Critical Details Research Articles

Lane detection using hough transformation and Yolov8

Autonomous vehicles necessitate the integration of advanced technologies such as computer vision and deep learning to comprehend and navigate their surroundings. A crucial yet challenging component of this integration is the accurate detection of lanes, which can be influenced by a multitude of varying lane characteristics and conditions. This research undertakes a comparative analysis of lane detection methodologies, explicitly focusing on traditional image processing techniques and Convolutional Neural Networks (CNNs). The evaluation utilized a sample of 500 images from the CULane dataset, which encompasses a diverse range of traffic scenarios. Initially, a method incorporating Gaussian blurring, Canny edge detection, and Hough line transformation was examined. Despite its efficiency, operating at 30 frames per second, this approach exhibited a high error rate (average Mean Squared Error (MSE) of 0.537), which is attributable to the loss of critical image details during the preprocessing stage. Subsequently, the performance of a fine-tuned YOLOv8 model, trained on a reformatted version of the CULane dataset was assessed. The combination of object detection and subsequent Hough transformation yielded high accuracy, demonstrating the model’s ability to learn and identify relevant lane features. The deep CNNs demonstrated superior performance over classical image processing techniques in terms of lane detection accuracy, thereby underscoring their potential applicability within the realm of autonomous vehicle technology

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

Simulating Virtual Organizations for Research: A Comparative Empirical Evaluation of Text-Based, Video, and Virtual Reality Video Vignettes

Due to recent technological developments, vignette studies that have traditionally been done in text or video formats can now be done in immersive formats using virtual reality—but are such virtual reality video vignettes superior to traditional vignettes? To address this question, we examine participants’ experiences within a fictitious organization by comparing their responses to a relevant and particularly sensitive organizational phenomenon presented either through written text, a video recording, or a virtual reality experience. The results indicate that participants prefer more immersive methods, and that these increase their attention to critical study details. Moreover, this augments the effect sizes of several measured employee reactions—particularly those with high emotional content—suggesting that virtual reality technology offers a promising avenue for developing ecologically valid vignette studies to measure employee affect. To facilitate and expediate the use of virtual reality video vignettes in organizational research, we provide organizational scholars with a step-by-step instructional guide to develop immersive vignette studies.

Visual attention methods in deep learning: An in-depth survey

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated into one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey on attention techniques to guide researchers in employing attention in their deep models. Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques, categorizing them by their most prominent features. We initiate our discussion by introducing the fundamental concepts behind the success of the attention mechanism. Next, we furnish some essentials such as the strengths and limitations of each attention category, describe their fundamental building blocks, basic formulations with primary usage, and applications specifically for computer vision. We also discuss the challenges and general open questions related to attention mechanisms. Finally, we recommend possible future research directions for deep attention. All the information about visual attention methods in deep learning is provided at https://github.com/saeed-anwar/VisualAttention

Enhancing Diagnostic Accuracy: A Hybrid Approach to Mitigate Speckle and Rician Noise in Medical Images

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach for speckle noise and separates images into magnitude and phase components for Rician noise. This allows for targeted filtering, utilizing customized versions of median and Gaussian filters, to effectively remove noise while preserving crucial image features. The proposed method was rigorously assessed using 200 contaminated brain and lung MRIs and radiographs, demonstrating superior performance compared to traditional filters based on crucial system of measurement like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). This research presents a promising solution for enhancing the quality and reliability of medical images, potentially leading to improved diagnostic accuracy and patient outcomes.

Insights into the Pharmacognostic Elucidation of Harumanis Mango (Mangifera Indica Linn.) Leaves Extracts as Therapeutic Agent

