MzSTools is a plugin for QGIS developed by the Institute of Environmental Geology and Geoengineering of the National Research Council (CNR-IGAG). The plugin has been designed as a set of practical and easy-to-use tools to carry out seismic microzonation (SM) studies, by producing standard compliant geographic database and maps, thus making them accurate, homogeneous and uniform for all municipalities in Italy. A geodatabase based on SQLite/SpatiaLite Relational Database Management System (RDBMS) has been designed to collect and store data related to elements such as: geognostic surveys; bedrocks and cover terrains; superficial and buried geomorphological elements; tectonic-structural elements; elements of geological instability such as landslide zones, liquefaction zones and zones affected by active and capable faults; homogeneous microzones in seismic perspective, microzones characterized by a seismic amplification factor. MzSTools assembles in a single software environment a set of useful tools in a configurable QGIS project template, comprising layers, symbol libraries, cartographic styles and print layouts for the SM maps. The plugin is open source and hosted on the GitHub platform, and available via the official QGIS plugins repository (https://plugins.qgis.org/plugins/MzSTools/).

Despite energy-related financial concerns and the growing demand for sustainability, many energy efficiency measures are not being implemented in industrial practice. There are a number of reasons for this, including a lack of knowledge about energy efficiency potentials and the assessment of energy savings as well as the high workloads of employees. This article describes the systematic development of an expert system, which offers a chance to overcome these obstacles and contribute significantly to increasing the energy efficiency of production machines. The system employs data-driven regression models to identify inefficient parameter settings, calculate achievable energy savings, and prioritize actions based on a fuzzy rule base. Proposed measures are first applied to an analytical real-time simulation model of a production machine to verify that constraints required for the specified product quality are met. This provides the machine operator with the expert means to apply proposed energy efficiency measures to the physical entity. We demonstrate the development and application of the system for a throughput parts cleaning machine in the metalworking industry.

The aim of this study was to determine the technical potential of the Polish Exclusive Economic Zone and to identify potential areas for offshore wind development. The paper proposes a GIS-based methodology in which a unique set of constraint-related criteria was employed to select the bests sites. These criteria included wind velocity, water depth, distance from nature conser-vation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, distance from coastline. The Analytic Hi-erarchy Process was employed to indicate the best locations. Accordingly, the total suitable marine area of 16218.67 square kilometers was divided into three parts, in which the highly suitable area accounted for 17.55%, the moderately suitable area was 48.56%, and the marginally suitable area was 10.26%. Two different spatial configurations for the deployment of individual wind turbines were considered to assess the offshore wind potential. First configuration could accommodate 3664 turbines with overall electrical capacity equal to 54.96 GW, whereas the second one 2347 turbines with overall electrical capacity equal to 35.2 GW. The technical potential only in highly suitable areas was equal to 15.24 GW and 9.15 GW, respectively. The results were presented in the form of maps over the domain covering with spatial resolution of 1 km x 1 km.

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to implement appropriate measures. These measures typically lead to an increased system complexity, which has to be addressed during operation. Whereas existing approaches from the field of supervisory and optimal control appear to be capable of mastering this issue, these are still not adopted in industry. Therefore, this work presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy supply systems. The procedure model combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies. By splitting the procedure model into five phases, expert knowledge can be integrated in a target-oriented manner. Subsequently to the description, the procedure model is exemplary applied to the example of an industrial heating supply system. Throughout an optimization study, the developed operating strategy is compared to a MPC strategy as well as a baseline strategy. Whereas the conventional MPC approach is the upper bound with regard to optimality, the developed operating strategy is able to generate comparable results. Compared to the baseline strategy, a relative reduction in operating expenses of 5.4 % to 37.0 % are achieved in this specific use case.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

The integration of 3D city models with Building Information Models (BIM), abbreviated as GeoBIM, facilitates improved data support to several applications, e.g. 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e. harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

For the purpose of improve the sustainability of products sold in the EU, Digital Product Passports is a mandatory solution. These passports would systematically gather information about products, such as detailing the materials involved to aid recycling and remanufacturing efforts, or recording the emissions produced throughout the manufacturing process. The overall goal is the establishment of a circular economy in the EU. Current research on Digital Product Passports is focused on collating content for sector-specific applications. However, achieving sustainability goals through Digital Product Passports will require new methods and technical solutions for digital collaboration between supply chain participants, especially in the manufacturing sector. Collaborative work on Digital Product Passports requires trust and traceability in data and access management to ensure privacy over the data contained. One way to enable this securely and confidentially is through the use of blockchain technology, in which stored data cannot be concealed, which is key to the traceability of digital product data documentation. This paper therefore presents a concept for the implementation of generically applicable cross-industry blockchain-based digital product passports. To this end, generic supply chain processes have been abstracted and condensed into the architecture of smart contracts. Great attention is paid to the establishment of traceability as well as privacy through the selection of the blockchain technology Secret Network in combination with Gaia-X functionalities. An illustrative use case for digital product passports in manufacturing is used to demonstrate the applicability of the presented concept. In this use case information about the digital nameplate and carbon footprint is exchanged securely and confidentially in a multi-tenant supply chain network based on the Industry 4.0 infomation model of the Asset Administration Shell.

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

In traditional databases, the entity resolution problem (which is also known as deduplication), refers to the task of mapping multiple manifestations of virtual objects to its corresponding real-world entity. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual object appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, even more challenging problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult---if not impossible---to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a Remote Sensing (RS) approach integrated with GIS to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mit-igating floods and preventing overtopping failure at the Wivenhoe Dam. Using QGIS software and a 25-meter resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied to identify the most optimal option based on dam wall di-mensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria, yielding one optimal choice. The study provides insights into the feasibility and ef-fectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation.

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

In the decarbonization of the steel, cement, and chemical industry in Germany, green hydrogen is expected to play a crucial role. The utilization of green hydrogen in the production processes of said industries requires organizations to modify their business model, requiring sustainable business model innovation (SBMI). Numerous tools and frameworks that support organizations in the process of SBMI have been proposed in the literature in recent years. However, the applicability of these tools and frameworks for steel, cement, and chemical companies that intend to utilize green hydrogen to produce their goods remains unexplored. By conducting a systematic literature review on tools and frameworks for SBMI, a literature and practice review to identify and analyze existing green hydrogen projects of steel, cement, and chemical companies, and an evaluation of the identified tools and frameworks in an evaluation matrix, this paper aims to assess the suitability of SBMI tools and frameworks for steel, cement, and chemical companies that plan to use green hydrogen to produce their goods. Based on the evaluation, the Cambridge Business Model Innovation Process (CBMIP) was identified as the most suitable SBMI framework.

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being “shared” between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour, before considering future directions in the field.

