Interpretation Of Data Research Articles

Understanding the adsorption of Reactive red 2 onto metal hydroxide sludge: analysis with physico-statistical steric and energetic perspectives.

This theoretical investigation delves into the analysis of Reactive red 2 (RR-2) adsorption isotherms on metal hydroxide employing a sophisticated double-layer model characterized by dual-energy levels within the realm of physical adsorption phenomena. An examination of five distinct statistical physics frameworks was undertaken to elucidate the modeling and interpretation of equilibrium data. Expression for the physico-chemical parameters involved in the adsorption phenomena was derived based on statistical physics treatment. Fitting experimental adsorption isotherms (308-333K) to a DAMTBS has revealed the number of anchored molecules per site, occupied receptor site density, and the number of adsorbed layers. The steric parameter n varies between 0.92 and 1.05. More importantly, it is evidenced that the adhesion mechanism of (RR-2) onto metal hydroxide as determined by the estimated adsorption energies (< 40kJ/mol) supports a spontaneous and exothermic physisorption process. Thermodynamic potential functions such as entropy, Gibbs free energy, and internal energy have been computed based on the most suitable model. This research advances our physical understanding of how metal hydroxide captures dye molecules RR-2 through adsorption reaction for water depollution treatment.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.Significance statement RNA-sequencing studies define differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate biological pathway information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse, despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER) to reduce data dimensionality from over 10,000 genes to 196 'latent variables' that map to known biological pathways. To validate this approach, we applied mousiPLIER to differential expression across mouse brain aging. We identified 26 functional pathways (latent variables) that varied across aging. Finally, we developed a web server to facilitate use of mousiPLIER by the scientific community.

Researching visual protest and politics with “extra-hard” data

A range of scholars have criticised scholarly tendencies to focus on “easy” data such as provided by the low-hanging fruit of Twitter hashtag networks (Burgess &amp; Bruns, 2015; Hargittai, 2020; Tromble, 2021). As a result, digital social research has been said to create a glut of studies that favour particular platforms, data forms, and networking dynamics, choices that may create ‘digital bias’ (Marres, 2017). These issues are particularly significant in visual data as the implicit nature of visuality means that platform spaces, text, and networked uses of visuals contribute to how visuals are interpreted in digital environments. In response to this issue, we present and critically reflect on new potentialities in software-based visual research on protest and politics, including: (1) rich cross-project comparisons; (2) complementing platform data with on-the-ground engagement, and (3) quali-quanti visual methods. These allow for rich data journeys through multi-modality, hybridity, comprehensive data curation, reiterative image data collection and interpretation, and the inclusion of contextual reflections in focused visual research, elements that provide meaning, texture, and context (= extra-hard data). We argue that visual digital methods consequently have the potential to provide nuanced, robust, and versatile analysis of visual data, if not necessitate these in a post-API age in which easy data access is no longer a given.

The Impact of COVID-19 on Selected Human Resource Management Functions: Recruitment &amp; Selection, Talent Management and Performance Management

The primary objective of this investigation was to assess the influence of the COVID-19 pandemic on the realm of human resource management. Due to the limited existing research in this area, the researcher conducted this study to contribute to the existing literature and provide guidance for organisations and human resource managers. Employing a qualitative approach, this paper conducted a literature review to gather data and utilized thematic data analysis for data interpretation. The results indicate that the COVID-19 pandemic has had and continues to have significant effects on three core human resource functions: recruitment and selection, performance management, and talent management. The transition to remote work has led to the digitization of recruitment and selection processes, posing challenges for HR professionals in managing performance. Additionally, the evolving landscape has underscored the importance of talent management as a critical function for organisational success.

A High-Resolution subtle fault mapping in Marlin oilfield, Brazil

Discriminating geological discontinuities is important for reservoir management because they influence storage capacity and fluid flow. Structural heterogeneities, such as faults and fractures, significantly impact the compartmentalization of the reservoir’s facies and affect the flow of hydrocarbons. These structures can act as the pore space for fluid storage, flow-control conduits, or barriers that allow the accumulation of exploitable volumes of oil and gas. Understanding these structures’ spatial and temporal development can significantly impact hydrocarbon exploration. Reservoir characterization is a critical process in the oil and gas industry that aims to comprehensively understand reservoir rocks and their fluid content and distribution. It is a multidisciplinary approach that combines geological, geophysical, and engineering datasets through statistical and deterministic mathematical models. Seismic datasets play a crucial role in reservoir characterization. Interpreters can use seismic data processing and imaging to interpret geological horizons and map faults, building complex geological models. Seismic inversion is also employed to estimate acoustic impedance and other petrophysical properties. Faults and fractures mapping are highly relevant themes in reservoir characterization. Identifying subtle faults and fractures is essential in specifying hydrocarbon migration paths and identifying bypassed oil accumulations. However, traditional seismic amplitude data interpretation should consider these structures more. In this study, we apply a high-resolution fault mapping workflow to map the subtle faults system at the Marlim field in the offshore portion of the Campos Basin. As an outcome, we imaged two separate fault trends inside and outside the Marlim reservoir.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

