This paper presents the design of a digital twin that blends aviation, gaming, simulation, and Geographic Information Systems (GIS) to create a synthetic environment within which strategies, laws and platforms for electric aviation may be tested out. This digital twin has been called Future Urban Synthetic Environment (FUSE). FUSE includes an in-built Unmanned Traffic Management (UTM) that can be used to run simulations to test the coordination of all urban traffic. It uses real GIS tagged imagery data and implements it at runtime into the game engine and thus link the optimised imagery loading to the visual performance of the simulation engine. FUSE provides a 3D digital twin of specified areas designed to simulate the effects (in terms of noise, visual impact, privacy) of drones and electric air taxis operating under various operational scenarios (such as number of deliveries allowed per day, maximum payload weight, no-fly areas, position of depots, vertiports, etc.). With so much high-fidelity data it is difficult for any game system to effectively render the environment and do justice to the detail whilst enabling enough of the landscape to be rendered to keep in focus the detail when looking out at the horizon.

Following the development and dissemination of new climate-smart agricultural technologies to farmers globally, there has been an increase in the number of socio-economic studies on the adoption of climate-smart integrated pests’ management (CS-IPM) technologies over the years. In this study, we review empirical evidence on adoption determinants of CS-IPM technologies and identify possible science-policy interfaces. Generally, our review shows that socioeconomic and institutional factors are influential in shaping CS-IPM adoption decisions of farmers. More specifically, income was found to positively influence the adoption of CS-IPM technologies while land size owned influences CS-IPM adoption negatively. Registered land tenure (registered secure rights) positively influences CS-IPM technologies’ adoption, implying that efficient land markets that enable competitive and fair distribution and access to land, more so by the vulnerable but efficient smallholder producers do indeed increase the adoption of CS-IPMs technologies. Social capital, achieved via farmers’ organizations is also central in fostering CS-IPM technologies’ adoption, just as markets reforms that minimize market failures regarding access to credit, labor, and agricultural information, which could indirectly hinder farmers’ use of CS-IPM practices. Functional extension systems that improve farmers’ awareness of CS-IPM do also improve CS-IPM technologies’ adoption. However, the adoption of CS-IPM technologies in Ghana and Benin is slow-paced because of factors like lack of access to farm inputs that facilitate uptake of these technologies, lack of credit facilities, and limited extension services among others. Interestingly, our review confirms that CS-IPM technologies do indeed reduce and minimize the intensity of pesticide usage and foster ecosystem (environmental and human) health. Therefore, this review unearths strategic determinants of CS-IPM adoption and makes fundamental guidance around climate-smart innovations transfer and environmental policies that should be prioritized to curb environmental pollution and ensure agricultural ecosystems’ sustainability.

The total amount of greenhouse gas emissions directly or indirectly generated by thermal power enterprises at any given time can be offset through afforestation, energy conservation and emission reduction. The present situation and control methods of CO₂ emission in China's coal-fired thermal power industry are introduced. The complex ecosystem is a unity of ecological functions composed of human society, economic activities and natural conditions. In the context of carbon neutrality and based on the theory of composite ecosystem, this paper divides the coal-fired thermal power plants in China into environmental management zones, calculates the atmospheric environmental capacity, and puts forward the concept of regional atmospheric environmental capacity, classification and zoning control. Finally, the management and control units are classified, and differentiated management and control requirements are put forward to provide a reference for regional air quality standard planning.

The present study aims to develop a risk-based approach to find optimal solutions for life extension management for offshore wind farms based on Markowitz’s modern portfolio theory, adapted from finance. The developed risk-based approach assumes that the offshore wind turbines (OWT) can be considered as cash-producing tangible assets providing positive return from the initial investment (capital) with a given risk attaining the targeted (expected) return. In this regard, the present study performs a techno-economic life extension analysis within the scope of the multi-objective optimisation problem. The first objective is to maximise the return from the overall wind assets, while the latter aims to minimise the risk associated with obtaining the return. In formulating the multi-dimensional optimisation problem, the life-extension assessment considers the results of a detailed structural integrity analysis, free-cash-flow analysis, and probability of project failure, local and global economic constraints. Further, the risk is identified as the variance from the expected mean of return on investment. The risk-return diagram is utilised to classify the OWTs of different classes using an unsupervised machine learning algorithm. The optimal portfolios for the various required rate of return are recommended for different stages of life extension.

Caesarean scar ectopic pregnancy (CSEP) is an uncommon form of ectopic pregnancy. This review defines types of CSEP, discusses pathophysiology, clinical presentation and diagnosis and compares common treatment options and outcomes. The pathophysiology is not yet fully understood. One third of patient are asymptomatic. In symptomatic patients vaginal bleeding and pelvic pain which is usually misdiagnosed as threatened or missed abortion. The main principles of treatment for CSEP are early diagnose, quick management, and maintain reproductive function as much as possible. Up to date; no consensus or guidelines for the treatment and management of CSEP. Management of CSEP depends on the clinical presentation and gestational age. Significant controversy exists regarding management, for this reason, several factors should be considered including age of the patient, myometrial thickness, clinical symptoms, hemodynamic status, fertility preservation.

The use of whole-body vibration (WBV) for therapeutic purposes is far from being standardized and only very recently an empirical foundation for reporting guidelines for human WBV studies has been published. Controversies about safety and therapeutic dosage stll exist. The present study aimed to investigate the metabolic and mechanical effects of low-intensity WBV in according to the ISO 2631 norm on subjects with obesity. 41 obese subjects (BMI≥ 35 kg/mˆ2) were recruited to participate in a 3-week multidisciplinary inpatient rehabilitation program including fitness training and WBV training. During WBV the posture was monitored with an optoelectronic system with 6 infrared cameras (Vicon, Vicon Motion System, Oxford, UK). The primary endpoints were: variation in body composition, factors of the metabolic syndrome, functional activity (sit-to-stand and 6-min walking test), muscle strength, and quality of life. Secondary endpoints were: modification of irisin, testosterone, growth hormone, IGF1 levels. We observed significant changes in salivary irisin levels, Group 2 (p<0.01) as compared to the control group, while muscle strength, function, and other metabolic and hormonal factors did not change after a 3-week low-intensity WBV training respect control group. Future studies are needed to deeper investigate the potential metabolic effect of low-intensity WBV in managing weight.

