Practical Applications Research Articles

An optimal trauma-informed pathway for PTSD, complex PTSD and other mental health and psychosocial impacts of trauma in prisons: an expert consensus statement

ABSTRACT People in prisons have high levels of trauma exposure throughout their lives. Presentations are often complex, with a high prevalence of PTSD and CPTSD and other mental health comorbidities. Prisons themselves can be stressful and traumatising environments. There are challenges in the delivery of effective treatments for PTSD and CPTSD. There is a need for the development of effective clinical pathways for these conditions that are embedded within trauma-informed organisational approaches. Responding to this need, this report is the result of a multidisciplinary expert consensus meeting and review of the research literature on PTSD, CPTSD, associated comorbidities and optimal approaches to trauma-informed practice. The group consisted of 24 expert representatives from psychology, psychiatry, healthcare, academia, social care and Welsh Government. The meeting commenced with presentations on various aspects of the clinical pathway for PTSD and complex PTSD in prisons, and of applications of trauma-informed practice within prisons. Small sub-groups then provided practical recommendations and solutions relevant to their assigned topic. Findings were presented to all meeting attendees for another round of discussion and debate, until consensus was reached. The resulting recommendations provide guidance to improve identification, treatment and support for people living in prison who have experienced trauma.

Few shot intelligent assessment method for compaction quality of earthwork considering uncertainty

Current practices lack reliable models for continuously assessing compaction quality from limited data, which hinders effective compaction quality control and the acceptance of earthwork. Using material source parameters as inputs for assessment models makes it infeasible to achieve a real-time assessment of the compaction quality in earthwork. These issues have hindered the popularization of emerging intelligent compaction (IC) and intelligent rolling compaction (IRC) technologies. Therefore, this study addresses the need for small-sample training by introducing an uncertainty-based multi-population genetic algorithm fused with a backpropagation neural network model (UB-MPGA-BP), enabling the real-time intelligent assessment of compaction quality based on few-shot learning. In case studies involving high- and low-liquid limit silts, existing models trained on large datasets were notably inadequate for small-sample evaluation tasks. However, the proposed method not only performed well in silt cases but also exhibited high performance in rockfill materials. The analysis revealed that in small-sample scenarios without reliance on material source parameters, the proposed method has the advantages of real-time accuracy, generalization, few-shot data-driven, and simple input parameters, which enable the rapid assessment of compaction quality. The model can potentially facilitate IC/IRC applications in other engineering practices.

Trividha Pariksha – Diagnostic Importance And Its Application In Clinical Practices

Trividha Pariksha – Diagnostic Importance And Its Application In Clinical Practices

Photoelectric Studies as the Key to Understanding the Nonradiative Processes in Chromium Activated NIR Materials.

In this study, we synthesized a series of Ga1.98-xInxO3:0.02Cr3+ materials with varying x values from 0.0 to 1.0, focusing on their broadband near-infrared emission and photoelectric properties. Interestingly, photocurrent excitation spectra exhibited behavior consistent with the absorption spectra, indicating the promotion of carriers into the band structure by the 4T1, and 4T2 states of Cr3+ ions. This association suggests that photocurrent in this material is influenced not only by valence to conduction band transitions but also by transitions involving Cr3+ dopants. Our investigation of luminescence quenching mechanisms revealed that nonradiative processes were not directly linked to thermally induced relaxation from the excited state 4T2 to the ground state 4A2, as usually suggested in the literature for this type of material. Instead, we linked it to the thermal ionization of Cr3+ ions. Unexpectedly, this process is unrelated to the transfer of electrons from Cr3+ impurities to the conduction band but is associated with the formation of holes in the valence band. This study provided novel evidence of luminescence quenching via the hole-type thermal quenching process in Cr3+-doped oxides, suggesting potential applicability to other transition metal ions and host materials. Finally, we demonstrated the dual-purpose nature of Ga1.98-xInxO3:0.02Cr3+ as a practical emitter for NIR-pc-LEDs and effective photocurrent for UV detectors. This versatility underscores these materials' practicality and broad application potential in optoelectronic devices designed for near-infrared and ultraviolet applications.