Mangifera indica L. (mango) of the Anacardiaceae family is a fruit with many phytochemicals that are extensively used in various traditional systems of medicine to prevent and treat various diseases. In Malaysia, harumanis is a local variety of mango widely cultivated in the northern region, namely Perlis. However, the drawback of this harumanis variety of mango is that research and information on its therapeutics are scarce and limited. Therefore, localized harumanis leaves were introduced to a series of pharmacognostic studies to provide critical details explaining harumanis as a potential therapeutic agent. Initially, the harumanis leaves were introduced to quality analysis tests, unveiling that the raw material used abided by proper post-harvesting protocols. Then, the harumanis leaves were extracted using ultrasonic aided extraction (UAE) with 100% methanol (13.73±0.24 %), 50% methanol (14.47±0.19 %), 100% ethanol (10.35±0.19 %), 50% ethanol (12.61±0.26 %), and distilled water (8.09±0.21 %). Next, the initial phytochemical screening revealed the presence of flavonoids, tannins, and saponins in all extracts. Subsequently, the phenolic and flavonoid content were quantified with the 50% methanol extracts recorded the highest total phenolic content (5.76 ± 0.02 mg/g), 50% ethanol extracts had the highest total flavonoid content (4.38 ± 0.04 mg/g). Fingerprinting analysis via FTIR spectroscopy was used to identify phytochemicals confirming the presence of flavonoids and phenolics compounds in all extracts. The extracts were then introduced to a DPPH inhibition assay with the highest IC50 value obtained from 50% methanol with 75.2 ± 0.22 microgram per milliliter. Next, the antimicrobial capabilities were tested using a disc diffusion study on E. coli, B. subtilis, and Face normal flora showing that the methanolic extract has superior abilities in microbial inhibitions with the value recorded at 13mm, 22mm, and 14mm respectively. Overall, we obtained valuable pharmacognostic data on the harumanis leaves extracts as potential antioxidant and antimicrobial agents.

Improved dual-aggregation polyp segmentation network combining a pyramid vision transformer with a fully convolutional network.

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style. Concretely, in the transformer branch, we adopted the B3 variant of pyramid vision transformer v2 (PVTv2-B3) as an image encoder for capturing multi-scale global features and modeling long-distant interdependencies between them whilst designing an innovative multi-stage feature aggregation decoder (MFAD) to highlight critical local feature details and effectively integrate them into global features. In the decoder, the adaptive feature aggregation (AFA) block was constructed for fusing high-level feature representations of different scales generated by the PVTv2-B3 encoder in a stepwise adaptive manner for refining global semantic information, while the ResidualBlock module was devised to mine detailed boundary cues disguised in low-level features. With the assistance of the selective global-to-local fusion head (SGLFH) module, the resulting boundary details were aggregated selectively with these global semantic features, strengthening these hierarchical features to cope with scale variations of polyps. The FCN branch embedded in the designed ResidualBlock module was used to encourage extraction of highly merged fine features to match the outputs of the Transformer branch into full-size segmentation maps. In this way, both branches were reciprocally influenced and complemented to enhance the discrimination capability of polyp features and enable a more accurate prediction of a full-size segmentation map. Extensive experiments on five challenging polyp segmentation benchmarks demonstrated that the proposed Dua-PSNet owned powerful learning and generalization ability and advanced the state-of-the-art segmentation performance among existing cutting-edge methods. These excellent results showed our Dua-PSNet had great potential to be a promising solution for practical polyp segmentation tasks in which wide variations of data typically occurred.

Multi-Frequency Field Perception and Sparse Progressive Network for low-light image enhancement

Images taken in low-light conditions often suffer from various types of degradation. While most current methods primarily focus on spatial domain information to address these degradation issues, they often overlook the importance of frequency domain information. In order to better solve the degradation problems of low-light images, the Multi-Frequency Field Perception and Sparse Progressive Network (MFSPNet) for low-light image enhancement is proposed through Leveraging the complementary strengths of the frequency domain and the spatial domain, aiming to tackle the challenges of degraded images in intricate scenarios. Specifically, we propose the frequency domain field feature filtering (FDFF) module, which utilizes image frequency distribution information to address issues such as noise and artifacts in low-light images while complementing the spatial domain. Subsequently, we embed different scales of FDFF into four heterogeneous branches to flexibly handle features at various levels of complexity. Furthermore, we design a sparse wing-shaped transformer block (SWTB) that enhances the focus on critical information and complex multi-scale details through adaptive sparse attention unit (ASAU) and illumination multi-scale fusion feedforward network (IMF-FN). In addition, we propose a progressive enhancement strategy for self-knowledge sublimation to gradually improve image quality. At last, we comprehensively assess the proposed network across multiple datasets. Compared to other methods, our approach achieved the highest PSNR scores, with improvements of 3.014 dB and 0.215 dB, respectively, over the next best results. Additionally, our method exhibited the highest SSIM gain. Abundant experimental outcomes demonstrate that our approach outperforms numerous present low-light image enhancement approaches in both objective evaluation metrics and subjective visual effects, showcasing outstanding performance and significant potential.