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery-supercapacitor hybrid power system. The hybrid power system consists of a Lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC-DC converter model is derived utilizing the first-principles method and compute the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power ele-ments’ life expectancies and supplies the power system with the required power

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

Over the last two decades, residential buildings have accounted for nearly 50 percent of total energy use in New Zealand. In order to reduce household energy use, the factors that influence energy use should be continuously monitored and managed. Building researchers and professionals have made efforts to investigate the factors that affect energy use. However, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the correlation between energy use data and residential building cost indicated that residential building cost in the construction phase and energy use in the operation stage were significantly correlated. These findings suggest that correct monitoring of building costs can help to identify trends in energy use. Therefore, this study proposes a time series model for forecasting residential building costs of five categories of residential building (one-story house, two-story house, townhouse, apartment, retirement village) in New Zealand. The primary contribution of this paper is the identification of the close correlation between household energy use and residential building costs and provide a new area for optimize energy management.

The aim of this study was to determine the impact of information technology in management of risks in the capital market-listed company is in Tehran Stock Exchange. The purpose of the present study is an applied descriptive approach. The target population for the survey, companies that from 2009 to the first half of 2015 have been a member of the Tehran Stock Exchange, through Cochranʼs sample size of 140 companies, respectively. We used cluster sampling method. In order to collect data from two questionnaires: risk management questionnaire Foakeh (2013) has 38 items and a standard questionnaire Chanvyas (2006) has 40 items, the whole five-item Likert scale questionnaire is above has been used. Data gathered through the questionnaire, sign the application was 21spss. For inferential analysis of the variables and to analyze the data from different statistical tests and regression was used Kolmogorov-Smirnov test. The results showed that information technology on risk management and its dimensions (primary market risk, market risk and the risk of secondary non-financial) impact.

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

The article analyzes Bernoulli 's binary sequences in representation of empirical events about the distribution of natural resources and population sizes. Considering the event as a nonlinear system, and consisting of two dependent random variables, with memory and probabilities in maximum finite or infinite lengths, constant and equal to ½ for both variables (stationary process). The expressions of the possible trajectories remain constant in sequences that are repeated alternating the presence or absence of one of the variables at each iteration (asymmetric). There are constant oscillations in the event except if the variables X₁ and X₂ are regulated as a function of time Y. It is observed that the variables X₁ and X₂ assume in time T_k → ∞ specific behaviors (geometric variable) that can be used as management tools for random systems. In this way, the article seeks to know from this analyzes, the maximum entropy of information in the system by a theoretical view and how to model resources distribution or containment in the given problem.

To remain competitive in the current market, an enterprise must differentiate itself based on higher value propositions. For this purpose, since improving the product or service performance can reach some limits, one potential solution is to move towards new combinations of products and services. This evolution, called servitization, leads to the generation of Product-Service Systems (PSS). Servitization requires not only a clear understanding of enterprise core business but also a clear vision on the prevailing trends and challenges of PSS development from both business and technological points of view. In addition, the evolution path should be aligned with the enterprise strategy. This paper first highlights the notion of symbiotic PSS where Product Systems and Service Systems, and their stakeholders, interoperate seamlessly based on a win-win approach. Then it proposes a PSS Conceptual Framework (PSS-CF) which can be applied in the early stages of servitization to increase the understating of PSS dimensions and to facilitate the prioritization of the servitization investments. The framework dimensions were discussed in several iterations, from both academic and industrial points of view, in the frame of a European research project. Moreover, the applicability of the framework was studied in four different industrial Use-Cases.

Servitization or productization, indicating evolution from product to service economy or vice-versa, can be considered as a successful strategy to gain competitiveness based on novel combination of products and services. To decrease the risks of servitization and to support the sustainable development of its main outcome, being Product Service System (PSS), it is required not only to have a clear and common understanding of the core business and processes but also to share the same definitions on (PSS) concepts as the main outcome of servitization. For this purpose, managers could be supported by abstract models with a limited number and high ratio of known concepts in the early stages of PSS development. Through an extensive literature review on this subject, followed by a structured conceptualization approach and discussions with domain experts, this paper proposes a Conceptual Model (PSS-CM). To validate the results, PSS-CM and its elements were discussed in several iterations, from both academic and industrial points of view, in the frame of a European research project. In the frame of this project, a case study was also performed to illustrate the instantiation of PSS-CM.

Silicon light funnels are three-dimensional subwavelength structures in the shape of inverted cones with respect to the incoming illumination. Light funnel arrays can serve as an efficient absorbing layers on account of their light trapping capabilities associated with the presence of high density complex Mie modes. Specifically, light funnel arrays exhibit broadband absorption enhancement of the of the solar spectrum. In the current study, we numerically explore the optical coupling between surface light funnel arrays and underlying substrates. We show that the absorption in LF array-substrate complex is higher than the absorption in LF arrays of the same height (~10% increase). This, we suggest, imply that a LF array serves as an efficient surface element that imparts additional momentum components to the impinging illumination, and hence optically excites the substrate by near-field light concentration, excitation of traveling guided modes in the substrate and mode hybridization.

