There is some evidence that aspects of nurse-doctor communication are associated with the quality of care and treatment patients receive whilst they are in hospital. To date, no studies have examined stakeholder perceptions of how patient care is influenced by clinical communication between nurses and doctors. We conducted a concept mapping study to generate a deep understanding of how clinical communication impacts patient care. Concept mapping has six phases: preparation, idea generation, structuring, representation, interpretation, and utilization. Twenty patients, 21 nurses, and 21 doctors participated in the study. Brainstorming generated 69 discreet statements about how nurse-doctor communication impacts patient care. The structuring (rating and clustering) phase was completed by 48 participants. The data interpretation workshop selected a five-cluster solution: effective communication, trust, patient safety, impediments to patient care, and interpersonal skills. On the final concept map, the five clusters were arranged in a circle around the center of the map. Clusters were relatively equal in size suggesting, each concept makes a broadly equal contribution to how nurse-doctor communication influences patient care. Our study suggests that there are multiple aspects of clinical communication that impact patient care. Candidate interventions to enhance nurse-doctor communication may need to consider the complex nature of interprofessional working. Registration: This study was prospectively registered with the Open Science Framework (OSF) (https://osf.io/9np8v/) prior to recruiting the first participant.

This research focuses on examining the responses of interest groups listed in the European Transparency Register to the ongoing Russia-Ukraine war. Its aim is to investigate the nuanced reactions of 2,579 commercial and business associations and 2,957 companies and groups to the conflict, as expressed through their X (Twitter) activities. Utilizing advanced text mining, NLP and LDA techniques, this study conducts a comprehensive analysis encompassing language dynamics, thematic shifts, sentiment variations, and activity levels exhibited by these entities both before and after the outbreak of the war. The results obtained reflect a progressive decreasing in negative emotions regarding the conflict over time. Likewise, multiple forms of outside lobbying are identified in the communication strategies of interest groups. All in all, this empirical inquiry into how interest groups adapt their messaging in response to complex geopolitical events holds the potential to provide invaluable insights into the multifaceted role of lobbying in shaping public policies.

Chinese texts often feature a substantial volume of normative content, and their analysis has increasingly become a focal point in recent semantic analysis endeavors. Enhancing the effectiveness of text classification and clustering is pivotal in unveiling the intricate semantics within extensive textual data. Empirical research within the realm of computational social science has underscored the need for improved explanatory power when it comes to general text clustering methods applied to normative texts. This study introduces an innovative hierarchical clustering method that incorporates part-of-speech (POS) features and pioneers a novel semantic network model. It achieves this by amalgamating a POS-based feature weight calculation method with hierarchical clustering techniques. To assess its efficacy, the method is rigorously evaluated across three diverse text datasets, encompassing normative reports, news articles, and social media content. The experimental results unequivocally demonstrate the superior performance of the POS-HC method, surpassing the traditional TF and TF-IDF feature extraction methods in terms of clustering accuracy, and surpassing some existing clustering methods. Furthermore, its classification effectiveness exhibits notable advantages, particularly in the semantic classification of normative texts.

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. By analyzing the relationship between elevation frequency and range, the proposed method compensates for quadratic range dependence. Through the construction of an elevation oblique subspace projection technique, an elevation adaptive filter is used to separate clutter from ambiguous regions. Residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.

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

The once near-extinct Russell’s viper has recently come back to the area beyond its historical range. A couple of decades ago, its distribution was confined to 11 districts of Bangladesh. Hence, the study aims to update the distributional map of Russell’s Viper (Daboia russelii) across the country and find out the surges' causal factors. Both primary and secondary data were utilized in this study. It revealed that the distributional range of this snake was expanded from 22 to 33 districts in 2024. The respondents noted that climatic stressors like high humidity accompanied by hot weather, increased salinity, and increased frequency of flooding facilitated the spreading of the viper across the country. The cropping pattern and crop diversification were another major contributing factor. The shifting of the single-cropped area to a multi-cropped area in the Barind tract increased its main prey rats. Contrarily, the killing of natural predators such as foxes, monitor lizards and mongoose led to the rise in the snake population. The destruction of vegetation coverage in the upper part of the Padma Basin facilitated the movement of Russell's vipers downstream.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

Fractal array antennas are multiband arrays having ultra wide band and space filling facility. But Side lobe levels and large number of antenna elements are the major designing challenges of these arrays. In this paper, design and analysis of octagonal fractal array antenna is investigated with Morse-Thue fractal density tapering technique (MTFDT). Due to the proposed technique, a remarkable improvement has observed in Side lobe levels and thinning of the elements can also be attained at the various iterations of octagonal fractal array antenna. These arrays are analyzed and simulated by MATLAB-15 programming.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

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

Marula seed cake (MSC) is a nutritionally-rich natural feed resource that can enhance the healthiness of animal-derived foods (ADFs) for human consumption. This study compared the health-related fatty acid (FA) profiles of MSC products from South Africa and Eswatini. Composite samples monthly collected from both countries were analysed for FAs. MSC products from both countries were found to be dominated by oleic acid (>70%), followed by palmitic, linoleic and stearic acids. Consequently, both products had their FA totals dominated by ƩMUFA followed by ƩSFA, ƩPUFA, Ʃn-6 PUFA and Ʃn-3 PUFA. Both oleic and stearic acids were higher (P < 0.01) whilst linoleic (P < 0.001), α-linolenic (P < 0.05), margaric (P < 0.05), palmitoleic (P < 0.05) and eicosatrienoic (P < 0.05) acids were lower in South African in comparison to Eswatini MSC. Consequently, South African MSC had higher ƩMUFA (P < 0.01) but lower ƩPUFA (P < 0.001), Ʃn-6 PUFA (P < 0.001) and Ʃn-3 PUFA (P < 0.05). Also, Eswatini MSC had higher n-6 : n-3 PUFA, PUFA : SFA (P = 0.001) and PUFA : MUFA (P < 0.05) ratios. Further, MSC products from both countries had similarly (P > 0.05) low atherogenicity and high desaturase indices. In conclusion, both country products are rich particularly in oleic acid and their incorporation into farm animal diets would increase content of the MUFA in ADFs and, consequently, improve health benefits to human consumers.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Infection with the malaria parasite Plasmodium, remains a major cause of illness and death world-wide. The development of evasion strategies by the parasite from host immunity and its adaptability to antimalarial drugs has raised the urgency for developing new strategies to combat this disease. One promising avenue is targeting telomeres – sequences found at chromosome termini– and telomerase – the enzyme that catalysis synthesis of telomeres using an intrinsic RNA template. As telomeres shorten critically, cells undergo replicative senescence or apoptosis. Plasmodium, lacking telomerase activity in its human stages, exploits alternative mechanisms, accelerating telomere shortening, aging, and immune evasion. Chemical telomerase inhibitors show promise as antimalarials. In this review, we examine the potential of targeting this system in malaria therapy, with telomerase inhibitors offering a novel approach. A systematic search on PubMed using Boolean techniques identified 246 relevant articles from 1985 to March 31, 2024. After filtering, 43 articles met inclusion criteria, supplemented by snowball sampling. The telomerase inhibitor drug strategy is widely applicable against cancer. However, given the drugability of telomerase and the absolute requirement for active telomere maintenance in the vast majority of parasites including plasmodium, telomerase and telomere maintenance could well represent the Achilles heel of the parasite

Maize silage is produced to alleviate forage shortage on dairy animals, particularly during the dry season. Microorganisms play a significant role in silage fermentation and thus determine the silage quality. Therefore, modulation of microorganisms in silage may help prohibit undesired bacteria and improve silage quality. Hence, this study evaluated the effects of tannin extract as an additive on fermentative quality, aerobic stability and microbial composition of maize silage. Therefore, a mini-silo experiment of maize with five treatments was conducted for 75 days. The results showed that additives modulate silage microorganisms. In addition, tannin extract reduced certain silage bacteria while promoting others. However, this is without affecting the silage’s fermentative quality and aerobic stability. All silage fermented well with a pH below 4.2. Tannin extract improved dry matter recovery of silage while reducing silage weight loss. Furthermore, tannin extract suppressed the establishment of undesired bacteria, such as genera Dysgonomonas, Gluconacetobacter and Clostridium, while promoting desirable bacteria, such as genera Lactobacillus and Weissella, which attributed to silage quality. Therefore, it is concluded that tannins can be strategically used as silage additives to modulate microbial composition of silage and improve silage quality by promoting the dominance of desirable bacteria in silage.

