christianity in Zambia is often used as a political weapon to gain political mileage and (ii) Christianity as a religion has been traditionalised in Zambia. It serves as a “national moral campus”, which compromises the nation’s position as a so-called “democratic” state and suppresses individual freedoms. Thus, it corrupts the very nature of fundamental practices of the religion itself, as it has simply blossomed into more of a norm than a religion. Understanding these dynamics is very crucial, especially in the context of how religion is perceived, experienced and exercised in the political arena to circumvent limited policy options for broader problem solving.
Genre and Genesis: Locating Covenants in the Qurʾān and the Bible
Authors: Samji Karim
Published: November 2024

The present article considers the intersection between genre and covenant in scripture in order to locate historical covenants and to trace their complex textual boundaries. Standing on the shoulders of Smith, Wellhausen, and Gunkel, the joint legacy left behind by Martin and Albrecht Noth is equally significant for both Biblical and Islamic studies. Nothian scholarship laid solid foundations for reconstructing the history of tribal traditions. Both of them together created a common frame of reference for studying primary and secondary themes (Themen), literary and documentary forms (Formen), introductory and concluding formulas (Formeln), and theological and political perspectives (Tendenzen), in addition to geographical, transitional, and framing devices (Schemata). In spite of this shared scholarly heritage, it appears that both Biblical and Qurʾānic studies have either been talking at cross-purposes or have reached a critical crossroads, as has happened in the case of covenant. All things considered, genre and tradition criticism allow us to move forward beyond the Methodenstreit in covenant studies.
Predicting Mutual Fund Stress Levels Utilizing SEBI’s Stress Test Parameters in MidCap and SmallCap Funds Using Deep Learning Models
Authors: Maheshwari Suneel , Naik Deepak Raghava
Published: November 2024

Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our study utilizes AMFI datasets from February 2024 to September 2024 which consisted of 14 variables. Among these, the primary variable identified in grading mutual funds is the stress test parameter, expressed as number of days required to liquidate between 50% and 25% of the portfolio, respectively, on a pro-rata basis under stress conditions as a response variable. The objective of our paper is to build and test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors. The results suggest that the simpler neural network model architectures show higher accuracy. We used Artificial Neural Networks (ANN) over other machine learning methods due to its ability to analyze the impact of dynamic interrelationships among 14 variables on the dependent variable, independent of the statistical distribution of parameters considered. Predicting stress levels with the highest accuracy in MidCap and SmallCap mutual funds will benefit investors by reducing information asymmetry while allocating investments based on their risk tolerance. It will help policy makers in designing controls to protect smaller investors and provide warnings for funds with unusually high risk.
Market Predictability Before the Closing Bell Rings
Authors: Zhang Lu , Hua Lei
Published: November 2024

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations.
High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China
Authors: Yu Kangsan , Wang Shumin , Wang Yitong , Gu Ziying
Published: November 2024

Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, APbox and APseg, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in APbox and APseg, respectively. Importantly, the APseg of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment.
Building Change Detection Network Based on Multilevel Geometric Representation Optimization Using Frame Fields
Authors: He Fachuan , Chen Hao , Yang Shuting , Guo Zhixiang
Published: November 2024

To address the challenges of accurately segmenting irregular building boundaries in complex urban environments faced by existing remote sensing change detection methods, this paper proposes a building change detection network based on multilevel geometric representation optimization using frame fields called BuildingCDNet. The proposed method employs a multi-scale feature aggregation encoder–decoder architecture, leveraging contextual information to capture the characteristics of buildings of varying sizes in the imagery. Cross-attention mechanisms are incorporated to enhance the feature correlations between the change pairs. Additionally, the frame field is introduced into the network to model the complex geometric structure of the building target. By learning the local orientation information of the building structure, the frame field can effectively capture the geometric features of complex building features. During the training process, a multi-task learning strategy is used to align the predicted frame field with the real building outline, while learning the overall segmentation, edge outline, and corner point features of the building. This improves the accuracy of the building polygon representation. Furthermore, a discriminative loss function is constructed through multi-task learning to optimize the polygonal structured information of the building targets. The proposed method achieves state-of-the-art results on two commonly used datasets.
EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources
Authors: Ji Hanjie , Guo Lixin , Zhang Jinpeng , Wei Yiwen , Guo Xiangming , Zhang Yusheng
Published: November 2024

