The alternating stress caused by periodic high-pressure injection and extraction in gas storage can potentially induce fault slippage, compromising the sealing integrity of faults within these facil-ities. Understanding the mechanical behavior of faults under alternating stress is crucial for en-suring the long-term stability and safety of gas storage operations. To explore the impact of fault dip angle and fault gouge strength on fault slip characteristics, fault samples were prepared with uniaxial compressive strengths of 20.1, 30.2, 42.4, and 51.4 MPa at two distinct dip angles. Triaxial compression experiments were conducted under alternating stress conditions corresponding to operational pressures at a specific gas storage in China. Results indicate that faults with dip angles of 30° and 45° tend to fail at their weakest points. Increasing strength of fault gouge shifts failure mechanisms from interfacial failure between gouge and surrounding rock towards internal gouge failure, often accompanied by shear failure across sections, resulting in characteristic "X"-shaped conjugate shear failures. The decrease in the ratio of bedrock strength to fault gouge strength elucidates the observed phenomena of initial reduction followed by increased fault deformation. Transition points for faults with 30° and 45° dips occur around the strength ratios of 1.7/1 and 1.2/1, respectively. Fault damage exhibits a negative correlation with fault gouge strength and a positive correlation with fault dip angle. Samples with higher strength fault gouge at a 30° dip angle generally incur less damage compared to those with lower strength fault gouge at a 45° dip angle. Moreover, higher maximum static friction coefficients denote greater fault resistance to slipping, with 30° faults consistently demonstrating superior resistance compared to 45° faults. Additionally, higher strength fault gouge consistently enhances slip resistance under identical dip angles.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

The collapsible loess will rapidly soften and lose its bearing capacity when soaked in water. Under a mild condition (20°C), the biomimetic inorganic agent, Diammonium phosphate (DAP), reacts with calcite in the collapsible loess, producing a stronger bonding material, hydroxyapatite (HAP), to modify and stabilize the soil. Uniaxial compression, permeability tests and morphological analysis using XRD and SEM/EDX microscopy were carried out to assess the effectiveness of DAP stabilization on the collapsible loess. The results indicated that HAP improved the inter-particle bonding within loess, filled the pores within particles, reduced the permeability, and consequently mitigated collapsibility of loess. The compressive strength of DAP-treated loess increased as DAP concentration increased. Following 28-days curing, the compressive strength of the loess treated with a 3.0 mol/L DAP solution was six times greater than that of the untreated group. DAP's reinforcement effect on loess was superior to that of cement. The compressive strength of DAP-treated loess was about double that of cement-treated loess and the permeability coefficient was reduced by more than 50% at equivalent solid content. Furthermore, DAP generated 68% fewer carbon emissions compared to Portland cement. Considering eco-friendly and sustainable development, DAP offers a more competitive alternative for modification and stabilization of loess.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

Phylogenomics has enriched our understanding that the Tree of Life can have network-like or reticulate structures among some taxa and genes. Non-vertical modes of evolution—such as hybridization/introgression and horizontal gene transfer—deviate from a strictly bifurcating tree model, causing non-treelike patterns. Here, we present a brief overview of a phylogenomic workflow for inferring organismal histories and compare methods for detecting reticulate evolution. We discuss how the timing of coalescent events can help disentangle introgression from incomplete lineage sorting and how horizontal gene transfer events can help determine the relative timing of speciation events. In doing so, we identify pitfalls of certain methods and discuss how to extend their utility across the Tree of Life. Workflows, methods, and future directions discussed herein underscore the need to embrace reticulate evolutionary patterns for understanding the timing and rates of evolutionary events, providing a clearer understanding of life’s history.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

Alignment of picornavirus proteinase/polymerase sequences reveals this family evolved into five ‘supergroups’. Interestingly, the nature of the 2A region of the picornavirus polyprotein is highly correlated with this phylogeny. Viruses within supergroup 4, the Paavivirinae, have complex 2A regions with many viruses encoding multiple 2A^NPGP sequences. In vitro transcription/translation analyses of a synthetic polyprotein comprising green fluorescent protein (GFP) linked to b-glucuronidase (GUS) via individual 2A^NPGPs showed two main phenotypes: highly active 2A^NPGP sequences - similar to Foot-and-Mouth Disease Virus 2A^NPGP and, surprisingly, a novel phenotype of some 2A^NPGP sequences which apparently terminate translation at the C-terminus of 2A^NPGP - without detectable re-initiation of downstream sequences (GUS). Probing databases with the short sequences between 2A^NPGPs did not reveal any potential ‘accessory’ functions. The novel, highly active, 2A-like sequences we identified substantially expands the toolbox for biomedical / biotechnological co-expression applications.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Background: Pediatric patients with polyuria polydipsia syndrome (PPS) represent a diagnostic challenge for clinicians because of the technical difficulties in performing the gold standard water deprivation test (WDT). Copeptin, a stable biomarker representing the C-terminal portion of the polypeptide chain of antidiuretic hormone, proves to be a more reliable diagnostic tool. Objective: To assess the diagnostic accuracy of copeptin baseline dosing, arginine/saline copeptin stimulation tests, and WDT. Establishing the diagnostic utility of copeptin in pediatrics, for differentiating central and nephrogenic diabetes insipidus, and primary polydipsia. Methods: comparative and non-comparative primary studies, relating to children, were included and searched for in PubMed, Cochrane Library, WOS, ScienceDirect, Scopus, Google Scholar, up to August 2024. QUADAS-2 tool was used to assess risk of bias and applicability. Meta-analyses used fixed effect models due to low heterogeneity and the HSROC model. Results: 11 studies included, with an overall Low bias, no significant applicability concerns. The mean pooled sensitivity = 0.98 (95% CI: 0.936–1.025), pooled specificity=0.947 (95% CI: 0.920–0.973), and AUC=0.972 (95% CI: 0.952–0.992), indicating excellent diagnostic accuracy. Conclusion: Stimulation methods for copeptin dosing represent effective and less invasive diagnostic test for children with PPS, and future development of standard copeptin testing protocols are needed.

The feeling of beauty is one of the positive human feelings. In architecture and design, it is associated with forms appropriate to their purpose. The usefulness of products to meet needs is also important. They are well received by users. The task of designers is to achieve perfection in their work. The rational design and use of artificial living environments are expressions of concern for natural resources (sustainable environment). These issues are addressed, i.e., by an interdisciplinary initiative of the New European Bauhaus. The aim of the paper is to expand knowledge about beauty and utility. They are important when choosing mass-produced goods. The article uses research methods: interpretive research, analyses, comparisons, logical argumentation, etc. Experimental qualitative research using a checklist was used. The results expand the knowledge on the selected topic and research scope of the research of the paper. The survey can be used to rationally evaluate architecture, interiors, and design facilities. It can be helpful for various aesthetic and functional strategies dedicated to different users. The paper demonstrates the role of beauty and functionality in the built environment. They are analyzed in the context of social and technical conditions, as well as contemporary sustainable design

We describe a forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to operationally forecast conditions in Northern and Central California. It was established in 2014 and has been periodically improved. In the current configuration, WRF forecasts are routinely performed multiple times each day on a 2-km grid, and the results are used as input to wildfire fuel moisture, fire probability, and wildfire spread models. Components of the system include a stochastically perturbed ensemble; an annually updated reanalysis covering more than 30 years; and validation against PG&E’s extensive mesonet and other observing networks. We describe the development and validation of the next operational edition. Configurations tested include irrigation triggered by crop growing season and an ensemble-based approach with intelligently sub-selected Global Ensemble Forecast System (GEFS) members. The latter approach allows for enhanced uncertainty sampling in WRF forecasts and can greatly improve WRF forecasts when calibrated by observations.

