For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

The informational synthesis of neural structures, processes, parameters and characteristics that allow a unified description and modeling as neural machines of natural and artificial neural systems is presented. The general informational parameters as the global quantitative measure of the neural systems computing potential as absolute and relative neural power were proposed. Neural information organizing and processing follows the way in which nature manages neural information by developing functions, functionalities and circuits related to different internal or peripheral components and also to the whole system through a non-deterministic memorization, fragmentation and aggregation of afferent and efferent information, deep neural information processing representing multiple alternations of fragmentation and aggregation stages. The relevant neural characteristics were integrated into a neural machine type model that incorporates unitary also peripheral or interface components as the central ones. The proposed approach allows overcoming the technical constraints in artificial computational implementations of neural information processes and also provides a more relevant description of natural ones.

Nowadays, unmanned vehicles are widely used in various fields. However, the safety of unmanned vehicles is directly determined by the autopilot systems, and sensor faults are a very critical factor that affects the normal operation of the autopilot systems. To efficiently and comprehensively test unmanned systems for sensor faults, this paper proposes a chip-level testing platform for unmanned vehicle autopilot systems based on FPGA hardware-in-the-loop simulation. Unlike existing testing platforms, the introduction of FPGA technology allows us to achieve nanosecond real-time simulation frequency, thus realizing chip-level simulation of sensors and black-box automated testing. Firstly, we propose a set of general platform and modeling methods, and the paper presents a detailed application of the four most common necessary sensors in autopilot systems as examples. Secondly, we propose for the first time a chip-level fault testing method for unmanned systems, and build a test platform with a quadrotor vehicle, the PX4 software platform, and the Pixhawk4 hardware platform as examples. Finally, the experimental results verify that the testing platform proposed in this paper can adapt to various autopilot systems and has high simulation test reliability.

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop to produce repetition rate multiplied pulses, and these pulses working as a pump signal induce XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line-spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line-spacing of the generated OFCs is respectively multiplied to be 10 GHz, 15 GHz and 20 GHz with a factor of 2, 3 and 4. The center of the OFCs is tuned in a 4-nm range by adjusting the wavelength of the probe signal.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

This paper studies the duration patterns of unemployment spells for immigrants and the determinants of unemployment’s completion into one of a number of studied labour market states in Finland. We estimate a duration model for unemployment with competing risks of its terminating into employment, labour market training or economic inactivity. Taking into account the wide period of observation and opportunities to analyse processes of labour market integration during various periods of economic development in Finland, in combination with the individualistic character of the labour careers of immigrants, this research is beneficial owing to the many various findings concerning labour market integration of immigrants. The approach undertaken in this research has a dualistic “descriptive-dynamic” character under which integration is understood as a never-ending process, which is conditioned by a time period of long-term existence and a context of solitary action. We find that transitions out of unemployment spells have a cyclical character; after every new “cycle” in unemployment, the probability of terminating unemployment decreases further. We also find that ascriptive factors make sense in the process of job-placement of immigrants from unemployment. Therefore, the gender, education and age of immigrants, as well as the effect of the period in which first unemployment occurred, potentially predict transitions out of unemployment and further labour market integration of immigrants.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

Nowadays cities, due to the increasing urbanization, are facing challenges spanning on multiple domains as mobility, energy, environment, etc. Big data gathered from legacy systems, from geographic information systems, operators, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city, the possibility to perform what-if analysis to observe the impact of possible changes. The few available solutions for formal scenario definition and analysis are limited to single domain analysis and provide proprietary formats and tools. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to define general processing and what-if scenarios in order to carry out analysis on different domains. The proposed solution responds to a series of identified requirements and implements NGSIv2 compliant data models and formal description of the urban context. As a case of study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

Automatic garments design class identification for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several methods, based on Hand-Engineered feature coding exist for identifying garments design classes. But, most of the time, those methods do not help to achieve better results. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data (images, sound, and text) more accurately. In this paper, we have applied deep CNN for identifying garments design classes. To evaluate the performances, we used two well-known CNN models AlexNet and VGGNet on two different datasets. We also propose a new CNN model based on AlexNet and found better results than existing state-of-the-art by a significant margin.

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance.

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

In the extensive monitoring of maritime traffic, maritime management frequently encounters incomplete automatic identification system (AIS) data, particularly in critical static fields like vessel types. This shortfall presents substantial challenges in safety management, necessitating robust methods for data completion. Distinct from road traffic, where road width imposes constraints, maritime vessels of identical classifications often choose significantly divergent routes, deviating by several nautical miles. Sole dependence on trajectory clustering is insufficient. Addressing this issue, we segments the maritime area into spatiotemporal grids, transforming vessel paths into sequences of grid encodings. Utilizing natural language processing techniques, we integrate the Word2vec word embedding approach with our novel biLSTM self-attention chunk-max pooling net (biSAMNet) model, enhancing the classification of vessel trajectories. This method is crucial for determining static details like vessel types. Employing the Taiwan Strait as a case study and benchmarking against CNN, RNN, and methods based on the attention mechanism, our findings underscore our model’s superiority. The biSAMNet achieves an impressive vessel classification F1 score of 0.94, using only 5-dimensional word embeddings, highlighting the effectiveness of Word2vec pre-trained embedding layers. This research introduces a novel paradigm for processing vessel trajectory data, greatly enhancing the accuracy of filling in incomplete vessel type details.

Nearly half a century has passed since the discovery of cytoplasmic inheritance of human chloramphenicol resistance. The inheritance was then revealed to take place maternally by mitochondrial DNA (mtDNA). Later, a number of mutations in mtDNA were identified as a cause of severe inheritable metabolic diseases with neurological manifestation, and the impairment of mitochondrial functions has been probed in the pathogenesis of a wide range of illnesses including neurodegenerative diseases. Recently growing number of preclinical studies has revealed that animal behaviors are influenced by the impairment of mitochondrial functions and possibly by the loss of mitochondrial stress resilience. Indeed, as high as 54% of patients with one of the most common primary mitochondrial diseases, mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome, present psychiatric symptoms including cognitive impairment, mood disorder, anxiety, and psychosis. Mitochondria are multifunctional organelles which produce cellular energy and play a major role in other cellular functions including homeostasis, cellular signaling, and gene expression, among other. Mitochondrial functions are observed to be compromised and to become less resilient under continuous stress. Meanwhile, stress and inflammation have been linked to the activation of the tryptophan (Trp)-kynurenine (KYN) metabolic system, which observably contributes to development of pathological conditions including neurological and psychiatric disorders. This narrative review discusses the functions of mitochondria and the Trp-KYN system, the interaction of the Trp-KYN system with mitochondria, and the current understanding of the involvement of mitochondria and the Trp-KYN system in preclinical and clinical studies of major neurological and psychiatric diseases.

The invasion of ship domain stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validated through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of collision risk. Finally, taking the high risk area of merchant and fishing vessels collision in the Minjiang River Estuary as an example, this paper conduct the analysis involves classifying ship collision scenarios, extracting risk data under different collision scenarios, and visually analyzing areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis is verified by accidents occurred in the recent three years.

