bluuebunny/crossref_metadata_2025 huggingface dataset:
An assessment of appraisals of dating relationship conflicts and perceptions of appropriate coping strategies with psychologically abusive interactions
Authors: Matheson Kimberly , Wornovitzky Daphne , Landry Jyllenna , Anisman Hymie
Published: December 2023

Introduction

Stemming from a stress appraisal and coping perspective, the present investigation developed a methodology for assessing how individuals appraise abusive dating relationship conflicts (Study 1) and the implications of such appraisals for informing coping responses to abusive interactions (Study 2).

Methods

Participants ranging in age from 17 to 29 years (Study 1: 102 males, 339 females; Study 2: 88 males, 362 females) completed a survey in which they were presented with a series of 10 scenarios that conveyed relationship conflict cues that were ostensibly aligned with various forms of psychological abuse.

Results

Factor analyses indicated that blatant actions conducted in privacy were differentiated from more ambiguous public forms of psychological abuse, in that the latter were appraised by both males and females as more abusive. Females were further likely to appraise blatant conflicts as more threatening but at the same time more resolvable. Participants who had encountered abuse in their own intimate relationships were especially likely to appraise conflicts as abusive, threatening and uncontrollable. Such appraisals were associated with greater endorsement of avoidant coping strategies in response to an abusive encounter, irrespective of personal relationship experiences.

Discussion

It is suggested that how individuals appraise relationship conflicts may be key to their ability to cope effectively with such encounters or to provide appropriate support to those experiencing psychologically abusive relationships.
Case report: Anti N-methyl-D-aspartate autoimmune encephalitis following a mildly symptomatic COVID-19 infection in an adolescent male
Authors: Hainmueller Thomas , Lewis Lambert , Furer Tzvi
Published: December 2023

Background

Antibodies against N-methyl-D-aspartate receptors are the most commonly identified cause of autoimmune encephalitis. While predominantly associated with malignancies, cases of anti-N-methyl-D-aspartate receptor autoimmune encephalitis have been reported after infections with the herpes-simplex virus or, more recently, in patients with severe COVID-19 disease.

Case presentation

A previously healthy 17-year-old male adolescent acutely developed psychosis with auditory and visual hallucinations, fluctuating mental status, and an isolated seizure 5 weeks after a mildly symptomatic COVID-19 infection. The symptoms continued to worsen, accompanied by catatonia, and additional neurological symptoms developed during the initial antipsychotic treatment. A diagnostic workup revealed antibodies against N-methyl-D-aspartate receptors in the cerebrospinal fluid without other major abnormalities. After establishing the diagnosis, initiation of immunomodulatory therapy stopped the symptom progression and led to full recovery within 2 months.

Conclusion

The case is remarkable in that anti-N-methyl-D-aspartate receptor autoimmune encephalitis developed shortly after a COVID-19 infection in an adolescent, despite the individual experiencing only mild COVID symptoms. The diagnosis should be considered in cases of acute-onset psychotic symptoms during or after COVID-19 infection, particularly in individuals without a prior psychiatric history, who present with atypical psychiatric or neurological features.
Risk analysis of depression among adult patients with epilepsy of different sex: a retrospective single-center study from China
Authors: Guo Wang , Li Yu-xuan , Zhang Yi , Lv Xue-rui , Wang Sheng-xue , Zhang Shuang-yuan , Wang En-si , Chen Xin-jie , Li Yun
Published: December 2023

Objective

To determine sex differences in the prevalence of depression and assess the risk factors for depression among adult patients with epilepsy from the Dali area of China.

Methods

We retrospectively analyzed the clinical data of adult patients with epilepsy who visited the First Affiliated Hospital of Dali University from January 2017 to January 2022. Patient Health Questionnaire-9 was used to assess depressive symptoms in patients with epilepsy. The risk factors of depression were analyzed by binary logistic regression among different sex in patients with epilepsy.

Results

There were significant sex differences in depression in patients with epilepsy (p < 0.001), and females were 4.27 times more likely to suffer from depression than males (95% confidence interval: 3.70–4.92). The risk factors for depression among female patients with epilepsy included occupation (p < 0.001), years with epilepsy (p < 0.001), seizure frequency (p < 0.001), seizure type (p < 0.001), etiology (p < 0.001), number of antiseizure medications used (p < 0.001), antiseizure medications (p < 0.001), and electroencephalogram findings (p < 0.001). The risk factors for depression among male patients with epilepsy included age (p < 0.001), ethnicity (p < 0.001), occupation (p < 0.001), years with epilepsy (p < 0.001), seizure frequency (p < 0.001), seizure type (p < 0.001), etiology (p < 0.001), number of antiseizure medications used (p < 0.001), antiseizure medications (p < 0.001), and electroencephalogram findings (p < 0.001).

