Police vehicle contamination by inorganic gunshot residue (iGSR) in Zagreb County Police Administration (Croatia): Analysis of characteristic and indicative particles across different vehicle parts and contributory risk factors.

Inorganic gunshot residue (iGSR) analysis, crucial for linking suspects to firearm use, faces challenges from potential environmental contamination, notably in police vehicles. The present study aimed to explore the level of iGSR contamination in police vehicles from the Zagreb County Police Administration (Croatia), considering particle types and their position in vehicles, and to identify associated risk factors. From December 2021 to April 2022, 65 of 86 police vehicles (margin of error: ±6% at a 95% confidence level) were sampled with GSR stubs on the drivers' seats, back seats, and backrests and analyzed using scanning electron microscopy and energy dispersive x-ray analysis (SEM/EDX). Characteristic particles were found in 63.1% of vehicles, 33.8% on the driver's seat, and 24.6% on the back seat/backrest. Indicative particles were found in 70.77% of vehicles, with a fairly even distribution. McNemar's chi-square analysis showed no significant disparities in positive sample ratios across vehicle parts or particle types (p > 0.05). In total, 228 characteristic and 166 indicative GSR particles were identified, with no notable correlation among them (p = 0.346). Logistic regression analysis identified the transportation of individuals involved in firearms incidents as a statistically significant factor influencing the presence of characteristic particles (p = 0.030). The findings suggest a considerable prevalence of iGSR in the analyzed Police Administration unit, highlighting the need for careful contamination management in police operations to preserve evidence integrity, particularly in cases when individuals who used firearms had been transported in the vehicle.

One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Beyond the shot: Exploring secondary transfer of gunshot residue on common surfaces and the impact of hand cleaning methods.

Understanding the presence, transfer dynamics and depletion of gunshot residues (GSR) on various surfaces is crucial for preserving evidence, reconstructing shooting incidents, and linking suspects to crime scenes. This study aims to explore the transfer and loss of GSR on commonly encountered surfaces such as ceramic, glass, metal, paper, and plastic, as well as the influence of different common hand cleaning methods on secondary transfer. Using scanning electron microscopy (SEM) combined with energy dispersive X-ray analysis (EDX) and automated detection software, we quantified highly indicative three-component characteristic particles (lead, barium, and antimony) on cups made from ceramic, glass, metal, paper, and plastic. Furthermore, we evaluated the amount of secondary transferred particles on these surfaces following various post-discharge hand cleaning methods: washing with water and soap, washing with only water, wiping with wet wipes, or using paper towels. The results demonstrate that counts of secondarily transferred GSR particles vary significantly among surfaces. Specifically, the transferred GSR count was highest on paper, followed by plastic, ceramic, metal, and glass respectively. Post-discharge hand cleaning methods, including washing with water and soap, washing with only water, cleaning with wet wipes, or with paper towel, resulted in substantial loss of GSR count on transferred surfaces. Among these methods, washing with water and soap showed the highest depletion. The empirical evidence provided by our results underscores the importance of considering surface properties, post-shooting activities, and the methods of sample collection and analysis when interpreting transferred GSR analysis. Despite challenges, these insights enhance our ability to link suspects to shooting crimes through careful consideration of the entire context.

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants' arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal-valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students' emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings.

Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study.

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4-10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement-disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual.

A Study on the correlation of the physico-chemical properties of Gunshot Residue (GSR) particles with distance using a 9×19 mm Indian ammunition.

The trio elements found in Gunshot Residue (GSR) are considered the key elements that are characteristic of GSR. To date, most forensic laboratories have mainly concentrated on employing carbon stubs analyzed by Scanning Electron Microscopy (SEM) coupled with Energy Dispersive Spectroscopy (EDS) to find IGSR on the hands and clothing of a person. A little elevated from the normal practice, this work is focused on the evaluation of compositional and morphological variations of GSR collected from muzzle end, trajectory, and target obtained by firing the ammunition of choice (9×19 mm Indian ammunition). Even though there may be variations in IGSR compositions within various locations of a weapon, this hasn't been investigated or documented up to this point. To ascertain whether it is possible to identify any variation in GSR particles gathered from these three different locations, the objective of this study is to investigate the structural characteristics and elemental composition of GSR to identify the distinctive parameters that allow for comparison and to establish the composition of the primer. The study also focuses on assessing any possible surface modification that may occur to GSR upon striking the target and establishing a correlation between GSR particles and propellant powder. The collected GSR samples were analyzed using a digital microscope, SEM/EDS, and EDXRF. It was discovered that the primer type showed a strong correlation to the elemental composition and morphology of GSR. By analyzing the GSR particles collected from the various sites as mentioned above, it was possible to identify the primer mixture used in the ammunition and its diversity in elemental concentration. The obtained GSR samples were not spherical but showed an elongated structure and possessed a diameter ranging from 695.4 µm-1.640 mm, 536.2 µm-1.412 mm, and 775.8 µm-1.772 mm respectively. However, the morphology and the size distribution of the particles collected from all three different points showed slight deviation as moving from ME towards TG. The obtained results could identify the primer mixture and diversity in its elemental concentration. The morphology and size distribution of GSR collected from three different points showed deviations.