Recycling information can be complex and often confusing which may subsequently reduce the participations in any waste recycling schemes. As a result, this research explored the roles as well as the importance of a holistic approach in designing recycling information using 15 expert-based in-depth interviews. The rationale was to offer a better understanding of what constitutes waste, recycling, and how recycling information should be designed and presented to make recycling more attractive/convenient. Based on the research participants’ perceptions with supports from the existing studies, this research sub-categorised recycling information into three different themes, termed the “WWW” of recycling information components. As a result, these components (or attributes) were extensively described (using findings of semi-structured interviews) to elicit pragmatic guidance for practitioners, policy-makers, and other stakeholders in designing structured communication or information strategies that may simplify and subsequently increase waste-recycling practices. The policy implications of holistic information in enhancing recycling are further discussed.

A population dynamics epidemic model is developed that incorporates age of infection and age of vaccination. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. The model analyzes the efficacy of vaccination in terms of vaccination age. The model is applied to the 2003 SARS epidemic in Taiwan and the current COVID-19 epidemic in New York State.

Introduction: Endoscopic biliary stenting is a widely utilized procedure in the realm of interventional gastroenterology, primarily employing plastic stents for short-term biliary drainage. This case series endeavours to investigate the occurrence and ensuing clinical ramifications of forgotten or retained biliary stents. Methods: Carried out over an 8-month duration at King George Hospital in Visakhapatnam, this case series encompasses 12 patients diagnosed with forgotten biliary stents. Diagnostic confirmation was established through a combination of X-ray, abdominal ultrasound, and magnetic resonance cholangiopancreatography (MRCP). Four patients experienced complications such as fractured distal pigtails and obstructed stents, thwarting retrieval efforts via endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopy. Results: This case series featured 12 patients with a median age of 48 years (range: 18-76), comprising 7 females (58.3%) and 5 males (41.7%). The primary indication for initial stent placement was choledocholithiasis in 10 instances (83.3%), while benign biliary strictures accounted for 3 cases (25%). On average, patients presented 31.3 months post-initial stenting, primarily manifesting cholangitis symptoms (83.3%). ERCP succeeded in stent removal in 8 cases (66.6%), while the remaining cases necessitated advanced procedures such as cholangioscopy, with two patients being referred for surgical intervention. It is noteworthy that there were no instances of mortality. Conclusion: This case series, conducted against the backdrop of the COVID-19 pandemic, highlights an increased prevalence of neglected plastic biliary stents. The principal complication encountered was cholangitis, often coexisting with common bile duct (CBD) stones. Standard ERCP techniques demonstrated reduced efficacy in instances characterized by fractured, obstructed, or displaced stents, necessitating the exploration of alternative strategies like cholangioscopy or surgical intervention. This compilation of cases underscores the pressing imperative for prompt stent retrieval and the enhancement of management protocols to proactively mitigate the potential for complications.

In this paper we propose a novel self-supervised approach of keywords and keyphrases retrieval and extraction by an end-to-end deep learning approach, which is trained by contextually self-labelled corpus. Our proposed approach is novel to use contextual and semantic features to extract the keywords and has outperformed the state of the art. Through the experiment the proposed approach has been proved to be better in both semantic meaning and quality than the existing popular algorithms of keyword extraction. In addition, we propose to use contextual features from bidirectional transformers to automatically label short-sentence corpus with keywords and keyphrases to build the ground truth. This process avoids the human time to label the keywords and do not need any prior knowledge. To the best of our knowledge, our published dataset in this paper is a fine domain-independent corpus of short sentences with labelled keywords and keyphrases in the NLP community.

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

Exposure to malachite green (MG) may pose great health risks to humans, thus it is of prime importance to develop fast and robust methods to quantitatively screen the presence malachite green in environment. Herein application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 ug L-1 (S/N=3) in lake water samples and ~ 0.5 ug L-1 in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–5000 ug L-1. Measurement of practical water samples fortified with MG at 0.01 and 0.1 mg L-1 gave a good validation of the established calibration curve. The average recoveries of malachite green in lake water and Carassius carassius fish farm effluent water were 114.9% (6.6% RSD) and 85.4% (9.2% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a weak pulling force automatically shifts the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

: In the realm of data management, data preservation stands as a critical undertaking aimed at preserving and upholding the integrity of data. Regardless of whether it concerns personal or enterprise data, the detrimental effects of malicious alterations implemented by attackers cannot be overlooked. Particularly in conventional industrial control environments, the prevalent practice involves the transmission of data from sensors to databases for storage purposes. However, it is essential to recognize that this process exposes the data to various vulnerabilities. Thus, to ensure the long-term security and reliability of the data, it becomes imperative to implement robust data preservation strategies within these industrial control systems. However, the reliance of these databases on physical hard disks introduces inherent vulnerabilities, including the potential for data loss due to disk damage or targeted malicious attacks. Consequently, it becomes imperative to prioritize the implementation of robust data preservation measures. These measures are crucial in mitigating the risk of disruptions and protecting critical data from compromise. By establishing effective data backup systems, employing advanced security protocols, and implementing proactive monitoring mechanisms, organizations can bolster their data preservation capabilities and safeguard against potential threats to data integrity and availability. As a result, many enterprises opt to store their data with third-party providers to ensure data integrity. However, this approach carries inherent risks. If the third-party service experiences an attack or if the data is tampered with, it becomes challenging to verify the integrity of the data. To address these concerns and ensure data preservation within the context of the Internet of Things (IoT), a growing number of individuals are integrating IoT with Distributed Ledger Technology (DLT). By leveraging DLT, the integrity of data can be ensured, reducing reliance on centralized third-party storage and enhancing security in the IoT ecosystem. In this article, IOTA is the DLT, which employs Directed Acyclic Graph (DAG) to store transaction information. Compared to Ethereum or other blockchain technologies, IOTA offers notable advantages in terms of transaction verification speed, making it highly suitable for real-time IoT environments. However, the conventional transmission path from sensors to IOTA nodes entails a complex route, involving multiple hardware devices before reaching the intended destination. This complexity poses challenges in ensuring data integrity during transmission and introduces vulnerabilities such as man-in-the-middle attacks or SQL injection attacks. To address these issues, we propose a method to streamline the transmission path between sensors and IOTA, specifically tailored for industrial fields with numerous IoT devices. Our approach involves preprocessing the data stored on the server using our method before uploading, ensuring data confidentiality, and leveraging IOTA to guarantee data integrity. To achieve the shortest path between IoT and DLT nodes, it becomes necessary to establish IOTA nodes on lower-level devices, such as Raspberry Pi or IoT controllers. By simplifying the transmission path, we can reduce the potential for tampering and enhance overall data security. Implementing our proposed method enables the assurance of data confidentiality and integrity during both transmission and storage on the server, strengthening the trustworthiness of the IoT, and IOTA integration.

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners’ site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: ’invertible games’ and ‘cyclical games'. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances these expressions can be simplified. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: ‘Tit-for-Tat' and ’Win-Stay, Lose-Switch'.