Monitoring of clients living with HIV encompasses a specific array of activities that routinely check on various aspects of ART following its initiation depending on the availability of resources in that particular state, among other factors as per guidelines from the World Health Organisation. This is a significant practice as it promotes positive health outcomes for patients and efficient utilisation of ART regimens to reduce resistance to the medication – hence it is a measure to guard avoidable costs. It also forms a basis on which decisions are made from. It can be done clinically by assessing for signs e.g., skin rash and symptoms e.g., nausea and vomiting, checking vital organ function and measuring weights across all populations, or can be also be done by assessing specimens for their respective viral loads, white blood cell count, pregnancy status or other infections such as hepatitis B. There have not been any special indications required for one to be eligible for monitoring during ART in developing countries; all qualify for routine monitoring even though some may be more frequent than others in cases of comorbidities such as renal failure where medications can be attributed to exacerbating the condition.

The mechanism of brain information processing unfolds within spatial and temporal domains inherently linked to the concept of symmetry. Biological evolution, beginning with the prevalent molecular chirality, results in the handedness of cognitive and psychological functions (the phenomena known as biochirality). The key element in the chain of chirality transfer from the downstream to upstream processes is the pyramidal neuron (PyrN) morphology-function paradigm (archetype). The most apparent landmark of PyrN is the geometry of the cell soma. However, “why/how PyrN soma gains the shape of quasi-tetrahedral symmetry” has never been explicitly articulated. Resolving the above inquiry is only possible based on the broad-view assumption that encoding 3D-space requires specific 3D-geometry of the neuronal detector and corresponding network. Accordingly, our hypothesis states that if the primary function of PyrNs, at the organism level, is sensory space perception, then the pyramidal shape of soma is the best evolutionary-selected geometry to support sensory-motor coupling.

In this very personal hunt for the meaning of the bacterial cell cycle, the snark, I briefly revisit and update some of the mechanisms we and many others have proposed to regulate the bacterial cell cycle. These mechanisms, which include the dynamics of calcium, membranes, hyperstructures and networks, are based on physical and physico-chemical concepts such as ion condensation, phase transition, crowding, liquid crystal immiscibility, collective vibrational modes, reptation, and water availability. I draw on ideas from subjects such as the ‘prebiotic ecology’ and phenotypic diversity to help with the hunt. Given the fundamental nature of the snark, I would expect that its capture would make sense of other parts of biology. The route therefore followed by the hunt has involved trying to answer questions like “why do cells replicate their DNA?”, “why is DNA replication semi-conservative?”, “why is DNA a double helix?”, “why do cells divide?”, “is cell division a spandrel?” and “how are catabolism and anabolism balanced?”. Here, I propose some relatively unexplored, experimental approaches to testing snark-related hypotheses and, finally, I propose some possibly original ideas about DNA packing, about phase separations, and about computing with populations of virtual bacteria.

Cartoons in the era of digital media are among the most prevalent medium of entertainment for the children, parents also encourage them to view such contents to engage the children which make an area need to be explained exhaustively by adopting new approaches. There are lot of studies in the past conducted to explain the impacts of the cartoons on the children however, it is remarkable fact that there is lack of the studies in the literature which directly address the observation about the above mentioned impacts in view of the teacher’s. This study deals with the educational, emotional, behavioral and Religious etc. impacts of cartoons on kids in view of teachers of the primary schools as they are considered as the one who can observer these impacts in a profound way. of the of Multan. Different areas of impacts have been analyzed in this study by using survey technique and analysis is conducted and presented in the teacher’s views in this study to examine what they feel that how cartoons are effecting the personalities of the children.

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz need to provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT) and fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

This study investigates the optical properties of Yttrium Orthoferrite thin films fabricated via plasma laser deposition. Yttrium Orthoferrite, a ferrimagnetic material known for its potential applications in spintronics and photonics, was deposited on single-crystal substrates under controlled conditions to analyze its optical characteristics. The influence of deposition time, on the film quality and optical properties was examined.

Individuals with low health literacy (HL) are known to have poorer health outcomes and to have higher mortality rates compared to individuals with higher HL: hence, the improvement of HL is a key outcome in modern healthcare systems. Healthcare providers are therefore asked to support patients’ literacy skills by encouraging the implementation of autonomy-supportive patient centered communication (PCC), which in turn requires the enhancement of patient engagement. Our main hypothesis is that the well-known relationship between autonomy-supportive PCC and HL is mediated by patient engagement which is known to play a role in HL promotion and that is related to PCC as well. The purpose of this study was to formulate a hypothetical structural equation model (SEM) linking PCC to patient engagement and HL. A cross-sectional survey design was employed involving 1007 Italian chronic patients. The hypothetical model was tested using SEM to verify the hypothesized mediation of patient engagement between PCC and HL. Results show that the theoretical model has a good fit indexes and that patient engagement fully mediates the relationship between PCC and HL. This finding suggests healthcare systems to implement a new paradigm where patients are supported to play an autonomous role in their own healthcare.

Although economic uncertainty and social media use are recognized as having a significant impact on religiosity, their role in religiosity has yet to be extensively examined in developing countries. This study explored the effects of economic uncertainty and social media use on religiosity in the Chinese context. By analyzing the publicly available data from the Chinese General Social Survey and World Values Survey, the findings showed that economic uncertainty significantly increased people's religious beliefs, while social media use had no significant impact on religious beliefs. Furthermore, social media use did not significantly moderate the relationship between economic uncertainty and religious belief. In contrast, educational level positively moderated the relationship between economic uncertainty and religious belief. These findings differ in part from the results of previous studies, presenting new evidence for theoretical integration in the field and revealing potential group differences in the strength of the relationship between economic uncertainty and religious belief.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

The fast decoding of low-density parity-check (LDPC) is crucial for its future applications. In this paper, we propose to use Early Termination (ET) and Force Convergence (FC) techniques to accelerate the decoding of two new local/global two-phases decoding algorithms to reduce the Globally Coupled (GC) LDPC codes. Specifically, ET is applied in the local decoding phase of two- phases decoding of GC-LDPC codes to skip the local decoding which is highly likely to fail and avoid the unnecessary iterations. FC is applied in global decoding phase where converged variable nodes are identified and removed from Tanner graph and the decoding can be speed up shrinking parity check matrices. Two variants are proposed for the ET operation in local decoding phase. ET-1 merely stops iteration if it meets the stopping criterion while in ET-1, the iteration number saved in local decoding is added up in global decoding to improve the decoding performance. The simulation results show that the ET-1-FC scheme can save up to 42% decoding time complexity in the low SNR region while keeping the error rate performance nearly unchanged, the ET-2-FC can improve the error rate performance by 0.18dB at bit error rate 0.001 or 0.23dB at frame error rate 0.1. In the waterfalling region of BER/FER curves, both schemes can save about 25% decoding time complexity.

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple input multiple output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML) based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP) consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.