Nutrients and polyphenols-rich Sorghum bicolor genotypes as complementary therapy for Alzheimer’s disease

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder and most common cause of dementia among older people. The main pathological hallmarks of AD are formation of insoluble amyloid beta senile plaques and paired helical filaments of neurofibrillary tangles. AD features gradual memory decline, mild to severe cognitive impairment, eventually total dependence of patients on caregivers. Currently available drugs have not been able to modify AD pathology. This has drawn increasing attention to plant food materials with high nutritional and bioactive constituents as potential complementary therapy for AD. Sorghum bicolor is a widely available cost-effective source of proteins, fats, crude fibres, biopeptides and polyphenols which are vital for human wellbeing. This review discussed the major mechanisms underlying AD pathology. The nutritional and bioactive constituents of Sorghum bicolor grains were extensively described. There is limited report on anti-AD activities of sorghum grains. Therefore, the pharmacological mechanisms of action including scavenging of reactive oxygen species, inhibition of oxidative stress, anti-acetylcholinesterase activity and modulation of mitophagy were only speculated. This comprehensive update suggests more robust innovative studies that will provide critical theoretical details necessary to promote utilization of sorghum grains as functional food or source of bioactive molecules for AD therapy.

A-Mode Ultrasound Bladder Volume Estimation Algorithm Based on Wavelet Energy Ratio Adaptive Denoising.

Assessing bladder function is pivotal in urological health, with bladder volume a critical indicator. Traditional devices, hindered by high costs and cumbersome sizes, are being increasingly supplemented by portable alternatives; however, these alternatives often fall short in measurement accuracy. Addressing this gap, this study introduces a novel A-mode ultrasound-based bladder volume estimation algorithm optimized for portable devices, combining efficient, precise volume estimation with enhanced usability. Through the innovative application of a wavelet energy ratio adaptive denoising method, the algorithm significantly improves the signal-to-noise ratio, preserving critical signal details amidst device and environmental noise. Ultrasonic echoes were employed to acquire positional information on the anterior and posterior walls of the bladder at several points, with an ellipsoid fitted to these points using the least squares method for bladder volume estimation. Ultimately, a simulation experiment was conducted on an underwater porcine bladder. The experimental results indicate that the bladder volume estimation error of the algorithm is approximately 8.3%. This study offers a viable solution to enhance the accuracy and usability of portable devices for urological health monitoring, demonstrating significant potential for clinical application.

Revised description of the blueberry bud mite, Acalitus vaccinii (Acari: Trombidiformes: Eriophyidae), and a key to all Eriophyoidea on Vaccinium

In 2014 the blueberry bud mite, Acalitus vaccinii (Acari: Trombidformes: Eriophyidae), was detected in the Mpumalanga province of South Africa for the first time causing significant damage to cultivated blueberries. This was the first detection of this pest outside of North America, to which it is native. However, its taxonomic description at that time lacked critical detail and omitted characters and life stages important for easy and accurate identification. Using an integrative taxonomic approach, we combined phase contrast light microscopy with low-temperature SEM and DNA barcoding data to revise the description of A. vaccinii using South African specimens. Additional characters not included in previous descriptions but reported here are the h1 (accessory) setae, leg I & II u’ (mesal) setae, and leg II bv (femoral) setae. Detailed descriptions and measurements of all life stages are included, along with a discussion of morphological variation and biology. Two DNA sequences of common barcode regions (nuclear and mitochondrial) are provided to further aid in identification. In addition, a key to all known species of eriophyoid mites present on Vaccinium is provided.