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

An Explainable Multichannel Model for COVID-19 Time Series Prediction

Introduction: The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications. Methods: An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation. Results: STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time. Conclusion: STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.

A Comprehensive Survey of Continual Learning: Theory, Method and Application.

To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.

A Survey on Information Bottleneck.

This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999. It provides an elegant balance between data compression and information preservation, and improves its prediction or representation ability accordingly. This survey summarizes both the theoretical progress and practical applications on IB over the past 20-plus years, where its basic theory, optimization, extensive models and task-oriented algorithms are systematically explored. Existing IB methods are roughly divided into two parts: traditional and deep IB, where the former contains the IBs optimized by traditional machine learning analysis techniques without involving any neural networks, and the latter includes the IBs involving the interpretation, optimization and improvement of deep neural works (DNNs). Specifically, based on the technique taxonomy, traditional IBs are further classified into three categories: Basic, Informative and Propagating IB; While the deep IBs, based on the taxonomy of problem settings, contain Debate: Understanding DNNs with IB, Optimizing DNNs Using IB, and DNN-based IB methods. Furthermore, some potential issues deserving future research are discussed. This survey attempts to draw a more complete picture of IB, from which the subsequent studies can benefit.

Inverse Optimal Adaptive Neural Control for State-Constrained Nonlinear Systems.

Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing works on solving this problem usually require a complicated and time-consuming learning procedure by employing neural networks, and the results are only applicable for simple or time-invariant constraints. In this work, these restrictions are removed by a newly proposed adaptive neural inverse approach. In our approach, a new universal barrier function, which is able to handle various dynamic constraints in a unified manner, is proposed to transform the constrained system into an equivalent one with no constraint. Based on this transformation, a switched-type auxiliary controller and a modified criterion for inverse optimal stabilization are proposed to design an adaptive neural inverse optimal controller. It is proven that optimal performance is achieved with a computationally attractive learning mechanism, and all the constraints are never violated. Besides, improved transient performance is obtained in the sense that the bound of the tracking error could be explicitly designed by users. An illustrative example verifies the proposed methods.

Kullback-Leibler Control in Boolean Control Networks.

This article addresses the Kullback-Leibler (KL) control problem in Boolean control networks. In the considered problem, an extended stage cost function depending on the control inputs is introduced; in contrast to a stage cost of the conventional KL control problems in the Markov decision process cannot take into consideration the control inputs. An associated Bellman equation and a matrix-based iteration algorithm are presented. The theoretical analysis shows that the proposed KL control results in an approximated form of conventional dynamic programming (DP). Furthermore, the convergence analysis is presented, with the weight parameter converging to zero and diverging to infinity. In practical application examples, a comparison of the conventional DP and proposed KL control is illustrated.

Tiny Object Tracking: A Large-Scale Dataset and a Baseline.

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in many vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel multilevel knowledge distillation network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination, and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://github.com/mmic-lcl/Datasets-and-benchmark-code.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Sequential Point Clouds: A Survey.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

A Fixed-Time Noise-Tolerance ZNN Model for Time-Variant Inequality-Constrained Quaternion Matrix Least-Squares Problem.

Presently, numerical algorithms for solving quaternion least-squares problems have been intensively studied and utilized in various disciplines. However, they are unsuitable for solving the corresponding time-variant problems, and thus few studies have explored the solution to the time-variant inequality-constrained quaternion matrix least-squares problem (TVIQLS). To do so, this article designs a fixed-time noise-tolerance zeroing neural network (FTNTZNN) model to determine the solution of the TVIQLS in a complex environment by exploiting the integral structure and the improved activation function (AF). The FTNTZNN model is immune to the effects of initial values and external noise, which is much superior to the conventional zeroing neural network (CZNN) models. Besides, detailed theoretical derivations about the global stability, the fixed-time (FXT) convergence, and the robustness of the FTNTZNN model are provided. Simulation results indicate that the FTNTZNN model has a shorter convergence time and superior robustness compared to other zeroing neural network (ZNN) models activated by ordinary AFs. At last, the construction method of the FTNTZNN model is successfully applied to the synchronization of Lorenz chaotic systems (LCSs), which shows the practical application value of the FTNTZNN model.