Conclusion

Adult female patients with epilepsy had a higher risk of depression than adult male patients with epilepsy. There were sex differences in the risk factors associated with depression among patients with epilepsy.
Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity
Authors: Rządeczka Marcin , Wodziński Maciej , Moskalewicz Marcin
Published: December 2023

This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.
Arthropod microbiota: shaping pathogen establishment and enabling control
Authors: Pavanelo Daniel B. , Piloto-Sardiñas Elianne , Maitre Apolline , Abuin-Denis Lianet , Kopáček Petr , Cabezas-Cruz Alejandro , Fogaça Andrea C.
Published: December 2023

Vector-borne diseases (VBDs) pose significant global health threats. The microbiota of arthropod vectors influences their fitness and pathogen acquisition and/or transmission. Here, we review the intricate interplay among the arthropod immune system, the microbiota, and pathogens that limits or favors infection. We focused on the most important arthropod vectors, namely mosquitos, phlebotomines, tsetse flies, triatomines, and ticks, and expanded our analysis to include the nonvector model Drosophila melanogaster for comparison. The microbiota and immune system of arthropod vectors are targets for the development of promising control strategies, such as paratransgenesis and anti‐microbiota vaccines. Further research should focus on elucidating the underlying mechanisms of vector–pathogen–microbiota interactions and optimizing anti-microbiota strategies. These approaches have the potential to combat VBDs and reduce their global impact.
IoT devices deployment challenges and studies in building management system
Authors: Chan Raymond , Yan Wye Kaye , Ma Jung Man , Loh Kai Mun , Yu Tan , Low Malcolm Yoke Hean , Yar Kar Peo , Rehman Habib , Phua Thong Chee
Published: December 2023

Deployment of IoT devices into an existing control system has increasingly become a new norm in recent years. Traditional approach of installing a new device into the system involves creating a separate network or infrastructure to ensure that the newly added devices do not affect the current system. However, this may not be the optimal solution for IoT devices, as they are designed to integrate and communicate with the existing systems. Therefore, it is important to understand how to properly deploy IoT devices and address the concerns of engineers. This research shares valuable experiences in deploying and integrating IoT devices into a campus Building Management System It covers considerations and requirements for the devices, as well as the deployment and integration challenges encountered during the process. These valuable experiences can serve as a useful reference for the industry when they need to install IoT devices in their infrastructure.
Potential food safety risks in tomato value chains in urban settings of Eastern Ethiopia: a qualitative investigation
Authors: Amenu Kebede , Megersa Bekele , Jaleta Megarsa Bedasa , Dinede Getachew , Worku Hable , Kasim Kemal , Taha Mukerem , Ibrahim Abdulmuen M. , Kedir Jafer , Mego Lina , Roesel Kristina , Roothaert Ralph , Srinivasan Ramasamy , Grace Delia , Knight-Jones Theodore
Published: December 2023

Background

The value chains of tomatoes in Ethiopia are largely informal which may pose potential food safety risks. Value chains (VCs) mapping was carried out with emphasis on tomatoes in two major cities in eastern Ethiopia—Harar and Dire Dawa—which were assessed to identify practices likely to result in unsafe food, considering production, transportation, retail, preparation, and consumption.

Methods

Qualitative methods were used to map the VCs to understand the flows, actors, and practices. Group discussions and key informant interviews were performed to better understand the processes, practices, beliefs, and food safety risks in these VCs.