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

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

Mitochondria are semiautonomous organelles, which are pivotal for the energy metabolism of eukaryotic cells. These organelles contain their own genetic system (mtDNA, mitogenome) that encodes to some structural RNAs and organellar proteins. The biogenesis and function of mitochondria is tightly regulated by nuclear-encoded protein cofactors. The mtDNAs of land plants are characteristically large, with a complex mode of gene organization and expression, in particularly at the post-transcriptional level. To become functional, the primary organellar transcripts undergo extensive maturation steps, which include endo- and exo-nucleolytic cleavage, RNA-base editing, and both ‘cis’- and ‘trans’-splicing events. These essential processing steps rely on the activities of a large set of nuclear-encoded factors. Among these, the RNA helicases serve as key players in organellar (mt-)RNA metabolism. This review summarizes the important roles played by these factors in regulating the highly dynamic processes of transcription, RNA processing, and translation within the mitochondria of land plants. We further discuss recent advancements in understanding how dysregulation of mitochondrial RNA helicases affects the organellar gene expression, respiratory functions, and hence the growth, development and physiology of land plants.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

The article presents a model for optimizing biomass supply chains using the linear programming framework integrated with a Geographic Information System. Based on the distance from a given type of biomass resource, its calorific value, price, and transportation costs, the model identifies the optimal source of biomass, allowing it to cover the demand for the required total energy value with the lowest possible costs. The case study includes the Połaniec power plant in southeastern Poland and potential sources of forest biomass and agricultural straw within 100 km of the plant. Unit costs of biomass varied depending on biomass availability and energy demands. For energy demand on the level of 1 PJ yearly, the lowest unit costs of biomass (4.08 €/MJ) were when all kinds of biomass were available, and the highest (5.47 €/MJ) was when only stacked wood was available. As energy demand increased, unit costs increased, and the ability to meet this demand with just one type of biomass decreased. The energy biomass sector can utilize the model to benefit both biomass producers and their final buyers.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

This review aims to consolidate varied information regarding Port Scanning, and examine the tools, techniques, and detection algorithms used. It explores TCP SYN, Full TCP, UDP, and stealth scans, and shows their functions and abilities. Paired with these techniques, it also shows the tools that they can be used by, such as NMap, Zmap, and MASSCAN. It discusses the various detection techniques such as Signature and Anomaly based detection, and discusses the real world impact of this technology, while commenting on the legal and ethical issues posed.

Molecular visualisation is fundamental in the current scientific literature, textbooks and dissemination materials, forming an essential support for presenting results, reasoning on and formulating hypotheses related to molecular structure. Visual exploration has become easily accessible on a broad variety of platforms thanks to advanced software tools that render a great service to the scientific community. These tools are often developed across disciplines bridging computer science, biology and chemistry. Here we first describe a few Swiss Army knives geared towards protein visualisation for everyday use with an existing large user base, then focus on more specialised tools for peculiar needs that are not yet as broadly known. Our selection is by no means exhaustive, but reflects a diverse snapshot of scenarios that we consider informative for the reader. We end with an account of future trends and perspectives.

This study employs Geographic Information System (GIS) techniques to evaluate the walkability of Onaizah City, which serves as a representative model for a Saudi city situated in the Qassim region, north of the capital city, Riyadh. The goal is to evaluate how easily pedestrians can navigate the street network and identify areas that excel or need improvement in terms of walkability. The results offer insights for planning and development supporting sustainable initiatives. Approach; The study involves integrating GIS data, such as street layouts, land use patterns, and demographic information. By analyzing factors like integration values and choice values within the street network the study gauges’ walkability aspects. Furthermore, correlation analysis explores how spatial arrangements, influence walkability. By studying the case of a medium-sized Saudi city, which can then be applied to several Saudi cities. Cost distance analysis and cost-back correlation calculations are employed to determine pedestrian routes. Findings and Discussion; Through GIS techniques the study effectively assesses the walkability of Onaizah City by identifying three walking paths with lengths of (1401m, 1283m, and 1507m). The study reveals pedestrians' actual city distance. Weighted criteria assign 40% to heritage sites, 30% to green spaces, and 15% each to mosques/commercial sites. Preserving access to heritage sites and incorporating areas enhance walkability. Conclusion: Study enhances walkability understanding, offering data-driven planning suggestions. Urban planners prioritize pedestrian connectivity, considering GIS effectiveness and physical environment importance. Findings support walkability research and sustainable urban development.

Highway alignment is an essential part of the highway planning and design phase. Highways are impacted by existing projects and surrounding context. The isolation of geo-technical analysis from highway planning and design also delays the planning process. This study therefore proposes a model that integrates building information modelling (BIM) and geographic information science (GIS) capabilities to facilitate the planning and design process. Semantic web technologies are used to integrate BIM and GIS data on a semantic level. The proposed model also helps to identify geohazards by providing geological analysis. The visualization of the proposed project can help reduce design errors and miscommunication, which, in turn, reduces project risk. In addition, the model facilitates highway alignment optimization by incorporating visualization, simulation, and analysis into the planning and design phase. The proposed model provides future opportunities for project professionals to have organized, reliable and dynamic ways to manage the project during construction.

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

Wetlands are recognized as one of the world’s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways.

In the last five years, European research and innovation programmes have prioritised the development of online catalogues and tools (handbooks, models, etc.) to facilitate the implementation and monitoring of Nature Based Solutions (NBS). However, only a few catalogues and toolkits within European programmes are directly related to mainstreaming of NBS for food production (i.e., edible NBS). Therefore, the main aim of this paper is to present existing NBS tools through the eyes of productive urban landscapes. We reviewed 32 projects related to NBS and 50 tools were identified and characterised. Then, the 6 tools already available, and providing indicators, were further analysed in terms of their format and knowledge domains. Our main conclusion demonstrates that there is a lack of tools capable of supporting users for planning and implementing edible NBS, calculating the food potential of the city and/or of individual edible NBS, including the needed resources for implementation and operation (water, nutrients, energy), and assessing their urban design value, environmental and socio-economic impacts. And when they do exist, there is a resistance to share the models and equations behind the tools to allow other projects to reuse or validate them, fact which is contrary to Open Science principles stood up by many research public agencies.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

We hypothesize that the first ancestral "ante-cell" molecular structures, i.e., first RNAs and peptides which gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear there, have leaved traces in RNAs and genes of present cells. We propose a circular RNA which could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the ante-cells have begun to join the living organisms. In particular, we propose that in present RNAs (ribosomal or messenger) playing an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) belonging to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in evolution of species.

Metabolomics is now considered to be a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense response to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in molecular breeding and genome editing programs to develop superior next generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted method. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) and matrix-assisted laser desorption/ionization (MALDI) have speed up metabolic profiling in plants. Further, we deliver a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula river. Using image processing procedures, 2D and 3D shape descriptors were determined. The Principal Components Analysis (PCA) was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed 3 or 4 main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (CSF, Compactness, Dimensionality). Considering the scraps, PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

.(1) Background: Groundwater resources management in dry lands, characterized by climate var-iability and population growth, is difficult. Exploration and exploitation of groundwater, due to inadequate surface water is very costly. The present study employed Analytic Hierarchy Process (AHP) and GIS to identify Ground-Water Potential (GWP) areas in a semi-arid region of Nigeria; (2) Methods: Land-use-land-cover, drainage density, slope, rainfall, static water level, soil media, vadose media and aquifer media were selected for GWP analysis. Parameters weights were de-termined using AHP and ranked based on their contribution to GWP by experts. The parameters were then integrated using weighted overlay tool in ArcGIS 10.5 to produce GWP map of the study area. Boreholes yield data from 245 wells were collected to determine the model accuracy and model validation; (3) Results: Results classified the study area into very high GWP (1.9%), high GWP (8.8%), moderate GWP (62%), low GWP (20.70%) and very low GWP (6.6%). Validation of the AHP model with boreholes yield data shows a correlation coefficient of 71.3% giving a good prediction; (4) Conclusions: AHP and GIS can be used to successfully map GWP areas which could serve as an exploration guide for sustainable management of groundwater resources in semi-arid areas.