The influence of growth temperature on the crystalline quality and optical properties of AlGaN at 325 nm, grown using plasma-assisted molecular beam epitaxy (MBE), was investigated. The growth temperature exhibited a significant impact on the growth mode and surface morphology. Specifically, at a growth temperature of 750 degrees Celsius, when appropriate Al and Ga flow rates were employed, the AlGaN layer exhibited exceptional surface morphology and favorable optical properties. The optical properties of the AlGaN material were explored, revealing the presence of two distinct non-radiative recombination centers characterized by different activation energies. The minority carrier lifetimes were measured as 560 ps at 10 K and 64 ps at room temperature. The underlying mechanism was analyzed from the perspective of carrier dynamics, shedding light on the observed phenomena.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

In order to comprehensively evaluate the employee satisfaction of mine occupational health and safety management system, an analytic method based on fuzzy analytic hierarchy process and 2- tuple linguistic model was established. Firstly, based on the establishment of 5 first- grade indicators and 20 second- grade ones, the weight of each indicator is calculated and validated by method of FAHP and root mean square model. Secondly, a path based on the time-ordered Weighted Averaging Operator (T-OWA) model is constructed. Finally, the model is validated by empirical analysis. The results demonstrate that the employee satisfaction of the mine occupational health and safety management system is of the “general” rank. The method including the comprehensive evaluation of employee satisfaction and the quantitative analysis of language evaluation information ensures the authenticity of the language evaluation information.

Information theory provides robust measures of multivariable interdependence, but classically does little to characterize the multivariable relationships it detects. The Partial Information Decomposition (PID) characterizes the mutual information between variables by decomposing it into unique, redundant, and synergistic components. This has been usefully applied, particularly in neuroscience, but there is currently no generally accepted method for its computation. Independently, the Information Delta framework characterizes non-pairwise dependencies in genetic datasets. This framework has developed an intuitive geometric interpretation for how discrete functions encode information, but lacks some important generalizations. This paper shows that the PID and Delta frameworks are largely equivalent. We equate their key expressions, allowing for results in one framework to apply towards open questions in the other. For example, we find that the approach of Bertschinger et al. is useful for the open Information Delta question of how to deal with linkage disequilibrium. We also show how PID solutions can be mapped onto the space of delta measures. Using Bertschinger et al. as an example solution, we identify a specific plane in delta-space on which this approach’s optimization is constrained, and compute it for all possible three-variable discrete functions of a three-letter alphabet. This yields a clear geometric picture of how a given solution decomposes information

Predicting the outcome of a future game between two National Basketball Association (NBA) teams poses a challenging problem of interest to statistical scientists as well as the general public. In this article, we formalize the problem of predicting the game results as a classification problem and apply the principle of maximum entropy to construct NBA maximum entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning team 74.4 percent of the time as compared to some other machine learning algorithms which is correct 69.3 percent of the time.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

This paper examines a persuasion game between two agents with one-sided asymmetric information, where the informed agent can reveal her private information prior to playing a Battle-of-the-Sexes coordination game. We find that in the presence of strategic uncertainty in coordination there exists an equilibrium where there is no 'unraveling' of information. We provide a purification argument for this mixed strategy equilibrium to strengthen the central result, which is robust to several extensions, including both-sided asymmetric information and imprecise information revelation.

Huge-scale video surveillance systems have become essential in crime prevention and situation recording. Traditional surveillance systems relied on human monitoring of video streams, which often led to errors and difficulties in understanding events. Furthermore, locating specific scenes within recorded videos required extensive human investigation. To overcome these challenges of inefficiency, inconvenience, and potential risks, we propose an intelligent analysis scheme that utilizes abnormal behavior recognition and metadata retrieval algorithms to replace human monitoring. The proposed method consists of three stages: i) metadata generation through object detection and tracking, ii) abnormal behavior recognition, and iii) SQL-based metadata retrieval. By incorporating specific information such as object color and aspect ratio, our technique enhances the usability of retrieval. Moreover, our abnormal behavior recognition module demonstrates robust classification capabilities for activities such as pushing, violence, falling, and crossing barriers. The proposed method can be seamlessly deployed on both edge cameras and analysis servers, making it adaptable to various surveillance setups. This approach revolutionizes the traditional surveillance paradigm, enabling more efficient, reliable, and secure video monitoring and analysis.

Recently deep cross-modal hashing networks have received increasing interests due to its superior query efficiency and low storage cost. However, most of existing methods concentrate less on hash representations learning part, which means the semantic information of data cannot be fully used. Furthermore, they may neglect the high-ranking relevance and consistency of hash codes. To solve these problems, we propose a Self-Attention and Adversary Guided Hashing Network (SAAGHN). Specifically, it employs self-attention mechanism in hash representations learning part to extract rich semantic relevance information. Meanwhile, in order to keep invariability of hash codes, adversarial learning is adopted in the hash codes learning part. In addition, to generate higher-ranking hash codes and avoid local minima early, a new batch semi-hard cosine triplet loss and a cosine quantization loss are proposed. Extensive experiments on two benchmark datasets have shown that SAAGHN outperforms other baselines and achieves the state-of-the-art performance.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

By using the particle swarm optimization algorithm for crystal structure prediction, we reveal a newly orthorhombic Cmcm structure of HfB4, which is more energetically superior to the previously proposed YB4-, ReP4-, FeB4-, CrB4-, and MnB4-type structures in the considered pressure range. The phonon dispersion and elastic constants calculations confirm that the new phase is dynamically and mechanically stable. The calculated large shear modulus (240 GPa) and high hardness (45.7 GPa) imply that the predicted Cmcm-HfB4 is a potential superhard material. Meanwhile, the directional dependences of the Young's modulus, bulk modulus, and shear modulus for HfB4 are systematically investigated. Further analyses of the density of states and electronic localization function indicate that the strong B-B and B-Hf covalent bonds greatly contribute to its high hardness and stability.

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flow in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the Physics-Informed Neural Network (PINN) method. This innovative approach combines artificial neural networks (ANNs) with physical equations, offering a more efficient and accurate way to model various complex processes and phenomena. The proposed mathematical model, based on the Euler equation, has been meticulously implemented using the Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In the research, a comprehensive comparative analysis was conducted between results obtained using the PINN method and those from conventional Computational Fluid Dynamics (CFD) approaches. The analysis highlighted the advantages of the PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing computation time, demonstrating its potential as a transformative modeling tool. The data derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulation has the potential to revolutionize the integrity of our energy infrastructure and utilization of gas resources. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

We explored the dynamics of the two interacting information systems. We show that for the Markovian marginal systems the driving force for information dynamics is determined by both the information landscape and information flux. While the information landscape can be used to construct the driving force to describe the equilibrium time reversible information system dynamics, the information flux can be used to describe the nonequilibrium time-irreversible behaviours of the information system dynamics. The information flux explicitly breaks the detailed balance and is a direct measure of the degree of the nonequilibriumness or time irreversibility. We further demonstrate that the mutual information rate between the two subsystems can be decomposed into the equilibrium time-reversible and nonequilibrium time-irreversible parts respectively. This decomposition of the mutual information rate (MIR) corresponds to the information landscape-flux decomposition explicitly when the two subsystems behave as Markov chains. Finally, we uncover the intimate relationship between the nonequilibrium thermodynamics in terms of the entropy production rates and the time-irreversible part of the mutual information rate. We found that this relationship and MIR decomposition still hold for the more general stationary and ergodic cases. We demonstrate the above features with two examples of the bivariate Markov chains.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