A model of a single-server queuing-inventory system (QIS) with catastrophes and a finite waiting room for consumer customers (c-customers) has been developed. When a catastrophe occurs, all inventory in the system's warehouse is destroyed, but c-customers in the system are still waiting for restocking. In addition to c-customers, negative customers (n-customers) are also taken into account, which displaces one c-customers (if any). The policy (?, ?) is used to replenish stocks. If upon arrival of the c-customer the inventory level is zero, then according to Bernoulli’s scheme this c-customer is either lost or joining the queue. The mathematical model of the studied QIS is constructed in the form of a two-dimensional continuous-time Markov chain (2D CTMC). Exact and approximate methods for calculating the steady-state probabilities of constructed 2D CTMCs are developed and closed-form expressions are derived for calculating the performance measures. The results of numerical experiments are presented, demonstrating the high accuracy of the developed approximate formulas, as well as the behavior of performance indicators depending on the reorder point. In addition, an optimization problem is solved to obtain the optimal reorder point to minimize the expected total cost.

Pipelines are regularly trenched and then forgotten, just allowed to run till failure and producing an overall incredibly poorly run system that is ripe for improvements. One of the most needed areas for improvement are proactive methods for condition assessment of existing pipelines. This can be divided into two main categories: proactive condition assessment technologies and nonstructural proactive condition improvement technologies. The objective of this paper is to identify the best option available on the market for both these areas of proactive condition assessment for existing pipelines. The compared proactive condition assessment technologies include Acoustic Resonance Testing, Ultrasonic Thickness Measurement Testing, Phased Array Ultrasonic Testing, Pulsed Eddy Current Testing, and the Multisensor ROV. The technologies compared for nonstructural proactive condition improvement were Jetting, Pigging and also included the main requirements for a proper catalog of pipe information that can be used to produce a ranked system for assessing which pipes are at highest risk for failure and can be used to efficiently direct resources for pipe maintenance. The final conclusion was that Acoustic Resonance Testing and the Multisensor ROV are the two best products available for the proactive condition assessment of existing pipelines available on the market from their versatility of application and amount of information gathered during pipe inspection. The best option available in the group of nonstructural proactive condition improvement technologies was pigging for its incredibly low risk and low social impact as well as potential for improvement in the near future. It was also concluded that every agency would be better off with a catalog of the pipe material, age, length, diameter, and burial depth to know the location of every pipe an agency owns as well as to rank every pipe in terms of risk of failure and impact of said failure to most efficiently direct resources.

The demand for improved service in mobile networks is always increasing. That means signal transmission with higher capacity is required. To achieve that goal a combined optical and wireless link using double polarization multiplexing has been developed. A high quality signal transmission is achieved by applying high polarization extinction ratio. The new concept is validated by experiments varying the bit rate and fiber length. A signal with 12 Gbit/s bit rate was transmitted over a 25 km long combined link with about 1.10-8 bit error rate. That result is much better than the already published data which were measured on links transmitting information with 2.5 Gbit/s bit rate. The polarization multiplexing technique is very practical for capacity enhancement in a specific section of an existing optical fiber by inserting a new link into it without deploying a new fiber.

Objective: This study aimed to demonstrate the availability of portable devices for nasal FeNO measurement, which could replace exhaled NO measurement based on the continuous aspiration of multiple flow measurement systems. Methods: A descriptive cross-sectional study. The healthy subjects without rhinosinusis disorders (control group) and those with diagnosed allergic rhinitis (AR group) were enrolled in the study. They underwent nasal and bronchial FeNO measurements by both fixed flow NObreath® connected to nasal mask and multiple flows HypAir FeNO®. Results: A total of 153 people were recruited including 63 healthy subjects and 90 subjects with AR. There were no statistically significant differences in anthropometric characteristics between the two groups (P > 0.05). There were a significant increase in FeNO values in the AR group compared to the control group; concentrations of nasal and bronchial FeNO mearsured by NObreath® were 76.0 ± 50.7 ppb (vs 36.0 ± 28.1 ppb, P < 0.01) and 32.3 ± 15.4 ppb (vs 9.2 ± 5.6 ppb, P < 0.001), and those mearsured by NObreath® Hypair FeNO® were 1796.9 ± 523.3 ppb (vs 400.2 ± 123.1 ppb, P < 0.001) and 37.3 ± 23.0 ppb (vs 17.4 ± 18.3 ppb, P < 0.01). A weak correlation between the values measured by NObreath® and Hypair FeNO® devices was observed (r = 0.023 and r = 0.089, respectively; p < 0.05). Conclusions: The handheld NObreath® device is effective in measuring FeNO concentrations in both normal subjects and AR patients. The advantages in price and size, creating a compact, low-cost device that is convenient for use and research.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

We use the theory of coherent measures to look at the problem of surplus sharing in an insurance business. The surplus share of an insured is calculated by the surplus premium in the contract. The theory of coherent risk measures and the resulting capital allocation gives a way to divide the surplus between the insured and the capital providers, i.e. the shareholders.

Utility perceived by individuals is believed to be different from the utility experienced by that individual. System dynamicists implicitly categorize this phenomenon as a form of bounded rationality and traditionally employ a simple smoothing function to capture it. We challenge this generalization by testing it against an alternative formulation of utility perception that is suggested by modern theories of behavioral economics. In particular, the traditional smoothing formulation is compared with the peak-end rule in a simple theoretical model as well as in a medium-size model of electronic health record implementation. Experimentation with the models reveals that the way utility perception is formulated is important and might affect behavior and policy implications of system dynamics models.

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASS). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML) based algorithms and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCC) by using real-time data to optimise their operations, enhance safety and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity aspects must be considered and properly addressed to prevent such complex systems from being tampered with. In this paper, we propose an approach to secure communication channels between the SCCs and the vessels using SECOM standard and the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly detection. The proposed approach is validated against a real implementation of the SCC deployed at Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

A micro decision-making utility model under uncertainty is presented as a complementary foundation for macro coronavirus models. The micro model consists of two functions, a risk averse utility function depending on wellness and a wellness random output which is a function of the input variable called “treatment” consisting of such elements as social distance, washing hands, wearing a face mask, and others. The decision maker selects a level of treatment that maximizes her/his expected utility, given the probabilities of the respective outputs. The focus is on how changes in a person’s psychological attitude towards the macro determined (announced) probabilities affects the optimum results of the model. Such changes create a micro-macro dynamic interaction which is briefly outlined. A short discussion of the model’s behavioral implications for health policy is also given.