Results

The two cities are supplied by two vegetable VCs: the first and larger being from distant producers in central Ethiopia, and the second from surrounding rural and peri-urban producers. The long-distance VCs involve producers, brokers, transporters, wholesalers, retailers, and consumers. The local producers, however, bring their fresh products directly to the cities without the involvement of VC actors other than final retailers and consumers. The study suggests microbiological contamination risks along tomato VCs, potentially including soil contamination, use of sewerage-contaminated irrigation water, untreated manure, unhygienic handling and storage conditions, and dirty contact surfaces during transportation and retailing. Tomatoes are mostly harvested by hand picking, collected, and sorted on the ground exposing to sunlight and physical bruising with potential contamination. More importantly, tomatoes are widely consumed raw without “a kill-step” that certainly contributes to food-borne infections. Suggestions by study participants for improving food safety and hygiene include funding toward improved infrastructure and facilities in the sectors, supporting VC actors with improved technology for quality production, and increasing awareness of good and hygienic practices. Consumers were particularly concerned about contamination with agrochemicals without much emphasis on the potential microbial contaminants. Fresh tomatoes are prepared in a variety of ways and are often consumed raw or slightly cooked. Further recommendations included using health extension workers to conduct awareness campaigns on improved food safety and hygienic practices.

Conclusion

The qualitative VC mapping generated useful information for designing intervention strategies, especially targeting developing food safety interventions and an awareness communication campaign.
Influence of three different manure treatments on antimicrobial resistance genes and mobile genetic elements
Authors: Flores-Orozco Daniel , Levin David , Kumar Ayush , Sparling Richard , Cicek Nazim
Published: December 2023

There is a growing concern about the potential dissemination of antimicrobial resistance into agricultural fields due to the application of manure as crop fertilizer. While reducing the usage of antibiotics in livestock production stands as the first alternative to prevent this problem, there is evidence that this may not be enough to eliminate antimicrobial resistance elements already present in manure microbiomes. This study employed a metagenomic approach to investigate the impact of common manure treatments, including aerobic storage, mesophilic anaerobic digestion (MAD), and solid-liquid separation, on the presence and abundance of antimicrobial resistance genes (ARGs), bactericides, and heavy metal resistance genes (BacMet), and mobile genetic elements (MGEs) in manure from three different farms, including one operating in an antibiotic-free environment. The results indicated that MAD was the best method to reduce the numbers of ARGs, BacMet, and MGEs, achieving reduction rates greater than 40%, 89%, and 68%, respectively. Manure storage significantly reduced BacMet levels (over 30%) and MGEs (28%) but had no significant effect on total ARG levels. Solids recovered through solid-liquid separation exhibited elevated levels of ARGs, BacMet, and MGEs, while the liquid fraction displayed levels similar to untreated manures. Correlation and co-occurrence modeling analyses indicated that changes in microbial communities, particularly fluctuations in aerobic and facultative communities belonging to Bacillota, Actinomycetota, and Pseudomonadota phyla, played a significant role in driving changes in ARGs, BacMet, and MGEs. The results also showed the presence of toxin-antitoxin and transposon systems near different ARGs. Overall, the results confirmed that genes conferring resistance to various antimicrobials and MGE capable of mobilizing them are widely spread in dairy farms; that even under the absence of antibiotics, the use of heavy metals and disinfectants may promote the maintenance of ARGs and MGEs, and; that treatment such as anaerobic digestion could reduce the risk of the spread of antimicrobial resistance.
Haystack Fires in Australia: Causes and Considerations for Preventative Management
Authors: Chowdhury Imtiaz F. , Moore Joseph R. , Broster John C.
Published: December 2023

The spontaneous combustion of hay when stacked after baling is an issue frequently encountered by farmers in Australia and elsewhere. While there is a basic understanding of why this occurs the interactions of the many factors involved mean that there is still no consistent methodology for its prevention. Recent technological advances in sensors and communications allow for the continual collection of quantitative data from hay bales or stacks for managers to utilize in their decision-making processes with regards to minimizing the risks of spontaneous combustion. This review discusses both the factors involved in the spontaneous combustion of haystacks and the types of sensors available for the monitoring of these factors. This includes advancements in sensor technologies and their practical applications in monitoring hay bale conditions.
Potential Use of Plant Growth-Promoting Bacteria to Enhance Growth and Soil Fertility in Marginal Areas: Focus on the Apulia Region, Italy
Authors: Racioppo Angela , d’Amelio Annalisa , De Santis Alessandro , Bevilacqua Antonio , Corbo Maria Rosaria , Sinigaglia Milena
Published: December 2023