Well logs reconstruction of petroleum energy exploration based on bidirectional Long Short-term memory networks with a PSO optimization algorithm

During petroleum energy exploration, estimating missing well logs from existing logging data is very meaningful. Due to a highly nonlinear relationship between various logging data and a challenge to express the complexity of underground geological conditions by deterministic functions, traditional methods are difficult to meet the need of accurate interpretations of log data and fine descriptions of reservoirs. Nevertheless, deep learning method provides an advanced means for reconstructing well logs, and can directly map existing logging data to missing well logs. In this paper, we adopt the PSO-BiLSTM network that combines the BiLSTM (Bidirectional Long Short-term Memory) network with the PSO (Particle Swarm Optimization) algorithm. BiLSTM is an excellent data-driven method that can extract bidirectional temporal well logging data. PSO algorithm enhances the performance of BiLSTM model in predicting missing well logs through hyperparameter optimization, global search capability, and adaptive learning. At the same time, RMSE, MAE, and MAPE are used as indicators to evaluate the performance of PSO-BiLSTM model. The results show that when a PSO-BiLSTM model is applied to well logs reconstruction experiments, the reconstructed value of a log curve is in good agreement with its real value. Compared to LSTM and BiLSTM models, a PSO-BiLSTM model provides the best accuracy and stability in predicting missing well logs. The PSO-BiLSTM model strengthens the extraction of relevant logging information and reduces the error of parameter adjustment. It has an important reference significance for the reconstruction of well logs in complex formations.

Interventions for insomnia in cancer patients and survivors-a comprehensive systematic review and meta-analysis.

Considering the persistent nature and higher prevalence of insomnia in cancer patients and survivors compared to the general population, there is a need for effective management strategies. This systematic review and meta-analysis aimed to comprehensively evaluate the available evidence for the efficacy of pharmacological and non-pharmacological interventions for insomnia in adult cancer patients and survivors. Following the PRISMA guidelines, we analyzed data from 61 randomized controlled trials involving 6528 participants. Interventions included pharmacological, physical, and psychological treatments, with a focus on insomnia severity and secondary sleep and non-sleep outcomes. Frequentist and Bayesian analytical strategies were employed for data synthesis and interpretation. Cognitive-Behavioral Therapy for Insomnia (CBT-I) emerged as the most efficacious intervention for reducing insomnia severity in cancer survivors, and further demonstrated significant improvements in fatigue, depressive symptoms, and anxiety. CBT-I showed a large post-intervention effect (g = 0.86; 95%CI : 0.57-1.15) and a medium effect at follow-up (g = 0.55; 0.18-0.92). Other interventions like BWL therapy, sleep medication, melatonin, exercise, mind-body therapies, and mindfulness-based therapies showed benefits, but the evidence for their efficacy was less convincing compared to CBT-I. Brief Behavioral Therapy for Insomnia showed promise as a less burdensome alternative for patients in active cancer treatment. CBT-I is supported as first-line treatment for insomnia in cancer survivors, with significant benefits observed across sleep and non-sleep outcomes. The findings also highlight the potential of less intensive alternatives. The research contributes valuable insights for clinical practice and underscores the need for further exploration into the complexities of sleep disturbances in cancer patients and survivors.

Recovering 3D Salt Dome by Gravity Data Inversion Using ResU-Net++

In geophysical research, gravity-based inversion is essential for identifying geological anomalies, mapping rock structures, and extracting resources such as oil and minerals. Traditional gravity inversion methods, however, face challenges such as the volumetric effects of gravity fields and the management of large, complex matrices. Unsupervised learning techniques often struggle with overfitting and interpreting gravity data. This study explores the application of various U-Net-based network architectures in gravity inversion, each offering distinct challenges and advantages. Nested U-Net, although effective, requires a high parameter count, leading to extended training periods. Recurrent Residual U-Net's implicit attention mechanism restricts its dynamic adaptability, while Attention U-Net's lack of residual connections raises concerns about gradient issues. This research comprehensively analyzes the training processes, core functionalities, and module distribution of these networks, including Residual U-Net++. Our synthetic studies compare these networks with traditional focused regularized gravity inversion for reconstructing density anomalies. The results demonstrate that Nested U-Net closely approximates the actual model, despite some redundancy. Recurrent Residual U-Net shows improved alignment with minimal redundancies, and Attention U-Net is effective in density prediction but encounters difficulties in areas of low density. Notably, Residual U-Net++ excels in inversion modeling, achieving the lowest misfit percentage and accurately replicating density values. In practical applications, Residual U-Net++ impressively reconstructed the F2 salt diapir in the Nordkapp Basin with well-defined boundaries that closely match seismic data interpretations. These results underscore the capabilities of Residual U-Net++ in geophysical data analysis, structural reconstruction, and inversion, demonstrating its effectiveness in both simulated settings and real-world scenarios.