Pipes have been utilized since ancient times and have seen significant renewal, modification, and replacement to improve their effectiveness and efficiency in the modern era. As early as 4000 BC, Egyptians used clay pipes for drainage systems. The U.S. has the largest network of energy pipelines in the world. The ASCE 2017 Infrastructure Report Card assigned grades of D and D+ to water and wastewater pipelines, highlighting a substantial need for improvement. The paper investigates the infrastructure required for the sustainability of pipeline systems, as well as the construction of underground systems using both open-trench and trenchless technologies, along with the associated risks in the US. It concludes that trenchless methods are increasingly preferred for pipeline construction due to technological advancements, reduced surface disruptions, shorter construction times, and lower social costs. Tackling this challenge will necessitate strategic investment, decisive leadership, thorough planning, and meticulous preparation for future demands. Enhancing pipeline infrastructure can stimulate the U.S. economy, whereas postponing action will lead to higher costs.

This paper provides a data set collected with an underground shaft mobile mapping system equipped with rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, and IMU Xsens MTi-30. The ground truth data was acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D Terrestrial Laser Scanner stations of a total 273,784,932 of 3D measurement points. The aim of the paper is to describe the data set of a mining shaft. Obtained mobile mapping data can be the basis for detailed analyzes of the technical condition of the shaft. This data set provides an end-user case study of realistic applications in mobile mapping technology. It provides ground truth data, mobile mapping data and software tools for manipulation and visualization. The project is released and maintained at https://michalpelka.github.io/mine-mapping-dataset/.

Many parameters and complex boundaries are involved in the spatial arrangement of an under-ground powerhouse for a hydropower station, which requires reference to many relevant cases and specifications. However, in practical applications, retrieving relevant cases or specifications is difficult, and there is a lack of knowledge of cascading logic among design parameters. For this question, a targeted knowledge graph based on knowledge graph management technology is established to support subsequent applications. This paper proposes a new concept of con-structing a knowledge graph for building information modeling (BIM) underground power-houses of hydropower stations. Firstly, the ontology skeleton of hydropower station spatial ar-rangement design, which represents the knowledge organization structure of the knowledge graph, is reconstructed by carefully analyzing the requirements for intelligent modeling of un-derground powerhouses. A large amount of unstructured data is identified based on optical character recognition (OCR) technology and is divided into words to extract correlation knowledge based on THULAC. In the next step, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in a Neo4j knowledge base to build a knowledge graph. Finally, the knowledge graph is serviced to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of spatial arrangement of an underground powerhouse of pumped storage hydropower stations.

Background: The predictive value of changes in C-reactive protein (CRP), procalcitonin and leukocyte levels, which are commonly used in the diagnosis of infection in sepsis and septic shock, remains a topic of debate. This study aims to evaluate the effectiveness of changes in CRP, procalcitonin, and leukocyte counts on the prognosis of patients admitted to the intensive care unit (ICU) with a diagnosis of pneumosepsis and pneumonia-related septic shock, and to investigate whether any of these markers have a superior predictive value over the others in forecasting prognosis. Methods: The study analyzed the demographic data, Charlson Comorbidity Index score (CCIS), CURB-65 score (confusion, uremia, respiratory rate, BP, age ≥ 65 years), and Acute Physiology and Chronic Health Evaluation II (APACHE-II) scores of 230 patients admitted to the ICU with a diagnosis of pneumosepsis and pneumonia-related septic shock between April 1, 2022, and December 31, 2023. The levels of CRP, procalcitonin, and leukocytes were also recorded and analyzed to assess their impact on 1-month and 3-month mortality outcomes. Results: According to multivariate logistic regression analysis, low BMI, male gender, high CCIS, CURB-65, and APACHE-II scores were found to be significantly associated with both 1-month and 3-month mortality (p<0.05). An elevated SOFA score was associated with 1-month mortality, while advanced age was related to 3-month mortality (p<0.05). Although there was no significant relationship between the first-day levels of leukocytes, CRP, and PCT and mortality, their levels on the third day were observed to be at their highest in both 1-month and 3-month mortality cases (p<0.05). Additionally, a concurrent increase in any two or all three of CRP, PCT, and leukocyte values was found to be higher in patients with 3-month mortality compared to those who survived (p=0.004). Discussion: Although PCT, CRP, and leukocyte values measured at the time of admission were not significantly associated with 1-month mortality in patients with pneumosepsis or pneumonia-related septic shock, the persistent elevation and concurrent increase of these values, along with male gender, advanced age, low BMI, and high CCIS, CURB-65, and APACHE-II scores, were found to be significantly associated with 3-month mortality.

Caudal type homeobox transcription factor 2 (CDX2) is a gastrointestinal cancer biomarker that regulates epithelial development and differentiation. Absence or low levels of CDX2 have been associated with poor prognosis and proposed as a chemotherapy response predictor. Tumour tissue of 668 patients with stage I-IV colorectal cancer were stained for CDX2 and stratified into two subgroups according to expression levels. Statistical tests evaluated CDX2 relationship with survival and chemotherapy response. Of 646 samples successfully stained, 51 (7.9%) had low and 595 (92.1%) had high CDX2 levels. Low CDX2 staining was associated with poor differentiation, presence of lymphovascular or perineural invasion, and more common in colon and right sided tumours. Overall survival (p&lt;0.001) and disease-free survival (p=0.009) was lower in low CDX2 expressing patients. Multivariable analysis validated CDX2 as an independent poor prognostic factor after excluding confounding variables. There was no statistically significant improvement in survival with adjuvant chemotherapy in stage II colon cancer (p=0.11). In the rectal cohort, there was no relationship between CDX2 levels and therapy response. While confirming the prognostic utility of CDX2 in colorectal cancer, our study highlights larger studies are required to confirm utility as a predictive chemotherapy biomarker, especially in left sided and rectal cancers.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

Genetic disorders are preeminent determinants of infant mortality. The inherited pediatric-onset genetic disorders have consequential stress on child growth and development: several congenital, complex and rare disorders with indistinguishable clinical symptoms where diagnosis always remains a challenging task. Traditional diagnosis methods include biochemical tests followed by chromosomal microarray and sequencing of a single gene or panel of genes. These methods had several limitations, but with the advent of whole-exome sequencing (WES), genetic testing has become cost-effective and transformative. Exome sequencing has been known for its effectiveness, which appropriately elucidates and distinguishes the heterogeneous disorders to avoid misdiagnosis and decode the underlying genetic alterations. WES has led to discovering genes and genomic variants in a broad spectrum of diseases, including autism, epilepsy, congenital heart diseases, neurodevelopmental diseases, cancer, nephrotic disorders, neural tube defects and fetal structural anomalies. WES is significant in producing immense genomic biomarkers that can be made as appropriate pharmacogenomic targets for drug therapy. In this article, we analyze the recent exploration of WES technology to revolutionize not only the process of genetic variation and disease detection but also the convention of preventative and targeted drug discovery.

In this paper, the results of a high-precision control survey carried out in the underground mining of the Oyu Tolgoi project using terrestrial laser scanning technology are presented. The purpose of the measurements was to ensure that the direction, total length and volume of the penetration and excavation works were being implemented in accordance with the planned and designed dimensions. The coordinates of the geodetic traverse checkpoints were also assessed. In addition, we checked a traverse network of 18 checkpoints in the tunnel of the selected underground level. These checkpoints were repeatedly measured four times by two using readings of the Trimble S7 1” total station and were processed using the Compass/Bowditch method with Trimble Business Center software. All points except for one were within the tolerance limits. Based on these points, the excavation length and volumes were determined using the terrestrial laser scanners (TLSs) Trimble X7 and Trimble SX12 [25]. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured using laser scanner was calculated with the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed a length of 20285.72m and a volume of 441128.80m3 for the seven underground mine levels, while their project values were 16158.26m length and 422837.50m3 volume.