The use of Geographical Information Systems (GIS) for biodiversity monitoring and conservation, shortened to Conservation GIS, is an influential tool that has revolutionized conservation efforts by providing spatially explicit data to inform conservation decision-making. However, Conservation GIS also faces challenges related to data quality and availability, technical limitations, as well as policy and governance issues. The diverse and rising uses of GIS for conservation has resulted in a need for continued research and development of Conservation GIS, including advancements in technology and data collection, integration with other fields such as machine learning and artificial intelligence, and collaborative approaches to Conservation GIS. Conservation GIS has the potential to make a significant impact on conservation efforts, and this article emphasizes the importance of its implementation to achieve the broader goal of biodiversity conservation for planetary health. In this review, we discuss the various applications of Conservation GIS, including biodiversity conservation and monitoring, habitat mapping and restoration, climate change mitigation and adaptation, wildlife tracking and management, and protected area management. We highlight some of the key knowledge gaps or research questions that need to be addressed in the future to further advance the field of Conservation GIS. By addressing these challenges and knowledge gaps, Conservation GIS can become a powerful tool for addressing the challenges facing biodiversity and ecosystems worldwide, and for promoting sustainable land-use practices.

As noted in the introductory quotation, an ideal map was long ago seen as the map of the map, the map of the map, of the map, and so on endlessly. This recursive perspective on maps, however, has received little attention in cartography. Cartography, as a scientific discipline, is essentially founded on Euclidean geometry and Gaussian statistics, which deal with respectively regular shapes, and more or less similar things. It is commonly accepted that geographic features – such as rivers, cities, streets and building – are not regular and that the Earth’s surface is full of fractal or scaling or living phenomena with far more small things than large ones at different levels of scale. This paper argues for a new paradigm in mapping, based on fractal or living geometry and Paretian statistics, and – more critically – on the new conception of space, conceived and developed by Christopher Alexander, that space is neither lifeless nor neutral, but a living structure capable of being more living or less living. The fractal geometry is not limited to Benoit Mandelbrot’s framework, but is extended towards Christopher Alexander’s living geometry and based upon the third definition of fractal: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Paretian statistics deals with far more small things than large ones, so it differs fundamentally from Gaussian statistics, which deals with more or less similar things. Under the new paradigm, I make several claims about maps and mapping: (1) Topology of geometrically coherent things – in addition to that of geometric primitives – enables us to see a scaling or fractal or living structure; (2) Under the third definition, all geographic features are fractal or living, given the right perspective and scope; (3) Exactitude is not truth – to paraphrase Henri Matisse – but the living structure is; and (4) Töpfer’s law is not universal, but scaling law is. All these assertions are supported by evidence, drawn from a series of previous studies. This paper demands a monumental shift in perspective and thinking from what we are used to on the legacy of cartography and GIS.

The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to a) transportation infrastructure b) inhabited areas and c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and GIS techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those, which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.

The recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) tools has had a pervasive effect on every sphere of human existence. Libraries and information centres are not immune to these technological advances. These disruptive technologies have transformed the traditional face of libraries and their operations. There are many available AI tools, for example, chatbots, literature review tools, plagiarism detection tools, and several other developments in the field that have perhaps made the functioning of libraries easier. However, there is growing concern and uncertainty among both traditional library and modern professionals regarding the possible job loss and security issues related to these new technologies, which the professionals must deal with. Librarians and information professionals must be ready to handle the unforeseen situation. However, it is to be observed how libraries cope with these uncertainties, and threats to the integrity, availability of data, and normal library housekeeping functions.

Targeted Protein Silencing (TPS) is an elegant approach to investigate protein function and its role in the cellular landscape, overcoming limitations of genetic perturbation strategies. In contrast to CRISPR/Cas9 and RNA interference, these systems act in a reversible manner and reduce off-target effects. Several TPS have been developed and wisely improved, including compartment delocalization tools and protein degradation systems. In this review, we focus on Anchor-Away, deGradFP, auxin inducible degron (AID) and dTAG technologies, and discuss their recent applications and advances. Finally, we propose Nano-Grad, a novel nanobody-based protein degradation tool to specifically proteolyze endogenous tag-free target protein.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

Electric torque wrenches play a crucial role in the assembly of high-strength bolts. This study focuses on three assembly processes for high-strength bolts and introduces a statistical analysis method to assess the quality of the bolt assembly. To enhance the performance, a novel, fully digital, Internet of Things (IoT) electric torque wrench is designed, incorporating a smart meter chip. This wrench achieves integration between the current sensor control method and the torque sensor control method using a simplified hardware circuit, ensuring high precision and stability. Additionally, this study employs the AC voltage self-stabilization method and Savitzky–Golay filtering method to enhance the accuracy and robustness of the wrench. Simulation experiments using Simulink and Matlab validate the effectiveness of these two methods. Furthermore, a cloud platform is utilized to enable rapid IoT access for the wrench, facilitating seamless connectivity and data transmission.

An information-theoretic data mining method is employed to analyze categorical spatiotemporal Geographic Information System land use data. Reconstructability Analysis (RA) is a maximum-entropy-based data modeling methodology that works exclusively with discrete data such as those in the National Land Cover Database (NLCD). The NLCD are organized into a spatial (raster) grid, and are available in consistent format for every five years from 2001 to 2021. An NLCD tool reports how much change occurred for each category of land use; for the study area examined the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. When the predicting cells are all Shrubs & Grasses there is a high probability for a 2021 state of EFO, while when the predicting cells are all EFO, there is a high probability that the 2021 state will be not-EFO. These findings are interpreted as detecting forest clear-cut cycles that show up in the data, and explain why this class is so dynamic. This study introduces a new approach to analyzing GIS categorical data and expands the range of applications that this entropy-based methodology can successfully model.

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

Geographic information systems (GIS) are critical for mapping the geographical distribution of illness prevalence, disease transmission patterns, and spatially modeling environmental elements of disease occurrence. This study was purposively to map and cluster five diseases such as cholera, diabetes, malaria, asthma and hypertension using GIS techniques in Southern Malawi. Getis-Ord Gi* statistic was used to determine spatial distribution of diseases. Data were acquired from Malawi Demographic Health Survey (DHS) and assessed geographically using spa-tial-statistical analytical techniques, including the kernel and hotspot models. These models were developed to investigate and exhibit the dissimilar patterns of the selected diseases, as well as to depict places of high concentration, and they may be useful in understanding local patterns of diseases and their geographical correlations. The study found that asthma is high in Mangochi, Balaka, Blantyre, Thyolo, Mulanje, Chikwawa and Nsanje districts. It was also revealed that hypertension patients were highly concentrated in Blantyre, Thyolo, Chikwawa, Zomba, Mangochi and Nsanje Districts. Mangochi, Phalombe, Nsanje and Chikwawa are high risk areas of Malaria and diabetes is highest in Blantyre. Cholera is high in Mangochi, Balaka, Blantyre, Thyolo, Mu-lanje, Chikwawa and Nsanje districts.