Sensing of Gunshot Residue components from real sample using Fluorescence Resonance Energy Transfer.

The present work represents a Fluorescence Resonance Energy Transfer (FRET) based sensing method for detecting Gunshot Residue (GSR) components. Two laser dyes Acf and RhB have been used as donor and acceptor respectively in the FRET pair. The real sample was collected after test firing in a forensic science laboratory. On the other hand, a standard GSR solution has been prepared in the laboratory. For the preparation of standard GSR solutions, we used the water solutions of the salts BaCl2, SbCl3, and Pb(NO3)2. The FRET efficiency was measured between Acf and RhB to sense the presence of GSR components (Pb+2, Ba+2, and Sb+3) in both real sample and standard solution by mixing the salts in aqueous solution. It has been observed that the FRET efficiency systematically decreases in the presence of GSR components. To amplify the FRET efficiency of the dye pair, inorganic clay dispersion (laponite) was used. The enhancement in FRET efficiency represents a better sensitivity of the proposed sensor. The current sensor is useful for the quantification of concentrations of the GSR components in a real sample.

Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements.

Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.

Gunshot residue (GSR): Frequency of residue types encountered in case work and background levels on control samples.

The evaluation of criminal cases involving the discharge of a firearm requires reliable and up to date information regarding the transfer and persistence of gunshot residue (GSR). Similarly, knowledge of background levels of GSR on relevant populations and awareness of the potential for contamination/secondary transfer is essential. In this paper we build on previous work published by this laboratory and provide an update on the frequency of gunshot residue types in discharged cartridge casings (DCC) encountered in casework within the Republic of Ireland. In conjunction, an examination of the types of firearms encountered in casework and the associated residue types is undertaken. Finally, a review of levels of GSR particles detected on control samples taken from members of An Garda Síochána, the Irish police is detailed. Control samples are taken before a police officer samples a detainee suspected of involvement in an incident where a firearm was discharged and/or subsequently handled.

Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering.

OBJECTIVES: In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. To address these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering. METHODS: In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method. RESULTS: Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods. CONCLUSIONS: The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.

Evaluation of bicyclist physiological response and visual attention in commercial vehicle loading zones.

INTRODUCTION: With growing freight operations throughout the world, there is a push for transportation systems to accommodate trucks during loading and unloading operations. Currently, many urban locations do not provide loading and unloading zones, which results in trucks parking in places that obstruct bicyclist's roadway infrastructure (e.g., bicycle lanes). METHOD: To understand the implications of these truck operations, a bicycle simulation experiment was designed to evaluate the impact of commercial vehicle loading and unloading activities on safe and efficient bicycle operations in a shared urban roadway environment. A fully counterbalanced, partially randomized, factorial design was chosen to explore three independent variables: commercial vehicle loading zone (CVLZ) sizes with three levels (i.e., no CVLZ, Min CVLZ, and Max CVLZ), courier position with three levels (i.e., no courier, behind the truck, beside the truck), and with and without loading accessories. Bicyclist's physiological response and eye tracking were used as performance measures. Data were obtained from 48 participants, resulting in 864 observations in 18 experimental scenarios using linear mixed-effects models (LMM). RESULTS: Results from the LMMs suggest that loading zone size and courier position had the greatest effect on bicyclist's physiological responses. Bicyclists had approximately two peaks-per-minute higher when riding in the condition that included no CVLZ and courier on the side compared to the base conditions (i.e., Max CVLZ and no courier). Additionally, when the courier was beside the truck, bicyclist's eye fixation durations (sec) were one (s) greater than when the courier was located behind the truck, indicating that bicyclists were more alert as they passed by the courier. The presence of accessories had the lowest influence on both bicyclists' physiological response and eye tracking measures. PRACTICAL APPLICATIONS: These findings could support better roadway and CVLZ design guidelines, which will allow our urban street system to operate more efficiently, safely, and reliable for all users.

New layers of regulation of the general stress response sigma factor RpoS.

The general stress response (GSR) sigma factor RpoS from Escherichia coli has emerged as one of the key paradigms for study of how numerous signal inputs are accepted at multiple levels into a single pathway for regulation of gene expression output. While many studies have elucidated the key pathways controlling the production and activity of this sigma factor, recent discoveries have uncovered still more regulatory mechanisms which feed into the network. Moreover, while the regulon of this sigma factor comprises a large proportion of the E. coli genome, the downstream expression levels of all the RpoS target genes are not identically affected by RpoS upregulation but respond heterogeneously, both within and between cells. This minireview highlights the most recent developments in our understanding of RpoS regulation and expression, in particular those which influence the regulatory network at different levels from previously well-studied pathways.