This paper explores various methods for inspecting gravity pipes. It emphasizes that understanding the initial process of pipe prioritization is crucial before discussing inspection methods. Given the extensive gravity pipeline systems in use today, inspecting all suspected pipelines without prioritization would be extremely costly and time-consuming, especially in a large country like India. Therefore, the paper highlights the importance of using algorithms and analysis tools to prioritize which pipelines need inspection. It also describes innovative inspection techniques, such as wave and sound analysis, which can improve inspection efficiency. The research provides an overview of the pipe inspection process and discusses cost-saving algorithms that can enhance field efficiency.

UAV has become powerful devices for both practitioners and researchers. In recent years, there has been a substantial growth in the usage of UAV for a wide range of applications in research and engineering. UAV are mostly utilized for image capturing techniques due to their time savings, low cost, minimum field work, and high precision. The purpose of this study is to generate an accurate and reliable 3D modelling process using UAVs photogrammetric technology; hence this study allows the techniques to evaluate the practicability of modelling and mapping inaccessible tunnels using images recorded by UAVs and photogrammetry. As a result, the approach was always employed to depict the true state of the place. This research incorporates three experiments. The first experiment is to build a 3D model using only one-directional light source and evaluate the accuracy of 3D photogrammetric model and conducted at the concrete tunnel. The second one is to build a 3D model of underground sewer tunnel using different light sources one-directional and omni-directional. The last experiment was to build 3D model using an UAV system which is developed based on first and second experiment’s result, evaluate an accuracy of 3D photogrammetric model and conducted at the tunnel named in the research as a “pedestrian tunnel. There is certain limitation in this study such as Adding a payload to a drone changed its weight distribution, which affected its stability. If the payload was not properly balanced, it caused the drone to fly unevenly, making it more difficult to control.

Studies have shown the unconditional danger of biological attacks on underground hydroelectric pumped-storage power stations. A hypothetical list of biological damage to water conduits and dams is determined. Relevant predictions are given, as well as accident events of hydraulic structures are described. A universal scheme for the application of hazard protection measures, which can be comprehensive, has been developed. Praemonitus praemunitus – precautions should be taken.

The coal mine underground reservoir is an appropriate solution between coal mining and groundwater resource protection and utilization. By calculating the storage capacity of a groundwater reservoir, the storage coefficient has been proved to be always an empirical value. Based on the mathematical derivation of the vertical fracture area ratio and the horizontal fracture area rate of the collapse zone and the fissure zone in the goaf area of the coal seam, the mathematical models of tem are derived, and the model for calculating the water storage coefficient is derived. The water storage coefficient derived from the theoretical model had more basis and more advanced than the traditional empirical value. By using this method, the practical calculation of No.1 underground reservoir of the DaLliuta Coalmine in Shenhua Shendong, has got a perfect matching with the actual groundwater storage capacity.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

The Active Distribution Networks (ADNs) are Multi-Input Multi-Output (MIMO) systems with coupled dynamics which cause interactions among the control loops of the Distributed Energy Resources (DERs). This undesired effect lead to performance degradation in the voltage control. To mitigate the effects of these unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In details, each DER is equipped with a double loop PI to control the active and reactive powers output by the Voltage Source Converter (VSC) which connects the DER to the network's node. Furthermore, to guarantee Ancillary Services (ASs), the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a two sequential steps procedure based on the (Internal Model Control) IMC technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in presence of model parameter uncertainty in the MIMO model.

Fumaric acid esters (FAEs) are small molecules with anti-oxidative, anti-inflammatory and immune-modulating effects. Dimethyl fumarate (DMF) is the best characterised FAE and is approved and registered for the treatment of psoriasis and Relapsing-Remitting Multiple Sclerosis (RRMS). Psoriasis and RRMS share an immune-mediated aetiology, driven by severe inflammation and oxidative stress. DMF, as well as monomethyl fumarate and diroximel fumarate, are commonly prescribed first-line agents with favourable safety and efficacy profiles. The potential benefits of FAEs against other diseases that appear pathogenically different but share the pathologies of oxidative stress and inflammation are currently investigated.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

The development of an underground gas storage (UGS) project and its subsequent management must ensure technical feasibility, commercial value and long-term efficiency. The UGS industry has borrowed much of its knowledge from other disciplines (primarily oil and gas reservoir engineering), but it has also developed its own technology. This paper provides a methodological approach based on current practices and available methods for designing and safely operating a UGS (including the so-called “delta-pressure” option to enhance UGS performance) and highlights what is special in UGS compared to oil and gas reservoirs.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utility data governance is proposed to manage the work process from the underground utility data capture to data usage. This is the backbone to support the coordination of different roles in the utility data governance and usage. Then, an initial design of the 3D underground utility data model is introduced to describe the 3D geometric and spatial information about underground utility data and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utility data for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.

Global warming and the associated increasing frequency of heatwaves are on the rise leading to numerous health complications. Diabetic patients are more vulnerable to heat exposure than healthy individuals, owing to their impaired sweating ability, however, little is known about how heat exposure may impact the function of skin cells, commonly keratinocytes. Our recent report provided the first proof-of-concept revealing that the metabolic composition of sweat EVs is associated with metabolic changes in healthy individuals and type 2 diabetes patients. Given this and the shared cellular proprieties of eccrine sweat glands with keratinocytes, we questioned whether the metabolic changes in sweat EVs may mirror the responses of keratinocytes upon heat exposure. To this, we exposed HaCat, which were maintained in low (mimicking healthy condition) and high glucose (mimicking diabetes condition) to acute (90 min), chronic (48 h), and repetitive heat exposure at 40 ⁰C. Through conducting a set of experiments including a targeted metabolomics approach, Greiss assay, western blotting analysis, and electron microscopy studies. The data demonstrate that heat exposure results in substantial changes of metabolite levels belonging to amino acids, pyrimidine metabolism, and glycolysis pathways in high glucose maintained HaCat cells when compared to those noted in low glucose condition. Exploring the molecular mechanisms through which heat exposure may impact the metabolic composition in keratinocytes, our discoveries highlight nitric oxide (NO) as a novel biomarker that the level is correlated with heat duration and frequency. Furthermore, data reveal that the HSP-70 protein levels and mitochondrial morphology are consequently altered. This study provides evidence of shared metabolic alterations in keratinocytes and sweat EVs collected from clinical study and proposes, for the first time, NO as a novel biomarker for monitoring changes in health and diabetes upon heat exposure.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

The paper addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a DT-SWIPT-assisted time allocation scheme, named DaTA, such that the wireless station can share the remaining energy after transmission to the HAP to those who have not transmitted to the HAP to minimize the energy consumption of the network. In addition to proposing a new frame structure, the paper also considers the SNR (Signal-to-Noise) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the paper, the energy consumption minimization problem is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. Simulation results showed that the proposed scheme can have better performance than the related work in terms of energy consumption and energy efficiency.