Given the significant spatial non-uniformity of marine evaporation ducts, accurately predicting the regional distribution of evaporation duct height (EDH) is crucial for ensuring the stable operation of radio systems. While machine-learning-based EDH prediction models have been extensively developed, they fail to provide the EDH distribution over large-scale regions in practical applications. To address this limitation, we have developed a novel spatiotemporal prediction model for EDH that integrates multiple environmental information sources, termed the EDH Spatiotemporal Network (EDH-STNet). This model is based on the Swin-Unet architecture, employing an Encoder–Decoder framework that utilizes consecutive Swin-Transformers. This design effectively captures complex spatial correlations and temporal characteristics. The EDH-STNet model also incorporates nonlinear relationships between various hydrometeorological parameters (HMPs) and EDH. In contrast to existing models, it introduces multiple HMPs to enhance these relationships. By adopting a data-driven approach that integrates these HMPs as prior information, the accuracy and reliability of spatiotemporal predictions are significantly improved. Comprehensive testing and evaluation demonstrate that the EDH-STNet model, which merges an advanced deep learning algorithm with multiple HMPs, yields accurate predictions of EDH for both immediate and future timeframes. This development offers a novel solution to ensure the stable operation of radio systems.
Terrestrial Photogrammetry–GIS Methodology for Measuring Rill Erosion at the Sparacia Experimental Area, Sicily
Authors: Palmeri Vincenzo , Di Stefano Costanza , Nicosia Alessio , Pampalone Vincenzo , Ferro Vito
Published: November 2024

Rill erosion is a major issue on a global scale, and predicting the presence, position, and development of erosive forms on hillslopes is a significant challenge for the scientific community. Several plot-scale investigations confirmed the reliability of the terrestrial photogrammetric (TP) technique for studying rill erosion and the reliability of a method for extracting the rill network from Digital Surface Models (DSMs) and measuring the corresponding volume. In this paper, for an intense erosive event that occurred at the Sparacia experimental area (Sicily, Southern Italy), TP surveys of three plots, with different length and steepness, incised by rills, were performed to reconstruct the DSMs. For each plot, the rill network was extracted from the DSMs, and the non-contributing network was distinguished from the contributing one, from which the soil loss and the consequent eroded volumes V were determined. The specific aims were to (i) establish the effect of plot steepness on rill depths and some morphometric characteristics of the drainage rill network; (ii) test and calibrate the relationship between V and the total rill length L, using all rill measurements available in the literature and those obtained in this study; and (iii) modify the V–L relationship by including climate forcing and assessing the related performance. The rill depths, h, the drainage frequency, and drainage density of the rill networks detected in the three plots were compared. The analysis demonstrated that h and the morphometric parameters of the contributing rill network increase with plot steepness s. In particular, the mean depth increases from 2.79 to 4.85 cm for slope increasing from 14.9 to 26%. Moreover, the drainage frequency of the contributing rill network varies from 0.16 m−2 for s = 14.9% to 0.47 m−2 for s = 26%, while the drainage density of the contributing rill network varies from 0.92 m−1 for s = 14.9% to 2.1 m−1 for s = 26%. Finally, using the data available in the literature and those obtained in this investigation, an empirical relationship between V and the total rill length L was firstly tested and then rearranged considering the event rainfall erosivity Re. Including Re in the rearranged equation guaranteed the best performance in V estimation.
Facial Movements Extracted from Video for the Kinematic Classification of Speech
Authors: Palmer Richard , Ward Roslyn , Helmholz Petra , Strauss Geoffrey R. , Davey Paul , Hennessey Neville , Orton Linda , Namasivayam Aravind
Published: November 2024