We were tasked by Canada’s COVID-19 Immunity Task Force to describe severe adverse events (SAE) associated with emergency department (ED) visits and/or hospitalizations in immune-mediated inflammatory diseases (IMID). At 8 Canadian centres, data were collected from adults with rheumatoid arthritis (RA), axial spondyloarthritis (AxS), systemic lupus (SLE), psoriatic arthritis (PsA) and inflammatory bowel disease (IBD). We administered questionnaires, analyzing SAEs experienced within 31 days following SARS-CoV-2 vaccination. About two-thirds (63%) of 1556 participants were female; mean age was 52.5 years. BNT162b2 (Pfizer) vaccine was the most common, with mRNA-1273 (Moderna) being second. Forty-nine percent of participants had IBD, 27.4% RA, 14.3% PsA, 5.3% SpA, and 4% SLE. Twelve SAEs leading to ED visit or hospitalization were self-reported, occurring in 11 participants. SAEs included 6 ED visits (including one Bell’s Palsy 31 days after first vaccination) and 6 hospitalizations (including one Guillain-Barré syndrome 15 days after the first vaccination). Two SAEs involved pericarditis, one in SLE (considered a serious disease flare) and one in RA. Thus, in the 31 days after SARS-CoV-2 vaccination in IMID, though several serious AEs occurred, these were relatively few. As SARS-CoV2 continues to be a common cause of death, our findings may help optimize vaccination acceptance.

With recent advancements in network technology and spread of Internet, use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges occur over time. Additionally, owing to the need for efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels, some have low, and others high levels. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast the individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few effects that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scales for self-esteem, implicit racism, and honesty); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us to theorize about the bistability of mind and body, but it proposes a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We begin with a review of the science of teams and a focus on human-machine team research in the laboratory versus the open; justifications for rejecting traditional social science while supporting our approach; a full understand of the complexity of the domain, tasks in the domain, and how teams address both; the mathematics involved; a review of results from our model versus the open field; a discussion of the results; conclusions; and the path forward to successfully advance the science of interdependence and autonomy.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

Management of sarcomas in specialist centers delivers significant benefits. In much of the world specialists are not available and development of expertise is identified as a major need. However the terms ‘specialist’ or ‘expert’ center are rarely defined. Our objective is to offer a definition for patient advocates and a tool for healthcare providers to underpin improving the care of people with sarcoma. SPAGN developed a discussion paper for a workshop at the SPAGN 2023 Conference, attended by 75 delegates. A presentation to the Connective Tissue Oncology Society (CTOS) and further discussion led to this paper. Core Principles were identified that underlie specialist sarcoma care. The primary principle is the multi-disciplinary team discussing every patient, at first diagnosis and during treatment. Principles for optimal sarcoma management include accurate diagnosis followed by safe, high-quality treatment, with curative intent. These Principles are supplemented by Features describing areas of healthcare, professional involvement, and service provision and identifying further research and development needs. These allow for variations because of national or local policies and budgets. We propose the term ‘Sarcoma Intelligent Specialist Network’ to recognize expertise wherever it is found in the world. This provides a base for further discussion and local refinement.

Background: This study aims to investigate the patient safety culture at a sports medicine hospital and explore quality of healthcare and associated factors. Methods: In a cross-sectional study design, the tool Hospital Survey on Patient Safety Culture (HSOPC) was administered online among staff at a sports medicine hospital in Doha, Qatar. About 319 staff participated from 898 staff who received emails (35.5%). Results: The results showed that 49.2% of the staff rated the patient safety grade as excellent and 36.1% as very good, which sums up to 85.3%. Factors associated with better patient safety grade were work climate (OR 10.3 95% CI (3.2 to 33.6); teamwork OR 7.6 95% CI (2.8 to 20.9) and team communication (OR 2.9 95% CI (1.1 to 7.6). Conclusions: Addressing the work climate, enhancing communication, and cohesive work within teams facilitates a culture of trust that improves patient safety grade. Ensuring quick return to play is a team effort involving multiple disciplines, hence a high performance and safety culture can enhance teamwork. Reducing punitive response to error and encouraging staff and supervisors are key areas that can improve safety culture in sports medicine organizations, and efforts should be placed in this area.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

(1) Background: Second Victims are health care workers negatively affected by adverse events and are prone to developing dysfunctional coping strategies compromising not only their own, but also patient safety. An effective way to address this issue is the implementation of peer support programs. This paper reports on the effectiveness of a peer support program at the Austrian Hietzing Clinic (KHI). (2) Methods: Telephone interviews were carried out after peer support sessions using a questionnaire. Descriptive analysis was carried out reporting frequencies and proportions for nominal variables, means with standard deviations for ordinal and interval-scaled variables. (3) Results: So far, 45 peer supporters have completed telephone interviews after a peer support session. The main reasons for the session were bad news from the private sphere and death of a patient. The peer supporters rated the helpfulness of the sessions for their colleagues as very high while not feeling very burdened themselves afterwards. (4) Conclusions: Underutilization of the program is probable. However, the high rating of helpfulness of the carried-out sessions by the peer supporters in combination with them not feeling very burdened, shows that the program is safe for the peer supporters and an upscaling is warranted.

The next-generation communication systems demand integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost, while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACS) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF) based target detection and parameters estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by iteratively method. Furthermore, to maximize the system’s sensing performance under communication and PT Quality of Service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, solved using through SDR technique. Simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS.

Painting style and creativity are fundamental to art, defining a work’s uniqueness and expression. They embody an artist’s personality, convey emotions, establish identity, and shape audience perception, adding depth and value to the work. Recently, diffusion models have gained popularity in art design, animation, and gaming, especially in original painting creation, poster design, and visual identity development. Traditional creative processes, while demanding unique imagination and creativity, often face challenges such as slow innovation, high reliance on manual efforts, high costs, and limitations in scalability. Consequently, exploring painting-style creative design through deep learning has emerged as a promising trend. In this paper, we introduce a novel network architecture, the Painting-Style Design Assistant Network (PDANet), for style transformation. To support this, we curated the Painting-42 dataset, comprising 4,055 works by 42 renowned Chinese painters. PDANet leverages this dataset to capture the aesthetic intricacies of Chinese painting, offering rich design references. Furthermore, we propose a lightweight Identity-Net for large-scale text-to-image (T2I) models, which aligns internal knowledge with external control signals, thereby enhancing the capabilities of existing T2I models. The trainable Identity-Net feeds image prompts into the U-Net encoder to generate diverse and stable images. Both quantitative and qualitative analyses demonstrate that our approach outperforms current methods, delivering high-quality generated content with wide-ranging applicability.

In this paper, we introduce a novel controlled quantum communication protocol utilizing a quantum random walk involving one sender, one receiver, and multiple controllers. Inspired by classical random walk theory, quantum random walk serves as the foundation of our proposed protocol. With this protocol, we demonstrate the ability to transfer any n- dimensional quantum state to any party facilitated by any m number of controllers. Furthermore, any of the (m+1) individuals has the freedom to accept the role of the receiver. Rigorous testing of the protocol’s performance is conducted through quantum state tomography. We have run different variations of the circuits in IBMQ QASM simulator. Additionally, we explore the effectiveness of a weak measurement-based protocol designed to mitigate amplitude-damping noise’s detrimental effects on quantum states. By analyzing fidelity versus amplitude damping noise-strength plots for scenarios with and without the weak measurement protocol, we provide valuable insights into its protective capabilities across various levels of noise. These find- ings illuminate the protocol’s potential applications in quantum information processing

The objective of this study is to investigate the correlation between the advancement of flood research and the utilization of Geographic Information Systems (GIS) and multi-criteria evaluation techniques, both from a theoretical and practical standpoint. Additionally, this research seeks to identify potential ways to address the aforementioned relationship. The present study employs a thorough literature review methodology. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a total of 28 papers were identified and included in the analysis, as they fulfilled the predetermined criteria. The findings of the study indicate that current research pertaining to the utilization of Geographic Information Systems (GIS) and multicriteria evaluation in flood control mostly centers around five key areas, namely natural factors, technical considerations, institutional factors, socio-economic factors, and financial factors. However, the aforementioned studies have identified specific areas of focus, including natural factors pertaining to climate, technical factors related to infrastructure, institutional factors concerning flood management strategies, socio-economic factors associated with demographics, and financial factors pertaining to financial obligations. However, there is a limited amount of existing research that specifically addresses flood management in developing nations. Therefore, it is anticipated that this study can serve as a valuable resource for future research endeavors, particularly in informing decision-making processes pertaining to the establishment of priorities in flood control plans. The primary subject of this study pertains to flood management occurrences within developing nations.