Human factors are the most relevant issues contributing to adverse events in obstetrics. Specific training of Crisis Resource Management (CRM) skills (i.e., problem solving and team management, resource allocation, awareness of environment, and dynamic decision-making) are now widespread and are often based on High Fidelity Simulation. In order to be used as a guideline in simulated scenarios, they need to be translated into specific and observable behavioral markers. To this purpose, we developed a set of observable behaviors related to the main elements of CRM in the delivery room. The observational tool was then adopted in a two-days seminar on obstetric hemorrhage where teams working in obstetric wards of six Italian hospitals took part to simulations. The tool was used as guide for the Io and as a peer-to-peer feedback. It was then rated for its usefulness in facilitating the reflection upon one’s own behavior, its ease of use, and its usefulness for the peer-to-peer feedback. The ratings were highly positive, around 4 in a 5-point scale. The CRM observational tool is therefore a useful, quick and easy solution to facilitate the debriefing, the peer-to-peer feedback and, most of all, the transfer of safe behavior from simulation to everyday practice.

Energy storage systems will play a key role for individual users in the future smart grid. They serve two purposes: (i) handling the intermittent nature of renewable energy resources for a more reliable and efficient system, and (ii) preventing the impact of blackouts on users and allowing for more independence from the grid, while saving money through load-shifting. In this paper we investigate the latter scenario by looking at a neighbourhood of 25 households whose demand is satisfied by one utility company. Assuming the users possess lithium-ion batteries, we answer the question of how each household can make the best use of their individual storage system given a real-time pricing policy. To this end, each user is modelled as a player of a non-cooperative scheduling game. The novelty of the game lies in the advanced battery model, which incorporates charging and discharging characteristics of lithium-ion batteries. The action set for each player comprises day-ahead schedules of their respective battery usage. We analyse different user behavior and are able to obtain a realistic and applicable understanding of the potential of these systems. As a result, we show the correlation between the efficiency of the battery and the outcome of the game.

Whereas waste management research has focused on mostly urban municipalities in South Africa, not much is known about the current performance of waste management services from the perspective of community residents in rural municipalities. This study reports on the status and effectiveness of waste management practices from the perspective of community residents in the Umkhanyakude and Zululand Districts in the rural areas of the KwaZulu-Natal province of South Africa. Making use of structured questionnaires, primary data was obtained by interviewing 333 respondents representing households in the study area. Results have revealed several shortfalls in the provision of municipal waste management services, except for solid waste removal from households (66%), and management of landfill sites (41%). The degree of dissatisfaction for waste management services rendered was very high (97.3%). Furthermore, the majority (61.4%) of respondents were not willing to participate in waste segregation due to lack of appropriate knowledge and infrastructure. The study also pinpointed some benefits associated with waste minimization, as well as barriers constraining effective waste recycling. Based on these findings, there is a dire need for transforming current waste management practices toward increased recycling rates by creating more environmental awareness and a supporting infrastructure.

The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as Remaining Useful Life (RUL). Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that this kind of black box models are opaque decision systems, and it may be hard to explain its outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This work presents a critical and comparative revision on a number of XAI methods applied on time series regression model for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. The model used during the experimentation is a DCNN trained to predict the RUL of an aircraft engine. The methods are reviewed and compared using a set of metrics that quantifies a number of desirable properties that any XAI method should fulfill. The results show that GRAD-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of Image Processing.

The goal of this paper is to contribute the perspective of a systems engineer to the effort to fight pandemics. The availability of low latency case data and effectiveness of social distancing suggest there is sufficient control for successful smoothing and targeting almost any desired level of low or high cases and immunity. This control proceeds from spontaneous public reaction to caseloads and news as well as government mediated recommendations and orders. We simulate multi-step and intermittent-with-feedback partial unlock of social distancing for rapidly-spreading moderate-mortality epidemics and pandemics similar to COVID-19. Optimized scenarios reduce total cases and therefore deaths typically 8% and up to 30% by controlling overshoot as groups cross the herd immunity threshold, or lower thresholds to manage medical resources and provide economic relief. We analyze overshoot and provide guidance on how to damp it. However, we find overshoot damping, whether from expert planning or natural public self-isolation, increases the likelihood of transition to an endemic disease. An SIR model is used to evaluate scenarios that are intended to function over a wide variety of parameters. The end result is not a case trajectory prediction, but a prediction of which strategies produce near-optimal results over a wide range of epidemiological and social parameters. Overshoot damping perversely increases the chance a pathogen will transition to an endemic disease, so we briefly describe the undershoot conditions that promote transition to endemic status.

Background: Handover is a critical process for ensuring quality and safety in healthcare. Considerable research suggests that poor handover results in significant morbidity, mortality, dissatisfaction, and excess financial costs. Despite this, little formal attention, education, and evaluation has been given to handover. There is also paucity of data on the opinions of practitioners on the safety of handover.Objectives: The aim of this study was to measure the perceived risk, degree of patient harm and the systems used to support handover, and to understand how this varied by care setting, type of clinical practice, location, or level of experience. Methods: An open, anonymous and confidential online questionnaire covering: (a) respondent characteristics; (b) peer-to-peer handover; (c) internal referrals; (d) discharges and transfers between organisations; and (e) leading and improving handover was conducted with healthcare practitioners and managers from various settings. Results: We gathered a total of 432 completed responses from 26 countries. The average reported performance of handover was rated as 3.9 out of 5.For each type of handover, 12 - 14% reported errors occurring more than weekly. Of those that knew the outcome of such errors, between 29% and 34% reported that they had witnessed moderate or severe harm. 12% and 17% of respondents believed that handover was high or very high risk (See table 4). These respondents were more likely to have witnessed moderate or severe harm, or to be more senior.A wide combination of handover systems was utilised by respondents. 28% - 32% relied exclusively on EPRs (with or without face-to-face contact). 21% used Office documents such as Word and Excel for peer-to-peer handover, and over 30% used hand-written or manual systems. Conclusions: This study suggests the need to do more — and go further — to improve communication and reduce risk during all types of handovers. Clinical leaders should find ways to train and support handover with effective systems, with less experienced staff being the primary focus. More research is needed to demonstrate the interventions that improve the safety of handover.