Fabrication Aspects and Performance Characterization of α-Al2O3/AlPO4 Based Sandwich Configuration Flow Channel Inserts and Coatings for High Temperature Liquid Metal Applications

Abstract Liquid metals (LMs) exhibit several key characteristics justifying their utilization as coolants and breeders for nuclear fusion reactors and advanced fission reactors. In fusion reactors, the LMs confront an exorbitantly high flow retarding force, due to the magneto-hydro-dynamics (MHD) effect, imposing significant demands on the pumping power and designs of ancillary coolant systems. Corrosion of structural materials leading to activated corrosion products and coolant chemistry control are some of the vital issues common to both fusion and fission reactors employing liquid lead (Pb) and its alloys. To address these concerns, different technological solutions such as flow channel inserts (FCIs) and high temperature compatible corrosion resistant coatings are being investigated to provide a chemical and/or electrical isolation between the LM and structural material for advanced reactors. In this study, three different prototype geometries (circular, square, and 90 deg bend) of steel-insulator-steel sandwich FCIs are fabricated for fusion reactor applications and an extensive characterization of the electrical insulation is performed over an operating temperature range of 100 °C–600 °C. Welding trials and pneumatic pressure tests up to 10 kg/cm2 (g) are performed on the assemblies to validate the electrical and mechanical integrity over typical fusion reactor operational regime. This paper presents detailed fabrication aspects along with quantitative estimations of insulation filling density, electrical insulation performance and, for the first time, a detailed systematic study of insulation degradation resulting from the combined effects of tungsten inert gas (TIG) welding, exposure to pressure and machining operations on these FCIs. The paper also provides critical details derived from the metallurgical examinations and visual observations from the destructive tests executed on the prototypes. Further, from an implementation perspective toward Lead-cooled Fast Reactors (LFRs), a preliminary feasibility assessment of the α-Al2O3/AlPO4 coating is performed through thin film deposition trials on planar and non-planar substrates followed by mechanical characterizations, such as coating thickness, surface roughness, adhesion strength and microhardness. Metallurgical analyses are presented and discussed to assess Pb ingress after 700 h of continuous exposure to molten Pb alloy at 300 °C–400 °C.

FASO-C: A rapid visualization technique based on optimized fusion with crossover-based atom search for multi-band imagery

Multi-band image fusion is a vital image processing technique used to combine data from multiple images or sensor bands, particularly in scenarios where a wide range of spectral information is available. Its primary objective is to create a single, comprehensive image that not only visually enhances the scene but also preserves critical details and characteristics present in each source. This paper proposes a novel image fusion technique that combines the useful features from a set of source images to provide a quick visualization of the scene. The approach is highly beneficial for multi-band images, including a large number of bands acquired by sampling the wavelength spectrum at narrow intervals. The fusion process depends on a multi-objective cost function, which optimizes the resultant fused image according to specific desired characteristics. By solving this optimization framework, an optimal set of weights is obtained using a novel Fusion-Driven Atom Search Optimization with Crossover (FASO-C) algorithm for the fusion of the image bands. Crossover operation enhances the exploitation capability of ASO, enabling the global optimal solution to be achieved quickly. To further validate the effectiveness of the proposed fusion model, various experiments were conducted to assess its performance using both subjective and objective metrics, as well as analyze its convergence.

The influence of sandwich panel cladding on horizontal structure stiffness

This paper presents a study on the influence of sandwich panel roof cladding on the horizontal stiffness of load-bearing steel structures (stressed skin action). A full-scale test was carried out on a steel monopitch portal frame structure made in two stages – without (bare steel structure) and with roof sandwich panel cladding. Data obtained from both stages of the test are presented and are used to demonstrate the significant increase in horizontal stiffness caused by the cladding system. The test data were then used to validate an FE numerical model. A roof cladding made of composite panels is relatively stiff in its own plane. However, the critical detail is the connection of the panels to the sub-structure (e.g. purlins or rafters), i.e. the screw bearing capacity. Even within the serviceability limits, the internal sheet of the composite panel suffers a certain level of permanent bearing damage. Based on the test data, the results of the FE numerical model and the analytical approach for calculating the shear stiffness of the sandwich panels, the approach for taking into account the influence of the cladding on the horizontal stiffness was derived and compared. This approach is believed to be safe and simple for practical use, and it leads to load-bearing structure members with more slender cross-sections. In addition, the data and results that are obtained expand the limited amount of available data related to stressed skin action.