Technology is in the way to reach in its climax but the basic understanding of science in many phenomena is still awaited even though the nature witnesses it. Scientific research reveals strong analogy between photon and electron. When an atom deals neutral state, it levitates electron of outer ring from the back surface while placing the bit-energy at front surface. Gravitation behavior of that electron starts at the centre of relaxation point by including the force of side pole where the pulling force of nearby unfilled state of that atom from the front surface results into depict forcing energy shape like Gaussian distribution symbol with both ends turned called unit photon. The inertia is being involved at each stage of changing direction of that electron by introducing the disappearances of forces of two poles against the appearances of forces of two opposite poles during rest to motion and motion to rest in the first half-cycle. The same is the case in the second half-cycle of that electron during rest to motion and motion to rest but it is under different introduction of the disappearances of forces against appearances of forces. However, at stage of levitating or gravitating period of electron, only one force is being involved at one time where the opposite force is disappeared. The uninterrupted confined inter-state electron-dynamics of atom under the availability of several bits of bit-energy results into generate forcing energy shape like a wave. Two bits of bit-energy where shape of bit for first half-cycle is like integral symbol and second half-cycle is like opposite integral symbol which are being placed along the configuring trajectory of inter-state electron-dynamics during forward-direction cycle and two bits of bit-energy shape in opposite order are being placed along the trajectory of inter-state electron-dynamics during back-direction cycle. Generating forcing energy of unit photon in each cycle is pushing to the rear side by remaining connected till the electron is not restoring the state of rest. Silicon atom is considered as a model system under neutral state. Uninterrupted confined inter-state electron-dynamics result into generate forcing energy that can travel immeasurable length and unavailability of necessitating bit-energy at any interrupted stage result into generate an overt photon. Inter-state electron-dynamics for at least two cycles generate an overt photon –a photon length twice to unit photon. Under certain interaction of unit photon, it divides equally into two bits of bit-energy instead of dividing into tits and bits of heat. The mechanism of generating photon characteristic current by the electron of neutral state atom validates that atoms are four-dimensional discs at centre of dealing no force. An isolated electron is being grounded under directed forcing energy to impinge a neutral state atom where the gained instantaneous velocity under merged energy resulting into distort atom at that point. Matter changes the role of energy and force under various sorts of interactions. Here, heat energy and photon energy explore matter at atomic and electron levels, thus, devise basis of science to describe.

The relationship between perception of anxiety and quality of life in caregivers of children with autism was verified. To verify quality of life, the Short Form Healthy Survey Item was used; perception of anxiety was assessed using the Beck Anxiety Inventory. 80 caregivers of children with autism participated, with 68.7% of caregivers being mothers. Of the total number of caregivers, 68.8% had a severe level of anxiety. Regarding quality of life, they demonstrated greater impairment in Limitation due to physical aspects, Limitation due to emotional aspects, Vitality and Pain. Caregivers with severe anxiety levels had worse quality of life in the domains of pain (p = 0.012), social aspects (p =&lt; 0.001), limitation due to emotional aspects (p = 0.001) and mental health (p =&lt; 0.001). However, in the functional capacity domain, caregivers with a moderate level of anxiety had a better physical capacity score (p = 0.001). There was a negative correlation between the general anxiety score and the general physical (p = 0.029) and general emotional component of quality of life (p =&lt; 0.001). It was found that caregivers of children with ASD present losses related to psychological factors, quality of life and the lack of social support.

Abstract Introduction: Poor structural quality of health facilities for tuberculosis (TB) diagnosis and treatment contributes to unfavorable health outcomes among TB patients. Improving the quality of healthcare services helps to avert TB-related morbidity. Despite these facts, the level of structural quality of health facilitieSs to deliver TB services is not in line with what the national TB and leprosy program (NTLP) recommends. Hence, the present study was conducted to assess the structural quality of health facilities to deliver quality TB services to TB patients among public health facilities in the Sidama region.Objectives: To determine the quality of the structural domain of TB services at public health facilities in the Sidama region of southern Ethiopia.Methods: An institution-based cross-sectional study was conducted from June to July 2023 among randomly selected public health facilities in the Sidama region. We included 40 randomly selected public health facilities that delivered TB services in the study area. Data was collected by experienced data collectors using facility audits, clinical observation checklists, structured questionnaires, and in-depth interviews. An Open Kit Mobile (ODK) application was used to collect data and exported to SPSS version 26. All statistical analysis was done using SPSS version 26.Results: The overall mean quality score for the structural dimension was 50.25 among the 40 health facilities audited in the study area.Conclusion: The quality of TB services regarding structural dimensions was poor in the study area. Therefore, any strategies must give careful attention to increasing the comprehensive structural dimension of quality TB services.

Existing Knowledge Graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieved entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel Retrieval-augmented Knowledge Graph (RAKG) model, which solves the above issues through two key innovations. First, we leverage the density matrix to make the model reason along the corrected knowledge path and extract an enhanced knowledge graph subgraph. Second, we fuse representations of questions and graph entities through a bidirectional attention strategy, in which two representations fuse and update by Graph Convolutional Network (GCN). To evaluate the performance of our method, we conduct experiments on two widely-used benchmark datasets CommonsenseQA and OpenBookQA. The case study gives insight into findings that the augmented subgraph provides reasoning along the corrected knowledge path for question answering.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Abstract In order to reduce the failure accidents of conventional packers, a kind of sealing structure of retrievable packers is designed, which can provide reference for optimizing the sealing performance and pressure capacity of packers. And theoretical calculation and experimental research on its sealing performance and pressure capacity are carries out through the finite element analysis on the contact with large deformation based on Abaqus and experimental. The results show that when the rubber cylinder of retrievable packers in the sealing process, the upper packer is subjected to greater stress and mainly plays a sealing role. In order to improve the sealing performance of packer, the height of upper and lower packer should be preferentially selected about 60 mm, the height of middle rubber cylinders should be about 50 mm, and the thickness of packer should be about 21 mm. The newly designed rubber cylinders can meet 150 ℃. Sealing and pressure-bearing function under the condition of 10000 Psi. The rubber cylinder of newly designed can satisfy the function of sealing and pressure-bearing under 140 ℃ and 10000 Psi. The whole experiment shows that the capacity of bearing pressure and sealing is stable, which can better meet the requirements of field use.

PURPOSE: Breast cancer tumors frequently have intratumor heterogeneity. Tumors with high intratumor heterogeneity have caused therapeutic resistance and have human epidermal growth factor receptor 2 (HER2) heterogeneity in response to HER2-targeted therapies. This study aimed to investigate whether high HER2 heterogeneity levels were clinically related to poor prognosis for HER2-targeted adjuvant therapies resistance in primary breast cancers. METHODS: This study included patients with primary breast cancer (n = 251) treated with adjuvant HER2-targeted therapies. HER2 heterogeneity was manifested by the shape of HER2 fluorescence in situ hybridization amplification (FISH) distributed histograms with HER2 gene copy number within a tumor sample. Those heterogeneity classified each tumor into the biphasic grade graph (high heterogeneity [HH]) group and the monophasic grade graph (low heterogeneity [LH]) group. Both groups were evaluated for disease-free survival (DFS) and overall survival (OS) for a median of ten years of annual follow-up. RESULTS: Of 251 patients with HER2-positive breast cancer, 46 (18.3%) and 205 (81.7%) were classified into the HH and LH groups, respectively. The HH group had more distant metastases and a poorer prognosis than the LH group (DFS: P < 0.001 (HH:63% vs LH:91% at 10 years), and OS: P = 0.012 (HH:78% vs LH:95% at 10 years). CONCLUSION: High HER2 heterogeneity is a poor prognostic factor in patients with HER2-positive breast cancer. A novel approach of heterogeneity, which is manifested by the shape of HER2 FISH distributions, might be clinically useful in the prognosis prediction of patients after HER2 adjuvant therapy.