Peri-urban forests often have extensive networks of greenways that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of flora and fauna and often overlook the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is not as high as expected. Having a quantification of their naturalness would be useful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on Geographic Information Systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index, while this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analysing the degree of naturalness of the trails, but if used together, it would be possible to add flexibility to the type of variables incorporated in the analysis.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Streptococcus pneumoniae is the causative agent of a multitude of diseases and further study into its pathogenies is vital. The pneumococcus is genetically malleable and several tools are available to manipulate this pathogen. In this study, we attempted to utilize one such tool, the Sweet Janus cassette, to replace the capsule locus with other capsule loci in our strain background and found that the efficiency of allelic replacement was low and the number of revertant false-positive colonies was high. We determined that the capacity to recombine capsule varied by the initial isolated colony, suggesting that frequency of reversion is dependent on the bacterial cell line. Alternative selection markers may further expand the application of Sweet Janus. We created novel cassettes that utilized chlorinated phenylalanine as an alternative counter-selection agent in conjunction with the Janus or Sweet Janus cassette, providing a new dual or triple selection marker. Moreover, we created cassettes that do not require engineered resistance in the background strain, including both single and dual selection markers. We were able to utilize all constructs in allelic replacement of the capsule loci. These novel constructs provide a new means for generating gene deletions in S. pneumoniae that expand experimental applications.

In the smart city, information and communications technologies (ICTs) are proposed as solutions to urban challenges, including sustainability concerns. While sustainability commonly refers to economic and environmental dimensions, the concept also contains a social component. Our study asked how smartphone applications (apps) address social sustainability challenges in urban transport, if at all. We focused on transport disadvantages experienced due to low income, physical disability, and language barriers. A review of 60 apps showed that transport apps respond to these equity and inclusion issues in two ways: (a) by employing universal design in general-use apps; including cost-conscious features; and providing language options (b) by specifically developing smartphone apps for persons with disabilities. The article discusses the study by positioning it in the literature of smart cities as well as socially sustainable transport.

This paper introduces an intelligent model that combines military expertise with the latest ad-vancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes. The model is based on direct input and val-idation from field decision-makers for their practical applicability and effectiveness. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes mili-tary-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algo-rithmic precision for maximal safety and effectiveness in demining

This study uses Landsat 8 and Digital Elevation Models (DEM) to show the dominant orientations of digital lineaments and morphotectonic features between measured trends and the tectonic evolution of the Upper Guajira, Colombia, in the northernmost region of the South American plate. Data from Landsat-8 and hillshaded images of three Digital Elevation Model (DEM) images with different resolutions (SRTM: 90m, ASTER-GDEM: 30m and Alos-Palsar: 12.5m), were used for the extraction and mapping of morpholineaments, drainage network and morphological features. Lineaments were analyzed by means of north azimuth frequency, length, density distributions, lithological distributions and geochronological periods. Tectonic control was supported by using the digitized geological map created by the Colombian Geological Service (SGC). Lineaments and faults were analyzed through the interpretation of a Riedel shear model as a result of the transtensional/transpressional tectonic arrangement of the Caribbean and South American plates. The directional trends of lineaments and faults indicate two dominant directions: NE-SW and E-W. The azimuth distribution analysis of measured structures and drainage channels show similar trends, except for some differences in the predominant directions of some drainage channels. The similarity in the orientation of lineaments, faults and drainage system highlights the degree of control exerted by underlying structures on the surface geomorphological features. Drainage channel classification illustrates the morphological and neo-tectonic complexity of the region. The extracted lineaments were divided into five geochronological groups based on the main ages of host rock formations according to the Colombian Geological Service (SGC) geological map. From the Cretaceous onward, the lineament azimuth frequency rotates from a NE-SW trend to a prominent E-W direction, which resembles the translation that Caribbean plate has been undergoing since the Cretaceous. Our results confirm that Remote Sensing techniques are reliable and useful to study the morphotectonic of an area and can be applied to zones of difficult access.

In this world that we share with them, they became unintended sentinels for the consequences of pollutant exposure, developing similar conditions to humans, and even earlier. This review focuses on the human-pet interaction and on the effects of the environment pets share with us. Alongside other species, canine and feline companions are veritable models in human medical research. The latency period for showing chronic exposure effects to pollutants is just a few years in them, compared to considerably more, decades in humans. Comparing the serum values of man's best friends to ours can indicate the degree of poisonous lead-load we are exposed to, for example, and of other substances as well. We can find 2.4 times higher perfluorochemicals from stain- and grease-proof coatings in canine companions, 23 times higher values for PBDEs in cats, and five times more mercury compared to the average levels obtained in humans. All these represent early warning signals. Taking into account all these, and the animal welfare orientation of today’s society, finding non-invasive methods to detect the degree of environmental pollution in our animals becomes paramount, alongside with the need to raise awareness on the risks carried by certain chemicals we knowingly use.

A growing body of evidences is showing that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, with reduction of host damage. Herein, we discuss how diagnostic parasitology may contribute to design clinical metagenomics pipelines and FMT programs, especially in pediatric subjects. The consequences of a more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.

The importance of frailty in older people is getting constant recognition as an important aspect both in terms of public health, as well in personal level for the appropriate management of an older person health condition. This is reflected by the continuously increasing number of research studies carried out in several settings across different countries. Sometimes this is very solid, but in other cases there is a considerable gap in terms of accurate and well grounded documentation of frailty status. This is the case in Greece, where we are missing clinically validated tools to approach frailty. We are missing frailty screening tools, such as for instance PRISMA 7, the gold standard tool of Fried criteria is somehow problematic since the question referring to physical activity originates from a questionnaire that has not been translated and validated, while Clinical Frailty Scale has been validated for the translation but not for the capacity to detect frailty. Aim of this study is to validate these tools for their accuracy to detect frailty by using a measurable index of frailty, previously proposed for use in clinical studies: the Short Physical Performance Battery (SPPB). 74 male and female participants have been evaluated for their frailty status using different tools. We observed that PRISMA 7 translation detects frailty only when one question is removed at a cut-off of ≥ 2 and indicated a sensitivity of 88.1%, and specificity of 99.9% with a good correlation with SPPB measurements (r = 0.858; p < 0.001). When CFS was validated using SPPB it demonstrated a very good correlation (r = -0.838; p < 0.001 respectively) as it was the case for the modified Fried Criteria (r = -0.725; p < 0.001). All items demonstrated a good correlation between them. We can here propose that we can accurately assess frailty status in the community setting by using a modified version of Fried criteria, Clinical Frailty Scale translation in Greek and we can screen for frailty by using the Greek translation of PRISMA 7 only after removing the item 6 of the questionnaire.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