Assessment of gene–disease associations and recommendations for genetic testing for somatic variants in vascular anomalies by VASCERN-VASCA

BackgroundVascular anomalies caused by somatic (postzygotic) variants are clinically and genetically heterogeneous diseases with overlapping or distinct entities. The genetic knowledge in this field is rapidly growing, and genetic testing is now part of the diagnostic workup alongside the clinical, radiological and histopathological data. Nonetheless, access to genetic testing is still limited, and there is significant heterogeneity across the approaches used by the diagnostic laboratories, with direct consequences on test sensitivity and accuracy. The clinical utility of genetic testing is expected to increase progressively with improved theragnostics, which will be based on information about the efficacy and safety of the emerging drugs and future molecules. The aim of this study was to make recommendations for optimising and guiding the diagnostic genetic testing for somatic variants in patients with vascular malformations.ResultsPhysicians and lab specialists from 11 multidisciplinary European centres for vascular anomalies reviewed the genes identified to date as being involved in non-hereditary vascular malformations, evaluated gene–disease associations, and made recommendations about the technical aspects for identification of low-level mosaicism and variant interpretation. A core list of 24 genes were selected based on the current practices in the participating laboratories, the ISSVA classification and the literature. In total 45 gene–phenotype associations were evaluated: 16 were considered definitive, 16 strong, 3 moderate, 7 limited and 3 with no evidence.ConclusionsThis work provides a detailed evidence-based view of the gene–disease associations in the field of vascular malformations caused by somatic variants. Knowing both the gene–phenotype relationships and the strength of the associations greatly help laboratories in data interpretation and eventually in the clinical diagnosis. This study reflects the state of knowledge as of mid-2023 and will be regularly updated on the VASCERN-VASCA website (VASCERN-VASCA, https://vascern.eu/groupe/vascular-anomalies/).

The effect of anatomic location on porcine models of burn injury and wound healing.

Porcine models are frequently used for burn healing studies; however, factors including anatomic location and lack of standardised wound methods can impact the interpretation of wound data. The objectives of this study are to examine the influence of anatomical locations on the uniformity of burn creation and healing in porcine burn models. To optimise burn parameters on dorsal and ventral surfaces, ex vivo and in situ euthanized animals were first used to examine the location-dependence of the burn depth and contact time relationship. The location-dependent healing in vivo was then examined using burn and excisional wounds at dorsal, ventral, caudal and cranial locations. Lactate dehydrogenase (LDH) and H&E were used to assess burn depth and wound re-epithelialization. We found that burn depth on the ventral skin was significantly deeper than that of the dorsal skin at identical thermal conditions. Compared with burns created ex vivo, burns created in situ immediately post-mortem were significantly deeper in the ventral location. In live animals, 2 out of 12 burn wounds were fully re-epithelialized after 14 days in contrast to complete re-epithelialization of all excisional wounds. Among the burn wounds, those at the cranial-dorsal site exhibited faster healing than at the caudal-dorsal site. This study showed that anatomical location is an important consideration for the consistency of burn depth creation and healing. These data support symmetric localization of treatment and control for comparative assessment of burn healing in porcine models to prevent misinterpretation of results and increase the translatability of findings to humans.

Structuration theory perspective of Big Data in a typical South African municipality

Background: Big Data, sourced from various digital sources, offers valuable insights for better decision-making, and organisations are implementing Big Data technologies to improve their services. This article is about how the City of Tshwane (CoT), a South African metropolitan municipality is using data collected from its various information and communication technology (ICT) projects to improve decision-making.Objectives: The study aims to identify the type of Big Data generated by South African municipalities and to establish ways in which knowledge, expertise and suitable management techniques impact the efficient application of Big Data in these municipalities.Method: A qualitative approach, a case study method and Semi-structured interviews were deemed fit to collect data while thematic analysis was employed to identify patterns and themes associated with participants’ experiences. Structuration Theory was used to analyse existing social structures that govern how Big Data is used in the CoT.Results: There is a lack of data integration and a Big Data management system in the CoT. However, the CoT is ready to embrace Big Data and it is currently establishing a unit to effectively analyse collected data.Conclusion: Big Data’s potential for developing effective data management systems has garnered significant attention in the public sector and the CoT is also ready to fully adopt and implement these technologies.Contribution: South African municipalities can effectively utilise Big data by establishing skilled staff, good infrastructure and suitable policies for efficient data generation and interpretation.