The concept of sustainable management in culture has been recognised in global strategic documents on sustainable development for more than a decade. It is also increasingly reflected in the cultural polices of particular states, and – very importantly –cultural managers responsible for shaping the cultural offer in cities are becoming more interested in this concept. Despite the increasing attention being paid to this topic among both practitioners and theoreticians of management, in none of these documents or other works can we find any content directly related to the possibility of applying this concept in a town which, due to political turmoil, has been divided by a national border. Hence, this gap was the direct impulse for taking up research in this field. In the article, by using different notions of the market, our own definition of a cross-border market for cultural services was developed, and the conditions for the functioning of this market were presented based on the example town of Cieszyn (Poland) and Český Těšín (Czech Republic). In the opinion of the authors of the article, the development and functioning of a cross-border market for cultural services is essential for the application of the concept of sustainable management of the cultural offer in a town divided by a border. For the purpose of the article, a survey and individual interviews with experts shaping the cultural offer in Cieszyn and Český Těšín were conducted. The results of the research prove that despite numerous cross-border Czech-Polish projects carried out by cultural institutions, there are still many barriers in the town which make it difficult for the residents to benefit from the cultural offer available on the other side of the border. These barriers limit the full implementation and application of the concept of sustainable management of the cultural offer.

The paper presents the final results from the research project “Urban Abacus of Building Energy performances (Abaco Urbano Energeticodegli Edifci – AUREE)” aimed at supporting the renovation and energy efficiency enhancement of urban building heritage financed by the “Ricerca Sistema Elettrico (MISE) program”. The crux of AUREE project is a Web – GIS GeoBlog portal with customized semantic dashboards aimed at sharing information on urban built environment and promoting the participation of local stakeholders to its improvement. As the latest development of this research, a workflow integrating the AUREE portal with BIM authoring and an open source IoT platform has been implemented and applied to an experimental case study concerning a public building in Carbonia (Italy). The headquarters of the sustainable energy research center Sotacarbo was selected as case study. The presented results proved that it was possible to create an open system, which could be easily consulted by "non-expert" users as well, to make available real-time monitoring data obtained from IoT sensors measuring the energy consumption, the indoor environmental conditions and the usage profiles of public buildings. The proposed open system could also be suitable to be later used as an effective tool to support the rising “energy communities”.

This report describes the experience in implementing the FAIR principles for the GoTriple Discovery Platform for the Social Sciences and Humanities (SSH). It shows how adherence to FAIR should be considered as a continuous process throughout the entire lifespan of any information management system, including GoTriple, rather than a static goal with decisions only made at design time. The report presents an introduction highlighting the importance of the FAIR principles, indicating how they can be assessed in data management systems. Then the GoTriple case is presented, with a general overview of this discovery platform before describing some of the implemented practices in support of FAIR. The Discussion section shows on the one hand some virtuous reuse of GoTriple data, together with one major pitfall in the platform’s FAIR implementation. In this sense, this report serves as a case study that can offer insights and actionable advice for those implementing information systems aligned, from the very outset, with the FAIR principles.

Pharmacy personnel often answer telephones to respond to pharmacy customers (subjects) who received messages from automated systems. This research examines the communication process in terms of how users interact and engage with pharmacies after receiving automated messages. No study has directly addressed automated telephone calls and subjects’ interactions. The purpose of this study is to test the interpersonal communication (IC) process of uncertainty in subjects in receipt of automated telephone calls from pharmacies. Subjects completed a survey of validated scales for Satisfaction (S); Relevance (R); Quality (Q); Need for Cognitive Closure (NFC). Relationships between S, R, Q, NFC, and subject preference to an automated telephone call (ATC) were analyzed to determine whether subjects contacting pharmacies display information seeking behavior. This research demonstrates that seeking information occurs if subjects: are dissatisfied with the content of the ATC; perceive that the Q of the ATC is high; perceive that the Q of ATC is high, and like receiving the ATC or with high NFC, and do not like receiving ATCs. Other interactions presented complexities amongst uncertainty and tolerance of NFC within the IC process.

While human activities and climate change are threatening available natural ecosystems, targeted studies on the biological cycle, propagation, and cultivation of threatened species are crucial both for their conservation and sustainable exploitation. This study was designed to bridge extant research gaps regarding the vulnerable local endemic Campanula pelviformis, a wild edible green traditionally consumed in Crete (Greece) with agro-alimentary and medicinal interest as well as ornamental value. New knowledge was obtained on the abiotic environmental conditions prevailing in its wild habitats (natural distribution mapped in Geographical Information Systems and linked with open-source databases). We collected seeds from wild-growing populations (three lowland, two semi-mountainous), we tested their germination at four different temperatures (10, 15, 20 and 25oC) and under different light conditions (light/dark and darkness), and we performed fertilization trails [integrated nutrient management (INF), chemical fertilization (ChF), control] examining morphological and physiological characteristics, above- and below-ground macro- and micronutrients contents, as well as their phenol content and antioxidant capacity. There were population and temperature effects on seed germination and their interaction was statistically significant. Campanula pelviformis germinated better at 10 and 15 oC (>85% for all populations) with no preference on light conditions (98.75% and 95% in light and dark conditions). The INF application increased root dry mass, chlorophyll content index and chlorophyll fluorescence compared to other treatments and was beneficial for macro- and micronutrients concentrations in above-ground parts compared to previously studied wild-growing material, while below-ground parts were positively impacted by both fertilization types. Total phenols and antioxidant capacity were both increased by ChF fertilization. The data furnished herein led to the re-evaluation and upgrade of the feasibility and the readiness timescale for its sustainable exploitation in different economic sectors.