Abstract Background: The highest incidence rate of covid-19 in Iran was reported from Shahroud County. This study was conducted by geographic information systems (GIS) to determine the geographical distribution of Covid-19 in 60 days. Study design: A cross-sectional study Methods: This study was conducted in counties covered by Shahroud University of Medical Sciences, namely Shahroud and Mayami, from February 20, 2020 to April 18, 2020. The GIS can better show the spread of epidemics. This software indicates geographical distribution of disease spread and is very helpful in controlling the epidemics. Therefore, maps of spatial distribution and risk of infection to COVID-19 were prepared in different regions of Shahroud county using Arc-GIS software to better implement health policies. Results: During this sixty-day period, 529 confirmed cases were detected, of which 51% were men and the average age was 55 years. The maps showed high-risk to risk-free regions. Shahroud and Bastam cities were known as the coronavirus hot spots. The eastern region of Shahroud has the highest number of cases but some high risk areas are spread throughout the Shahroud city due to high infectivity of virus. Risk-based time maps indicated a reduction in the risk of infection at the end of the research period due to some mitigation and suppression strategies. Conclusions: Shahroud and Bastam are the most dangerous cities that, the number of patients and dissemination has decreased over time because of tracking definite patients and people in contact with them and implementation of preventive care.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

Reuse of patterns is a self-evident approach for managing interoperability concerns. Although patterns for resolving interoperability barriers exist in the literature, no study exists on adoption of interoperability patterns by Geographic Information Systems (GIS) practitioners in industry. Thus there is limited understanding of pattern re-usability, yet the advantages offered by interoperability patterns provide a reasonably sound justification for their usage. This paper examines the adoption of proven interoperability best practices in the GIS industry. An empirical study that involved the use of semi-structured interviews was employed to gather data from GIS developers on domain interoperability best practices. Results indicated that industry and communities of practice have been converging on the technical level to ensure interoperability of GIS concerns. Semantic interoperability and related patterns are least understood, yet semantic barriers still exist. This is partly due to the complexity associated with the top-down approach used to develop semantic interoperability solutions. Therefore, this study proposes research into resolving barriers in the adoption of interoperability patterns that reduce complexity while solving semantic interoperability barriers.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. Onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skills levels, potentially starting novices with graphical IDEs before introducing Emacs. Key takeaways include literate programming aids comprehension but requires rigorous onboarding and reinforcement; Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.

Ecological corridors function as a viable way to mitigate the environmental impact arising from forest fragmentation by interconnecting forest fragments through various techniques. In this context, the objective of this study is to propose a route for the implementation of an ecological corridor in the Itapemirim river watershed. The specific objectives were: (i) to delimit the permanent preservation areas (APP) of the Itapemirim river watershed and compare land use and land cover in the delimited areas; (ii) to calculate landscape ecology indices and select the forest fragments with the highest potential for ecological corridor implementation using Fuzzy logic; (iii) to assess costs and trace the best route for ecological corridor implementation, considering distance and physical impediments; (iv) to assess land costs and expropriation costs to delimit the ecological corridor in the study area. To map land use and land cover, the MapBiomas platform was used, based on Landsat 8 satellite images. The permanent preservation areas were delimited according to criteria established by Law No. 12.651 of May 25, 2012, which establishes parameters, definitions, and limits for APP. The characterization and structural quantification of some landscape ecology indices were performed using the QGIS 3.26 computational application, through the LecoS 3.0.1 plugin and Fragstats 4.2. The connected forest fragments were the Caparaó National Park, the Serra das Torres State Natural Monument, and the fragments selected through the application of Fuzzy logic to the landscape ecology indices. The corridor was delimited according to the lowest cost route, considering land use and land cover, APP, fragment potential, slope, and subnormal clusters. For each cost raster image, its respective statistical weights were calculated using the Analytic Hierarchy Process (AHP) hierarchical method, as well as for the analysis of priority areas for forest restoration, considering land use and land cover, APP, pedology, lithology, and biological importance. The interconnection between protected areas and fragments with the ecological corridor followed the orientation described by CONAMA nº 09/96. Based on the development of the work, the following results were identified: the highest land use and land cover class is pasture. Of the area designated for permanent preservation, 68.58% is in conflicting use with the legislation. The bare land value per hectare of the pasture class is the second highest among the bare land value per LULC values, representing 64.28% of the total. The priority area map showed that 31.86% of the area was classified as of very high or high importance and 42.97% as low or very low priority for forest restoration. Thus, it is concluded that the least cost path algorithm associated with the result generated by the multi-criteria decision method (AHP) constitutes an important tool for planning and implementing an ecological network by taking into account the primary factors for decision-making regarding the location of the best route.

The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled the sky in the teraelectronvolt regime, initiating the so-called “TeV revolution” at the beginning of the new millennium. This revolution was also facilitated by the implementation and adaptation of statistical tools for analyzing the shower images collected by these telescopes and inferring the properties of the astrophysical sources that produce such events. The image reconstruction techniques, the background discrimination and the signal-detection analyses are just a few of the pioneering studies applied in the last decades in the analysis of IACTs data. This (succinct) review has the intent of summarizing the most common statistical tools that are used for analyzing data collected with IACTs, focusing on their application in the full analysis chain, giving references to the past literature for a deepened examination.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

Flood events are becoming more severe, causing significant problems to human communities, including physical, psychological, and material damage. For both flood forecasting in emergency response situations and flood mapping, georeferencing and data curation are paramount in the context of prevention or preparedness. Hence, data display, data management, and articulation with numerical simulation results must occur on GIS platforms. Our research is motivated by recent advances in Web and GIS technologies, social sensing and high-performance computing, and an envisaged wider availability of remote sensing data. This paper presents and discusses an innovative Web GIS platform named "RiverCure Portal" or "RCP" for short. This platform combines observations and hydrodynamic modelling tools to support various stages of the flood risk management cycle, including operational response, emergency preparedness, and risk assessment. RCP is a multi-organisation, multi-context digital platform with flexible configuration features to define and support multiple sensor types and modelling options, satisfying the various needs of different organisations and stakeholders. In addition, this paper discusses the RiverCure Approach, which encompasses the following tasks directly supported by the RCP platform: defining the context and involved geometries, associating sensors to the context, pre-processing and generating the context mesh, defining the simulation event, running the simulation event, and analysing the results from the simulation event. Thus, the RCP streamlines and simplifies data analysis and simulation procedures to meet decision-makers' needs. The novelties discussed in this paper include the design and discussion of a Web GIS platform that allows (i) to manage flood data and results of simulations at several contextual levels by different stakeholders such as domain experts, decision-makers, researchers, or students; (ii) to process and curate sensed data obtained from physical and social sensors; and (iii) update the state and values of the parameters of simulation tools through continuous data assimilation techniques for forecasting purposes. Finally, this paper supports the explanation and discussion with a running example, "Águeda 2016 flood" event, which dataset is publicly available for further study and experimentation.

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

The paper summarizes in a step by step ‘recipe’ format a model of developing new ideas (Ideation) that can be used to form a new venture or develop a new product. The proposed ‘recipes’ are widely used and can be applied in tandem with other prevalently available models or tools. The intention of the paper is also to serve as a resource for entrepreneurs and innovators, and the links and references munificently used in the paper are serving this purpose. The author has used the outline and the tools described here in numerous Ideation classes taught for over a decade in several countries around the world. The paper’s intention is not to conduct a systematic review on the subject of new venture creation or propose new research agenda (for that, see for example Shepherd et al., 2021). The purpose is to provide an effective and efficient set of tools, models and techniques that can assist the entrepreneur in her journey to create and achieve her dreams. One such tool is the matrix of Reasonings-Outcomes introduced here for the first time.