Speech Sound Disorders (SSDs) are prevalent communication problems in children that pose significant barriers to academic success and social participation. Accurate diagnosis is key to mitigating life-long impacts. We are developing a novel software solution—the Speech Movement and Acoustic Analysis Tracking (SMAAT) system to facilitate rapid and objective assessment of motor speech control issues underlying SSD. This study evaluates the feasibility of using automatically extracted three-dimensional (3D) facial measurements from single two-dimensional (2D) front-facing video cameras for classifying speech movements. Videos were recorded of 51 adults and 77 children between 3 and 4 years of age (all typically developed for age) saying 20 words from the mandibular and labial-facial levels of the Motor-Speech Hierarchy Probe Wordlist (MSH-PW). Measurements around the jaw and lips were automatically extracted from the 2D video frames using a state-of-the-art facial mesh detection and tracking algorithm, and each individual measurement was tested in a Leave-One-Out Cross-Validation (LOOCV) framework for its word classification performance. Statistics were evaluated at the α=0.05 significance level and several measurements were found to exhibit significant classification performance in both the adult and child cohorts. Importantly, measurements of depth indirectly inferred from the 2D video frames were among those found to be significant. The significant measurements were shown to match expectations of facial movements across the 20 words, demonstrating their potential applicability in supporting clinical evaluations of speech production.
Research on Beampattern Synthesis of Conformal Array Based on Space-Variability Suppression
Authors: Liu Sijia , Pan Minghai
Published: November 2024

In order to solve the problem that the array beampattern varies greatly with the angle during spatial scanning, this paper presents a synthesis method of the conformal array beampattern based on “spatial-variability (SV)” suppression. In order to obtain a stable beampattern, the convex optimization method is used to solve the weighted value of the amplitude and phase of the elements under the condition of satisfying the set beam gain threshold so that the optimized main lobes of the beams with different directions approach the main lobe of the reference beam. Since beams of different dimensions have different broadening degrees after optimization, we weighted the objective function and solved the optimal weights by the golden section search method (GSM) to improve the comprehensive optimization results of two-dimensional beams. The simulation results demonstrate the effectiveness of the proposed method.
Supported Influence Mapping for Mobile Robot Pathfinding in Dynamic Indoor Environments
Authors: Stawarz Paweł , Ozog Dominik , Łabuński Wojciech
Published: November 2024

Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors.
Demonstration of a Validated Direct Current Wearable Device for Monitoring Sweat Rate in Sports
Authors: Xuan Xing , Rojas Daniel , Lozano Isabel Maria Diaz , Cuartero Maria , Crespo Gastón A.
Published: November 2024

Sweat rate magnitude is a desired outcome for any wearable sensing patch dedicated to sweat analysis. Indeed, sweat rate values can be used two-fold: self-diagnosis of dehydration and correction/normalization of other physiological metrics, such as Borg scale, VO2, and different chemical species concentrations. Herein, a reliable sweat rate belt device for sweat rate monitoring was developed. The device measures sweat rates in the range from 1.0 to 5.0 µL min−1 (2 to 10 µL min−1 cm−2), which covers typical values for humans. The working mechanism is based on a new direct current (DC) step protocol activating a series of differential resistance measurements (spatially separated by 800 µm) that is gradually initiated by the action of sweat, which flows along a customized microfluidic track (~600 µm in width, 10 mm in length, and 235 µm in thickness). The device has a volumetric capacity of ~16 µL and an acquisition frequency between 0.010 and 0.043 Hz within the measured sweat rate range. Importantly, instead of using a typical and rather complex AC signal interrogation and acquisition, we put forward the DC approach, offering several benefits, such as simplified circuit design for easier fabrication and lower costs, as well as reduced power consumption and suitability for wearable applications. For the validation, either the commercial sweat collector (colorimetric) or the developed device was performed. In five on-body tests, an acceptable variation of ca. 10% was obtained. Overall, this study demonstrates the potential of the DC-based device for the monitoring of sweat rate and also its potential for implementation in any wearable sweat platform.
Validation of Multi-Frequency Inductive-Loop Measurement System for Parameters of Moving Vehicle Based on Laboratory Model
Authors: Marszalek Zbigniew , Duda Krzysztof
Published: November 2024