Concept of Processing and Interpretation of Spatiotemporal Vector Data

The purpose of this work is to develop an approach to complex processing and interpretation of spatio-temporal vector data. In this regard, it is necessary to analyze existing methods for visualizing vector and tensor fields and organizing a database for their storage and develop a concept which contains the main advantages of these methods and allows one to interpret spatial data represented as a tensor. The developed concept proposes an approach to field visualization based on drawing superellipse glyphs at each point in space. Axes of glyphs correspond to the rank of the visualized tensor, and attribute values are expressed by varying the color gradient in its monochrome representation. The architecture of a web-based application, which implements the storage of spatial data in a hybrid DBMS, and interpretation of the necessary data upon request is also proposed. For the physical storage of tensor information, a hybrid relational-hierarchical model is proposed, in which the representation of direct spatial data and the corresponding metadata, including those characterizing the source of information, are separated. It is advisable to present the final result and intermediate data in GeoJSON format. This format is actually currently the standard for presenting geospatial data and is supported by almost all software platforms and libraries aimed at solving problems of this kind.

Conceptualising performance in public services co-production: from ideas to measures in neighbourhood watch schemes

PurposeThis paper delves into a pivotal juncture within the co-production literature, intersecting with the ongoing debate about performance challenges in public sector accounting scholarship. It explores how public managers conceive and measure the performance of co-produced public services.Design/methodology/approachA case study is conducted on three instances of neighbourhood watching – that is, a type of collective co-production – in a homogeneous institutional setting. The analysis and interpretation of empirical data are guided by a systematic conceptual space delineating the qualities that performance criteria can take in contexts where public services are produced.FindingsFindings reveal that when the co-production activation is driven by both state and lay actors, public managers tend to conceptualise and measure its performance in a way that contributes to building a more structured co-productive space, where the roles to play, how to interact and what to achieve are clearly defined.Originality/valueThis paper breaks new ground by scrutinising the conceptualisation of performance in settings where public services involve actors beyond traditional public administrations. By exploring the diverse “shapes” and meanings that performance can take in co-production arrangements, this paper enriches discussions on how public sector accounting can inform co-production literature.

Ways of interdisciplinary approaches to advocating for nursing home residents with dementia.

To explore how nursing home staff advocate for residents with dementia. Phenomenographic qualitative research. Twenty nursing home staff from four disciplines (six nurses, four physical therapists, five social workers and five care workers) were purposively recruited from three different nursing homes. Data were collected through semi-structured interviews conducted from February 2023 to March 2023, and the analysis followed the sequential steps of phenomenographic analysis. The analysis identified five categories of description: focusing on what happened, finding the gaps in perspectives, how to bridge for finding a common perspective, how to tailor care such that each resident receives equitable care and how to establish interdisciplinary sharing for a consistent advocative pattern. Their structural relationship was also identified as an outcome space. The cyclical advocacy structure illustrated that nursing home staff engage in an ongoing process of advocacy during conflict situations as part of interdisciplinary care, emphasizing continuity of care rather than separate occurrences of care. This study revealed that, in advocating for residents with dementia, nursing home staff adopted an approach that fosters consistent care and proactive prevention, achieved through the formation of shared knowledge applicable uniformly across similar situations. This study contributes significantly to the continuing education or training of interdisciplinary staff in nursing homes. The revelations of the study hold significance not only for the practical application but also for the theoretical advancement of concepts related to safeguarding the dignity, human rights and personhood of residents with dementia, with the ultimate goal of enhancing their quality of life within nursing homes. Reporting complied with the COREQ criteria for qualitative research. Nursing home directors have contributed to the validation of data analysis and interpretation.