In order to explore more spectrum resources to support sensors and its related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensors paradigm is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attack from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attack, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. Finally, the numerical simulation results demonstrate that compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability, and average number of samples under various attack ratios and probabilities.

The burden of hypertension remains unacceptably high globally, particularly in low- and middle-income countries (LMICs). Workplace offer enormous potential as an idea setting for early detection and treatment of hypertension among the working class. Analysis of such a Workplace Screening Programme can reveal information about its potential impact. Machine learning techniques such as k-Means Clustering are untapped tools for such analyses. We set out to deploy this tool for the analysis of our university annual medical screening of workforce for hypertension. An anonymized dataset containing the demography and blood pressure measurements values obtained from staffs of different departments/units in an academic institution was obtained. The total number of samples or data points are 1, 723 in which the input dataset contains six features, including year category (2018, 2019, 2021,2022), Department/Unit (academic and non-academic), gender (male and female), while the target output is the blood pressure status (low, normal and high) respectively. Analysis of the dataset was carried out using machine learning techniques. In this retrospective analysis, it was observed that the mean age for this working class is 42 years old. Similarly, it was discovered that hypertension was prevalent among members of staff above the age of 40 irrespective of their gender or professional category (academic or non-academic). The analysis also revealed that there was a steady decline in the prevalence of hypertension from 2018 to 2022. From the research, it is evident that the adoption of machine learning techniques for periodic analyses of workplace health status screening initiative (especially for hypertension) is effective, feasible, and sustainable to diagnose and control hypertension among the working class.

Introduction. Intra-abdominal cystic formations represent heterogeneous pathologies with varied localization and clinical manifestation. The first challenge of a giant intra-abdominal cystic lesion is identifying the organ of origin. The clinical presentation of intra-abdominal cystic lesions varies from acute manifestations to non-specific symptoms or accidental discovery. Case presentation: A 2-year-old girl presents to the emergency unit with a fever of 38.5 Celsius, loss of appetite, and apathy. The investigations showed a gigantic intra-abdominal mass whose organ belonging could not be specified. Postoperatively, a giant mesenteric lymphangioma was evident, which was completely excised. Discussions Giant cystic formations modify the anatomical reports and become space-replacing formations, and the starting point is even more challenging to assess preoperatively. Nevertheless, the careful evaluation of the characteristics of the formation, the effect on the adjacent organs, the age of the patient, and the clinical picture can provide elements of differential diagnosis.

The enterprise software sector is experiencing a significant transformation driven by rapid technological progress and evolving business needs. An effective release management process is crucial for the solution providers and customers, as they incorporate the principles of Industry 4.0 and 5.0. With the growing dependence on software solutions and services, ensuring their quality, reliability, and security is highly necessary. The release management process incorporates strategic planning, regression testing, accurate deployment, and continuous monitoring of software updates. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) create a unique challenge to the release management process. The existing research needs to address the necessary adaptations for Industry 5.0, mainly in product-based software organizations, to the release management process without a change management system. After working with several software solution stakeholders, based on the responses from SaaS project stakeholders, we have presented a revised software release readiness workflow by incorporating a change management module. The preliminary result based on data over a limited period of time indicates that the revised workflow has the potential to improve the management of slippage, particularly by more effectively addressing NFR-related issues, thereby resulting in more predictable and reliable release cycles.

In a completely randomized design (CRD), 400 day-old Ross 308 broilers were randomly allotted to 5 diets with 0, 5, 10, 15 and 20% MSC, each with 8 replicates of 10. Weekly feed intake (FI), body weight gain (BWG) and feed conversion efficiency (FCE) were calculated whilst haemato-biochemistry was measured at d42. Overall, FI was linearly (P &lt; 0.05) and quadratically (P &lt; 0.01) decreased by MSC, of which the optimum inclusion was 15%, as BWG was linearly (P &lt; 0.001) and quadratically (P &lt; 0.05) decreased and FCE linearly decreased (P &lt; 0.01) by MSC, of which the optimum dietary inclusion was 10%. Also, MSC linearly and quadratically decreased slaughter weight (P &lt; 0.001 and P &lt; 0.05, respectively), hot carcass weight (P &lt; 0.001 and P &lt; 0.05, respectively) and cold carcass weight (P &lt; 0.001 and P &lt; 0.05, respectively). Similarly, it linearly decreased white blood cells (P &lt; 0.01) and lymphocytes (P &lt; 0.05) as it linearly (P &lt; 0.001) and quadratically (P &lt; 0.01) decreased symmetric dimethylarginine (SDMA) and linearly (P &lt; 0.001) increased serum cholesterol. In conclusion, up to 10% MSC can be incorporated into broiler diets in replacement of SBM without adverse effects.

In humans, age-associated degrading changes are observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psy-chological abilities, and memory. Cross talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, ge-nome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirali-ty, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging re-lated to irreversible PTMs SP has been associated with the nontrivial interplay between poor so-matic and mental health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated “collapse” of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet un-der-appreciated hallmark of aging.

Corporate Sustainability has garnered increasing attention within the business community, as corporates communicate to influence its stakeholders for building sustainable relationships. There has been a surge in research exploring its connection to firm performance but, existing studies lack a cohesive and concentrated approach. This study employs scientometric and bibliometric methods to analyse scientific production which signals a scarcity of research from developing countries, indicating a need for greater investigation. Focussing on specific subfields of Corporate Sustainability and Firm Performance, we analyze and narrow down the scope of our study to a focussed sample. Employing use of structured methods and PRISMA Protocol, this scientifically rigorous study, points towards identification of research themes linking sustainability practices to firm performance for achieving business excellence. It goes beyond mapping of literature, thereby establishing linkages to Sustainable Development Goals (SDGs). Our analysis reveals clusters centered on Environmental Performance, Firm Performance, Corporate Sustainability Reporting, Corporate Social Performance and Green Supply Chain Management with significant citations related to various SDGs. We make use of time series analysis to determine the future trends of research in the field. This scientometric and bibliometric analysis study contributes to a more focused understanding of the linkages between corporate sustainability and firm performance, emphasizing the need for cohesive research approaches to address the complexities of sustainable business practices and their implications for financial performance and the economy.

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.

Implementing dietary screening tools into clinical practice has been challenging, including in Nigeria. This study evaluated the impact of the Nigerian dietary screening tool (NiDST) on patient-clinician communication and barriers to and facilitators of implementation. Mixed methods approach was used to collect data from patients (n=151) and clinicians (n=20) from outpatient clinics in Nigeria. Patients completed the validated 25-item NiDST prior to outpatient consultations. Both patients and clinicians completed the Measurement Instrument for Determinants of Innovations (MIDI) questionnaire to assess implementation determinants post-consultation. Semi-structured interviews were conducted for in-depth feedback. Fidelity of implementation was 92% for NiDST-reported dietary discussion, with a mean completion time of < 6 minutes and an accepted marginal increase in consultation time (< 10 mins). For clinicians, 25% reported time constraints and their additional nutritional knowledge as barriers, while facilitators of NiDST implementation were the clarity and completeness of the NiDST, clinical relevance and improved patient-clinician communication, as reported by all the clinicians. Over 96% of patients reported the NiDST as quick to complete, with 90.7% reporting self-reflection on dietary intake. This study demonstrated the NiDST's potential to enhance patient-clinician communication and highlighted major facilitators of implementation in clinical practice to improve dietary discussion in Nigeria.