The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu ge-othermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean-Himalayan high-temperature geothermal belt and is characterized by abundant geothermal resources. However, due to its considerable depth, exploration poses significant risks, resulting in a total utilization rate of less than 0.5% of the total reserves. This study employs natural seismic data to perform a tomographic analysis of the geothermal system in the Midu basin. By examining the P-wave velocity (Vp) and velocity ratio of P-wave and S-wave (Vp/Vs) at various depths, the findings reveal that the basin comprises two distinct structural layers: the thrust basement of the Mesozoic and Paleozoic eras and the strike-slip extensional sedimentary layer of the Cenozoic era. A low-velocity anomaly in the central basin corresponds to the loose Cenozoic sedimentary layer. In contrast, high-velocity anomalies at the basin edges correlate with boundary faults and the Mesozoic-Paleozoic strata. Below a depth of 4 km, the Red River Fault and MiDu Fault continue to dominate the basin's structure, whereas the influence of the Malipo Fault dimin-ishes. The MiDu Fault exhibits higher thermal conductivity than the Yinjie Fault. It interfaces with multiple carbonate and basalt formations characterized by well-developed pores and fractures, making it a crucial conduit for water and a control point for geothermal storage. Consequently, the existence of high temperature geothermal resources for power generation should be concentrated around the Midu fault on the western side of the basin, while the Yinjie fault area is more favorable for advancements in heating and wellness.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

Effective management of water distribution systems requires an integrated approach, but none of the available frameworks for one are in broad use in the water supply industry. Frameworks developed include a management standard of the American Water Works Association and Distribution System Optimization, a methodology for physical, hydraulic, and water quality performance assessment. The intelligent systems framework also offers a pathway to integration, but it lacks a definite structure. The voluntary aspect of adopting innovations within the fragmented and unregulated nature of the water utility industry poses a barrier to adoption of such innovations. Another barrier is the uncoordinated arrangements of water research stakeholders with different incentive structures. Intelligent water systems offer a way to incentive utilities to encourage implementation. They can provide a bottom-up approach where utilities see advantages, as opposed to a top-down approach where they are expected to adopt a method without seeing clear benefits. Research to develop new and improved tools is needed, but the research roadmap should prioritize implementation.

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

This paper presents a methodology for determining the sensitivity of the main air flow directions in ventilation subnetworks to changes of aerodynamic resistance and of air density in mine workings. Formulae for determination of the sensitivity of the main subnetwork air flows by establishing the degree of dependency of the air volume stream in a given working on the variations in resistance or air density of other workings of the network have been developed. They have been implemented in the VentGraph mine ventilation network simulator. This software, widely used in Polish collieries provides an extended possibility to predict the process of ventilation, air distribution and, in the case of underground fire, also the spread of combustion gasses. The new method facilitates assessment by mine ventilation services of the stability of ventilation systems in exploitation areas and determine of the sensitivity of the main subnetwork air flow directions to changes of aerodynamic resistance and air density. Recently in some Polish collieries new longwalls are developed in seams located deeper then the bottom of the intake shaft. Such solution is called “exploitation below the level of access” or “sublevel”. The new approach may be applied to such developments to assess the potential of changes of direction and air flow rates. In addition, interpretation of the developed sensitivity indicator is presented. While analyzing air distributions for sublevel exploitation, application of current numerical models for calculations of the distribution results in tangible benefits, such as the evaluation of the safety or risk levels for such exploitation. Application of the VentGraph computer program, and particularly the module POŻAR (fire) with the newly developed options, enables an additional approach to the sensitivity indicator in evaluating air flow safety levels for the risks present during exploitation below the level of the intake shaft. The analyses performed and examples presented enabled useful conclusions in mining practice to be drawn.

The Atlantic Canada Together Enhancing Acute Stroke Treatment (ACTEAST) project is a Modified Quality Improvement Collaborative (mQIC) designed to improve ischemic stroke treatment rates and time-based efficiency in Atlantic Canada. This study specifically evaluates the implementation of the mQIC in Nova Scotia using qualitative methods. The mQIC spanned six months and included two Learning Sessions, multiple webinars, and a per-site virtual visit. Each session was followed by an action planning period to guide implementation efforts over the following 2-4 months. The Consolidated Framework for Implementation Research (CFIR) was utilized to develop a pre-tested, semi-structured interview guide, which aimed to uncover barriers and facilitators to mQIC implementation. Interviews were conducted with 14 healthcare professionals, generating 454 references that were coded into 28 CFIR constructs. Notably, 84% of these references were positively framed as facilitators. Despite this, significant barriers were identified, including resource availability, competing priorities, communication challenges, and difficulties engaging key stakeholders. Some barriers were particularly prominent during specific phases of implementation. The study findings offer valuable insights into the implementation of quality improvement initiatives in stroke care. They underscore the importance of recognizing and addressing context-specific barriers, while also leveraging identified facilitators to drive successful implementation and ultimately improve patient outcomes.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

As a major coal country, China faces the issue of significant gas emissions during the coal mining process. This study aims to improve the utilization efficiency of mine gas, reduce greenhouse gas emissions, and promote the low-carbon and green transformation of the coal industry. A 10kW gas regenerative thermal oxidation (RTO) experimental system was constructed. The effects of initial methane concentration, low-temperature flue gas proportion, and operating load on combustion temperature, methane oxidation rate, high-temperature flue gas energy, and system thermal efficiency were studied. The results show that when the combustion temperature is below 600°C, the CH4 combustion reaction cannot proceed effectively, and the system temperature continuously decreases and cannot be maintained stable. The experiment determines the stable operating methane concentration range of RTO. In this experimental system, the lower limit of the initial methane concentration is 0.28%, corresponding to an 86% methane oxidation rate. As the initial methane concentration decreases, the combustion temperature also decreases, and the methane oxidation rate follows suit. The higher the low-temperature flue gas proportion, the higher the combustion temperature, and the system thermal efficiency and output heat decrease with the increase of the low-temperature flue gas proportion. This experiment explores multiple factors affecting regenerative thermal oxidation, providing an basis for ensuring the safe and stable operation of the system and optimization. Improving the thermal insulation and heat exchange performance of the storage body can expand the lower limit of the initial methane concentration, thereby increasing the stability and thermal efficiency of the system.

Today is the era of nanoscience and nanotechnology, which finds applications in the field of medicine, electronics, or environmental cleanup. Even though the nanotechnology is in its emerging phase, but still it provides solutions to numerous challenges. Nanotechnology and nanoparticles are found very effective because of their unique chemical and physical properties, high surface area, but their high cost is one of the major hurdles in its wider application. So, the synthesis of nanomaterials especially 2D nanomaterials from the industrial, agricultural and other biological activities could provide a cost-effective technique. The nanomaterials synthesized from such waste not only minimizes the pollution but also provides an eco-friendly approach towards the utilization of the waste. In the present review work, the emphasis has been given on the types of nanomaterials, different methods for the synthesis of 2D nanomaterials from the waste generated from industries, agriculture and their application in electronics, medicine and catalysis.

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.