Soil degradation is a global problem and refers to the reduction or loss of the biological and economic productive capacity of the soil resource. In Europe, the countries most affected by soil degradation are undoubtedly those of the Mediterranean basin. Among these, Italy shows clear signs of degradation, with different characteristics, especially in the southern regions, where climatic and meteorological conditions strongly contribute to it. Apulia, the Tavoliere plain in particular, is a fragile and very sensitive ecosystem due to its intrinsic characteristics and the level of anthropic exploitation. Agricultural production pays the highest price, as increasing desertification due to climate change and the loss of agricultural land severely limit the extent of land available to produce food for an ever-growing population. Plant growth-promoting bacteria (PGPB) could be a low-cost and long-term solution to restore soil fertility, as they provide a wide range of benefits in agriculture, including increasing crop productivity, improving soil nutrient levels and inhibiting the growth of pathogens. This review shows how PGPB can be used to improve the quality of soils, their impact on agriculture, their tolerance to abiotic stresses (drought, salinity, heavy metals and organic pollutants) and their feasibility. The use of PGPB could be promoted as a green technology to be applied in marginal areas of Apulia to increase soil fertility, reduce pollution and mitigate the impacts of abiotic stresses and climate change. This is supported by a series of studies showing that the growth of plants inoculated with PGPB is superior to that of non-inoculated plants.
Blue Light Damage and p53: Unravelling the Role of p53 in Oxidative-Stress-Induced Retinal Apoptosis
Authors: Fietz Agnes , Corsi Francesca , Hurst José , Schnichels Sven
Published: December 2023

In the digital age, the widespread presence of electronic devices has exposed humans to an exceptional amount of blue light (BL) emitted from screens, LEDs, and other sources. Studies have shown that prolonged exposure to BL could have harmful effects on the visual system and circadian rhythm regulation. BL is known to induce oxidative stress, leading to DNA damage. Emerging research indicates that BL may also induce cell death pathways that involve the tumor-suppressor protein p53. Activated p53 acts as a transcription factor to regulate the expression of genes involved in cell cycle arrest, DNA repair, and apoptosis. This study aimed to explore the implication of p53 in BL-caused retinal damage, shedding light on the potential mechanisms of oxidative-stress-induced retinal diseases. BL-exposed porcine retinal cultures demonstrated increased p53- and caspase-mediated apoptosis, depending on exposure duration. Direct inhibition of p53 via pifithrin α resulted in the prevention of retinal cell death. These findings raise concerns about the long-term consequences of the current daily BL exposure and its potential involvement in various pathological conditions, including oxidative-stress-based retinal diseases like age-related macular degeneration. In addition, this study paves the way for the development of novel therapeutic approaches for oxidative-stress-based retinal diseases.
N-Acetylcysteine and Atherosclerosis: Promises and Challenges
Authors: Cui Yuqi , Zhu Qiang , Hao Hong , Flaker Gregory C. , Liu Zhenguo
Published: December 2023

Atherosclerosis remains a leading cause of cardiovascular diseases. Although the mechanism for atherosclerosis is complex and has not been fully understood, inflammation and oxidative stress play a critical role in the development and progression of atherosclerosis. N-acetylcysteine (NAC) has been used as a mucolytic agent and an antidote for acetaminophen overdose with a well-established safety profile. NAC has antioxidant and anti-inflammatory effects through multiple mechanisms, including an increase in the intracellular glutathione level and an attenuation of the nuclear factor kappa-B mediated production of inflammatory cytokines like tumor necrosis factor-alpha and interleukins. Numerous animal studies have demonstrated that NAC significantly decreases the development and progression of atherosclerosis. However, the data on the outcomes of clinical studies in patients with atherosclerosis have been limited and inconsistent. The purpose of this review is to summarize the data on the effect of NAC on atherosclerosis from both pre-clinical and clinical studies and discuss the potential mechanisms of action of NAC on atherosclerosis, as well as challenges in the field.
Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices
Authors: Farman Haleem , Nasralla Moustafa M. , Khattak Sohaib Bin Altaf , Jan Bilal
Published: December 2023