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time. Based on Weight Agnostic Neural Networks (WANNs) methodology, we propose an automated searching neural net architectures strategy that can perform various tasks such as identify zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

Forecasting streamflow accurately is essential to achieve an efficient integrated water resources management strategy and provide consistent support to water decision-makers. We present a simple, low-cost and robust approach for forecasting monthly and yearly streamflow during the hydrological year in course, applicable to headwater catchments. It combines the use of regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. It is based on a probabilistic comparison of the progression of the current hydrological year with the historic observed series. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain. The root-mean-square error and correlation coefficient were used to measure the accuracy of the model and the results showed good levels of reliability. The outputs are the probabilistic monthly and yearly streamflow and 80% confidence interval. Further reductions in prediction errors may be achieved from increasing the number of observed years. These risk-based predictions are of great value, especially, before the intensive irrigation campaign starts (usually in April), when Water Authorities are to take responsible management decisions about the best allocation of the available water volume between the different water users and environmental needs.

The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT 4 (release May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT writes essays of limited length of about 50% of the stipulated word count which are primarily descriptive without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior school assignment, but fall far short of what would be expected in senior school, let alone at a college and university level.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

This study explores the strategic importance of leveraging supplier relationships for competitive advantage in the e-commerce industry. Strong supplier relationships are crucial for enhancing operational efficiencies, cost management, quality assurance, innovation, sustainability, and overall supply chain resilience. Through collaborative partnerships, e-commerce companies can streamline logistics, reduce costs, ensure consistent product quality, and foster innovation through joint development initiatives. Ethical sourcing and sustainable practices play a pivotal role in supplier relationship management (SRM), aligning with consumer expectations and enhancing brand reputation. Advanced technologies such as AI, blockchain, and IoT further transform SRM practices by improving transparency, efficiency, and decision-making capabilities across the supply chain. The study also discusses challenges in managing global supplier relationships, emphasizing the importance of effective communication, cultural sensitivity, and strategic risk management. By addressing these challenges and nurturing trust-based relationships with suppliers, e-commerce firms can navigate market complexities and achieve sustainable growth. This research provides insights for e-commerce companies seeking to optimize their supplier relationships and maintain competitive advantage in a dynamic market environment.

This qualitative research explores Supplier Relationship Management (SRM) strategies within the context of e-commerce customization, aiming to understand how businesses effectively manage relationships with suppliers to meet personalized consumer demands. Through semi-structured interviews with industry professionals, key themes such as trust, communication, technological integration, strategic alignment, sustainability, and collaboration emerged as critical factors influencing SRM effectiveness. Trust was found to be foundational, fostering cooperation and commitment between e-commerce businesses and suppliers through transparent interactions. Effective communication and the integration of advanced technologies like AI and blockchain enhanced operational efficiency and innovation capabilities, enabling businesses to deliver tailored customer experiences. Strategic alignment between businesses and suppliers proved essential for shared success and mutual benefit, while sustainability practices increasingly shaped consumer preferences and brand reputation. Collaboration facilitated joint innovation and accelerated product customization, underscoring the importance of partnerships in achieving strategic goals. However, challenges including supply chain complexity, managing consumer expectations, and regulatory compliance highlighted the need for continuous improvement and adaptive strategies. Overall, this research contributes valuable insights for e-commerce businesses seeking to optimize SRM strategies, enhance supply chain efficiency, and navigate the complexities of customization in a competitive digital marketplace. By leveraging trust, communication, technology, strategic alignment, sustainability, and collaboration, businesses can effectively meet evolving consumer demands and achieve sustainable growth.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

This study investigates green logistics strategies adopted by businesses to enhance sustainability within their supply chains, particularly focusing on reducing carbon footprints and environmental impacts associated with transportation and distribution activities. Through qualitative analysis and interviews with supply chain managers, industry experts, and sustainability officers, this research provides insights into motivations, strategies, challenges, and benefits related to green logistics practices. Key findings highlight the critical role of aligning green logistics with corporate sustainability goals. Advanced technologies such as telematics, GPS tracking, and data analytics are instrumental in optimizing transportation routes, reducing fuel consumption, and minimizing emissions. Additionally, strategies like using eco-friendly vehicles, energy-efficient warehouse operations, and implementing reverse logistics are identified as effective means to mitigate environmental impacts. Collaboration and partnerships with suppliers, logistics providers, and stakeholders are essential for sharing best practices and fostering joint initiatives. Despite benefits such as cost savings and enhanced corporate reputation, challenges include high initial investments, the absence of standardized metrics for environmental impact, and regulatory complexities. However, regulatory compliance also acts as a driver for green logistics adoption and presents opportunities for improving sustainability performance. The integration of sustainability goals with logistics strategies underscores the interdisciplinary nature of sustainable supply chain management. This holistic approach encompasses aspects of entrepreneurship, emotional intelligence, marketing, and supplier relationship management.

The rapid evolution of technology has reshaped the global economy, intensifying competition and prompting industry players to embrace innovation for sustained competitiveness. In Indonesia, Business Incubators, overseen by Universities and Provincial/City Governments, are pivotal. However, understanding the determinants of Business Incubator Performance remains limited. This study investigates 24 statement items aiming to uncover the factors influencing Technology Business Incubators in Indonesia, with a focus on knowledge management as a mediating variable derived from a knowledge-based perspective. Employing a quantitative approach, Structural Equation Modeling (SEM) is utilized to scrutinize these factors' impact on Business Incubator Performance. The findings reveal that knowledge management fully and partially mediates between funding support, government assistance, incubator governance, and Business Incubator Technology Performance. This research offers valuable insights for entrepreneurs and stakeholders, emphasizing the significance of funding, governmental backing, incubator governance, and knowledge management in enhancing Incubator Business Technology Performance in Indonesia.