Genetic factors influence medication response (pharmacogenetics), affecting the pharmacody-namics and pharmacokinetics of many medicaments used in clinical care. The ability of medica-tions to cross the blood-brain barrier (BBB) represents a critical putative factor in the effectiveness and tolerability of various central nervous system (CNS) medications at a pharmacokinetic (dosing) level. Pharmacogenetics has the potential to personalise medicine to a greater extent than has been possible, with the potential to help reduce heuristic delays to effective tolerable pharmacotherapy. Here, we critically examine and summarise the evidence, particularly for ABCB1 polymorphisms associated with various CNS medications. These transporters appear to have a role in BBB pharmacogenetics. We identify some of the most promising variants for clinical translation while spotlighting the complexities of the involved systems and limitations of the current empirical literature.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

An analysis of workplace accidents in the mining sector has been done using the database from the Spanish administration between the period 2005-2015 and applying data mining techniques. Data has been processed by means of the software Weka. Two scenarios were chosen regarding the accidents database, surface and underground mining. The most important variables involved in occupation accidents and their association rules have been determined. These rules are formed by several predictor variables that cause an accident, defining its characteristics and context. This study exposes the 20 most important association rules of the sector, either surface or underground mining, based on statistical confidence levels of each rule obtained by Weka. The outcomes display the most typical immediate causes with the percentage of accident basis of each association rule. The most typical immediate cause is body movement with physical effort or overexertion and type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident change in both scenarios. Data mining techniques have been proved as a very powerful tool to find out the root of the accidents, apply corrective measures and verify their effectiveness, either for public or private companies.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Background: With its impressive natural language processing and comprehension skills, ChatGPT-4 (Chat Generative Pre-Trained Transformer) is a technology that shows great promise for use in a number of medical fields. This article provides a thorough description of the possible uses of OpenAI's state-of-the-art language model, ChatGPT-4, in musculoskeletal imaging. Purpose for review: MSK Radiologists need to evolve about their knowledge and understanding of the roles of ChatGPT and its implications in daily practice. We aim to emphasize the advantages of ChatGPT-4 on the musculoskeletal imaging field and its potential to enhance radiologist working checklist review items, and the overall productivity of radiology reports. Recent findings: The fact of that specialists in radiology, rheumatology, orthopedic surgery, and other fields are conversant with ChatGPT. Furthermore, opinions are generally well, despite the fact that many people have reservations about the new technology, which is still developing ^[1]. Compared to GPT-4V based ChatGPT, GPT-4 based ChatGPT showed noticeably better diagnostic accuracy. Although ChatGPT, which is based on GPT-4, performed diagnostically on par with radiology residents, however, it demonstrated lower performance than a musculoskeletal radiologist ^[2].

The objective of this research is to propose a utility function that calculates the possible level of satisfaction that a music consumer would achieve after listening to a song according to the emotional response obtained from a song. Assuming that the consumer's musical preferences are satisfied if the predominant emotions of his current emotional state increase; the probabilistic emotional-musical model (Lugos Abarca, 2023) is used to construct such a utility function. By posing a hypothetical scenario, realistic results were obtained, which guarantee, in principle, its use in theoretical practice. The originality of this research lies in proposing a mathematical tool to study, quantitatively, the decision making of a musical consumer influenced by his emotions. The main conclusion being its importance in the music industry, since the function could predict, theoretically, whether a song will be accepted or rejected by the consumer in comparison with the competition.

The purpose of this review is to summarize current knowledge on lymph node dissection (LND) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP). Despite a growing body of evidence, utility, therapeutic and prognostic value of such approach as well as optimal extent of LND, remain unsolved issues. Although LND is the most accurate staging procedure, the direct therapeutic effect is still not evident from the current literature which limits the possibility of establishing clear recommendations. This indicates the need for further robust and adequately designed high quality clinical trials.

Multichannel EEGs were obtained from healthy participants in the eyes-closed no-task condition (where the alpha component is typically abolished). EEG dynamics in the two conditions were quantified with two related binary Lempel-Ziv measures of the first principal component and with three measures of integrated information including the more recently proposed integrated synergy. Both integrated information and integrated synergy with model order p=1 had greater values in the eyes closed condition. If the model order of integrated synergy was determined with the Bayesian Information Criterion, this pattern was reversed, and in common with other measures, integrated synergy was greater in the eyes open condition. Eyes open versus eyes closed separation was quantified by calculation of the between-condition effect size. Lempel-Ziv complexity of the first principal component showed greater separation than the measures of integrated information. The performance of the integrated information measures investigated here when distinguishing between indisputably different physiological states encourages caution when advocating for their use as measures of consciousness.

The Aβ cascade and alternations of biomarkers in neuro-inflammation, synaptic dysfunction and neuronal injury followed by Aβ have progressed. But the question is how to use the biomarkers. Here, we examine the evidence and pathogenic implications of protein interactions and the time order of alternation. After the deposition of Aβ, the change of tau, NfL and NG is the main alternation and connection to others. The neuro-inflammation, synaptic dysfunction and neuronal injury function is exhibited prior the structural and metabolic changes in the brain following Aβ deposition. The time order of such biomarkers compared to the tau protein is not clear. Despite the close relationship between biomarkers and plaque Aβ deposition, several factors favor one or the other. There is an interaction between the proteins that CSF SNAP-25, VILIP-1 and YKL-40 can predict the brain amyloid burden. The Aβ cascade hypothesis could be the pathway, but not all subjects are converted to AD, even with very high elevated Aβ. The interaction of biomarkers and the time order of change require further research to identify the right subjects and right molecular target for precision medicine therapies.

With a data revolution underway for some time, there is an increasing demand for formal privacy protection mechanisms that are not so destructive. Hereof microaggregation is a popular high-utility approach designed to satisfy the popular k-anonymity criteria while applying low distortion to data. However, standard performance metrics are commonly based on mean square error, which will hardly capture the utility degradation related to a specific application domain of data. In this work, we evaluate the performance of k-anonymous microaggregation in terms of the loss in classification accuracy of the machine learned models built from perturbed data. Systematic experimentation is carried out on four microaggregation algorithms that are tested over four data sets. The empirical utility of the resulting microaggregated data is assessed using the learning algorithm that obtains the highest accuracy from original data. Validation tests are performed on a test set of non perturbed data. The results confirm k-anonymous microaggregation as a high-utility privacy mechanism in this context and distortion based on mean squared error as a poor predictor of practical utility. Finally, we corroborate the beneficial effects for empirical utility of exploiting the statistical properties of data when constructing privacy preserving algorithms.

The Safe Drinking Water Act Amendments of 1996 require community water systems in the United States to send consumers Consumer Confidence Reports (CCRs). CCRs contain information on detected contaminants and required educational information about drinking water. The authors of this study developed a survey to evaluate how utilities track consumer feedback, understanding, and the role of the CCR in shaping consumer perceptions about water quality. Responses from this survey indicate it is common for utilities to indirectly track the effectiveness of their CCRs, but few utilities indicated directly evaluating consumer understanding or the effect of CCRs on consumer perceptions.