Fire detection employing vision sensors has drawn significant attention within the computer vision community, primarily due to its practicality and utility. Previous research predominantly relied on basic color features, a methodology that has since been surpassed by adopting deep learning models for enhanced accuracy. Nevertheless, the persistence of false alarms and increased computational demands remains challenging. Furthermore, contemporary feed-forward neural networks face difficulties stemming from their initialization and weight allocation processes, often resulting in vanishing-gradient issues that hinder convergence. This investigation recognizes the considerable challenges and introduces the cost-effective Encoded EfficientNet (E-EFNet) model. This model demonstrates exceptional proficiency in fire recognition while concurrently mitigating the incidence of false alarms. E-EFNet leverages the lightweight EfficientNetB0 as a foundational feature extractor, augmented by a series of stacked autoencoders for refined feature extraction before the final classification phase. In contrast to conventional linear connections, E-EFNet adopts dense connections, significantly enhancing its effectiveness in identifying fire-related scenes. We employ a randomized weight initialization strategy to mitigate the vexing problem of vanishing gradients and expedite convergence. Comprehensive evaluation against contemporary state-of-the-art benchmarks reaffirms E-EFNet’s superior recognition capabilities. The proposed model outperformed state-of-the-art approaches in accuracy over the Foggia and Yar datasets by achieving a higher accuracy of 0.31 and 0.40, respectively, and its adaptability for efficient inferencing on edge devices. Our study thoroughly assesses various deep models before ultimately selecting E-EFNet as the optimal solution for these pressing challenges in fire detection.
Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning
Authors: Chen Ke , Xiao Bo , Wang Chunyang , Liu Xuelian , Liang Shuning , Zhang Xu
Published: December 2023

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.
Joint Channel and Power Assignment for Underwater Cognitive Acoustic Networks on Marine Mammal-Friendly
Authors: Xue Libin , Cao Chunjie
Published: December 2023

When marine animals and underwater acoustic sensor networks (UASNs) share spectrum resources, problems such as serious harm caused to marine animals by underwater acoustic systems and scarcity of underwater spectrum resources are encountered. To address these issues, a mammal-friendly underwater acoustic sensor network channel power allocation algorithm is proposed. Firstly, marine animals are treated as authorized users and sensor nodes as unauthorized users. Considering the interference level of sensor nodes on authorized users, this approach improves network service quality and achieves a mammal-friendly underwater communication mechanism. Secondly, to maximize the utility of unauthorized users, the algorithm incorporates a network interference level and node remaining energy into a game-theoretical framework. Using channel allocation and power control, a game model is constructed with a unique Nash equilibrium point. Finally, through simulation, it can be found that the proposed algorithm can obtain a stable optimal power value, and with the increase of network load, the system capacity of the proposed algorithm is significantly improved than that of the traditional cognitive radio technology and the common spectrum allocation algorithm, and the transmitted power of nodes can be controlled according to the size of the residual energy, so as to comprehensively improve the overall performance of the network.
Robust Watermarking Algorithm for Building Information Modeling Based on Element Perturbation and Invisible Characters
Authors: Zhou Qianwen , Zhu Changqing , Ren Na
Published: December 2023

With the increasing ease of building information modeling data usage, digital watermarking technology has become increasingly crucial for BIM data copyright protection. In response to the problem that existing robust watermarking methods mainly focus on BIM exchange formats and cannot adapt to BIM data, a novel watermarking algorithm specifically designed for BIM data, which combines element perturbation and invisible character embedding, is proposed. The proposed algorithm first calculates the centroid of the enclosing box to locate the elements, and establishes a synchronous relationship between the element coordinates and the watermarked bits using a mapping mechanism, by which the watermarking robustness is effectively enhanced. Taking into consideration both data availability and the need for watermark invisibility, the algorithm classifies the BIM elements based on their mobility, and perturbs the movable elements while embedding invisible characters within the attributes of the immovable elements. Then, the watermark information after dislocation is embedded into the data. We use building model and structural model BIM data to carry out the experiments, and the results demonstrate that the signal-to-noise ratio and peak signal-to-noise ratio before and after watermark embedding are both greater than 100 dB. In addition, the increased information redundancy accounts for less than 0.15% of the original data., which means watermark embedding has very little impact on the original data. Additionally, the NC coefficient of watermark extraction is higher than 0.85 when facing attacks such as translation, element addition, element deletion, and geometry–property separation. These findings indicate a high level of imperceptibility and robustness offered by the algorithm. In conclusion, the robust watermarking algorithm for BIM data fulfills the practical requirements and provides a feasible solution for protecting the copyright of BIM data.
Local Thickness Optimization of Functionally Graded Lattice Structures in Compression
Authors: Decker Thierry , Kedziora Slawomir
Published: December 2023