Evaluation of serum mid-infrared spectroscopy as new prognostic marker for first-line bevacizumab-based chemotherapy in metastatic colorectal cancer

Background and aimsBevacizumab-based chemotherapy is a recommended first-line treatment for metastatic colorectal cancer (mCRC). Robust biomarkers with clinical practice applicability have not been identified for patients with this treatment. We aimed to evaluate the prognostic yield of serum mid-infrared spectroscopy (MIRS) on patients receiving first-line bevacizumab-based chemotherapy for mCRC. MethodsWe conducted an ancillary analysis from a multicentre prospective study (NCT00489697). All baseline serums were screened by attenuated total reflection method. Principal component analysis and unsupervised k-mean partitioning methods were performed blinded to all patients’ data. Endpoints were progression-free survival (PFS) and overall survival (OS). ResultsFrom the 108 included patients, MIRS discriminated two prognostic groups. First group patients had significantly lower body mass index (p = 0.026) and albumin levels (p < 0.001), and higher levels of angiogenic markers, lactate dehydrogenase and carcinoembryonic antigen (p < 0.001). In univariate analysis, their OS and PFS were shorter with respective medians: 17.6 vs 27.9 months (p = 0.02) and 8.7 vs 11.3 months (p = 0.03). In multivariate analysis, PFS was significantly shorter (HR = 1.74, p = 0.025) with a similar trend for OS (HR = 1.69, p = 0.061). ConclusionBy metabolomic fingerprinting, MIRS proves to be a promising prognostic tool for patients receiving first-line bevacizumab-based chemotherapy for mCRC.

Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

Adaptability and prospects for the use of introduced representatives of the genus Weigela in different climatic conditions

The use of highly decorative and beautifully flowering shrubs in urban green spaces, especially in sharply continental climatic conditions, is extremely important. In this regard, representatives of the genus Weigela are highly valued in the in different climatic conditions. The main indicators of eco-physiological adaptability of plants, such as intensity of transpiration and photosynthetic productivity, water deficiency and the total water content, quantity of the plastids, have been studied for the representatives of highly ornamental shrubs of the genus Weigela Thumb. The object of the research was the following representatives of the genus Weigela: W. praecox, W. coraeensis, W. floribunda, W. florida and W. florida ʺVariegataʺ, which were introduced into different climatic conditions in order to climate train and replenish the dendro-collections. Physiological research was conducted, during the vigorous vegetative period (May–June months). All the measurements were carried out within the 11:00–13:00 time period, in 9 repetitions (7–10 shoots were chosen for each sample). The nature of ecological adaptability of the studied species, along with the perspective of their application in the landscaping practices in different climatic zones was revealed. At the same time it was revealed that the studied species can be used not only as solitary plants (especially on the background of green lawns) but also in groups in long blooming gardens (with flowers with different colours and blooming phases) and in decorating rock gardens.

Investigating the Endophyte Actinomycetota sp. JW0824 Strain as a Potential Bioinoculant to Enhance the Yield, Nutritive Value, and Chemical Composition of Different Cultivars of Anise (Pimpinella anisum L.) Seeds.

