Practical Applications Research Articles

Effect of Tillage Practices and Hydrogel Applications on Yield and Yield Attributing Traits of Maize (Zea mays L.) over the Years

This study investigated the effects of different tillage practices and hydrogel applications on maize yield in sandy loam soils at the Agricultural Research Station, Karimnagar, PJTSAU, Telangana state over a period of four years (2015-2018) during the Kharif season. The experiment was designed as a split plot with four main treatments i.e., Conventional tillage (CT), Conventional tillage with residue mulching (4 t/ha), Zero tillage (ZT) and Zero tillage with residue mulching (4 t/ha). These were combined with three sub-treatments involving hydrogel application i.e., Control (no hydrogel), Hydrogel at 2.5 kg/ha and Hydrogel at 5.0 kg/ha. The study was replicated three times to ensure reliability of the results. The combination of zero tillage with residue mulching yielded the highest maize grain yield (4435 kg/ha). This suggests that this practice is more effective in retaining soil moisture, which is critical under rainfed conditions. The addition of hydrogel, regardless of the rate (2.5 kg/ha or 5.0 kg/ha), did not lead to any significant improvement in maize yield. The use of zero tillage combined with residue mulching is beneficial for maize production in sandy loam soils under rainfed conditions, primarily due to enhanced moisture retention. However, hydrogel application did not provide any additional yield benefits in this context.

Bacteriophages: Sustainable and effective solution for climate-resilient agriculture

Abstract Climate change entails significant challenges to global food security and agricultural sustainability, prompting the need for innovative solutions to enhance climate resilience in farming practices. Bacteriophages (phages), viruses that specifically target and infect bacteria, provide promising applications in numerous agricultural practices to address these challenges. With traits such as specificity towards bacterial pathogens along with self-replication, self-dosing, and adaptability properties phages offer potential as effective biocontrol agents while exerting minimal harm on non-target organisms or their surrounding environment. In this review, various applications of phages have been explored including increasing crop resilience, improving soil health, preserving food, managing water resources, and adapting to emerging pests and diseases. Additionally, the benefits of these applications are also explored, such as improving sustainable practices, enhancing plant growth and yield, and supporting pollinator health.Although they offer great promise, there are still several obstacles such as limited host range, environmental stability, and regulatory challenges, so further research and policies are required for their integration into agricultural systems. Furthermore, education and public awareness are essential for farmers to adopt phage-based biocontrol. By harnessing the potential of phages, agriculture can mitigate the adverse effects of climate change, promote sustainability, and ensure food security in a changing climate.

Synergizing lean management and circular economy: Pathways to sustainable manufacturing

AbstractOur research focuses on integrating circular economy (CE) and Lean Management (LM) within the manufacturing sector, a proactive response to the escalating pressures of globalization, environmental sustainability, and market competition. Through a comprehensive systematic literature review, the research uncovers how CE principles can fortify and enrich LM practices, the role of LM in facilitating a transition towards CE, their combined impact on manufacturing performance, and the crucial environmental factors influencing this integration. The findings underscore that CE principles significantly strengthen LM practices by advocating waste minimization and resource optimization, thereby contributing to operational efficiency and sustainability. Conversely, LM aids the transition to a CE by optimizing resource use and minimizing waste, aligning with sustainability goals. LM supports recycling and remanufacturing by employing Just‐in‐Time and value stream mapping methodologies, augmented by Industry 4.0 technologies for seamless CE integration. Despite challenges like integration barriers and the need for organizational change, the synergy between CE and LM offers substantial benefits for environmental performance, operational efficiency, and competitive advantage. Our paper enriches academic knowledge and provides practical applications in sustainable manufacturing practices, offering a roadmap for organizations striving for sustainable operational excellence. Moreover, it also paves the way for future studies to further explore the synergistic potential of CE and LM through empirical studies, quantitative analyses, and the exploration of Industry 4.0 technologies.

A Primer on Trauma-Informed Practice in Refugee Law

This article is intended as a primer on trauma-informed practice for professionals working in the area of refugee law. Refugees and asylum seekers are amongst the most vulnerable groups in the community, and the process of seeking asylum itself can intensify stress. Trauma-informed practice is founded on ‘safety first’ and ‘do no harm’. It reflects adherence to core principles, rather than a prescribed set of practices and procedures. The core principles of trauma-informed practice include safety, trust, choice, collaboration and empowerment. This article primarily focusses on professional applications of trauma-informed practice at an individual level. By taking steps to integrate these principles into interpersonal interactions with refugees and asylum seekers, professionals can better support their needs in the refugee law context.