This paper presents a new method for optimizing the thickness distribution of a functionally graded lattice structure. It links the thickness of discrete lattice regions via mathematical functions, reducing the required number of optimization variables while being applicable to highly nonlinear models and arbitrary optimization goals. This study demonstrates the method’s functionality by altering the local thickness of a lattice structure in compression, optimizing the structure’s specific energy absorption at constant weight. The simulation results suggest significant improvement potential for the investigated Simple Cubic lattice, but less so for the Isotruss variant. The energy absorption levels of the physical test results closely agree with the simulations; however, great care must be taken to accurately capture material and geometry deviations stemming from the manufacturing process. The proposed method can be applied to other lattice structures or goals and could be useful in a wide range of applications where the optimization of lightweight and high-performance structures is required.
Hydraulic Analysis of a Passive Wedge Wire Water Intake Screen for Ichthyofauna Protection
Authors: Zielina Michał , Pawłowska-Salach Agata , Kaczmarski Karol
Published: December 2023

A passive wedge screen, thanks to its many functional and environmental advantages, has recently become a popular type of surface water intake for municipal and industrial purposes. The design solutions proposed in this paper for a passive wedge wire screen intake model and two different deflectors have been experimentally tested under conditions that can be considered as no-flow conditions at the hydraulic flume. There was only a slight flow associated with the operation of the screen, while there was almost no flow in the hydraulic channel itself, such that it would be considered a watercourse. A hydraulic analysis was carried out, including velocity distribution around the screen as well as the determination of head losses with or without deflectors installed inside the screen. Lower inlet and inflow velocities to the surface of the water intake reduce the risk of injury or death to small fish and fry as well as attracting pollutants understood as sediments, debris, and plant remains floating in the river. In order to achieve the lowest possible maximum inlet and inflow velocities at the highest possible intake capacity, it was necessary to equalize the approach velocity distributions. It was shown that by using the proposed deflectors, the approach velocity distributions were equalized and the maximum values of inflow and inlet velocities were reduced. A water intake screen with a deflector with an uneven porosity distribution equalized the approach velocities better than a deflector with equal openings, but the differences were small. Installing the wedge screen model reduced the maximum inlet velocity from exceeding 2 m/s to a value of 0.08 m/s, and after installing deflectors with equal and unequal openings to values of 0.06 m/s and 0.05 m/s, respectively. In addition to laboratory tests, the paper describes the numerical simulations performed in ANSYS Fluent software. The results of the simulations made it possible to obtain a broader study, as well as to compare the velocity values obtained at the measuring points during the laboratory tests.
Skin Imaging: A Digital Twin for Geometric Deviations on Manufactured Surfaces
Authors: Ghanbary Kalajahi Elnaz , Mahboubkhah Mehran , Barari Ahmad
Published: December 2023

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces.
Statistical Analysis of Ceiling and Floor Effects in Medical Trials
Authors: Arslan Janan , Benke Kurt
Published: December 2023

Exploratory data analysis and statistical moments were used to investigate the potential impact of ceiling and floor effects in medical trials. A total of 150 treatment-naive eyes were assessed in a retrospective case study of patients who were treated with anti-VEGF injections for wet age-related macular degeneration. The experimental results revealed that ceiling and floor effects are problematic in data analysis and may result in serious errors when using standard parametric tests. The case study provided insights relating to methodology in medical trials, experimental data analysis, and statistical inference, as applied to the interpretation of treatment response limits. Suggestions are provided for statistical data pre-processing and post-processing when significantly skewed distributions are present in response groups.
Employing Active Learning in Medium Optimization for Selective Bacterial Growth
Authors: Zhang Shuyang , Aida Honoka , Ying Bei-Wen
Published: December 2023

Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.
Octopus vulgaris Exhibits Interindividual Differences in Behavioural and Problem-Solving Performance
Authors: Dissegna Andrea , Borrelli Luciana , Ponte Giovanna , Chiandetti Cinzia , Fiorito Graziano
Published: December 2023

By presenting individual Octopus vulgaris with an extractive foraging problem with a puzzle box, we examined the possible correlation between behavioural performances (e.g., ease of adaptation to captive conditions, prevalence of neophobic and neophilic behaviours, and propensity to learn individually or by observing conspecifics), biotic (body and brain size, age, sex) and abiotic (seasonality and place of origin) factors. We found more neophilic animals showing shorter latencies to approach the puzzle box and higher probability of solving the task; also, shorter times to solve the task were correlated with better performance on the individual learning task. However, the most neophilic octopuses that approached the puzzle box more quickly did not reach the solution earlier than other individuals, suggesting that strong neophilic tendency may lead to suboptimal performance at some stages of the problem-solving process. In addition, seasonal and environmental characteristics of location of origin appear to influence the rate of expression of individual traits central to problem solving. Overall, our analysis provides new insights into the traits associated with problem solving in invertebrates and highlights the presence of adaptive mechanisms that promote population-level changes in octopuses’ behavioural traits.
Collagen Structured Hydration
Authors: Biswal Satyaranjan , Agmon Noam
Published: December 2023