Anise (Pimpinella anisum L.) seeds have various nutritional and therapeutic benefits and are thus considered a valuable addition to animal and human health. Hence, in this study, we aimed to induce the nutritive and biological value of anise seeds. To this end, the potential biofortification effect of the endophytic Actinomycetota sp. JW0824 strain, isolated during the fall of 2023 from the medicinal plant Achyranthes aspera, exhibiting natural distribution in the Jazan region of Saudi Arabia, was investigated in four varieties of anise seeds from Egypt, Tunisia, Syria, and Morocco. Results revealed significant increments (p < 0.05) in the seed dry weight percentage (DW%) and oil yields. In line with increased biomass accumulation, the metabolism of the primary and secondary metabolites was increased. There were differential increases in proteins, sugars, flavonoids, alkaloids, phenols, vitamins (e.g., β-carotene, ascorbic acid), and essential oil components (e.g., phenylpropanoids and monoterpenes), along with their precursor phenylalanine. Consistently, the activity of L-phenylalanine aminolyase (PAL) was increased in the Egyptian and Tunisian varieties at 83.88% and 77.19%, respectively, while 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS) activity increased in all varieties, with a significant 179.31% rise in the Egyptian variety. These findings highlight the beneficial effects of Actinomycetota sp. JW0824 as a bioinoculant for anise seeds, suggesting its potential application in agricultural practices to improve seed yield and quality. Further field trials are recommended to assess the commercial viability of this endophyte for enhancing anise seed production and potentially benefiting other plant species.

Impact of a biostimulant enriched in betalain degradation products on ROS signaling, proline accumulation, and phytohormone homeostasis

This study investigates the potential of a biostimulant derived from Selenicereus undatus peel waste and enriched in betalain degradation products (BDP), to influence Arabidopsis thaliana seedling development. Notably, lower BDP concentrations enhanced seedling development, while higher dosages exhibited adverse effects. Assessment of mitochondrial activity in both seeds and purified organelles showed that the tested biostimulant did not affect mitochondrial activity or integrity, highlighting its independence from mitochondrial performance. Mechanistically, BDP-enriched biostimulant modulated ROS-signaling, diminishing H2O2 by regulating the enzymatic activity and gene expression of SOD, CAT, GPX, and GR. Particularly, analyzing their different isoform via qRT-PCR, the primary cellular compartment where detoxification occurred were identified. Furthermore, biostimulant was able to influence proline-accumulation, altering both the expression of metabolism (PC5S, P5CR and OAT) and catabolism (PDH and P5CDH) related genes. Finally, the BDP-enriched biostimulant altered phytohormone levels, mainly affecting ABA/ABA-glu, tZea/tZea-rib, and tZea/IAA. Concerning GAs, the increase in GA4 and GA7 suggested an involvement of GA13ox, a hypothesis encouraged by qRT-PCR analysis. In summary, this study underscores the potential of BDP-based biostimulant as sustainable promoters of plant growth, influencing critical regulatory pathways during germination. Further research is necessary to explore their extensive applications in agricultural practices.

What does “urgency” mean when prioritizing cancer treatment? Results from a qualitative study with German oncologists and other experts during the COVID-19 pandemic

PurposeCancer care in Germany during the COVID-19 pandemic was affected by resource scarcity and the necessity to prioritize medical measures. This study explores ethical criteria for prioritization and their application in cancer practices from the perspective of German oncologists and other experts.MethodsWe conducted fourteen semi-structured interviews with German oncologists between February and July 2021 and fed findings of interviews and additional data on prioritizing cancer care into four structured group discussions, in January and February 2022, with 22 experts from medicine, nursing, law, ethics, health services research and health insurance. Interviews and group discussions were digitally recorded, transcribed verbatim and analyzed using qualitative content analysis.ResultsNarratives of the participants focus on “urgency” as most acceptable criterion for prioritization in cancer care. Patients who are considered curable and those with a high level of suffering, were given a high degree of “urgency.” However, further analysis indicates that the “urgency” criterion needs to be further distinguished according to at least three different dimensions: “urgency” to (1) prevent imminent harm to life, (2) prevent future harm to life and (3) alleviate suffering. In addition, “urgency” is modulated by the “success,” which can be reached by means of an intervention, and the “likelihood” of reaching that success.ConclusionOur analysis indicates that while “urgency” is a well-established criterion, its operationalization in the context of oncology is challenging. We argue that combined conceptual and clinical analyses are necessary for a sound application of the “urgency” criterion to prioritization in cancer care.