Two distinct measures of information, connected respectively to the amplitude and phase of the wave function of a particle, are proposed. There are relations between the time derivatives of these two measures and their gradients on the configuration space, which are equivalent to the wave equation. The information related to the amplitude measures the strength of the potential coupling of the particle (which is itself aspatial) with each volume of its configuration space, i.e. its tendency to participate in an interaction localized in a region of ordinary physical space corresponding to that volume. The information connected to the phase is that required to obtain the time evolution of the particle as a persistent entity starting from a random succession of bits. It can be considered as the information provided by conservation principles. The meaning of the so-called "quantum potential" in this context is briefly discussed.

In a recent paper, we have shown how gravity and information can be related. The relation is based on the thermodynamic extension of the gravitational system under consideration using the holographic principle. Using this idea, we were able to derive the Gullstrand-Painlevé (GP) metric. In this short paper, we re-derive the GP metric using information. However, we now extend the spacetime to 5D with the fifth dimension being time, that is, the (3+1)D spacetime is now promoted to (3+1+1)D spacetime. We also comment on the possible Braneworld interpretation of this model.

Now-a-days, the thrust area of materials’ research is to develop multifunctional materials, which have multiple properties in a single functional material. We are interested to develop multifunctional polysilanes having special functional properties such as optical activity and photoluminescence (PL) properties. In this investigation, poly(methyl phenyl silane) (PMPS) was integrated with a chiral unit and a photoactive unit by in situ photopolymerization of (R)-N-(1-phenyl ethyl) methacrylamide (R-NPEMAM) and disperse red 1 methacrylate (DR1MA) monomer in the presence of PMPS. PMPS acted as a macrophotoinitiator and by UV exposure it produced macroradicals which initiated polymerization of chiral and photoactive monomers. Thus a block copolymer of PMPS-b-(R-NPEMAM-ran-DR1MA) was synthesized.The synthesized multifunctional organic inorganic hybrid polymer samples were characterized by FTIR and NMR spectroscopy. The molecular weights of the samples were measured by GPC analysis. The optical, chiroptical and photoluminescence properties were studied. The narrow band at about 1638 cm^-1 is due to C=O stretching vibration of -CONH- of the chiral unit. A wide absorption peaks at 3444 cm^-1 is the characteristic of NH trans stretching vibration of secondary amide group. The asymmetric stretching of NO₂ group appeared at 1481 cm^-1 and the characteristic peak at 1427 cm^-1 was observed due to azo (-N=N-) group stretching of DR1MA unit. The Si-Si band of PMPS appeared at about 462 cm^-1 in FTIR spectra. The optical absorbance observed at 272 nm is due to π-π* transition of aromatic ring and at 330 nm corresponds to σ-σ* transition of Si-Si bond. The other electronic absorption observed in the visible region at 475 nm corresponds to the combined contribution of n-π* and π-π* transition of DR1MA unit. The photoluminescence properties of such polymers were studied for the variation of concentration of the polymer solution and variation with excitation energy such as 275 nm, 325 nm and 475 nm in THF solvent. Such synthesized multifunctional photoactive organic-inorganic polymers having unusual optical, chiroptical and photoluminescence property may find novel optoelectronic (friend foe identification and secretive code identification) applications.

The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amout of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatial resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. Besides, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to variaty cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation.

Abstract: NFinFET transistors with fin channel length 90nm and a planar MOSFET transistor with channel length 180nm and 90nm are presented with characteristic curves at various gate biases. A finalized algorithm with kink effects is effectively responsible for addressing the field effect transistors. The algorithm includes the modified conventional current-voltage formula and a nonlinear heat-associated kink solution which is simplified as a Gaussian form. Three parameters in modified model includes kN (related with channel width, channel length, and gate oxide capacitor, and proportional to the mobility of carriers)Vth(Threshold Voltage), and (the inverse of Early Voltage). Those parameters are determined to minimize the discrepancies between the measured data and the fitting values, but leave kinks located at around (VGS－Vth), which are deliberately eliminated by the Gaussian form because of the agitation of thermal kink effects. The whole fitting is made to be as close as the as-measured IDS-VDS. In the mean time, those determined parameters are physically meaningful after the analysis has been done.

Orthogonal Frequency Division Multiplexing (OFDM) while being an efficient scheme for high data rate wireless communications has drawbacks such as higher Peak-to-Average Power Ratio (PAPR). To reduce PAPR, use of multiple signal representation technique such as Partial Transmit Sequence (PTS) is one of the favored techniques. However, the use of conventional PTS technique need excessive number of complex calculations in order to search for all permissible combinations of phase sequences causing steep increase in complexity in terms of complex computations. Paper aims to reduce the cumbersome process of phase selection by making use of the similarity of the phase vectors. The phase vectors are obtained sequentially and thus minimize the number of changes from one phase vector to another. Theoretical analysis shows that computational complexity is significantly reduced with the help of this proposed novel technique. We have also demonstrated that PAPR values are similar i.e. PAPR reduction capability remains similar but at reduced complexity.

The need for cross-modal retrieval (CMR) among users is growing because of the swift advancements in computer vision and natural language processing. Promoting home operating systems is greatly aided by the openKylin home operating system. It is focused on enhancing the functionality of the operating system with safe and dependable operating technologies. Practical implications abound for integrating computer vision research into home operating systems. A multimodal retrieval approach based on openKylin is presented to improve the usability of text search graphs, audio, and video. The overview of pertinent domestic and international research is the main topic of this article. This technological advancement. It then offers details on the design, implementation procedure, and final implementation outcomes for demonstration. The works of natural operations show that this strategy has excellent accuracy and performance.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Recent advancements in web-based geospatial software and smartphone technology have popularized the process of voluntary production and sharing of geospatial data by individual citizens. Through such Volunteered Geographic Information (VGI) activities, people across the world participate in online mapping projects (such as OpenStreetMap) to insert their spatial information. The quality of data generated by such VGI activities has profound impacts on online mapping projects and their spatial database. In this study, we examine the VGI contribution pattern in OpenStreetMap through three case study neighborhoods located in three major cities: Tehran, London, and Los Angeles, and investigate how it might affect the process of quality assessment of VGI.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

-- This paper examines the critical role of business analysis in shaping small and medium-sized enterprises’ strategies for better odds of success in the ever-evolving Nigerian economy and within the competitive markets. Business analysis ability, as a knowledge-supporting factor, focuses on understanding consumer needs, planning, data collection, analysis, and presentation to decision-makers. The process begins with strategic planning and meticulous data collection, facilitating a deep understanding of the business and consumer behavior and process potential. Analytical skills unearth the true potential of organizational processes, particularly in product creation, resulting in distinctive and highly valued products. Such products align with theories like the resource-based view or VRIO theory, driving increased sales volume—a key performance metric. By leveraging analytical capabilities, Nigerian SMEs can navigate complex market dynamics, create value-driven businesses, and differentiate themselves from competitors thus increasing the odds of success. This paper sets the stage for further research into the impact of business analysis on organizational success within dynamic market landscapes.