This qualitative study investigates the adoption of Lean Principles in Supply Chain Management (SCM), aiming to explore the motivations, challenges, and outcomes associated with Lean implementation across diverse organizational contexts. Through semi-structured interviews and thematic analysis, insights were gleaned from stakeholders involved in SCM processes, revealing the critical role of organizational culture, leadership commitment, and strategic alignment in driving successful Lean initiatives. The findings underscore the importance of fostering a culture of continuous improvement, where employees are empowered to proactively identify and address inefficiencies. Leadership emerged as pivotal in setting the tone, providing resources, and championing Lean initiatives, while strategic alignment ensured integration with broader organizational goals and objectives. Operational practices and processes were central, highlighting the need for customization and contextual adaptation in Lean implementation. Despite the potential benefits, the outcomes and impact of Lean adoption varied depending on factors such as organizational maturity and industry context. The study also revealed broader cultural and organizational changes catalyzed by Lean adoption, fostering innovation, collaboration, and adaptability. In summary, this study contributes to our understanding of Lean adoption in SCM, offering practical insights for organizations seeking to enhance efficiency and competitiveness in today's dynamic business landscape.

The advancement of mechanization in forestry has increased productivity in the forestry sector, bringing positive and negative impacts that require a deeper understanding for sustainable forest management. This study aimed to apply a simplified instrument for assessing damage and environmental impacts in forest harvesting of commercial eucalyptus plantations, using a combination of methodologies. The methodology used combined interaction networks and impact assessment matrices, carrying out field surveys, transposing them to interaction networks and weighting them through assessment matrices, resulting in environmental indices (ES) for prioritizing actions. The study was conducted on a commercial eucalyptus plantation in the municipality of São Pedro, São Paulo, Brazil. The mechanized harvesting of the area consists of the structure of a module with a mobile unit consisting of a harvester and forwarder. The results indicated that wood transport presented the highest ES, both positive and negative. The most significant negative impacts (ES) were the depletion of water resources and erosion, while the positive impacts included regional development and job creation. The most notable changes, positive and negative, were observed in the physical and anthropic environment, with a lesser impact on the biotic environment.

The link between the world of communication and the world of racing is provided by telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The aim of this research was to integrate telemetry into the battery bank of a racing electric vehicle to find the optimal values of current and voltage that optimize the charging process, thus improving the vehicle’s performance in competition using Response Surface Analysis. Particularly, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data was obtained, a response surface design was adapted with current, voltage, and temperature as factors, varying from low to high values, with the objective function being to minimize the battery’s charging time. Applying the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charge time being 6961 seconds, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.

Pinus occidentalis, Swartz is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are very scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable and Schumacher-Hall—across nine modeling variants (MVs). An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant's performance rigorously. Modeling variant SH02 was most effective in the Dry Ecological Zone, showing superior performance in both fitting and validation phases. Similarly, SH03 emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. The SH equation variants SH02 and SH03, provide reliable and precise volume estimations, allowing optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants' consistency in parameter estimation. This tailored approach employed ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts.

The development of a Digital Track Model (DTM) within Kazakhstan's railway system presents an innovative approach to enhancing efficiency and safety across the network. This paper investigates the economic viability of DTM implementation, offering a comprehensive mathematical model that highlights potential reductions in maintenance costs and improvements in system accuracy and durability. The findings advocate for DTM's potential to significantly elevate railway operations, suggesting a pivotal step towards modernizing Kazakhstan's transit infrastructure.

This study presents a systematic review of the impact of Industry 4.0, digitalization, and information systems on the procurement function with a focus on the Portuguese reality. Despite global trends emphasizing digital transformation and innovation, the procurement practices in Portugal seem to lag. The adoption of cutting-edge technologies and the comprehensive integration of digital solutions within the procurement and supply chain management sector are yet to materialize fully in the Portuguese SME context. The benefits and obstacles of these technologies in transitioning procurement into a more strategic role are analyzed. By examining existing literature, critical factors for organizations to consider when implementing these technologies and the potential benefits are identified. The results indicate that digitalization has resulted in a data-driven approach to procurement, with the aid of advanced analytics and artificial intelligence for decision-making. Industry 4.0 has facilitated smarter factories, warehouses, and logistics operations with a focus on automation, connectivity, and real-time data. Procurement can more efficiently manage suppliers, track contracts, identify cost-saving opportunities, and increase sustainability with the aid of information systems. The study also highlights potential challenges for organizations. Overall, this study provides insightful findings on the role of digitalization in transforming procurement and the challenges and opportunities that organizations may face.

Water recycling and reuse are corner stones in the water management that may be compromised by the presence of pollutants. Among them, pharmaceuticals can overcome the standard water treatments and require sophisticated approaches to remove them. Sorption is an economically affordable alternative limited by the need of sorbents that exhibit sorption coefficient (Kd) higher than 500 L/kg to be useful in wastewater treatment plants (WWTPs). We report that th cross-linking of dextrin (Dx) with divinyl sulfone (DVS) in presence of 1 mmol or 5 mmol of ibuprofen (IBU) yields the insoluble polymers pDx1 and pDx5 with improved affinity for IBU, Kd higher than 600 L/kg for ciprofloxacin (CIP) and ofloxacin and an outstanding Kd higher than 4000 L/kg for erythromycin (ERY), when assayed against a cocktail of 6 drugs. The characterization of the polymers by XRPD, TGA and SEM reveals that both pDx1 and pDx5 share similar features, with an ERY Kd of 13 x 103 for pDx1 and 6.4 x 103 for pDx5. The facts that new affinities and improvements in Kd can be achieved by cross-linking Dx in presence of other molecules that promote a pre-organization, broaden the applications of DVS cross-linked polysaccharide as sustainable and eco-friendly sorbents.