Experimental and Statistical Analysis of Concrete Eco-Cobble Using Organic and Synthetic Fibers

The environmental impact of traditional construction materials necessitates the development of sustainable alternatives. This study evaluates eco-cobbles as novel building materials designed to reduce environmental footprint while maintaining performance standards. The objectives were to develop an eco-friendly cobble alternative and assess its effectiveness through laboratory tests. Eco-cobbles were synthesized using recycled and bio-based materials and tested for compressive strength, flexural strength, and water absorption at 14 and 28 days. The compressive strength ranged from 11.5 MPa to 26.8 MPa, with a maximum value observed at 28 days in a mixture containing 95% concrete and 5% polyethylene terephthalate (PET). Flexural strength varied from 9.1 MPa to 28.7 MPa, with the highest value achieved in a mixture of 95% concrete and 0% fibers. Water absorption rates ranged from 2.1% to 6.6%, demonstrating an effective balance between performance and durability. Environmental assessments indicated a 30% reduction in resource consumption and a 40% decrease in carbon footprint compared to traditional cobble production methods. The findings demonstrate that eco-cobbles not only meet performance standards but also offer significant environmental benefits with a 99% compliance from the results obtained by response surface methodology plots, confirming that eco-cobbles offer a viable, sustainable alternative to conventional materials, with the potential for broader application in eco-friendly construction practices.

Techno-economic analysis on temperature vacuum swing adsorption system integrated with pre-dehumidification for direct air capture

In addressing the practical application of direct air capture (DAC) technology to achieve net-zero greenhouse gas emissions by 2050, the intricate climatic environment presents a significant challenge in selecting capture adsorbents with excellent adsorption performance, high moisture tolerance and stability. This study seeks to assess the efficacy of a temperature vacuum swing adsorption (TVSA) system for DAC, integrated with two distinct pre-dehumidification methods: condensation dehumidification and solid desiccant dehumidification. Initially, to balance enhanced adsorption capacity with the energy required for regeneration in the presence of water vapor, the optimal techno-economic performance is achieved at a 40 % relative humidity (RH). This corresponds to 15.49 GJ/t of heat consumption and 194.08 $/tonCO2 of CO2 the levelized cost of DAC (LCOD). Following dynamic simulation, the dehumidification system effectively controlled the outlet RH. Furthermore, economic analysis indicates that reducing environmental air to 40 % RH is more suitable and cost-efficient for both integrated systems. The optimal LCODs are 200.29 $/tonCO2 for the solid desiccant dehumidification system and 197.16 $/tonCO2 for the condensation dehumidification system, which could effectively save 7.09 % and 5.61 % of LCOD, respectively, compared to systems without a dehumidification process.

A Distorted Light Field Image Correction Method Based on Improved Hough Transform

Introduction: In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images. Method: In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image. Result: The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one. Conclusion: In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.

Mengintegrasikan Kecerdasan Emosional, Kecerdasan Spiritual Dan Kecerdasan Buatan Dalam Praktik-Praktik Manajemen Sumber Daya Manusia Pada Organisasi Bisnis

The increasing complexity and dynamism of the business world make the integration of Emotional Intelligence (EQ), Spiritual Intelligence (SQ), and Artificial Intelligence (AI) in Human Resource Management (HRM) practices a holistic approach to enhancing workplace effectiveness and well-being. Emotional Intelligence plays a crucial role in managing interpersonal relationships, communication, and conflict management, which are vital for effective leadership and teamwork. Spiritual Intelligence supports the search for meaning and purpose in work, fostering an organizational culture aligned with personal and collective values. On the other hand, Artificial Intelligence automates processes, provides data-driven analytics, and improves operational efficiency through advanced technology. Integrating these three forms of intelligence can create a more productive, innovative, and fulfilling work environment. This article reviews the fundamental theories of each type of intelligence and explains how their application in HRM practices can yield significant benefits for organizations.

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.

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.

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.