SatCoBiLSTM: Self-attention based hybrid deep learning framework for crisis event detection in social media

Social media platforms have emerged as vital sources of sharing real-time information during crises, enabling users to share critical details about disasters and ensuring timely awareness. This study addresses the daunting challenge of effectively detecting crisis events from a vast, noisy stream of short text data. To address the above-mentioned issue, we present a self-attention-based hybrid deep learning framework, SatCoBiLSTM, designed to extract hierarchical textual features and crucial information from textual data. The proposed model integrates a multi-scale convolution and BiLSTM layer to extract local and contextual relations, alongside a self-attention layer to select and focus on the most relevant crisis features. Various experiments have been conducted on three benchmark real-world crisis datasets to examine the proposed model’s performance. SatCoBiLSTM achieved an impressive F1-score of 96%, 94%, and 95% on the three public datasets. It also shows a promising improvement in the F1-score, compared to the state-of-the-art (SOTA) and baseline methods, by 1%, 1%, and 6%, respectively, showing its effectiveness in crisis event detection. An ablation analysis has been carried out to investigate the validity of each integrated layer in our model. The SatCoBiLSTM model’s capability to identify crisis-related information highlights its potential to enhance real-time awareness during disasters. Eventually, the study advances crisis event detection and lays the foundation for future research to process and handle short-textual data in noisy environments.

FERONIA: A Receptor Kinase at the Core of a Global Signaling Network.

Initially identified as a key regulator of female fertility in Arabidopsis, the FERONIA (FER) receptor kinase is now recognized as crucial for almost all aspects of plant growth and survival. FER partners with a glycosylphosphatidylinositol-anchored protein of the LLG family to act as coreceptors on the cell surface. The FER-LLG coreceptor interacts with different RAPID ALKALINIZATION FACTOR (RALF) peptide ligands to function in various growth and developmental processes and to respond to challenges from the environment. The RALF-FER-LLG signaling modules interact with molecules in the cell wall, cell membrane, cytoplasm, and nucleus and mediate an interwoven signaling network. Multiple FER-LLG modules, each anchored by FER or a FER-related receptor kinase, have been studied, illustrating the functional diversity and the mechanistic complexity of the FER family signaling modules. The challenges going forward are to distill from this complexity the unifying schemes where possible and attain precision and refinement in the knowledge of critical details upon which future investigations can be built. By focusing on the extensively characterized FER, this review provides foundational information to guide the next phase of research on FER in model as well as crop species and potential applications for improving plant growth and resilience.

Fatigue study on improved composite box girder bridge with corrugated steel webs

The purpose of the study is to estimate the fatigue behavior of an improved composite box girder bridge with corrugated steel webs (ICBGBCSW) based on a three-dimensional vehicle-bridge coupled vibration system (VBCVS). In the VBCVS, the benchmark of the ICBGBCSW widely used in the highway bridges of Gansu Province of China was adopted as the bridge model, and a 6-axle fatigue load model established based on the traffic data of China was adopted as the fatigue vehicle loading. Based on the established VBCVS, the critical fatigue details of the ICBGBCSW were determined and the effects of pavement roughness condition (PRC) and vehicle speed on the stress ranges experienced by critical fatigue details were first investigated. Then, the fatigue live of critical fatigue details were evaluated based on the deterministic analysis method and reliability analysis method. The results show that the fatigue life of the ICBGBCSW can reach more than 375 years from a deterministic perspective under the current traffic conditions of Gansu Province of China and that the fatigue reliability index of the ICBGBCSW sharply decreases to the target fatigue reliability index of 2 if the PRC stays in the “Poor” class and the “Very poor” class for eight years and two years, respectively. The results obtained in the study can provide a technique support for the wide application of the ICBGBCSW and provide a reference for the bridge management agency to make plans for bridge pavement maintenance.