This review article seeks to systematically identify appropriate ways to measure managerial skills. The systematic literature review was conducted according to the criteria in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, and the eligibility criteria were declared through the PICOS tool (population, interventions, comparators, outcomes and study); a tool based on 6,803 records identified bibliometrically in the Web of Science database. The literature review stages determined a reduced set of articles that presented valid and reliable measurement scales covering some determinant constructs on managerial skills for employability such as fundamental, emotional, managerial, leadership, decision-making, communication and teamwork skills.

Efficient commencement of clinical trials is pivotal for their successful execution. The purpose of this study was to determine and examine the factors that contribute to trial start-up delays. The study assessed 20 clinical trials' start-up stages in order to identify relevant variables. Results showed that trials with an emphasis on devices, trials that did not outsource, trials that utilized less auxiliary services, and trials that employed interventional designs had quicker start-up times than observational studies. Furthermore, approval times were substantially shortened by using a centralized Institutional Review Board (IRB) rather than a local IRB. Furthermore, compared to studies that successfully enrolled subjects, those who failed to do so required more time to complete contracts and budgets and secure IRB clearance. This study underscores the diverse impact of clinical trial features on start-up timelines. Understanding these factors is crucial for optimizing the initiation process of clinical trials.

This study introduces a neural network-based approach to predict dust emission, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), the research predicts PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. The model is trained on extensive field data from the almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a Mean Squared Error (MSE) of 0.02 and a Mean Absolute Error (MAE) of 0.01, indicating a high degree of precision in forecasting PM2.5 levels. The study also finds a strong correlation between certain operational parameters and PM2.5 emissions, highlighting specific areas for optimization in harvesting techniques. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

The Indorama Eleme Petrochemical Limited (IEPL) is found in the southern part of Nigeria more specifically on the Rivers State southern religion Nigeria. It consists of five distinct units, each of which generates its own unique trash at a rate of around 3115.98 tonnes per year. This study had the goal of concentrating on the management of the processing wastes in order to minimize the negative effects that they have on the environment. It is necessary to have an understanding of the number, nature, and make-up of IEPL's industrial wastes in order to effectively manage and regulate the development of waste there. As a result, we decided to collect data through the use of questionnaires. The classification of industrial wastes was accomplished by doing a comparative study and synthesis of research that were relevant to the management of petrochemical waste. Because an individual coding system is required for the integrated management of industrial waste, we gave each type of trash a code that consists of thirteen digits. The primary components of trash were catalysts (9.58 percent), metallic materials (7.62 percent), plastic barrels (35.77 percent), coke (12.66 percent), wood (4.47%), oil (4.15%), glass (0.028 percent), cooling tower packaging (6.7 percent), and other material (18.83 percent). According to the findings of the investigation into the physical qualities of the wastes, only 11.81% of these residues were liquid, while 88.19% were solid. The current handling of these wastes has been experiencing significant difficulties as of late. garbage management at IEPL was particularly challenging due to the wide variety of wastes produced there and the dangerous nature of the wastes themselves (88.19% of garbage was hazardous waste). Recycling and reuse of garbage may have been the greatest decision in some situations; yet, incineration and disposal of waste are also necessary options.

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.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification is possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.

The Last Planner System (LPS) aims to enhance planning reliability by reducing variability in construction processes. While LPS applications have been explored in construction and detailed design, its application in architectural design remains underrepresented due to its abstract nature. This study addresses this gap by proposing an LPS framework tailored for architectural design, utilizing LPS metrics to assess planning reliability. Key issues hindering formal planning methods' implementation are identified, and relevant LPS principles are aligned with these challenges, culminating in a conceptual LPS model designed for architectural projects. Building upon the conceptual model, an implementation model is then developed and put into practice within an architectural design company in the United States, resulting in measured improvements in planning reliability and responsiveness. Additionally, it unveils hidden challenges associated with emerging tasks, guiding future design process enhancements. This study demonstrates how tracking design planning performance with LPS metrics can promote LPS adoption in architectural design, offering a benchmark for necessary interventions to achieve desired performance in architectural design.

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD), in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea-states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30 years long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea-states according to different return periods, and also for performing a sensitivity test for established empirical formulas to estimate wave run-up at this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment and planning.

Background: The oldest old people (≥ 85 years) represent the fastest growing age group in Western populations. Knowledge on early outcomes of acute ischemic stroke (AIS) in very old female patients remains limited. Methods: We assessed gender differences and female-specific clinical data and in-hospital mortality in very old patients with AIS. Acute cerebral infarction was diagnosed in 506 very old female patients from a total of 4,600 acute cerebrovascular infarctions available in an ongoing 24-year hospital stroke data bank. A comparative analysis of vital status at discharge (alive versus dead) was performed in terms of cardiovascular risk factors, clinical data, and early outcomes. Results: The overall in-hospital mortality rate was 20.4%. In multivariate analysis, sudden onset, altered level of consciousness, and neurological, respiratory, cardiological or hemorrhagic complications, and lacunar infarcts appeared to be independent predictors of in-hospital mortality. Conclusions: Female patients ≥85 years with AIS are an unfavorable subgroup of stroke patients with a high risk of early mortality, which may be due to comorbidity, stroke subtype distribution, and higher frequency of medical complications. However, the presence of lacunar infarcts showed a significant protective effect.