The paper presents research on a system for measuring the parameters of a vehicle in motion and the process of validating it under laboratory conditions. The measurement system uses four inductive-loop (IL) sensors, two slim ILs and two wide ILs. The vehicle speed, wheelbase, length, and overhangs are all determined on the basis of a vehicle magnetic profile (VMP) waveform. VMPs are captured from the continuous IL-based impedance measurement. The impedance measurement for a single IL is performed simultaneously at three carrier frequencies. The uncontrolled measurement conditions in the field on a real road test bed (RTB), including the speed of passing vehicles, motivated the development of a laboratory test bed (LTB). This LTB serves as a model of an existing measurement setup installed on the road, i.e., the RTB. The LTB includes IL sensors and a movable model of the vehicle made in 1:50 scale. The LTB enables validation of the whole measurement system in the vehicle speed range from 10 km/h up to 150 km/h in 5 km/h increments in fully controlled conditions. The measurement results are presented in the distance domain, calculated from the VMPs and the measured speed. The largest errors in estimating vehicle-model body parameters, on a natural scale, do not exceed 4.3 cm.
Unmanned Aerial Vehicle Photogrammetry for Monitoring the Geometric Changes of Reclaimed Landfills
Authors: Pasternak Grzegorz , Pasternak Klaudia , Koda Eugeniusz , Ogrodnik Paweł
Published: November 2024

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans.
Running Gait Complexity During an Overground, Mass-Participation Five-Kilometre Run
Authors: Jones Ben , Heller Ben , van Gelder Linda , Barnes Andrew , Reeves Joanna , Wheat Jon
Published: November 2024

Human locomotion contains innate variability which may provide health insights. Detrended fluctuation analysis (DFA) has been used to quantify the temporal structure of variability for treadmill running, although it has been less commonly applied to uncontrolled overground running. This study aimed to determine how running gait complexity changes in response to gradient and elapsed exercise duration during uncontrolled overground running. Sixty-eight participants completed an overground, mass-participation five-kilometre run (a parkrun). Stride times were recorded using an inertial measurement unit mounted on the distal shank. Data were divided into four consecutive intervals (uphill lap 1, downhill lap 1, uphill lap 2, downhill lap 2). The magnitude (SD) and structure (DFA) of stride time variability were compared across elapsed exercise duration and gradient using a repeated-measures ANOVA. Participants maintained consistent stride times throughout the run. Stride time DFA-α displayed a moderate decrease (d = |0.39| ± 0.13) during downhill running compared to uphill running. DFA-α did not change in response to elapsed exercise duration, although a greater stride time SD was found during the first section of lap 1 (d = |0.30| ± 0.12). These findings suggest that inter- and intra-run changes in gait complexity should be interpreted in the context of course elevation profiles before conclusions on human health are drawn.
The Biggest Decision of Your Life(Time)? Examining the Politics of Married at First Sight
Authors: Majic Samantha , Murib Zein
Published: November 2024

Lifetime’s “Married at First Sight” (MAFS) aired its seventeenth season in 2024, averaging 2.58 million viewers per weekly episode. In this paper, we ask, how does MAFS reflect and intervene in contemporary marriage politics, particularly regarding race, gender, class, and sexuality in the U.S.? To answer this question, we draw on scholarship about marriage as a political institution, and on reality TV as a window into contemporary socio-economic issues. Using interpretive, feminist methods of analysis, we find that MAFS reflects and intervenes in contemporary marriage politics by offering viewers a very traditional and exclusionary version of the institution at a time when it and everything else (reproductive rights and same-sex marriages, to name just two examples) is in flux. However, even as it attempts to offer a “balm” to all of this upheaval, in practice, the show’s “experimental results” offer something more complex, which both reflects the contemporary realities of marriage and attracts viewers.
Future Increase in Extreme Precipitation: Historical Data Analysis and Influential Factors
Authors: Zhang Hengfei , Mu Xinglong , Meng Fanxiang , Zheng Ennan , Dong Fangli , Li Tianxiao , Xu Fuwang
Published: November 2024

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.
A Systematic Review of the Literature on Climate Justice: A Comparison Between the Global North and South
Authors: Arruda Filho Marcos Tavares de , Torres Pedro Henrique Campello , Jacobi Pedro Roberto
Published: November 2024

This study analyzes the characteristics of publications on climate justice on a global scale and between countries in the North and South, using the guidelines of the PRISMA model. A total of 826 relevant papers were identified in five databases, with 717 from the Global North and 109 from the South. There is a concentration of work in the countries of the Global North. The social and economic disparities between countries in the North and South are reflected in the distribution of publications. The focus of climate justice research in the countries that make up the Global South is mainly on the socio-environmental context and the social vulnerability of the population; in this group, it is the first time that this theme has been expressed in the analysis.
Review of the Seasonal Wastewater Challenges in Baltic Coastal Tourist Areas: Insights from the NURSECOAST-II Project
Authors: Konkol Izabela , Kuligowski Ksawery , Szafranowicz Piotr , Vorne Virpi , Reinikainen Anu , Effelsberg Nina , Christensen Morten Lykkegaard , Svensson Maria , Zviedris Janis , Dvarioniene Jolanta , Cenian Adam
Published: November 2024