Introduction: Decreasing overweight and obesity prevalence requires precise data at sub-national levels to monitor progress and initiate interventions. This study aimed to estimate baseline age-standardized overweight prevalence at the lowest administrative units among women, 18 years and older, in seven African countries. The study aims are synonymous with indicator 14 of the global non-communicable disease monitoring framework. Methods: We used the most recent Demographic and Health Survey and administrative boundaries data from the GADM. Three Bayesian hierarchical models were fitted and model selection tests implemented. The age-standardized prevalence of overweight among adult women at national, first and second administrative levels were individually reported in each country in the form of maps and tables. Results: Substantial variation in the age-standardized prevalence of adult female overweight was noted across several second-level administrative units. In numerous locations in Tanzania, Nigeria and Zimbabwe, more than half of the adult female population were overweight and in one location in Tanzania, over 72% of the adult female population were overweight. These estimates were roughly twice the national level overweight prevalence and, in some cases, roughly 10 – 20% greater than the overweight prevalence in first-level administrative units. Conclusion: The observed overweight burden in subnational administrative units suggests the presence of an epidemic tantamount to the situation in more affluent economies. African countries lack the resources to effectively handle the fallout from such epidemic, therefore motivating the need for increased urgency in adopting WHO obesity-related intervention guidelines and implementing more rigorous studies to validate the study findings.

Aim: The aim of the study is to analyze quantitatively the scientific literature covering nursing studies on patient safety. Method: The Web of Science database is queried in this retrospective bibliometric analysis to identify relevant publications. The study themes, contributing journals, countries, institutions, and authors were evaluated quantitatively. Thus, a total of 5470 publications concerning patient safety in nursing were included and analyzed. Findings: The number of studies conducted increased every year from 1992 to 2023. While the country whose articles receive the most citations (31072) is the United States of America (USA), Turkey is ranked as last with the lowest number of citations (581). Journal of Clinical Nursing, with 338 articles, appeared to have the highest number of publications, Chaboyer W. appeared to have the highest number of citations with a total of 1426 and 45 publications. The countries that have the strongest international cooperation for research in this field are the USA, England, Australia and China. Results: After examining the literature in the field of patient safety in nursing through bibliometric analysis, it is seen that themes such as patient safety, medication errors, nursing care, nursing, education, systematic review, quality care and leadership were emphasized as important considerations.

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

This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHS) in building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective to leverage Structural Health Monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into the façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHS is defined to provide a complete system for façade monitoring. This integrated system includes the Piezoelectric Generator (PEG), Supercapacitor (SC), Power Conditioner Circuit (PCC), Fibre Optic Sensing (FOS) Interrogator, and the IoT Gateway trans-mitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector, but, raising issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHS for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of Piezoelectric Energy Harvesting technology in building envelope monitoring.

The purpose of this study is to develop and evaluate the effectiveness of a medical helicopter air transport training program for emergency medical personnel. The program was developed according to Rapid Prototyping Instructional System Design (RPISD) and finalized after content validity and usability evaluation. The effectiveness of the program was evaluated using a single-arm pre-post design to measure the performance of 11 convenience-sampled emergency medical technicians after the program was applied. Data analysis was performed using the SPSS 26.0 Program and Wilcoxon Signed-Rank test. The results of this study are summarized as follows. First, the developed doctor helicopter air transport training program applied RPISD, and the development stage was divided into analysis, design and usability evaluation, and development stages; the final program was derived. Secondly, the developed medical helicopter air transport training program is composed of a blended learning training program that combines online theory lectures and offline simulations for a total of 8 sessions. Third, as a result of applying the developed doctor helicopter air transport training program to 11 emergency medical workers, both pre- and post-air transport subjective job performance and objective job performance were statistically significantly increased. The Dr. Helicopter air transport training program developed in this study was found to be a program that can sufficiently evaluate the job performance of emergency medical personnel in air transport. Through this study, we hope that this training program can directly contribute to improving the survival rate of critically ill patients in the actual air transport field.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

Lichen Amyloidosis (LA) is a type of cutaneous amyloidosis characterized by brownish hyperkeratotic and itchy papules on the lower leg, back, forearm or thigh. It is associated with itching and Atopic Dermatitis (AD) according to an etiopathogenetic mechanism that has not yet been fully elucidated. Currently, available therapies for this condition include oral antihistamines, laser, cyclosporine, topical corticosteroids and phototherapy, but in light of the overlap with AD, Dupilumab may also be indicated. We report the case of a female, 52 years old, suffering from AD and LA for about 27 years. She had lesions attributable to both diseases on the trunk and lower limbs associated with severe itching and had proved resistant to cyclosporine therapy. It was decided to opt for Dupilumab with induction of 2 fl of 300 mg and maintenance with 1 fl every other week. The therapy proved effective, returning a total resolution of both diseases one year after the beginning of the treatment. Dupilumab demonstrated efficacy and safety in LA related to AD and led to clinical and quality of life improvement in this patient. Therefore, Dupilumab should be considered when treating LA. Further studies should be conducted focusing on the efficacy of the drug on LA (whether or not related to AD), changes in skin lesions after discontinuation, and the safety of long-term application.

This paper represents the first investigation into the consensus problem of linear time-varying multi-agent systems utilizing an event-triggered communication scheme. First, a general event-triggered consensus control scheme is proposed for a general category of linear time-varying multi-agent systems. Under some suitable assumptions, it is demonstrated that all agents’ states will converge exponentially, with Zeno behaviour being ruled out. Second, the consensus problem in a network of linear time-varying multi-agent systems with a spanning tree is investigated using the proposed control strategy. It demonstrates that the consensus issue for the specified system can be reformulated as a stabilization problem for an error system through a time-varying linear transformation. Then, the event-triggered consensus problem is just a special instance of the general event-triggered consensus problem mentioned above. Finally, to illustrate the efficacy of the event-triggered method proposed in this study, simulation results are shown.

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: unbalanced PSI protocol based on Cuckoo filters, unbalanced PSI protocol based on single cloud assistance, and unbalanced PSI protocol based on dual cloud assistance, each addressing the shortcomings of the previous solution. These protocols primarily optimize database computational load, storage capacity, protocol execution time, and security. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery.

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

Background: Over 900,000 cases of COVID-19 and 23,000 deaths have been reported till 13 July in India. Preserving the limited healthcare workforce is part of the strategy against the pandemic. Mortality and morbidity data have a role in customising this strategy. At this time, there is no published study on COVID-related mortality among doctors or other healthcare workers in India. Methods: A multi-pronged search was made for all reported deaths linked with COVID-19 among doctors in India. Details of COVID-linked deaths reported by mainstream media and by multiple professional social media sources were collected, screened, verified and analysed. Violent deaths occurring in the setting of pandemic-related work were separately listed. Deaths from other diseases were excluded. Results: Among 108 COVID-linked deaths among doctors, there were four pandemic-related violent deaths including three road accidents and a suicide. Of the 104 non-violent deaths, 55.5% were below 60 years of age, while 29.6% and 21% were below the age of 50 and 40 respectively. The average age at death was 56.3 (range 22 - 96). Over half of the deaths occurred among general practitioners, while surgical specialties accounted for 27% of the mortality. The geographic distribution of deaths of doctors correlated with the reported number of COVID-19 patients in each state. The total number of COVID-related healthcare worker deaths was 136, out of which eight (5.8%) were violent, and occurred in young individuals with an average age of 27.8 years. Conclusions: The majority of the 104 COVID-related non-violent deaths among doctors (55.5%) occurred below the age of 60. The average at death was 56.6 years. The states with highest number of COVID-19 cases had greater number of doctor deaths. Violent deaths among young healthcare workers in the setting of the pandemic requires special attention.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the rodent local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctor/ that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions.