Keywords: underground engineering; numerical simulation; excavation length effect, major principal stress; displacement; damage initiation; CPU time

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

The high prevalence of thyroid nodules and increased availability of neck ultrasound have led to an increased incidence of diagnostic thyroid fine needle aspirations, with approximately 20% yielding indeterminate results. The recent availability of molecular tests has helped guide the clinical management of these cases. This paper aims to review and compare three main commercially available molecular cytology platforms in the U.S. – Afirma GSC, Thyroseq GC, and ThyGeNEXT+ThyraMIR. Sequential improvements of the Afirma GSC and Thyroseq GC tests have led to higher positive and negative predictive values, sensitivity, and specificity. Comparative studies revealed similar diagnostic performance between these tests, with considerations for factors such as cost and processing time. Thyroseq GC provides detailed genomic information and specific management recommendations. ThyGeNEXT+ThyraMIR, though less studied, presents promising results, particularly in miRNA analysis for weak driver mutations. Challenges in interpreting results include variations in reporting and the evolving nature of testing platforms. Questions persist regarding cost-effectiveness and the utility of ultrasound characteristics in selecting candidates for molecular testing. While molecular testing has primarily served diagnostic purposes, advancements in understanding genetic alterations now offer therapeutic implications. FDA-approved options target specific genetic alterations, signaling a promising future for tailored treatments.

Cancer/testis (CT) antigens exhibit selective expression predominantly in immunoprivileged tissues in non-pathological contexts but are aberrantly expressed in diverse cancers. Because of their expression pattern, they have historically been attractive targets for immunotherapies. The investigation of mechanistic roles of CT antigens in promoting oncogenesis has historically been a prominent research question, and a growing number of studies implicate CT antigens in promoting almost all the hallmarks of cancer. This suggests that CT antigens may act as cancer drivers. CT antigens are expressed in head and neck squamous cell carcinomas, although their role in the pathogenesis, prognostication, and treatment for this family of cancers remains poorly studied. Given that CT antigens hold intriguing potential as therapeutic targets and as biomarkers for prognosis and therapeutic response and that they can provide novel insights into oncogenic mechanisms, their further study in the context of head and squamous cell carcinoma is warranted.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

There are various modeling techniques adopted for predicting the labor productivity that incorporate the influence of various factors, but neural networks are found to have strong pattern recognition and higher accuracy to get reliable estimates. To develop a construction labor productivity (CLP) model for concreting activities, data were collected through a questionnaire. The most critical influencing parameters were ranked according to their relative importance index (RII). Then the strength of the relationship between CLP and influencing parameters was analyzed using correlation coefficient results generated in Python program. Finally, the CLP model which represents the output of the crew within a certain factor was successfully developed using artificial neural networks (ANNs). Five objectives and six subjective critical influencing parameters were selected using RII. Crew experience, age of workers, and placement technique are the top three influencing parameters that have a strong relation with CLP with correlation coefficient values of 0.5681, 0.5349, and 0.5227 respectively. The developed model within these influencing parameters has a higher capability to predict the output of labor with a 92% coefficient of determination (R2) and a mean squared error of 0.316%. Therefore, the application of such a model for the accurate estimation of labor productivity is recommended.

The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Through the use of machine learning algorithms like the Support Vector Machine, it has been show that burn extent can be accurately mapped from hyperspatial drone imagery in both grasslands and forests. Despite these successes, hyperspatial imagery must be acquired via drones, requiring large amounts of time and resources to capture areas much smaller than the large catastrophic fires which result in the majority of the lands burned each year by wildland fires. To overcome this difficulty, high spatial resolution satellite imagery from Worldview2 can be substituted for hyperspatial drone imagery, allowing for larger regions of images to be acquired more easily and efficiently. Additionally, Worldview2 trades spatial resolution for spectral resolution and extent, capturing images in 8 multispectral bands as opposed to 3 band imagery in the visible spectra. This research examines the utility of each of the 8 bands observed in Worldview2 imagery using an Iterative Dichotomiser 3 decision tree, then uses these bands to map burn extent and biomass consumption. Several classifications of burn extent and biomass consumption are produced and compared based on the bands used as inputs. The results show that using Worldview2 imagery to map burn extent and biomass consumption results in highly accurate maps, with slight improvements when additional bands are added.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an energy optimization approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAV). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared to EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Smart electronic devices and telemedicine are widely used in humans' daily lives. This provides remote treatment of patients using information and communications technology. New telemedicine technologies, such as the Internet of Medical Things (IoMT), make it easier for medical and computing devices to communicate regularly and effectively. Critical motivations for adapting the IoMT are reduced cost, increased quality of life, and timely medical intervention. IoMT is significant because it enables continuous, real-time patient monitoring during routine everyday activities using a variety of wearables and sensors. With big data, the IoMT technology makes excellent use of Artificial Intelligence (AI) to support disease detection and health condition prediction, alerting patients and healthcare providers. Many research studies have been conducted to explore various aspects of IoMT and its applications in the real world. However, it is challenging to comprehend all the techniques and solutions proposed by the research community. Therefore, this survey sheds light on some crucial aspects of IoMT technologies and explores the potential research gaps and directions the research community could tackle. The survey examines and discusses the characteristics of IoMT standards, protocols, and types. It then delves into the layers of IoMT and distinguishes them into Fog and edge. The studies published under each type were explored, and the limitations of these works were highlighted. The research gaps and directions on IoMT approaches and technologies were also highlighted. With such findings and research directions, further research endeavors could be carried out to address the issues and existing limitations in the IoMT.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGen database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined by Mendelian randomization. 253 genes that exerted trans-regulatory impacts on 1,439 genes were identified. Majority of the identified gene-pairs aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. Genes with higher gene expression heritabilities displayed broader regulatory impact in the network. The network displayed the scale-free topology, this provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and higher expression of HHEX is associated with lower triglycerides and risk of type 2 diabetes. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The identified network displayed scale-free topology indicating the hub genes of a tissue-specific network could be targeted to prevent disease outcomes.

Several tumor types have been efficiently treated with PARPis, which are now approved for the treatment of ovarian, breast, prostate, and pancreatic cancer. The BRCA1/2 genes and mutations in many additional genes involved in the HR pathway may be responsible for the HRD phenomenon. In the present study, an NGS assay was used to analyze 513 genes associated with targeted and immuno-oncology therapies in 406 samples. In addition, the %gLOH values of 24 samples were also calculated using the Affymetrix technology to compare the results obtained by the two methodologies. HR variations occurred in 20.93% of the malignancies, while BRCA1/2 gene alterations in 5.17%. %LOH was highly correlated with alterations in the BRCA1/2 genes since 76.19% (16/21) of the BRCA1/2 positive tumors had a high %LOH value (p=0.007). Moreover, the LOH status was highly correlated with the TP53 and KRAS statuses, but there was no association with the TMB value. Lin's Concordance Correlation Coefficient for the 24 evaluable samples simultaneously examined by both assays was 0.87, indicating a nearly perfect agreement. In conclusion, the addition of gLOH analysis could assist the detection of additional patients eligible for PARPis

Due to production space and operating environment requirements, mine production equipment often breaks down, which seriously affects the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the number of the equipment, while and the ore quality was stable. Moreover, the improved optimization algorithm converged fast and was characterized by high robustness.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within smart cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviour to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms in order to build a system that makes the complete control of the UPTS a tangible reality.