Field testing of user-friendly perennial malaria chemoprevention packaging in Benin, Côte d’Ivoire and Mozambique

BackgroundPerennial malaria chemoprevention (PMC) aims to protect children at risk from severe malaria by the administration of anti-malarial drugs to children of defined ages throughout the year. Sulfadoxine-pyrimethamine (SP) has been widely used for chemoprevention in Africa and a child-friendly dispersible tablet formulation has recently become available.MethodsThis qualitative non-interventional observational study was conducted in Benin, Côte d’Ivoire, and Mozambique between February and June 2022. Prototype blister packs, dispensing boxes and job aids designed to support dispersible SP deployment for PMC were evaluated using focus group discussions (FGD) and semi-structured in-depth individual interviews (IDI) with health authorities, health personnel, community health workers (CHWs) and caregivers. The aim was to evaluate knowledge and perceptions of malaria and chemoprevention, test understanding of the tools and identify gaps in understanding, satisfaction, user-friendliness and acceptability, and assess the potential role of CHWs in PMC implementation. Interviews were transcribed and imported to ATLAS.ti for encoding and categorization. Thematic content analysis used deductive and inductive coding with cross-referencing of findings between countries and participants to enrich data interpretation. Continuous comparison across the IDI and FGD permitted iterative, collaborative development of materials.ResultsOverall, 106 participants completed IDIs and 70 contributed to FGDs. Malaria was widely recognised as the most common disease affecting children, and PMC was viewed as a positive intervention to support child health. The role of CHWs was perceived differently by the target groups, with caregivers appreciating their trusted status in the community, whereas health authorities preferred clinic-based deployment of PMC by health professionals. Empirical testing of the prototype blister packs, dispensing boxes and job aids highlighted the context-specific expectations of respondents, such as familiar situations and equipment, and identified areas of confusion or low acceptance. A key finding was the need for a clear product identity reflecting malaria.ConclusionSimple modifications profoundly affected the perception of PMC and influenced acceptability. Iterative quantitative investigation resulted in PMC-specific materials suited to the local context and socio-cultural norms of the target population with the aim of increasing access to chemoprevention in children most at risk of severe malaria.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): −37.8–107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: −68.4–73.4 min) and 32.1 min (95% LoA: −54.4–118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19–2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16–1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Fortifying Transparency: Enhancing Corporate Governance through Robust Internal Control Mechanisms

Purpose: This study explores how robust internal control mechanisms enhance corporate governance by fostering organizational transparency and accountability. The focus is on the interplay between internal controls and corporate governance practices, emphasizing their collective role in promoting organizational success and sustainability. Research Design and Methodology: The study employs a qualitative approach, conducting a comprehensive literature review of peer-reviewed articles, books, and reports. Using relevant keywords, data were collected from academic databases such as PubMed, Scopus, Web of Science, and Google Scholar. Thematic analysis was used to categorize and synthesize recurring themes and patterns, ensuring objectivity and rigor in data interpretation. Findings and Discussion: The findings of this study have significant practical implications. They reveal that effective corporate governance frameworks, underpinned by robust internal control mechanisms, not only enhance transparency, accountability, and ethical conduct but also mitigate risks, safeguard assets, and ensure compliance with regulations. This underscores the real-world relevance of the research and its potential to guide organizational practices. Implications: This study highlights the need for organizations to comprehensively integrate internal control mechanisms into their governance frameworks. It also emphasizes the importance of fostering a culture of integrity, adopting technological innovations, and maintaining compliance with evolving regulations. However, the study also points to the need for future research, particularly longitudinal studies, and interdisciplinary approaches, to further understand the dynamic governance-control relationship. This opens opportunities for further exploration and the development of more effective corporate governance practices.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Once upon a Time, There Was a Piece of Wood: Present Knowledge and Future Perspectives in Fungal Deterioration of Wooden Cultural Heritage in Terrestrial Ecosystems and Diagnostic Tools.

Wooden Cultural Heritage (WCH) represents a significant portion of the world's historical and artistic heritage, consisting of immovable and movable artefacts. Despite the expertise developed since ancient times to enhance its durability, wooden artefacts are inevitably prone to degradation. Fungi play a pivotal role in the deterioration of WCH in terrestrial ecosystems, accelerating its decay and leading to alterations in color and strength. Reviewing the literature of the last 25 years, we aimed to provide a comprehensive overview of fungal diversity affecting WCH, the biochemical processes involved in wood decay, and the diagnostic tools available for fungal identification and damage evaluation. Climatic conditions influence the occurrence of fungal species in threatened WCH, characterized by a prevalence of wood-rot fungi (e.g., Serpula lacrymans, Coniophora puteana) in architectural heritage in temperate and continental climates and Ascomycota in indoor and harsh environments. More efforts are needed to address the knowledge fragmentation concerning biodiversity, the biology of the fungi involved, and succession in the degradative process, which is frequently centered solely on the main actors. Multidisciplinary collaboration among engineers, restorers, and life sciences scientists is vital for tackling the challenges posed by climate change with increased awareness. Traditional microbiology and culture collections are fundamental in laying solid foundations for a more comprehensive interpretation of big data.