Many manufacturing sectors require high surface finishing. After machining operations such as milling or drilling, undesirable burrs or insufficient edge finishing may be generated. For decades, many finishing processes have been handmade-basis; this fact is accentuated when dealing with complex geometries especially for high value-added parts. In recent years, it’s a tendency of trying to automate as far as possible this kind of processes, repeatability and time/money savings are main purposes. Based on that idea, the aim of this work is to check new tools and strategies for finishing aeronautical parts, especially critical engine parts made on Inconel 718, a very ductile nickel alloy. Automating edge finishing of chamfered holes is a complicated but really important goal. In this paper, flexible abrasive tools were used for this purpose. A complete study of different abrasive possibilities was carried out, mainly focusing on roughness analysis and final edge results obtained.

Malassezia species are part of human commensal microbiota and is also related to diseases. Little is known about the interaction of these microorganisms with their host. Here we established two standard culture conditions for Malassezia spp. to perform infection assays using C. elegans and T. molitor. Invertebrate hosts infected by Malassezia spp. cultured in M9M resulted in higher death rate on survival assays when compared to yeasts cultured in standard Dixon medium indicating that M9M cultured Malassezia species have increased virulence. The culture and infection conditions established in this work, using invertebrate alternative models, are valuable tools to understand Malassezia-host interaction.

Green spaces take part a vital role in reducing the harmful effects occurred through the process of rapid urbanization. This study focuses on evaluating the change of green space and per capita green space in Colombo District, Sri Lanka in between 2008 and 2015 using Landsat images. The NDVI differencing method and classification method were used to detect the change of land cover. According to the results, a gradual decline of green space from 629.1km2 to 591.16km2 with a rate of 6.03% can be depicted. The change is considerable in Kaduwela, Moratuwa and Maharagama DSDs and quite better in the areas such as Avissawella, Homagama and Padukka. The per capita green space has been decreased from 272.361 m2 to 248.811 m2. Though these rates are very well higher than the UN, EU and WHO standards for the district, Colombo DSD do not comply with UN and EU standards. The case would turn into worst in 2025, as the predictions with current rates. Therefore, the study recommends taking immediate actions to make a greener city. Further, a tax system is proposed to get the attraction of the general public.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

Green open spaces (GOS) are well known to be vital to enhancing the standard of living conditions in urban environments. The World Health Organization (WHO) says it is important to have at least nine square meters per citizen. The European Environment Agency (EEA) stipulates that 5000 square meters of green space will be reachable within 300 meters to residents. GOS pattern in Kyrenia Region is not adequate or successful in that regard. The process of urban planning has been neglected in the Kyrenia region and these areas have lost their significance and value. This research evaluates the level of accessibility of green spaces in Kyrenia Region. Three urban areas, Kyrenia, Karavas and Agios Epiktitos have been analyzed within this framework. In order to do that, GOS areas and their pattern are researched by deploying geographical information analysis. Accessibility to these areas is measured by using Euclidean and network distance analysis based on 300, 800 and 5000 meters. Mainly, Euclidean distance and network distance methods are measured from building units and results showing accessibility values are constructed. According to the results, network accessibility is very low within the study region. Additionally, Kyrenia, Karavas and Agios Epiktitos have shortage in GOS per capita. To offer effective strategies for urban planning, the factors influencing the accessibility and also the usage of GOS are addressed.

Wind energy is one of the most attractive renewable energy sources because of its low operating, maintenance, and production costs as well as its low environmental impact. The goal of this study is to discover the best locations in Bangladesh where wind farms can be built and operated efficiently. This study applied the GIS and AHP methodologies to examine the eight important parameters upon which the suitability of locations is highly dependent. This analysis finds that Bangladesh has large regions appropriate for wind farm installation, with 3718.76 km² and 16631.14 km² classified as "very high" and "high" suitability, respectively. It was also observed that wind speed, land slope, and elevation each had a height-weighted criterion of 32 %, 27 %, and 12 %, respectively, when picking suitable locations. However, the viability of this study in identifying suitable sites has been evaluated based on ROC and AUC techniques and found satisfactory as per AUC value. The knowledge gained from this study will help the sustainable and renewable energy development authority (SREDA), Bangladesh to expedite the renewable energy investment process and will ensure the great certainty. The findings of this research can be considered as baseline information in the wind energy sector.

This study identified high-risk locations (hotspots), using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013-2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. Accident concentration analysis was carried out using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I Statistics (Spatial Autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and Network spatial weight matrix approaches of Getis-Ord Gi* statistic for hotspot analysis were used for the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95% - 99%. However, no hotspots exist for 2014 and 2015 since the pattern is random. The spatial autocorrelation analysis of the overall accident locations with a z-score = 0.0575, p-value = 0.9542, and Moran's I statistic = -0.0089 showed that the distribution of accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the Northbound and Southbound directions of the Abaji-Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features.

Rangelands are a key resource present all over the world as it covers half of emerged lands. They are even more important in drylands, where they cover 48% of the total area. Intensification and the additional pressure added by climate change push these systems towards desertification. Over the last two decades, we have developed and applied System Dynamics (SD) models for the study of Mediterranean grasslands. In addition, we have developed procedures and analysis tools, such as global sensitivity analysis, stability analysis condition, or risk analysis, in order to detect the main drivers of these socio-ecological systems and provide indicators about their long-term sustainability. This paper reviews these works, their scientific background, and the most relevant conclusions, including purely technical and rangeland-related ones as well as our experience as systemic modelers in a world driven by field specialists.

Nickel-based superalloys are attractive to many industrial sectors (automotive, military, energy, aerospace etc.). However, their physical properties make them difficult to machining using traditional tools. Therefore, the new materials for the machining of Ni-based alloys are required. Ceramic-based composites could act as a tool to replace the current materials. The incentives for this paper are to provide an overview of existing ceramic composites and draw some conclusions that will help in solving the problem of choosing materials for processing of Ni-based superalloys. Despite the diversity of ceramic composites in this work the focus was on the SiAlON ceramic.

The diagnosis and treatment of autoimmune diseases are major challenges. Their signs, symptoms, and their clinical severity change throughout the course of the disease. The available treatments for these conditions do not result in complete remission, and their long-term use generates several side effects. The main challenges in this field are the development of more sensitive diagnostic tools, and effective treatments. Studies in patients and animal models reported alterations in monocyte phenotype, activation, and function. These cells play an essential role in the pathogenesis of chronic inflammatory states and have become a target for extracorporeal monitoring and specific intervention. Nanoparticles (NPs) are ultrafine particles that can be used as contrast agents in diagnostic imaging techniques to detect specific cells in inflammatory infiltrates in tissues that are not easily accessible by biopsy. In addition, NPs can be designed to deliver drugs to a cell population or tissue. This review describes several experimental pieces of evidence demonstrating that monocytes and macrophages can specifically uptake various types of NPs, thus becoming detectable in vivo by magnetic resonance imaging and susceptible to be modulated by therapeutic agents. Hence, nanoparticles targeting mononuclear phagocytes represent promising tools for diagnosing and treatment.