Current-voltage characteristics curves of NFinFET are presented and are fitted with the modi-fied current-voltage (I-V) formulas, where the modified term in the triode region is demon-strated to be indispensable. In the as-known I-V formula, important parameters need determin-ing to make both the measured data and the fitting data as close as possible. Those parameters include kN (associated with the sizes of the transistor and mobility), (associated with Early Voltage), and Vth (the threshold voltage). The differences between the measured data and the fit-ting data vary as the applied source-drain bias, proving that the mobility of the carriers is not consistently constant. On the other hand, a modified formula, called kink effect factor, is nega-tively or positively added simulating solitary heat waves, which disturb the propagation of car-riers and thus influence the Source-Drain current (IDS). The new statistical standard deviations () are then found to be effectively suppressed as the kink effect is taken into account.

This article evaluates Europe's building sector's circular economy (CE). This industry is respon-sible for 42% of energy consumption, more than 50% of extracted materials, 30% of Europe's wa-ter and waste generation, and 35% of greenhouse gas (GHG) emissions. This study focuses on peer-reviewed articles from Scopus and Web of Science databases. Of the first 1750 publications, 2.9% were eligible for a full-text reading and analysis. Most of the trending studies, 92%, pro-mote the circular economy concept through construction materials rather than analyzing the im-pact of the construction sector on the environment and finding solutions for better implementa-tion, and 41% promote recycling and reuse as the only options. New Design solutions are in 12% of the studies, six-fold more than Law and Legislation, only 2%. Finding an optimal combination of assessing the life cycle of buildings and components and searching for different ways of man-aging the Construction and Demolition Waste at end-of-life is between 33% and 39%. Ultimately, one evaluated CE proposed frameworks for the building industry based on 10R principles varia-tions. One presents an alternative framework for a circular strategy for the building industry that focuses on Upcycling, replacing the Recover principle.

With the rapid development of Internet and the widely usage of smart devices, massive multimedia data are generated, collected, stored and shared on the Internet. This trend makes cross-modal retrieval problem become a hot issue in this years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others uses adversarial learning technique to abate the heterogeneity of multi-modal data. However, very few works combine correlation learning and adversarial learning to bridge the inter-modal semantic gap and diminish cross-modal heterogeneity. This paper propose a novel cross-modal retrieval method, named ALSCOR, which is an end-to-end framework to integrate cross-modal representation learning, correlation learning and adversarial. CCA model, accompanied by two representation model, VisNet and TxtNet is proposed to capture non-linear correlation. Beside, intra-modal classifier and modality classifier are used to learn intra-modal discrimination and minimize the inter-modal heterogeneity. Comprehensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed ALSCOR has better performance than the state-of-the-arts.

Timely processing of observations from hyper-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances for the physical retrieval of surface temperature and emissivity. The new radiative transfer model improves computational time by a factor of ≈ 7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the simultaneous retrieval of surface temperature and emissivity in three infrared channels (8.7, 10.8, 12 μm). The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e. average differences are generally well below 0.01.

We present a new approach to retrieve Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently don’t conduct any AOD retrieval in the regions with large water-leaving radiances in the visible spectrum. Over the global coastal water regions in all cloud-free conditions, this unavailability of AOD retrievals due to the inherent limitation in existing DT algorithm is ~20%. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm is negligible regardless of water turbidity. This refinement, with the assumption that the aerosol single scattering properties over coastal turbid water are similar to that over the adjacent open-ocean pixels, yields ~18% more of MODIS-AERONET collocated pairs for six AEROENT stations in the coastal water regions. Furthermore, comparison with these AERONET observations show that the new AOD retrievals are in either equivalent or better accuracy than those retrieved by the MODIS operational algorithm (over coastal land and non-turbid coastal water). Combining the new retrievals with the existing MODIS operational retrievals not only yield an overall improvement of AOD over those coastal water regions, but also successfully extend the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides, and thereby, aerosol impacts on regional air quality and climate are expected to be significant.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Plants belonging to the genus Irvingia are widespread across the African and Southeast Asian regions of the world. Irvingia gabonensis, Irvingia malayana, and Irvingia grandifolia are among the commonly used species in ethnomedicine, especially in Africa. Fever, scabies, toothache, inflammation, and liver and gastrointestinal disorders are among the pathological conditions that are reverted by Irvingia plants upon traditional preparations. Modern pharmacological investigations have substantiated the ethnomedicinal uses of Irvingia spp. Reports on the phytochemical analysis of Irvingia plants have revealed the presence of a number of secondary metabolites, such as flavonoids, phenolic compounds, tannins, saponins and alkaloids. Based on the foregoing, the present study provides a comprehensive evaluation of reports on the ethnomedicinal use, phytochemistry, pharmacology, and toxicity of plants from the genus Irvingia. Relevant information on Irvingia plants was mostly obtained from major scientific databases from their inception until July 2023. As a result, more than forty compounds have been identified in Irvingia spp. proving the abundance of secondary metabolites in these plants. Reports have pointed out modern pharmacological activities, such as antiprotozoal, antimicrobial, antioxidant, antidiabetic, anti-inflammatory, and hepatoprotective activities. The present study provides more insights for the successful utilization of Irvingia plants and may guide further research on their therapeutic potential in the treatment of various diseases.

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lip-idomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

(1) Background: Truing to find a fast and representative method for the approximate calculation of the culture density and the cell content of useful pigments, the examination of the absorption spectra of cultures of the dinoflagellate Amphidinium carterae, the haptophyte Isochrysis galbana, the chlorophyte Nephroselmis sp. and the filamentous cyanobacterium Anabaena sp. were chosen, as representative species of different taxa. (2) Methods: The experimental cultures were made in small volumes with the discontinuous method, under specific conditions each one and during the culture the absorption spectra, the density of the culture and the concentration in the pigments chlorophyll, total carotenoids and phycocyanin was recorded. (3) Results: The algal density can be quite precisely predicted as the regression equation of the correlation of the OD value of 750 nm drawn from each absorption spectrum and the measured algal biomass was very strong. The same applies also for the relevant correlations between OD 750 nm and the recorded pigments. (4) Conclusions: Absorption spectra of microalgal cultures can be an easy, low cost and non-invasive method to get the necessary information for predicting the right time to collect an ideal combination of maximum biomass and useful pigments, provided that the interpretation of the spectra is done considering that they are usefully applicable for that purpose in a case sensitive manner in terms of species and prevailing conditions.

Microsoft has implemented several measures to defend against macro viruses, including the use of Anti-malware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to apply malicious macros as their essential attack vector. In this paper, we analyze 77 obfuscation features from the attacker’s point of view and extract 46 suspicious keywords in macro. We first combine the above two types of features to train machine learning models on a public dataset. Then, we carry out the same experiment on self-constructed dataset, a collection of newly discovered samples, to see if our proposed method could discover the unseen malicious macros. Experimental results demonstrate that, comparing with the existing researches, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection further improve the detection performance.