The NURSECOAST-II project addresses the challenge of managing wastewater in near-coastal tourist destinations around the Baltic Sea, particularly from small treatment plants (<2000 PE) that experience fluctuating flows due to seasonal tourism. These fluctuations make it difficult to meet environmental standards, potentially harming both the environment and tourism. The project has created a GIS-based inventory of small wastewater treatment plants within 100 km of the coast. This inventory includes crucial operational data like flow rates, pollutant levels, and treatment technologies. Initial findings reveal significant discrepancies in data management, regulations, and treatment standards across the Baltic Sea region countries, as EU legislation does not uniformly cover plants under 2000 PE. Key findings highlight that small treatment plants are often undocumented, their environmental impact underestimated, and regulations vary widely. Small plants can significantly contribute to nutrient pollution, affecting the Baltic Sea, particularly in local areas. The data gathered will support local authorities in identifying gaps and improving management strategies. This study stresses the need for harmonized data collection and reporting methods across countries and suggests establishing a unified database accessible to both specialists and the public. The status of the collected data depending on the type of data and country was as follows: 38.11% from Denmark, 46.14% from Estonia, 26.36% from Finland, 15.56% from Germany, 23.47% from Latvia, 34.77% from Lithuania, 14.51% from Poland, and 45.40% from Sweden. Ultimately, this project aims to enhance wastewater management, protect the environment, and improve tourist satisfaction in coastal regions.
Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach
Authors: Kohanpour Ehsan , Davoodi Seyed Rasoul , Shaaban Khaled
Published: November 2024

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems.
Biogas Production from a Solar-Heated Temperature-Controlled Biogas Digester
Authors: Makamure Francis , Mukumba Patrick , Makaka Golden
Published: November 2024

This research paper explores biogas production in an underground temperature-controlled fixed dome digester and compares it with a similar uncontrolled digester. Two underground fixed-dome digesters, one fitted with a solar heating system and a stirrer and the other one with an identical stirrer only, were batch-fed with cow dung slurry collected from the University of Fort Hare farm and mixed with water in a ratio of 1:1. The solar heating system consisted of a solar geyser, pex-al-pex tubing, an electric ball valve, a water circulation pump, an Arduino aided temperature control system, and a heat exchanger located at the centre of the digester. Both the digesters were intermittently stirred for 10 min every 4 h. The digester without a heating system was used as a control. Biogas production in the two digesters was compared to assess the effect of solar heating on biogas production. The total solids, volatile solids, and the chemical oxygen demand of the cow dung used as substrate were determined before and after digestion. These were compared together with the cumulative biogas produced and the methane content for the controlled and uncontrolled digesters. It was observed that the temperature control system kept the slurry temperature in the controlled digester within the required range for 82.76% of the retention period, showing an efficiency of 82.76%. Some maximum temperature gradients of 7.0 °C were observed in both the controlled and uncontrolled digesters, showing that the stirrer speed of 30 rpm was not fast enough to create the needed vortex for a uniform mix in the slurry. It was further observed that the heat from the solar geyser and the ground insulation were sufficient to keep the digester temperature within the required temperature range without any additional heat source even at night. Biogas yield was observed to depend on the pH with a strong coefficient of determination of 0.788 and 0.755 for the controlled and uncontrolled digesters, respectively. The cumulative biogas was 26.77 m3 and 18.05 m3 for controlled and uncontrolled digesters, respectively, which was an increase of 33%. The methane content increased by 14% while carbon dioxide decreased by 10% from the uncontrolled to the controlled scenario. The percentage removal of the TS, VS, and COD was 66.26%, 76.81%, and 74.69%, respectively, compared to 47.01%, 60.37%, and 57.86% for the uncontrolled situation. Thus, the percentage removal of TS, VS, and COD increased by 19.25%, 16.44%, and 16.89%, respectively.