The multidisciplinary nature of the work required for research in the Covid-19 pandemic has created new challenges for health professionals in the battle against the virus. They need to be equipped with novel tools and resources ---that have emerged during the pandemic--- to gain access to breakthrough findings, know the latest developments, and to address their specific needs for rapid data acquisition, analysis, evaluation, and reporting. Because of the complex nature of the virus, the healthcare systems worldwide are severely impacted as the treatment and the vaccine for Covid-19 disease are not yet discovered. This leads to frequent changes in regulations and policies by governments and international organizations. Our analysis suggests that given the abundance of information sources, finding the most suitable tool for a given task is one of such challenges. But health professionals and policymakers need access to the most relevant, reliable, trusted, and latest information and tools that can be used in their day-to-day tasks of Covid-19 research and analysis. In this article, we present our analysis of various novel and important tools that have been specifically developed during the Covid-19 pandemic and that can be used by the health professionals community to help in advancing their analysis and research. These tools comprise of search engines, information repositories for literature and clinical trials, data sources, dashboards, and forecasting models. We present list of the minimally essential tools to serve a multitude of purposes, from hundreds of those developed since the beginning of the pandemic. A critical analysis is provided for the selected tools based on 17 parameters that can be useful for researchers and analysts for their evaluations. These parameters make up our evaluation framework and have not been used previously for analysis and evaluation. Hence, knowledge of the tools will not only increase the productivity but will also allow to explore new dimensions for using existing tools with more control, better management, and greater outcome of their research. In addition, the parameters used in our framework can be applied for future evaluations of similar tools and health professionals can adapt them for evaluation of other tools not covered in this analysis.

This research aimed to determine the most viable locations for establishing solar energy facilities in Yemen through the utilization of the Analytical Hierarchy Process (AHP), Best-Worst Method (BWM), and Geographic Information System (GIS). A total of twelve key criteria were meticulously selected and evaluated based on their impact on the appropriateness of sites for solar power plants. These criteria were amalgamated using a weighted overlay tool, resulting in a map that classified regions as either optimal, highly suitable, or suitable. The findings of the study indicated that the eastern, southern, and northern parts of Yemen are prime candidates for solar energy installations due to their moderate climate and flat topography. On the other hand, the western regions were deemed less favorable. The breakdown of site classifications revealed that 38% of areas were optimal, 61% were highly suitable, and only 1% were considered suitable, based on the assigned weights from the AHP and BWM methodologies. Moreover, a sensitivity analysis was carried out to evaluate the impact of adjusting the weights of Global Horizontal Irradiance (GHI) and slope on site classifications. A minor increase in the weights of GHI (from 30% to 31%) and slope (from 10% to 11%) resulted in a significant alteration in the suitability categories, highlighting the importance of weight adjustments in multi-criteria decision-making processes. In conclusion, the study emphasizes the effectiveness of AHP, BWM, and GIS in assessing site suitability for solar energy facilities, as well as the significance of sensitivity analyses. These approaches provide valuable insights for decision-makers, aiding in strategic planning and fostering sustainable energy development in Yemen.

Unmanned aerial vehicles (UAV) have emerged as a solution to day to day survey tasks, allowing users to visualize phenomena in real-time. This paper explores the capabilities of UAV or drones in the collection of accurate, geo-tagged data quickly, including photogrammetry software processes to deliver standardized data output. In order to explore the capabilities of UAV, Gatu Township in Centenary, Muzabani District of Zimbabwe was chosen from the national mapping topographic series. This study demonstrates the efficiency of data collection using drones, generate 2D orthomosaics in real time, so that analysts can easily visualize land cover and identify any changes, map and model large areas to produce data for 2D and 3D models. The recent development of innovative optical image processing has further lowered the costs high resolution topographic surveys.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand, therefore the need to optimise water resources in this area is crucial, and climate change could worsen this condition. In citrus cultivation, which is one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are im-plemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies constitutes valuable information for the stakeholders. Therefore, the objective of this study was to determine the prob-ability of presence of citrus in Sicily at changing of the percentage of water deficit application, in order to analyse change in the surface area and localisation of the crop. The methodology was based on the application of Species Distribution Models and Geographic Information Systems to the case study of the Province of Syracuse in Sicily. Different geostatistical and machine learning models were applied, based on 3-decades bioclimatic variables, DTM and irrigation; assessment of the outcomes was carried by using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that affected most the probability of presence. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory and a decrease of citrus pres-ence in southern areas of the considered territory, where climate conditions are less favourable in terms of temperature and precipitations, thus providing useful information for decision support tools in agriculture and land use policy.

COVID-19 pandemic event requires a rapid response from various organizations at the international and national levels. One important response is the provision of information sharing facilities and monitoring of the spread of cases around the world, JHU CSSE developed the Dashboard in January 2020 and followed by WHO the same month for the WHO COVID-19 dashboard. Both dashboards have distributed information as expected by the user with their respective pros and cons. JHU CSSE Dashboard provides faster information with good access to mobile device users even though the display and color selection are less attractive. Information on the WHO COVID-19 Dashboard is often late but more data appearances and variations and comparisons between countries can be made. In the Indonesian context, ESSC for COVID-19 Geoportal as Esri Indonesia initiative has been developed with the support of data and information from various parties and developed with the principles of big data management which are fully supported by adequate spatial portal developer software from Esri. Particularly in Indonesia, there is not yet an adequate system to support spatial based decision making at the local level, therefore the development of a GIS dashboard to support provincial and district governments is highly recommended.

This short note proposes a national Geographic Information System (GIS) - based health infrastructure to deal with epidemics and pandemics. Currently, there is no pan-India health infrastructure available that can compile, update, and report the spread of epidemic diseases. It not only curtails the opportunity of finding the real-time data on the spatial distribution of a disease but prevents one to inquire into the causes of the disease through epidemiological analysis. The proposed infrastructure in this study is a pan-India one and can broadly be divided into two parts, hotspot mapping and accessibility to services. In the first part, hospitals are proposed to act as nodes of data collection, sending data to a national GIS portal. This portal shall have the capabilities of plotting the data using map rendering services such as Google and Bing Maps. This way, hotspots can be visualized in no time, benefitting the government and common citizenry alike. The second part deals with the accessibility of citizenry to a wide range of services, i.e., healthcare services, grocery outlets, emergency services, baby food, and many other essential services of the day to day life. In order to implement this, we propose that the government need to enforce a mandatory submission of locational coordinates of all Goods and Services Tax (GST) enrolled service providers. Once the coordinates are submitted, the government can effectively control the opening and closing of services and inform the citizenry at the same time. The proposed infrastructure is going to help deal with the extraordinary situations during epidemic and pandemics similar to what the world is currently facing in the form of Corona Virus Disease 2019 (COVID-19). Furthermore, the infrastructure can be scaled up or down as per the spread of a disease. The health-GIS platform proposed in this concept paper, shall help India in controlling and managing the epidemic more efficiently.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

The objective of this study is to examine different types of bias present in assessment tools and techniques, identify and eliminate any forms of favoritism, and suggest effective approaches for advancing equity and objectivity in assessment procedures. The research utilizes a qualitative methodology, incorporating surveys and focus group discussions with educators and students to collect data. Thematic analysis is a method employed for the purpose of analyzing qualitative data. The results suggest that students hailing from diverse cultural backgrounds perceive partiality in the language and cultural context of evaluations, leading to diminished scores. According to academic professionals, standardized tests are subject to bias, and offering accommodations that cater to linguistic and cultural diversity may improve the academic achievement of students from diverse cultural backgrounds. The findings of this research are of great importance to professionals in the domains of education, policy development, and academia who seek to improve assessment practices and promote equitable educational opportunities for all learners.