Virus-infected Flu is a common disease. To date, no specific drugs are available to manage the symptoms of cough, headache and sputum production. An alternative Chinese herb medicine is introduced for virus-infected Flu or similar infection. Before hospitalization, some of patients may scare for cross-infection with mild symptoms or hardly go to hospital if encountered a temporary lockdown or quarantine. Some Chinese practice self-treatment of cough, headache and sputum production by inhalation of volatile chemicals from onion and garlic. Author used to take the same alternative approach of inhalation of onion, garlic or scallions for self-treatment when suffered virus caused flu with cough, headache and sputum production at onset disease. In this article, the biomedical effects of onion and garlic are reviewed. To help patients with mild symptoms of virus infected Flu, a simple home-based treatment was suggested to self-treatment because of temporary isolation and hardly going to hospitalization. The alternative approach may also suggest for some mild virus infected respiratory diseases caused by virus at onset disease.

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

The mental health impact of the environmental crisis, particularly eco-anxiety, is a growing research topic whose measurement still lacks consensus. In this article, we use item response theory (IRT) to better un-derstand what existing questionnaires measure. To conduct this review, we used open-access data from the short French version of the Climate Change Anxiety Questionnaire (Mouguiama-Daouda et al. 2022, Heeren et al., 2023). Our results suggest that, of the different models tested, the unidimensional model seems to be the most appropriate for measuring eco-anxiety. The item difficulty parameter extracted from the IRT enabled us to discuss the level of severity of the items making up this tool. The Climate Change Anxiety Questionnaire is more appropriate for measuring severe to moderately severe eco-anxiety. Ave-nues for improving this questionnaire and the measurement of eco-anxiety in general are then discussed.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrum in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (in-dices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

(1) Background: As part of the Scottish Governments five-year recovery plan to address the backlog in NHS care following the COVID-19 pandemic, community pharmacies in Scotland are planned to provide a Hospital Discharge Medicines Supply and Medicines Reconciliation Service. We aimed to qualitatively explore patients’ experience of this new service; (2) Method: Adult patients (≥18yrs age) who consented to participate in the Community Pharmacy Hospital Dis-charge and Medicines Reconciliation Service were invited for interview within 21 days of dis-charge from hospital. Qualitative, one-to-one, semi-structured, patient interviews were conducted by telephone and audio-recorded using Microsoft Teams®. The interview audio recordings were transcribed verbatim and underwent thematic analysis; (3) Results: 12 patients were inter-viewed, evenly split by sex and with a median age of 62 years (range; 36 to 88 years). Analysis generated main five themes; Patient Engagement, Stakeholder Communication, Practical Factors, Human Factors, and Comparative Experiences. Many of these were interdependent; (4) Con-clusions: Patients appreciated that the service ensured a quicker discharge from hospital. Good stakeholder communication, practical factors (including choice, location, and the realities of ob-taining their medication from the community pharmacy), and a pre-existing and trusted rela-tionship in their usual community pharmacy were the key factors that regulated the patient ex-perience. In the main, patients were positive about the introduction of this new service. However, a lack of previous relationship or trust with a community pharmacy, and previous experiences with medication supply problems were factors which had the potential to negatively impact on patient experiences.

The Polyurethane (PU) mixture is a high−performance engineering material with different dynamic properties compared to a hot−mixed asphalt mixture (HMA). This paper analyzed whether the Kramers−Kronig (K−K) relation and thermo−rheological simple properties were applicable to the PU mixture. Based on the result discussion, the PU mixture exhibited thermo−rheological simple properties within the test conditions. The time−temperature superposition principle (TTSP) was applicable for the PU mixture to construct the dynamic modulus master curve using the standard logistic Sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak−Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The Core−Core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K−K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results. The phase angle of the PU mixture cannot be derived from the dynamic modulus data and currently needs to be derived from the laboratory measurement.

This article presents an Internet of Things (IoT)-enabled system meticulously engineered for comprehensive environmental assessment, encompassing air quality, temperature, and humidity. Simultaneously, the devised system integrates cutting-edge MQ6 sensors, adept at detecting ambient gases, with a primary focus on monitoring Liquefied Petroleum Gas (LPG) and Butane gases concentrations in the atmosphere. This distinctive feature not only facilitates the identifi-cation of hazardous gases leakages, particularly common in household LPG usage, but also empowers preemptive measures. The environmental parameters, air quality, temperature, and humidity, are precisely gauged through the utilization of MQ135 and DHT11 sensors. The con-trolling and managing of the hardware component of the system (i.e., device) is assured by an Arduino UNO, that orchestrates the collection of sensor data, transmitting it seamlessly to both the ThingSpeak cloud and a Convolutional Neural Network (CNN) model. By interfacing with ThingSpeak, the device ensures the systematic storage and visualization of environmental data, streamlining monitoring and analysis processes. In the event of perilous gases detection, the system promptly triggers an alarm, concurrently dispatching a real-time notification to the device owner via the integrated If-This-Then-That (IFTTT) application programming interface (API). To further enrich environmental awareness, an embedded microphone captures audio recordings, subsequently processed by a CNN-based deep learning model. This model employs advanced techniques such as spectrogram generation, Convolution2D, and MaxPooling2D within a Se-quential Model architecture, to attain elevated levels of accuracy, furnishing insightful interpre-tations of ongoing activities within the environment.

(1) Background: The study of microbiome is being crucial for his role in major systemic diseases, in particular the oral and gut microbiota. In the last years the study of microorganism correlated for example with neurodegenerative disease is increasing the prospective of a possible link between gut microbiota and brain. Here we report a new case report about a patient evaluated genetically firstly for dementia and late-onset dyskinesia, and then tested with 16S metagenomics sequencing. (2) Methods: Starting from buccal swab, we extracted bacterial DNA and then we performed a NGS metagenomics sequencing based on the amplification of hypervariable regions of the 16S in bacteria. (3) Results: The sequencing revealed the presence of Spirochaetes Phylum, a pathogenic bacterium generally known to be able to migrate to the Central Nervous System and not only. (4) Conclusions: Oral infections as our results shows could be possible contributing factors to various neurodegenerative conditions.

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

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

In this research paper, design and development of pyramidal horn antenna for J-band application is reported. It is particularly designed for 17 dB gain and half beam width about 25 degrees at 6.93 GHz. Horn aperture, horn axial length and distance from the throat of the antenna to aperture are the main design constraints which are calculated and used for the antenna design and simulation. Beam width in E-plane and H-plane horn is calculated and it is 19.18 dB and 22.86 dB respectively. The reported antenna design shows good performance for J-band in radiometry, satellite, and radar applications.

Porter's diamond model was used because it is an index in the field of competitiveness and increasing the index of competition in the international arena. In order to measure actions in the field of commerce and marketing trends and its compliance with Porter's diamond model, an organization named Isfahan Chamber of Commerce, Industries, Mines and Agriculture was selected from among the existing non-governmental organizations in Iran. This non-governmental organization, having different departments has members and subgroups related and influential in the field of commerce and marketing. In order to answer the question raised in this research, that is, to determine the position of the Isfahan Chamber of Commerce, Industries, Mines and Agriculture in Porter's diamond model, by referring to this organization in person, the available documents, information and data available in a descriptive-survey manner. It was reviewed until October 2022. All key activities and works performed in this organization in the period from March 2019 to March 2022 were identified and separated according to the internal factors of Porter's Diamond model. Finally, the credibility and effectiveness of this non-governmental organization as a link between the private sector and the government was measured in the Porter's diamond model.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Cell-to-cell distant mechanical communication has been demonstrated by using in-vitro and in-vivo models. However, the molecular mechanisms underlying long-range cell mechanoresponsive interactions remain to be more elucidated. This study further examined the roles of α-Catenin and Piezo in traction force-induced rapid branch assembly of airway smooth muscle (ASM) cells on Matrigel hydrogel containing type I collagen. Our findings demonstrate that siRNA-mediated downregulation of α-Catenin or chemical inhibition of Piezo activity significantly reduced both cell directional movement and branching assembly. In regarding the role of N-cadherin in regulating branch assembly but not directional migration, our results further confirmed that siRNA downregulation of α-Catenin caused a remarked reduction of focal adhesion formation, as assessed by focal Paxillin and Integrin α5 localization. These observations implied that mechanosensitive α-Catenin was involved in both cell-cell and cell-matrix adhesions. Additionally, Piezo showed partial localization with Paxillin in focal adhesions, which was inhibited by α-Catenin downregulation with siRNA. This provides a plausible clue for Piezo mechanosensing traction force in the hydrogel. Collectively, our findings highlight the significance of α-Catenin in regulating cell-matrix interactions along with possible interpretation for Piezo mechanosensation at focal adhesions during cell-cell distant mechanical communication.