Improved method for stress detection using bio-sensor technology and machine learning algorithms.

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

Structural segregation in GSR from mercuric primers. A preliminary study.

Since the second half of the XX century, primer mixtures based on mercury fulminate have become a rare occurrence on small ammunition markets in Western Europe and North America. As a consequence, Hg-containing gunshot residue (GSR) particles have not been as deeply investigated as residues from lead-based primer mixtures. As a matter of fact, no mention of GSR particles from mercuric primers is made by the current ASTM standard procedure for gunshot residue analysis. However, those laboratories dealing with ammunition and firearms produced in Eastern Europe or Asia still have a forensic interest in Hg-containing GSR. In this paper, a brief description of chemical composition and inner morphology of GSR particles from three different mercuric primers is reported. Regarding composition, arguments are given to promote SbSnHg residues to Characteristic of GSR particles when mercuric primers are discharged. From a morphological point of view, presence of inner nodules and other inhomogeneities were shown in GSR particles milled in a FIB/SEM. Moreover, mercury vaporization under the electron beam was observed for a particle reduced to a lamella. Mercury evanescence in GSR was interpreted in terms of mercury segregation during particle formation and higher mobility of Hg atoms in presence of defects (vacancies) in a strained lattice.

Cognition-Emotion Interaction during L2 Sentence Comprehension: The Correlation of ERP and GSR Responses to Sense Combinations.

This study mainly examined the role of the combination of three senses (i.e., auditory, visual, and tactile) and five senses (i.e., auditory, visual, tactile, olfactory, and gustatory) in the correlation between electrophysiological and electrodermal responses underlying second language (L2) sentence comprehension. Forty subjects did two acceptability judgment tasks, encompassing congruent and semantically/pragmatically incongruent sentences. The event-related potential (ERP) and galvanic skin response (GSR) data for both the target and final words of the sentences were collected and analyzed. The results revealed that there is an interaction between cognitive and emotional responses in both semantically and pragmatically incongruent sentences, yet the timing of the interaction is longer in sentences with pragmatic incongruity due to their complexity. Based on the ERP and GSR correlation results, it was further found that the five-sense combination approach improves L2 sentence comprehension and interest in learning materials yet reduces the level of excitement or arousal. While this approach might be beneficial for some learners, it might be detrimental for those in favor of stimulating learning environments.

Conditioning to true content and artificial intelligence in psychophysiological intention recognition.

OBJECTIVE: The objective is to introduce a novel method for classical conditioning to true content (CtTC), and for the first time, apply this approach in the concealed information test (CIT) to effectively discern intentions. During CtTC, participants are trained to exhibit electrodermal responses whenever they recognize true content on a screen. Additionally, the objective is to evaluate a novel CIT-dataset preprocessing algorithm, employed to enhance machine learning (ML) classification performance. METHODS: A total of 84 participants were evenly divided into four groups. Two groups of participants devised plans for stealing money from a supermarket, while the other two groups did not engage in any planning. One planning group and one non-planning group underwent CIT examination, while the remaining groups were subjected to CtTC. RESULTS: The CIT accuracy initially stood at 52 % and increased to 71 % after Z-score and ML classification (McNemar test, p < 0.05). Conversely, the CtTC accuracy was 76 % and significantly improved to 93 % following Z-score and 95 % following ML classification (McNemar test, p < 0.05). In the best-performing classifiers, CtTC exhibited significantly superior metrics for guilty/innocent classification compared to CIT (Fisher's exact test, p < 0.05, power 1 - ß > 0.90). In the CtTC group, reactivity and sensitivity significantly increased, indicated by higher EDR amplitudes (p < 0.05, two-tailed t-test, power 1 - ß = 0.89) and the number of EDRs (p < 0.05, Fisher's exact test, power 1 - ß = 0.90). There was no statistically significant difference between the Z-score and ML classification. CONCLUSIONS: In the assessment of intentions, CtTC enhances both the sensitivity and accuracy of the CIT.

Plasma oxidative stress marker levels related to functional brain abnormalities in first-episode drug-naive major depressive disorder.