Rapid advances in the screening, diagnosis, and treatment of genetic disorders has increased the number of conditions that could be detected through universal newborn screening (NBS). However, addition of conditions to the Recommended Uniform Screening Panel (RUSP) and implementation of nationwide screening has been a slow process taking several years to accomplish for individual conditions. Here we describe web-based tools and resources developed and implemented by the newborn screening translational research network (NBSTRN) to advance newborn screening research and support NBS stakeholders. NBSTRN’s tools include the Longitudinal Pediatric Data Resource (LPDR), the NBS Condition Resource (NBS-CR), the NBS Virtual Repository (NBS-VR), and the Ethical, Legal, and Social Issues (ELSI) Advantage. Research programs including the Inborn Errors of Metabolism Information System (IBEM-IS), BabySeq, EarlyCheck, and Family Narratives Use Cases have utilized NBSTRN’s tools, and in turn contributed research data to further expand and refine these this resource. Additionally, we discuss ongoing tool development to facilitate expansion of genetic disease screening in increasingly diverse populations. In conclusion, NBSTRN’s tools and resources provide a trusted platform to enable NBS stakeholders to advance NBS research and improve clinical care for patients and their families.

In the context of growing global political tension and introduction of world trade barriers, the urgent task is to develop new tools for assessing their consequences. In the paper we present the agent-based model of trade wars, considering organizations, states and residents generated using initial statistical data. Simulation determines changes in output and supplies of organizations under trade restrictions. Results of calculations on the developed model and comparison of various model complexes forecasts with real consequences of trade wars between the USA and China in 2018 and Western countries against Russia in 2022 are presented. Within calculations four scenarios were considered: (1) baseline, (2) new restrictions between China and the USA, (3) more serious sanctions against China and Russia by the USA and the EU, (4) a global trade war. In the second scenario deviation GDP of the USA and China from the baseline scenario does not exceed 0.5%. In the third scenario, the range of countries involved is expanding, and the fall in GDP in them is expected at the level of 0.7-1%. In the fourth scenario, the entire world economy experiences a serious slowdown, and the EU are facing the most severe consequences, going into recession.

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under-represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well-known and well-researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling-origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy-to-implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series.

Faba bean (Vicia faba L.), a member of the Fabaceae family, is one of the important food legumes cultivated in cool temperate regions. It holds great importance for human consumption and livestock feed because of its high protein content, dietary fibre, and nutritional value. Major faba bean breeding challenges include its mixed breeding system, unknown wild progenitor, and genome size of ~13 Gb, which is the largest among diploid field crops. The key breeding objectives in faba bean include improved resistance to biotic and abiotic stress and enhanced seed quality traits. Major progress on reduction of vicine-convicine and seed coat tannins, the main anti-nutritional factors limiting faba bean seed usage, have been recently achieved through gene discovery. Genomic resources are relatively less advanced compared to other grain legume species, but significant improvements are underway due to a recent significant increase in research activities. A number of bi-parental populations have been constructed and mapped for targeted traits in the last decade. Faba bean now benefits from saturated synteny‐based genetic maps, along with next-generation sequencing and high-throughput genotyping technologies that are paving the way for marker-assisted selection. Developing a reference genome, and ultimately a pan-genome, will provide a foundational resource for molecular breeding. In this review, we cover the recent development and deployment of genomic tools for faba bean breeding.

There is increasing use of wood-based composites in industry not only because of the shortage of solid wood, but above all for their better properties such as: strength, aesthetic appearance, etc. compared to wood. Medium density fibreboard (MDF) is a wood-based composite that is widely used in the furniture industry. The goal of the research conducted was to determine the effect of the type of coating on the drill cutting blades on the value of thrust force (F_t), cutting torque (M_c), cutting tool temperature (T) and surface roughness of the hole in drilling MDF panels. In the tests three types of carbide drills (HW) were used: not coated, TiAlN coated and ZrN coated. The measurement of both the thrust force and the cutting torque was carried out using an industrial piezoelectric sensor. The temperature of the cutting tool in the drilling process was measured using an industrial temperature measurement system using a K-type thermocouple. It was found that the value of the maximum temperature of the tool in the drilling process depends not only on the cutting speed and feed rate, but also on the type of coating of the cutting tool. The value of both the cutting torque and the thrust force is significantly influenced by the value of the feed rate and the type of drill coating. The effect of varying plate density on the surface roughness of the hole and the variation of the value of the thrust force is also discussed. The results of the investigations were statistically analysed using a multi-factorial analysis of variance (ANOVA).

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

There are many modern and traditional methods in valuing the performance of the company among them EVA, CVA are modern methods where as EPS, ROI, ROE etc. are some of the traditional methods. modern and traditional methods are not only used to check the performance of a company but it also used in investment decisions by the investors. Stern Stewart, managing partner of M/s Stern Stewart & Co. introduced a modified concept of economic profit in 1990 in the name of Economic Value Added (EVA) as measure of business performance and CVA is only a cash consideration in EVA. In this paper an attempt is made to study which method is used by the investors while taking the investment decisions i.e. traditional methods are modern methods. 100 respondents (Investors) are questioned and collected their views, we are assuming that there is no impact of traditional or modern methods while taking investment decisions by the investors.

The history of agricultural terraces dates back to agricultural society; and throughout history, they have enabled agricultural activities to be carried out on lands with irregular relief. They have also served as an essential element of rural landscapes due to their architectural features, building materials, and techniques, as well as the landscapes they create. Furthermore, terraces also contribute to soil conservation by preventing erosion, adding organic matter to the soil, and harvesting rainwater. The significance of agricultural terraces in the cultural landscape has triggered the need to define and classify them. In this regard, agricultural terraces across many different locations have found their place on the List of Globally Important Agricultural Heritage Sites (GIAHS) and UNESCO cultural heritage lists. In the present study, the agricultural terraces of the Erikli neighborhood of the Uzundere district were mapped through surveys, drone shots, and orthophotos, and analyzed based on the size and density indices previously used in the classification of Alpine terraces. The study is important in terms of demonstrating the use of drone technology in mapping agricultural terraces. The results of the study revealed that 51.6% of the agricultural terraces in the study area had very steep slopes and 22195 meters of total stone wall length, the average density index was 523.29 m/ha, and 76.4% of the terraces were meso and macro in character. Moreover the study shows that the terraces in the region are more susceptible to deterioration.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.