Osteoarthritis (OA) is the most common degenerative joint disease that causes chronic pain and disability. Different innate immune components, including macrophages, T cells, and neutrophils, participate in osteoarthritis pathophysiology. Neutrophils are the most abundant circulating leukocytes with multiple specialized functions contributing to innate and adaptive immune functions. Although neutrophils produce proinflammatory cytokines and chemokines, reactive oxygen species (ROS), matrix-degrading enzymes, and neutrophil extracellular traps (NET) that promote joint degradation as the first recruit cells in an inflamed joint, these cells also play an important role in joint repair by regulating the immune response, releasing anti-inflammatory factors, and activating some protective genes. In this review, various aspects of neutrophil biology, their role in inflammation and its association with osteoarthritis, and possible therapeutic approaches to target neutrophils for the treatment of osteoarthritis are described. Understanding neutrophil heterogeneity and their mechanisms of action in joint inflammation, provides a potential strategy for OA management.

Infectious illnesses have always posed a threat to human health, with tuberculosis (TB) being a major concern. The use of various medications and antibiotics in the fight against such illnesses has led to the emergence of drug-resistant infectious diseases, which have become increasingly dif-ficult to manage as medical and scientific research has advanced. Inadequate medical infrastruc-ture, incorrect treatment practices that exacerbate resistance patterns, and transmission within communities and healthcare facilities are the main factors contributing to the spread of drug-resistant TB. Though treating individuals with multiple drug resistance (MDR) and extreme drug resistance (XDR) strains is challenging, early identification of resistance and the imple-mentation of a well-designed treatment regimen can result in a cure. Community-based inter-ventions that address socioeconomic barriers to adherence can also enhance treatment outcomes. While there have been few studies and proposed conceptual models on how to manage and prevent various drug-resistant TB mutations and lineages, a model aimed at limiting and controlling such mutations has been developed. This paradigm seeks to bridge the gap by facilitating the exchange of knowledge among healthcare professionals (HCP) in healthcare facilities (HCF), diagnostic laboratories (DL), and research institutes (RI), particularly for underprivileged communities in the Eastern Cape. The model guide will also monitor and evaluate TB management plans

The paper presents a new type of hydraulic clutch operating with magnetorheological (MR) fluids and the results achieved from both theoretical analysis and experimental measurement. The hy-draulic clutch system of its own design with MR working fluid and a rotating magnetic field lo-cated is designed. The principle of this clutch is to use a rotating magnetic field created by an al-ternating current electromagnet to set the MR fluid in motion. In the tests of the hydraulic clutch with a rotating magnetic field, MR fluid produced on their own, consisting of solid iron particles of various diameters mixed with a silicone oil, are used as working fluids. The rheological properties of MR fluids are assessed on the basis of tests carried out with a Brookfield DV2T rheometer equipped with a magnetic device for generating the magnetic field of its own design. The char-acteristics of the hydraulic clutch with MR working fluid and the rotating magnetic field are tested on a specially built test stand. As a result of the research, it has been found that the torque transmitted by the clutch is the greater, the higher the rotational speed of the magnetic field and the lower the rotational speed of the beaker in which the working fluid is placed, and that the greatest torque occurs for the working fluid with the highest iron content. Based on the analysis of the structure and characteristics of the clutch in which the magnetic field is used, it has been shown that the design of the developed clutch is similar to that of an induction clutch, and its characteristics correspond to the characteristics of the eddy current clutch. Therefore, the proposed new clutch working with MR fluid and the rotating magnetic field can be applied to stationary power transmission system similarly to an eddy current clutch.

The paper considers the symmetries of a bit of information corresponding to one, two or three qubits of quantum information and identifiable as the three basic symmetries of the Standard model, U(1), SU(2), and SU(3) accordingly. They refer to “empty qubits” (or the free variable of quantum information), i.e. those in which no point is chosen (recorded). The choice of a certain point violates those symmetries. It can be represented furthermore as the choice of a privileged reference frame (e.g. that of the Big Bang), which can be described exhaustively by means of 16 numbers (4 for position, 4 for velocity, and 8 for acceleration) independently of time, but in space-time continuum, and still one, 17th number is necessary for the mass of rest of the observer in it. The same 17 numbers describing exhaustively a privileged reference frame thus granted to be “zero”, respectively a certain violation of all the three symmetries of the Standard model or the “record” in a qubit in general, can be represented as 17 elementary wave functions (or classes of wave functions) after the bijection of natural and transfinite natural (ordinal) numbers in Hilbert arithmetic and further identified as those corresponding to the 17 elementary of particles of the Standard model. Two generalizations of the relevant concepts of general relativity are introduced: (1) “discrete reference frame” to the class of all arbitrarily accelerated reference frame constituting a smooth manifold; (2) a still more general principle of relativity to the general principle of relativity, and meaning the conservation of quantum information as to all discrete reference frames as to the smooth manifold of all reference frames of general relativity. Then, the bijective transition from an accelerated reference frame to the 17 elementary wave functions of the Standard model can be interpreted by the still more general principle of relativity as the equivalent redescription of a privileged reference frame: smooth into a discrete one. The conservation of quantum information related to the generalization of the concept of reference frame can be interpreted as restoring the concept of the ether, an absolutely immovable medium and reference frame in Newtonian mechanics, to which the relative motion can be interpreted as an absolute one, or logically: the relations, as properties. The new ether is to consist of qubits (or quantum information). One can track the conceptual pathway of the “ether” from Newtonian mechanics via special relativity, via general relativity, via quantum mechanics to the theory of quantum information (or “quantum mechanics and information”). The identification of entanglement and gravity can be considered also as a ‘byproduct” implied by the transition from the smooth “ether of special and general relativity’ to the “flat” ether of quantum mechanics and information. The qubit ether is out of the “temporal screen” in general and is depicted on it as both matter and energy, both dark and visible.

The holographic principle states that the information about the volume of space is stored on its boundary. Assuming this holds, we can explain the aspects of gravity. The spacetime needs to be extended to 5D to explain gravity in this context. This naturally explains the four classical tests of gravity namely the gravitational redshift, the perihelion precession, the bending of light, and the gravitational time delay. We also show a novel way in which the universe expands in an accelerated manner at late times without the need for any form of dark energy.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

Link prediction is an unbalanced early retrieval problem, whose goal is to prioritize a small cohort of positive links on top of a list largely populated by unlabelled links. Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class because, in practice, a negative class does not exist. Previous studies explained that AUC-ROC is not apt for unbalanced class problems and is misleading for early retrieval problems, therefore standard AUC-ROC is not appropriate for evaluation of link prediction. However, some scholars argue that an AUC-ROC like evaluation accounting for the relative positioning of the few positive links among the vastness of unlabelled links remains a valid concept to pursue. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work also for unbalanced early retrieval problems such as link prediction.