Collagen is a triple-helical protein unique to the extracellular matrix, conferring rigidity and stability to tissues such as bone and tendon. For the [(PPG)10]3 collagen-mimetic peptide at room temperature, our molecular dynamics simulations show that these properties result in a remarkably ordered first hydration layer of water molecules hydrogen bonded to the backbone carbonyl (bb-CO) oxygen atoms. This originates from the following observations. The radius of gyration attests that the PPG triplets are organized along a straight line, so that all triplets (excepting the ends) are equivalent. The solvent-accessible surface area (SASA) for the bb-CO oxygens shows a repetitive regularity for every triplet. This leads to water occupancy of the bb-CO sites following a similar regularity. In the crystal-phase X-ray data, as well as in our 100 K simulations, we observe a 0-2-1 water occupancy in the P-P-G triplet. Surprisingly, a similar (0-1.7-1) regularity is maintained in the liquid phase, in spite of the sub-nsec water exchange rates, because the bb-CO sites rarely remain vacant. The manifested ordered first-shell water molecules are expected to produce a cylindrical electrostatic potential around the peptide, to be investigated in future work.
Maximizing the Extraction of Bioactive Compounds from Diospyros kaki Peel through the Use of a Pulsed Electric Field and Ultrasound Extraction
Authors: Athanasiadis Vassilis , Chatzimitakos Theodoros , Bozinou Eleni , Kotsou Konstantina , Palaiogiannis Dimitrios , Lalas Stavros I.
Published: December 2023

The persimmon fruit (Diospyros kaki Thunb.) is renowned for its exceptional health benefits, which can be attributed to its abundance of bioactive compounds. This study aimed to optimize the extraction of bioactive compounds from persimmon peel, an underexplored waste biomass, within the frame of sustainability and a circular economy. For this reason, a comprehensive multi-factor extraction approach was employed. Specifically, diverse methods including a pulsed electric field and ultrasonication combined with simple stirring were explored. Through this systematic approach, the most efficient extraction process was determined, resulting in elevated yields of bioactive compounds, including polyphenols, ascorbic acid, and total carotenoids. Among the identified phenolic compounds, rutin emerged as the most abundant, with concentrations reaching up to 172.86 μg/g. Utilizing partial least squares analysis, the maximum predicted values for the bioactive compounds were determined, with total polyphenols reaching 7.17 mg GAE/g, ascorbic acid at 4.93 mg/g, and total carotenoids at 386.47 μg CtE/g. The antioxidant activity of the extracts was evaluated with the ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, and H2O2 scavenging assays. The recorded antioxidant performance underscored the substantial potential of persimmon peels as a source of cost-effective extracts with high antioxidant activity. This study not only contributes to optimizing the bioactive compounds’ extraction from persimmon peel but also highlights the process’s viability by producing valuable extracts with antioxidant properties at low cost.
Spatiotemporal Gait Variability in Children Aged 2 to 10 Decreases throughout Pre-Adolescence
Authors: Applequist Bryon C. , Motz Zachary L. , Kyvelidou Anastasia
Published: December 2023