The deformation of underground gateroads tends to be asymmetric and complex. Traditional instrumentation fails to accurately and conveniently monitor the full cross-sectional deformation of underground gateroads. Here, a full cross-sectional laser scanner was developed together with a visualization software package. The developed system used polar coordinate measuring method and the full cross-sectional measurement was realized by 360° rotation of laser sensor driven by an electrical motor. Later on, the potential impact of gateroad wall flatness, roughness and geometrical profile as well as coal dust environment on the performance of the developed laser scanner were evaluated. The studies show that a high-level flatness is favorable in application of the developed full cross-sectional deformation monitoring system. For a smooth surface of gateroad, the sensor cannot receive reflected light when the incidence angle of laser beam is large, causing data loss. Conversely, the roughness surface shows its priority as the diffuse reflection light can be received by the sensor. With regards to the coal dust in measurement environment, the fine particles of floating coal dust in the air can lead to the loss of measurement data to some certain due to scattering of laser beam.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Background: Frailty assessment allows the identification of patients at risk of death, and it is a challenge in both emergency departments and support wards. The aim here was to study the ability of the Frail-VIG Index (FI-VIG) in order to discriminate frailty groups of older adults and garner its correlation with mortality in an emergency department short-stay unit (2) Methods: Our observational, single-center, prospective study consecutively included patients over 65-years-old admitted to the unit between March 1, 2021, and April 30, 2021. (3) Results: A total of 302 patients were included (56% women), with a mean age of 83 ± 8 years, and 39.1% of them had a functional disability whilst 16.5% of them had dementia. A total of 174 patients (58%) met the frailty criteria (FI-VIG ≥0.2): 111 (63.8%) had mild frailty (FI-VIG 0.2 - 0.36), 52 (29.9%) had moderate frailty (FI-VIG 0.36 - 0.55), and 11 (6.3%) had advanced frailty (FI-VIG >0.55). Mortality at 30 days, 6 months, and 1 year was analyzed: no frailty was 6.3%, 10.8%, and 12.5%, respectively; mild frailty was 10.8%, 22.5%, and 22.5%, respectively; moderate frailty was 25%, 34.6%, and 42.3%, respectively; and advanced frailty was 36.4%, 54.5%, and 3.6%, respectively. This shows the significant differences between the groups (1-year mortality p<0.001). Mild frailty vs. non-frail HR was 2.47 (95%CI 1.12 - 5.46), moderate frailty vs. non-frail HR was 6.93 (95%CI 3.16 - 15.23), and advanced frailty vs. non-frail HR was 11.29 (95%CI 3.54 - 36.03). The mean test time was 7 minutes. (4) Conclusions: There was a strong correlation between frailty degree and mortality at 1, 6, and 12 months. FI-VIG Index is a fast and easy-to-use tool in this setting, and its routine implementation in SSUs could enable early risk stratification in order to detect vulnerable patients with specific needs.

Helicobacter pylori is the most common human gastric infection. H. pylori stool antigen lateral flow immunochromatography assay (HpSA-LFIA) is considered one of the most cost-effective and rapid non-invasive assays (active tests). The evaluation of this test is crucial for accuracy and utility assurance. This study aimed to evaluate the polyclonal antibody-based HpSA-LFIA in comparison to a monoclonal antibody-based ELISA kit. Methodology: Stool samples were collected from 200 gastric patients for HpSA-LFIA and semi-quantitative HpSA-ELISA. Statistical analysis of the diagnostic values was performed using MedCalc software. Chi-square tests were used to determine the effects of gender and age. Results: The obtained results found that HpSA-LFIA achieved promising sensitivity (93.75%) and NPV (98.00%). However, it had poor specificity, PPV, and accuracy, respectively, 59.76%, 31.25%, and 65.31%. LR+ & LR- were 2.33% & 0.1%, respectively. Gender had no significance on the di-agnostic parameters of HpSA-LFIA. Age groups had irrelevant sensitivity; however, specificity was significantly higher in patients over 45 years. Conclusion: It was concluded that HpSA-LFIA was not accurate enough to be the sole test for di-agnosis and needs other confirmatory tests in case of positive conditions

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

Designing high-voltage underground transmission lines poses complex challenges in heat management, trench optimization, and determining cable ampacity. This article introduces an innovative proposal that focuses on adjusting the dimensions of the thermal backfill as a primary strategy to enhance ampacity compared to the traditional approach of increasing the cross-sectional area of the cable core. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization, restart strategies, and parameter self-adaptation implemented in MATLAB. The objective of this approach is to provide more efficient solutions than traditional MATLAB PSO, demonstrating improved convergence and more accurate results with a success probability of 66.1\%. Although traditional PSO is 81\% faster, the proposed PSO stands out for its precision. Additionally, the incorporation of thermal backfill results in an 18.45\% increase in cable ampacity. Variations in the thermal resistivity of the soil, backfill, and ambient temperature are highlighted as sensitive factors affecting ampacity and backfill dimensions. This method is presented as a crucial tool in the early stages of the project and underground installation in operation with maximum ampacity, contributing to the continuous improvement of energy efficiency.

The purpose of this study was to recommend to the government about the appropriate land use based on geoelectric and hydrochemical works. The results were supposed to be used as models for effective land use. During the study, a groundwater survey using a geoelectrical method was conducted and was used to map an aquifer flow. In general, Solok groundwater mapping was meant to provide information necessary to optimize follow-up activities for the use of clean water. It was also intended to minimize the negative impacts of exploitation and utilization of groundwater. This research aimed to provide information on groundwater conditions in Solok. It focused on the survey of groundwater sources, the inversion of measurement data on Sclumberger electrode configuration geoelectric in thirteen districts, and chemical analysis of pH, Fe, Nitrite, and DHL. The results show there was a great potential for groundwater-unconfined aquifers.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Background: Autonomic balance measurement (ABM) is a valuable tool in clinical practice for assessing the functioning of the autonomic nervous system (ANS), which controls involuntary bodily functions. Here we summarize the current understanding of the utility of autonomic balance measurement in clinical practice. Methods: A narrative review of relevant literature was conducted, focusing on studies investigating the use of autonomic balance measures in various clinical settings. Results: ABM holds potential benefits in clinical practice, including aiding in diagnosing autonomic dysfunction, monitoring treatment efficacy, and predicting potential health complications. ABM can be crucial for diagnosing various conditions, including cardiovascular diseases (Heart failure, arrhythmias, and hypertension), neurological disorders (Parkinson's disease, Alzheimer's disease, and diabetic neuropathy), in sports medicine athletes can undergo ABM to optimize training regimens, enhance performance, assessing the impact of training stress, preventing overtraining, and promoting overall well-being and probably to assess potential health risks, in metabolic syndromes ABM can aid in the early detection of autonomic neuropathy, a diabetes-related complication, allowing for timely intervention and better management of the disease, and psychiatric disorders (Anxiety, depression, and post-traumatic stress disorder). In addition, ABM can be helpful in monitoring of treatment efficacy, by tracking changes in autonomic balance, clinicians can assess if treatments for conditions impacting the autonomic nervous system are effective. ABM can also be helpful in predicting outcomes by facilitating early recognition of future health complications in certain populations. However, limitations exist, including non-specificity, variability in measurements, and limited evidence for routine use in specific clinical settings. Conclusion: Incorporating autonomic balance measurement into routine clinical assessments provides a more comprehensive understanding of a patient's health. It complements traditional diagnostic methods, offering additional insights into the body's regulatory mechanisms. While autonomic balance measurement offers potential benefits, careful interpretation and consideration of its limitations are crucial for its effective use in clinical practice. ABM remains a valuable and versatile tool in clinical practice for a deeper understanding of the autonomic nervous system, enabling early detection, targeted interventions, and improved management of various medical conditions across different specialties.