(1) Background: Prefabricated zirconia crowns are used to restore teeth in children. The purpose of this study was to evaluate the removal of these crowns with the erbium, chromium: yttrium-scandium-gallium-garnet (Er,Cr:YSGG) laser; (2) Methods: Twelve primary and 13 permanent teeth were prepared and prefabricated zirconia crowns were passively fitted and cemented with two resin modified glass-ionomer cements. Surface areas of prepared teeth and crowns were calculated. Crowns were removed using two laser settings: 4.5 Watts, 15 Hertz, 20 water/ 20 air, and 5 Watts, 15 Hertz, 50 water/50 air. The retrieval time and temperature changes were tested recorded. Data were analyzed using ANOVA with Tuckey’s adjusted post hoc pairwise comparison t-test; (3) Results: The average time for crown removal was: 3 minutes, 47.7 seconds for permanent; and 2 minutes 5 seconds for primary teeth. The mean temperature changes were 2.48℃ (SD=1.43) for permanent; and 3.14℃ (SD=1.88) for primary teeth. The time to debond was significantly positively correlated with tooth inner surface area and volume, outer crown volume, and the cement volume; (4) Conclusions: Use of the Er,Cr:YSGG laser is an effective, safe and non-invasive method to remove prefabricated zirconia crowns cemented with RMGI cements from permanent and primary teeth.

Climate change is affecting the planet, compromising survival and altering forest ecosystems. Investigating climatic variations in all regions is important. The aim of this research was to assess such variations in the W and Pendjari Biosphere Reserves in Benin from 1985 to 2015 and predict climate up to 2045. Drought frequency was assessed by using the Standardized Precipitation and Evapotranspiration Index (SPEI) and correlation was performed between climatic variables. Then a predictive analysis was computed. From 1985 to 2015, there was change in the climate with few severe dry and wet years while no extreme climatic condition was noticed. Most of the years showed nearly normal climatic conditions. The predicted climate indicates an absence of extreme conditions up to 2045 but an increase in the annual humidity is expected. A low decrease in annual mean temperature with high confidence is expected. Decision-makers can better organize adaptation programs and sensitize people in the reserves on their role in environmental conservation and mitigation of climate change using these outputs.

The present work investigates primarily the morphology evolution of the body-centered-cubic (BCC)/B2 phases in AlxNiCoFeCr high-entropy alloys (HEAs) with increasing Al content, which has been neglected so far. There exist two types of microscopic morphologies of BCC and B2 phases in this HEA series: one is the weave-like morphology induced by the spinodal decomposition, and the other is the microstructure of a spherical disordered BCC precipitation on the ordered B2 matrix that appears in HEAs with a much higher Al content. The shape of coherent precipitates is found to be closely related to the lattice misfit between BCC and B2 phases, which is sensitive to Al. The mechanical properties, including the compressive yielding strength and microhardness of the AlxNiCoFeCr HEAs, are also discussed in light of the concept of the valence electron concentration (VEC).

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

Owing to the nondiffracting, self-accelerating, and self-healing properties, Airy beams of different nature have become a subject of immense interest in the past decade. Their interesting properties have opened doors to many diverse applications. Consequently, the questions of how to properly design spatial manipulation of Airy beams or how to implement them in different setups have become important and timely in the development of various optical devices. Here, based on our previous work, we present a short review on the spatial control of Airy beams, including the interactions of Airy beams in nonlinear media, beam propagation in harmonic potential, and the dynamics of abruptly autofocusing Airy beams in the presence of a dynamic linear potential. We demonstrate that under the guidance of nonlinearity and external potential, the trajectory, acceleration, structure, and even the basic properties of Airy beams can be adjusted to suit specific needs. We describe other fascinating phenomena observed with Airy beams, such as self-Fourier transformation, periodic inversion of Airy beams, and the appearance of spatial solitons in the presence of nonlinearity. These results have promoted the development of Airy beams, and have been utilized in various applications, including particle manipulation, self-trapping, and electronic matter waves.

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alter-ations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospi-talized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19 negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chro-matography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites al-tered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid seg-regated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify po-tential biomarkers for the diagnosis and prognosis of the disease.

In an age of market liberalization, globalization, and expanding agribusiness, there is a danger that small-scale farmers will find difficulty in fully participating in the market economy. One of the solutions to solve the above situation is to strengthen linkages between farmers and firms through contract farming. The purpose of this study is to assess the factors affecting the level of linkages between businesses and farmers in crop development in Thai Binh province, Vietnam. The two main research methods used were exploratory factor analysis (EFA) and regression analysis with survey data which was conducted from 300 farmers, 10 firms, and 15 factors related to contract-farming. The results indicated that there are 5 factors that significantly affect the level of participation of farmers in the linking process, the order of impacts from strong to weak is as follows: (1) Commitment and sharing of benefits and risks between farmers and firms in the linking process, (2) Issues related to management skills and enterprises capacity of link firms, (3) Price issues that farmers are interested in in the process of implementing links with firms, (4) Policy environment - Institutions related to farmer contracts between firms and farmers, (5) Household awareness about the benefits brought about by farmer contracts with firms. Based on results, a number of specific solutions have been proposed to enhance the efficiency of farming contracts between firms and farmers in cultivation production.

Soil moisture maps are essential for hydrological, agricultural and risk assessment applications. To best meet these requirements, it is essential to develop soil moisture products at high spatial resolution which is now made possible using the free Sentinel-1 (S1) SAR (Synthetic Aperture Radar) data. Some soil moisture retrieval techniques using S1 data relied on the use of a priori weather information in order to increase the precision of soil moisture estimates, which required access to a weather forecasting framework. This paper presents an improved and fully automated solution for high-resolution soil moisture mapping in bare agricultural areas. The proposed solution derives a priori weather information directly from the original Sentinel images, thus bypassing the need for a weather forecasting framework. For soil moisture estimation, the neural network technique was implemented to ensure the optimum integration of radar information. The neural networks were trained using synthetic data generated by the modified Integral Equation Model (IEM) model and validated on real data from two study sites in France and Tunisia. Main findings showed that the use of radar signal averaged over grids of a few km2 in addition to radar signal at plot scale instead of a priori weather information, provides good soil moisture estimations. The accuracy is even slightly better comparatively to the accuracy obtained using a priori weather information.

The abundant use of lithium-ion batteries (LIBs) in a wide variety of electric devices and vehicles will generate a large number of depleted batteries, which contain several valuable metals such as Li, Co, Mn, and Ni present in the structure of the cathode material (LiMO2). The present work investigates chemical, technological, and environmental aspects in the treatment of such wastes, development of a methodology for the extraction of lithium, cobalt, nickel, manganese, and graphite by a carbochlorination pyrometallurgical process. Mixtures of cathode and anode materials (called black mass, mixed oxides of Li, Co, Ni, Mn, and graphite) from different LIBs, carbon black (as reducing agent), and CaCl2 (as chlorinating agent) were used. Non-isothermal thermogravimetric tests up to 850°C and isothermal tests at 700°C of the mixtures in an inert atmosphere were carried out. It was experimentally observed that the LiMO2-C-CaCl2 reaction takes place at 700°C. LiCl, Ni, and Co were obtained as final products, and to a lesser extent, CoO, NiO, and MnO2. CaCO3 was also obtained as a by-product. The obtained results show that carbochlorination is an efficient and effective alternative route for the extraction and recovery of metals from different LIBs, focused on the sustainability and circular economy

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.