Major Depressive Disorder (MDD) is marked by abnormal brain function and elevated plasma oxidative stress markers. The specific relationship between these factors in MDD remains unclear. In this study, we conducted resting-state fMRI scans on fifty-seven first-episode, drug-naive MDD patients and sixty healthy controls. Plasma levels of oxidative stress markers (superoxide dismutase (SOD) and glutathione reductase (GSR)) were assessed using ELISA. Our results revealed a positive correlation between plasma SOD and GSR levels in MDD patients and the amplitude of low-frequency fluctuation (ALFF) values in key brain regions-thalamus, anterior cingulate gyrus, and superior frontal gyrus. Further analysis indicated positive correlations between plasma SOD and GSR levels and specific ALFF values in MDD patients without suicidal ideation, with these correlations not significant in MDD patients with suicidal ideation. Additionally, seed-based whole-brain functional connectivity analysis demonstrated a negative correlation between plasma GSR levels and connectivity between the thalamus and insula, while plasma SOD levels showed a positive correlation with connectivity between the thalamus and precuneus. These findings contribute to our understanding of MDD's pathophysiology and heterogeneity, highlighting the association between plasma oxidative stress markers and functional abnormalities in diverse brain regions.

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks.

Working memory tasks, such as n-back and arithmetic tasks, are frequently used in studying mental workload. The present study investigated and compared the sensitivity of several physiological measures at three levels of difficulty of n-back and arithmetic tasks. The results showed significant differences in fixation duration and pupil diameter among three task difficulty levels for both n-back and arithmetic tasks. Pupil diameters increase with increasing mental workload, whereas fixation duration decreases. Blink duration and heart rate (HR) were significantly increased as task difficulty increased in the n-back task, while root mean square of successive differences (RMSSD) and standard deviation of R-R intervals (SDNN) were significantly decreased in the arithmetic task. On the other hand, blink rate and Galvanic Skin Response (GSR) were not sensitive enough to assess the differences in task difficulty for both tasks. All significant physiological measures yielded significant differences between low and high task difficulty except for SDNN.Practitioner summary: This study aimed to assess the sensitivity levels of several physiological measures of mental workload in n-back and arithmetic tasks. It showed that pupil diameter was the most sensitive in both tasks. This study also found that most physiological indices are sensitive to an extreme change in task difficulty levels.

Cr and CeO2 promoted Ni/SBA-15 framework for hydrogen production by steam reforming of glycerol.

Ni/SBA-15 meso-structured catalysts modified with chromium and CeO2 (Ni-Cr-CeO2/SBA-15) were utilized to produce hydrogen from glycerol steam reforming (GSR). The catalysts were synthesized by a one-pot hydrothermal process and extensively characterized by analytical techniques such as N2 adsorption-desorption (BET), H2-temperature programmed reduction (H2-TPR), powder X-ray diffraction (PXRD), inductively coupled plasma-optical emission spectrometry (ICP-OES), and transmission electron microscopy (TEM). The low-angle XRD reflections affirmed that the catalysts were crystalline and possessed a 2D-ordered porosity. The BET results depicted that all the catalysts exhibited a good surface area ranging from 633 to 792m2/g, and the pore sizes were consistently in the mesoporous range (between 3 and 5 nm). TEM analysis of both calcined and spent catalysts revealed that the metal active sites were embedded in the hybrid CeO2-SiO2 support. Overall, the Ni-based catalysts exhibited higher glycerol conversion -12Ni-SBA-15-99.9%, 12Ni3CeO2-SBA-15-89.4%, and 8Ni4Cr3CeO2-SBA-15-99.7%. Monometallic 12Ni/SBA-15 performed exceptionally well, while 12Cr/SBA-15 performed poorly with the highest 71.48% CO selectivity. For short-term GSR reactions, CeO2 addition to 12Ni/SBA-15 did not have any effect, whereas Cr addition resulted in a 32% decrease in H2 selectivity. The long-term stability studies of 12Ni-SBA-15 showed H2 selectivity of ~ 64% and ~ 98% glycerol conversion. However, its activity was short-lived. After 20-30 h, the H2 selectivity and conversion dropped precipitously to 40%. The doping of mesoporous Ni/SBA-15 with Cr and CeO2 remarkably enhanced the long-term stability of the catalyst for 12Ni3CeO2-SBA-15, and 8Ni4Cr3CeO2-SBA-15 catalyst which showed ~ 58% H2 selectivity and ~ 100% conversion for the entire 60 h. Interestingly, Cr and CeO2 seem to improve the shelf-life of Ni-SBA-15 via different mechanistic pathways. CeO2 mitigated Ni poisoning through coke oxidation whereas Cr bolstered the catalyst stability via maintaining a well-defined pore size, structural rigidity, and integrity of the heterogeneous framework, thereby restricting structural collapse, and hence retard sintering of the Ni active sites during the long-term 60 h of continuous reaction. Hydrogen generation from renewable biomass like glycerol could potentially serve as a sustainable energy source and could substantially help reduce the carbon footprint of the environment.

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors.

Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers' inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers' inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers' physiological features and attention.