Optical Wireless Communication (OWC) technology has gained significant attention in recent years due to its potential to provide high data rate wireless connections through the large license-free bandwidth available. A key challenge in OWC systems, similar to high-frequency Radio Frequency (RF) systems, is the presence of dead zones caused by obstacles like buildings, trees, and moving individuals, which can degrade signal quality or disrupt data transmission. Traditionally, relays have been used to mitigate these issues. Intelligent Reflecting Surfaces (IRS) have recently emerged as a promising solution, enhancing system performance and flexibility by providing reconfigurable communication channels. This paper presents an overview of the application of IRS in OWC systems. Specifically, we categorize IRS into two main types: mirror array-based IRS and metasurfaces-based IRS. Furthermore, we delve into modeling approaches of mirror array-based IRS in OWC and analyze recent advances in IRS control, which are classified into system power or gain optimization oriented, system link reliability optimization oriented, system data rate optimization oriented, and system security optimization oriented approaches. Finally, we discuss the key challenges and potential future directions for integrating IRS with OWC systems, providing insights for further research in this promising field.

In recent decades, reports of deforestation, mass extinctions, biodiversity loss, and emergencies like floods and droughts have clearly highlighted the impact of human activities on these outcomes. This underscores the need for significant lifestyle changes to mitigate this impact. Pro-environmental behavior (PEB) change involves adopting actions that protect the environment and reducing environmentally harmful activities. While qualitative research may provide unique insights into PEB barriers that are challenging to capture through quantitative methods, reports of qualitative studies are scattered across different literatures with little integration. With the present review, we bring together insights from qualitative studies on PEB barriers and examine whether those studies reveal barriers compatible with existing barrier frameworks from environmental psychology. We identified 38 qualitative studies on barriers to PEB change. Our analysis revealed that most of the barriers to individual pro-environmental action identified in qualitative studies align with environmental psychology frameworks. These barriers were experienced not only by individuals in their role as consumer or citizen, but also by community-level actors (e.g., seeking to implement pro-environmental policies) and industrial decision-makers (e.g., aiming to reduce the ecological footprint of their business activities). Our findings underscore the multifaceted nature of barriers to PEB change and emphasize the importance of addressing structural and institutional factors alongside individual-level interventions.

Our decisions today are part of an economic thinking which aims to fix problems and increase the efficiency of capital markets. Our economy is still largely approached as a system which is unre-lated to our environment or social returns like community well-being, social cohesion, equitable distribution of resources, etc. To fully incorporate sustainability into cost-benefit analysis, busi-nesses need to change their financial approach, showcasing how long-term thinking impacts prof-its. Money's value is not static and evolves with time and technology. We propose to introduce a variation of cost/benefit and present value calculations. Precise calculations reveal the benefits of long-term strategies over short-term profit focus. As financial systems evolve and technology advances, the accurate calculation of money's time value will continue to play a crucial role in shaping economic landscapes and guiding financial choices. In this article, we demonstrate that updated calculations could be a key enabler to visualize the benefits of long-term thinking or “patient finance” as compared to a short-term focus on profit. We introduce the concept and calculation of a Present Future Value (PFV).

The process of developing therapies to treat rare diseases is fraught with financial, regulatory, and logistical challenges that have limited our ability to build effective treatments. Recently, a novel type of therapy called antisense therapy has shown immense potential for the treatment of rare diseases, particularly through single-patient N-of-1 trials. Several N-of-1 antisense therapies have been developed recently for rare diseases, including the landmark study of milasen. In response to the success of N-of-1 antisense therapy, the Food and Drug Administration (FDA) has developed unique guidelines specifically for the development of antisense therapy to treat N-of-1 rare diseases. This policy change establishes a strong foundation for future therapy development, and addresses some of the major limitations that previously hindered the development of therapies for rare diseases.

Hemophagocytic lymphohistiocytosis (HLH) is a serious haematologic condition that can be re-lated to various diseases, including tuberculosis (TB). The patient is a previously healthy 26-years-old man, originally from Western Africa, admitted to hospital for fever and weight loss. Given the results of computed tomography (CT) scan, ocular examination and microbiologic tests, a miliary TB with pulmonary, lymph nodal and ocular involvement was diagnosed. Following introduction of antitubercular treatment (ATT) an increase in inflammation indexes and severe pancytopenia were observed; at this point the patient presented six of eight diagnostic criteria for HLH and the diagnosis of HLH secondary to TB was raised. Therefore, HLH treatment with high dose of dexamethasone was started with a good clinical response. We performed a literature re-view of TB-related HLH, which shows a high mortality rate; ATT is necessary to ensure patient survival to remove the antigenic driver. Our patients developed HLH after initiation of ATT as a paradoxical reaction, that may be linked to the release of antigens due to the bactericidal effect of ATT.

Background: Our previous study highlighted important healthcare access and utilization concerns among new immigrants. Hence, in this study, we aimed to explore the role of public and patient involvement (PPI) may have in enhancing the accessibility and their contribution to migration health studies. Method: An open and in-depth interactive consecutive virtual discussion was conducted among volunteer asylum seekers and refugees from Eritrea and Syria in Switzerland. The novel PPI establishment had three phases; inception, training phase, and contribution phase. Result: At the beginning, the concept of PPI was apprehensive to grasp, as it was a new approach. After training and consecutive discussions, volunteers were ardent to engage actively. Hence, among the prime findings were the PPI's role and effect in raising awareness, PPI's role in exploring and communicating the healthcare system accessibilities and utilization among their community, the impact of PPI in echoing the healthcare needs of their fellow migrants, and PPI's contribution in enhancing and strengthening migration health researches. Conclusion: The PPI volunteers in the study were keen to raise their community's awareness through their networks, and bridge the research gap between researchers and the public.

Road traffic accidents are associated with different factors, such as non-signalized road networks, inexperienced drivers, lack of communication skills, distraction, and visual or cognitive impairment of road users, which have led to this increase in traffic accidents. Understanding these factors in alignment with road user communication skills can provide solutions that can reduce traffic fatalities. Road users’ communication is predominantly formal in nature, but informal means of communication also exist but are not given proper attention during the driving training and licence testing processes. Therefore, this study investigated communication skills between drivers as a factor contributing to traffic accidents in South African cities. Consequently, 16 traffic scenarios of communication between drivers were simulated using PTV Vissim and Blender software, and a semistructured interview questionnaire was used to collect data from South African road users. The questionnaire was further used to evaluate the confidence level of road users concerning informal communication. The data were analysed via regression analysis to establish the relationship between traffic accidents and miscommunication between drivers. The information gathered and analysed from this study helps to understand the informal communication types between drivers and establish where communication loss exists, which ultimately leads to a traffic accident. The results also show that misinterpretation of communication signals can present the possibility of traffic safety risk.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

Endometriosis is known to be a chronic, debilitating disease. The pathophysiological mechanisms of endometriosis development include local chronic inflammation and a certain degree of local immune deficit. We investigated the relationship between the endometriosis severity, IL-8, IL-10, BDNF, VEGF-A serum and tissue levels, patient-related pain, and physical activity in a cohort of 46 patients diagnosed with endometriosis who underwent surgery. The same panel of biomarkers was investigated in a control group of 44 reproductive-aged patients with non-endometriotic gynecological pathology who underwent surgical intervention. Our data show a high statistical significance between tissue expression of IL-8, IL-10, patient-related pain and the severity of endometriosis. No relationship was identified between serum or tissue levels of VEGF-A and BDNF and the severity of endometriosis. These results validate the presence of local chronic inflammation and immune deficit, thereby creating, alongside other studies in the field, an opportunity for the development of innovative and personalized treatment approaches in endometriosis.