Background: Children’s gait is traditionally understood to mature as young as three years old through pre-adolescence. Studies looking at gait biomechanics suggest that gait matures around three years old, while studies investigating gait variability propose a much later maturation. The studies that have examined children’s gait variability did so while the children walked around a track or down hallways that created a discontinuous gait, potentially affecting the measures of variability and the efficacy of the results. Purpose: Therefore, the purpose of our study was to investigate the development of gait dynamics and gait variability in children in a more continuous fashion, in this case, by walking on a treadmill. Methods: To accomplish this, we included four age groups of children, ranging 2–10 years old, walking on a treadmill for at least three minutes while stride time and stride length were collected. Stride time and stride length’s variability was then analyzed using linear (mean, standard deviation, coefficient of variation) and nonlinear (sample entropy, detrended fluctuation analysis) measures across the varying ages of our participants. Results: Interestingly, both the linear and nonlinear variabilities of the stride time and stride length measures decreased as the groups of children got older. Specifically, CV ST (2–3 (9.3 ± 4%), 8–10 (3.6 ± 0.7%), p < 0.05) and CV SL (2–3 (11.4 ± 3%), 8–10 (4.6 ± 1%), p < 0.05) were our strongest linear measures, and DFA α ST (2–3 (0.97 ± 0.12), 8–10 (0.82 ± 0.10), p < 0.05) and DFA α SL (2–3 (0.91 ± 0.04), 8–10 (0.81 ± 0.03), p < 0.05) were our strongest nonlinear measures, particularly between the youngest and oldest groups. This trend of variability decreasing with age suggests that as children’s gait matures, their gait becomes more stable and reliable. Significance: Our study rejects the notion that children’s gait is mature by the age of three, as some would suggest. By analyzing the variability of stride time and stride length, we can see that even later into childhood, children’s gait continues to change and evolve.
Special Issue: Immune-Mediated Neurological Disorders
Authors: Marsili Luca , Colosimo Carlo
Published: December 2023

For a long time, the immune system has been considered responsible for only a minority of neurological conditions involving the central and peripheral nervous system (CNS, PNS), respectively, namely multiple sclerosis and myasthenia gravis (with myastheniform syndromes) [...]
Artificial Neural Network (ANN)-Based Pattern Recognition Approach Illustrates a Biphasic Behavioral Effect of Ethanol in Zebrafish: A High-Throughput Method for Animal Locomotor Analysis
Authors: Myrov Vladislav O. , Polovian Aleksandr I. , Kolchanova Sofiia , Galumov Georgii K. , Schiöth Helgi B. , Bozhko Dmitrii V.
Published: December 2023

Variations in stress responses between individuals are linked to factors ranging from stress coping styles to the sensitivity of neurotransmitter systems. Many anxiolytic compounds can increase stressor engagement through the modulation of neurotransmitter systems and are used to investigate stress response mechanisms. The effect of such modulation may vary in time depending on concentration or environment, but those effects are hard to dissect because of the slow transition. We investigated the temporal effect of ethanol and found that ethanol-treated individual zebrafish larvae showed altered behavior that is different between drug concentrations and decreases with time. We used an artificial neural network approach with a time-dependent method for analyzing long (90 min) experiments on zebrafish larvae and found that individuals from the 0.5% group begin to show locomotor activity corresponding to the control group starting from the 60th minute. The locomotor activity of individuals from the 2% group after the 80th minute is classified as the activity of individuals from the 1.5% group. Our method shows three clusters of different concentrations in comparison with two clusters, which were obtained with the usage of a statistical approach for analyzing just the speed of fish movements. In addition, we show that such changes are not explained by basic behavior statistics such as speed and are caused by shifts in locomotion patterns.
Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures
Authors: Park Sangmin , Lee Sanghoon , Jang Hyeonwoo , Yoon Guwon , Choi Myeong-in , Kang Byeongkwan , Cho Keonhee , Lee Tacklim , Park Sehyun
Published: December 2023

The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need to devise an effective strategy for urban disasters. In the event of a fire, it is difficult to evacuate in the early stages due to the loss of detection function, difficulty in securing visibility, and confusion over evacuation routes. Accordingly, for rapid evacuation and rescue, it is necessary to build a city-level fire safety service and digital system based on smart technology. In addition, both forest and building fires emit a large amount of carbon dioxide, which is the main cause of global warming. Therefore, we need to prepare both energy and fire management to achieve carbon neutrality by 2030. In this study, we developed an AI-based smart fire safety system for efficient urban integrated management using a city-based fire safety architecture. In addition, we designed a fire management infrastructure and an energy management system for buildings. The proposal was demonstrated by building a test bed in the A building, and the AR-based mobile/web application was tested for optimized evacuation management. Furthermore, AI-based fire detection and the optimal evacuation of occupants were implemented through deep learning-based fire information data analysis. As a result, this paper presents four points for safety and energy management, and we demonstrate that the optimization of occupant evacuation ability and energy saving can be achieved. We also analyze the efficiency of the data transfer rate to prevent data communication delays by using Virtual Edge Gateway (VEG) management. In the future, we expect that the appearance of future fire and energy management buildings through this research will produce more accurate data prediction technology and the development of cutting-edge smart technology in smart city infrastructures.