Empire-Laden Theory

James Clerk Maxwell’s theories of electromagnetism are distinctively Victorian products. Analysis of his often ignored theory of electric absorption and the Maxwellians’ “leaky condenser” reveals that critical details of these theories were shaped by Victorian electrical technology, namely capacitors and undersea telegraphy. Between appearances in his “Dynamical Theory” and Treatise, Maxwell’s theory of electric absorption evolved. It shifted his understanding of electrical action in the dielectric, bolstered central concepts in his broader electromagnetic theories, provided hope for a beleaguered experimental program to confirm his electromagnetic theory of light, and even led him to distort his expression for Ohm’s law. Simultaneously, the technological influences behind his theories come with their own histories. Maxwell draws heavily upon the testimony of cable engineer Fleeming Jenkin to the Joint Committee on the Construction of Submarine Telegraphs, formed to rescue the industry after multiple failed attempts to lay an Atlantic cable. Maxwell’s reliance on this testimony given to this committee imprints the financial and imperial ambition that initially spurred these cables’ construction onto his electromagnetic theories. A substance discussed in this testimony, gutta-percha, also connects Maxwell’s theory to the extractive global trade of this resource. The success of this committee in reforming the telegraph industry links Maxwell’s theories to the colonial, economic, and ecological fallout of the rapid global expansion of Britain’s undersea telegraph network.

Pharmacists' Counseling and Benzodiazepines Dispensing for Sleep Disorders: A Simulated Patient Study in Iran.

Sleep is critical for good health and quality of life, but many people struggle with sleep disorders. Pharmacists are on the front lines, helping patients manage these problems. However, there is growing concern that some pharmacists are dispensing benzodiazepines over-the-counter and failing to provide proper counseling. This study examined how pharmacists in Iran performed in these areas. Between January and April 2022, we conducted a cross-sectional study in three major Iranian cities, using a "simulated patient" to observe how pharmacists interacted with them. In total, 431 pharmacies participated, and we used detailed forms to record the pharmacists' behavior. We then analyzed the data using descriptive statistics and the Chi-square tests. Of 549 visits, 78.5% were managed by pharmacists, whereas the remainder were managed by other pharmacy staff. 79.7% of pharmacists evaluated the patient before deciding whether or not to prescribe the medication and 58.9% provided a kind of counseling for their offered medication, but just 10.6% of pharmacies had a private counseling area. Despite regulations that require a valid prescription for benzodiazepines, 9.2% of pharmacies dispensed diazepam, and 13.2% dispensed alprazolam without requesting one, and when counseling was offered, it often lacked critical details. These findings raise serious concerns. There are deficiencies in how pharmacists and patients interact, with many pharmacists spending minimal time assessing patient needs. The high rates of benzodiazepine dispensing without valid prescriptions and inadequate counseling point to a need for stricter protocols and more training. To address these issues, health-care professionals and policymakers must collaborate to improve the quality and safety of sleep disorder treatment in community pharmacies.

Online misinformation can distort witnesses' memories. Analysis of co-witness discussions using an online version of the MORI-v technique.

The memory conformity effect occurs when people witness a given incident and then talk to each other about it, and the statement of one person affects the memory account of another person with respect to that incident. The main objectives of this experiment were (1) to examine the effectiveness of a modified version of the MORI-v technique in inducing the memory conformity effect and (2) to investigate how the manner in which participants discuss the observed event influences the magnitude of this effect. In general, the modified online MORI-v technique consists of the following main elements: (1) original material, that is, two versions of a short film which are identical except for certain critical details; for example, in one version, a thief puts on a red cap, but in the other version it is black; (2) the collaborative recognition test, that is, a discussion about the original material which leads to mutual misinformation; and (3) an individual recognition test that checks the effect of the discussion on the memory account of the original material. A total of 72 participants (36 pairs) aged 18-54 took part in the research. Participants were tested using the online MORI-v technique: They were familiarized with the original material on their computers at home, and then they talked about it via a video communication app and completed an individual recognition test on their computers. Importantly, the discussions were recorded and analyzed in detail after the experimental session. Using the online MORI-v technique, the effect of memory conformity was demonstrated, that is, in the individual recognition test, the proportion of correct answers to questions about discussed details (related to misinformation) was lower than the proportion of correct answers to questions about non-discussed details. It was also demonstrated that if one participant introduced misinformation during the discussion about a particular item and the other did not question it, the latter's answer to that item during the individual recognition test was most often incorrect. However, if one participant introduced misinformation during the discussion about an item and the other questioned it, the latter's answer about that item during the individual recognition test was most often correct.