The IoT is recognized as one of the most important areas of future technology and is gaining vast attention from a wide range of industries. Although, after 20 years from the first published literature (2002) the technology (as a whole) is not yet mature. In this study we will review the basics of IoT with a general approach, by addressing the problems of a standard architecture, vulnerabilities and use cases of this promising technology. Moreover, we will review some of the communication protocols that have invented especially for IoT technology, security threats and general implementation challenges.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Background: With the rapid improvement of the healthcare technologies, security and privacy of the most sensitive data, health data is at risk. Protecting patient privacy has many components, even though the data are in electronic format. Although patient privacy is thoroughly discussed in the literature, there is no study presenting all components of patient privacy protection. Methods: In this study, a complete evaluation framework is presented, as an evaluation tool, an inventory is developed, reliability and validity of the inventory is examined. Study is conducted in three phases: Conceptual framework development, inventory development and an evaluation case study. In the evaluation, fuzzy conjoint analysis is employed to handle the subjectivity and am-biguity. As the result of the evaluation, the institution is given a patient privacy protection ma-turity grade, between 1 and 5, where 1 is the worst and 5 is the best grade. Results: In the case study, XXX’s biggest hospital, employing 800 nurses, is evaluated. Half of the nurses, 400, have partici-pated in the study. Literature tells healthcare institutions do not invest enough to protect patient privacy, and the study results support this finding. Institution’s maturity grade resulted as level 2, which is bad. Conclusion: The study measures he privacy maturity with many evaluation com-ponents. Result of the evaluation explains to the patients and the public if their data is safe or not. With this maturity grade, patients have an idea about which institution to choose, public can conclude the institutions’ reliability in terms of patient privacy.

Pulmonary complications are frequently observed in patients with ulcerative colitis (UC), with large airway disease being the most prevalent manifestation. In this case study, we explore the potential for Immunosuppressant, a monoclonal antibody, to induce pulmonary hemorrhage—a side effect not previously documented with its use. The subject, a young male in his twenties with a six-year history of UC, tragically died suddenly. Found in a pool of blood without external injuries, extensive post-mortem examinations were required to determine the cause of death. The patient had been on biologic therapy, receiving Immunosuppressant intermittently over the past two years, including an infusion five days prior to death. External examination revealed bloody, pinkish foamy material expelling from his nose and mouth. An autopsy supported an atypical presentation of UC, while histopathology confirmed diffuse pulmonary hemorrhage, marked by edema and emphysematous changes. Additionally, there was a notable lymphomonocytic infiltration around the arterial walls, and signs of bronchiolitis and focal pleural fibrosis were evident. We concluded that the cause of death was Diffuse Alveolar Hemorrhage (DAH), directly associated with the recent administration of Immunosuppressant. This finding underscores the need for vigilant pulmonary monitoring in patients treated with monoclonal antibodies to promptly identify and manage potential fatal adverse reactions.

Communicating about sex is important in generating positive perceptions about one’s sexual and romantic life. The last decade has provided insight into the theoretical applications on sex communication research. While the focus on extant research focuses on the psychological processes’ individuals employ to talk about sex, no theoretical framework exists that identifies key constructs that provides a parsimonious understanding about the communicative processes of sex and sexuality. This review sought to identified key constructs that are suitable to develop a sex talk theory. In the current project, we reviewed 24 studies including 8,698 participants (Mage = 24.98) that applied a theoretical framework to sex research. This review identified three salient constructs (e.g., disclosure, cognitive, and relational and behavioral) that are important for a sex talk theory. Last, this review indicated that scholarship should explain the relationships between the key constructs. We provided several recommendations that scholars should take when developing a theory in sex communication research.

Aortic stenosis (AS) is a prevalent valvular disorder that often requires surgical aortic valve replacement (SAVR) to alleviate symptoms and improve survival. However, the selection of an appropriate prosthetic valve size is crucial to avoid patient-prosthesis mismatch (PPM), which can lead to suboptimal hemodynamic outcomes. Von Willebrand factor (VWF), a key component of hemostasis, has been shown to be altered in AS patients and may contribute to the hemostatic imbalance observed following valve replacement. This prospective study aimed to investigate the impact of PPM on postprocedural VWF levels in patients undergoing SAVR for severe AS. The study included 31 consecutive patients diagnosed with severe AS who underwent SAVR. By focusing on the relationship between PPM and VWF dynamics, this study sought to provide valuable insights into the potential role of VWF as a marker for hemostatic changes associated with PPM, ultimately informing strategies for optimal valve selection and perioperative management to minimize complications.

Background: Permanent tracheostomy because of total laryngectomy surgery entails great conse-quences for the patient regarding respiratory physiopathology such as the filtering, humidifying, and heating function of the air by the nose is lost. The use of special stomal filters can determine adequate protection of the trachea-broncho-pulmonary system with a reduction in respiratory pathologies. In fact, in most cases laryngectomee patients are first cigarette smokers who for this reason already have also respiratory diseases such as chronic obstructive pulmonary disease (COPD). Despite the availability of tracheal filters, multiple times, as reported in the literature, the patient tends to limit their use due to reported breathing difficulties, especially in conditions of intense breathing. Methods: The objective of this clinical study was to evaluate the most suitable stomal filter for the laryngectomee patient during physical activity. The filters studied were an INHEALTH device (Blom-Singer SpeakFree HME); two ATOS devices (Provox® Life™ Energy HME and Provox® Life™ Home HME); and an FAHL device (Laryvox HME Sport). Results: For this purpose, the performances of 31 laryngectomee patients, subjected to medi-um-high physical effort, were analyzed through a standardized pneumological test, the Six Minute Walking Test (6MWT) which involves a sustained walk lasting six minutes, with evaluation , every 60 seconds, heart rate, oxygen saturation and meters travelled; furthermore, it examines two sub-jective indices, namely, the basal and final dyspnea index and the initial and final muscular fatigue index.

We report the case of a 61-year-old woman who was referred to the Oral Surgery and Implantology Unit of the University Hospitals of Geneva to take care edentulous sites after dental avulsions of teeth 11 and 21 following traumatic shock due to endotracheal intubation under general anaesthesia. The dental history revealed that the patient had a history of generalized chronic periodontitis that had been treated several years earlier. Treatment consisted, for the first time to our knowledge, of immediate insertion of implants at sites 11 and 21 with simultaneous bone augmentation in a single surgical procedure and immediate restorations.

We present a case report of a 26-years-old female suffering from Coffin Siris Syndrome, who underwent orthodontic treatment and surgery to solve her malocclusion and improve her aes-thetics and functional occlusion. Methods: The presurgical phase involved multibracket self-ligating attachments, Damon prescription. The patient underwent maxillofacial surgery to correct the severe skeletal malocclusion and relocate the bone bases in the right relationship. The post-surgical orthodontic treatment was performed to complete the alignment. Results: The pa-tient improved her aesthetics and functional abilities. Conclusions: Syndromic patients can un-dergo orthodontictreatment if comorbidities and collaboration allow it. Support and collaboration of families and psychotherapists must be considered, but clinical cases of syndromic patients can be faced and solved. Obviously, each syndromic patient is considered unique, and the risk-benefit ratio must be correctly assessed for each one.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching methods, and simple course experiments, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. Time and space limitations allow people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional and deep learning methods of digital image processing to improve the relevant project practice and scientific research exploration ability and refer for similar research conclusions.