In the past decades, the world is actively working towards the global goal of net-zero emission. To decrease emissions, there is a notable trend of electrification in transportation, which is a transition from traditional fuel-based systems to electrical power systems onboard different transportation platforms. For this transition, it is important to study the electrical structure, specifically the onboard microgrid, powered by various energy sources. In this paper, traditional energy management strategies for onboard microgrid systems are discussed, which usually require complicated optimization algorithms and high computation capabilities. Driven by the recent advancements in information technologies, artificial intelligence and digital twin have gained much interest within the transportation sector. These technologies can effectively utilize data to achieve intelligent decision-making, optimize resource utilization, and save energy consumption. This paper presents an overview of the usage of these emerging information technologies in energy management strategies, providing an overall summary and classification of the practical applications. In addition, after examining the potential challenges associated with artificial intelligence and digital twin, this paper also discusses future trends in this field.

Background: In a Beveridgean decentralized healthcare system, like the Italian one, where regions are responsible for their own health planning and financing, the analysis of patients’ migration appears very interesting as it has economic and social implications. The study aims to analyze both patients’ migration for hospital rehabilitation and if the beds endowment is a driver for these flows; Methods: from 2011 to 2019, admissions data were collected from the Hospital Dis-charge Cards database of the Italian Ministry of Health, population data from Italian National Institute of Statistics and data of beds endowment from the Italian Ministry of Health website. To evaluate patients’ migration, we used Gandy’s Nomogram, while to assess if beds endowments are mobility drivers, we have created two matrices, one with attraction indexes (AI) and one with escape indexes (EI). The beds endowment, for each Italian region, were correlated with AI and EI. Spearman’s test was carried out through STATA software; Results: Gandy’s Nomogram showed that only some northern regions had good hospital planning for rehabilitation. A statistically significant correlation between beds endowment and AI was found for four regions, while with EI for eight regions. Conclusions: Only some northern regions appear able to satisfy the care needs of their residents, with an attraction minus escapes positive epidemiological balance. The beds endowment seems to be a driver of Patients’ migration, mainly for escapes. Certainly, the search for mobility drivers needs further investigation given the events in Molise and Basilicata.

Visual data on the geographic distribution of carbon storage helps policy maker to formulate countermeasures on global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data in distribution of forest carbon storage between the last to forest surveys (1993-2015). This study established a model to achieve an estimation that capable to demonstrate the distribution of forest carbon storage. This model uses land use, stand morphology, carbon conversion coefficient databases accordingly for 51 types of major forest in Taiwan. An estimation on 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha–1, where natural forests and plantations respectively contributed of 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr–1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and event of coverage shifting with time by combining to the two forest surveys in Taiwan.

The construction industry has witnessed the increasing use of digital tools and smart solutions, particularly, in the realm of buildings energy automation within the concept of smart cities. While to truly realize the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, appreciated as the critical unit of urban development. To support this interplay of smart solutions between buildings and neighbourhoods, this article aims to collect and review all smart solutions presented in existing scientific articles, technical literature, and realized European projects. These solutions are classified under two main sections of buildings and neighbourhoods, which are investigated through five domains: buildings energy-related uses, renewable energy sources, water, waste, and open space management. Showcasing the quantitative outcomes demonstrates the potential benefits of implementing smart solutions from buildings to neighbourhoods. Moreover, this research concludes that the true enhancement of energy conservation goes beyond the buildings’ energy component and can be genuinely achieved by integrating intelligent elements of the neighbourhood, due to their strong interdependencies. Future research is recommended to focus on assessing the effectiveness of these solutions towards resource conservation.

Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning to optimize teaching and achieve excellent student learning. An international consortium developed a professional development six-module course on educational neuroscience and online community of practice applying design thinking. A mixed methods research design was employed to investigate the attitudes of thirty-two (N=32) participating academics using a survey comprising eleven closed and open questions. Data analysis methods included descriptive statistics, correlation, generalized additive model and grounded theory. The overall evaluation demonstrated a notable satisfaction level with regard to the quality of the course. Given the power of habits, mentoring and peer interactions are recommended to ensure the effective integration of theoretical neuroscientific evidence into teaching practice.

Pygoscelis penguin populations in the Antarctic Peninsula have dropped dramatically in the last 50 years. The main probable cause is the reduction in Krill (Euphausia superba), the most important feeding item for Pygoscelis penguins during breeding. The scientific community has expressed concerns on the potential that competition with the fishery during periods of low krill availability might be exacerbating the effects of climate change. By bringing together data on breeding success of penguin colonies throughout the Antarctic Peninsula with information of krill availability from acoustic survey and krill fishery monitoring data, we were able to show that fishery has had in the past an effect over breeding success. That is a consequence of a management strategy based on a constant catch level enabling the fishery to maintain the same levels of production even when krill availability is low. The total catch limit may have represented a substantial amount of the available krill biomass in some years, and we detected that when the catch goes over 5% of the available biomass during summer, breeding success of penguins decreased by one third. We discuss the implications of our findings to the revised ecosystem-based management strategy of the krill fishery in Antarctica.

The ecotoxic effect of glyphosate, a commonly used Parthenium control herbicide, was evaluated in laboratory on biological and fitness attributes of Zygogramma bicolorata. Bioassay of glyphosate was carried within a minimum range of field recommended dose. Indirect exposure experiment reveals that glyphosate caused maximum mortality of 3^rd larval instars, extends the development stages of larvae, pre pupation and pupation. Significant negative effect was observed on sex ratio, fecundity, egg viability and on other fitness attributes. The study demonstrated the non-compatibility of glyphosate and unsafe with Zygogramma bicolorata. The study concludes that owing to acute toxicity of glyphosate at recommended field dose may be used in combination with Z. bicolorata for successful control of Parthenium weed, but needs to be evaluated under natural field conditions.

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.

Growth models of uneven-aged forests on the diameter class level can support silvicultural decision making. Machine learning brings added value to the modeling of dynamics at the stand or individual tree level based on data from permanent plots. The objective of this study is to explore the potential of machine learning for modeling growth dynamics in uneven-aged forests at the diameter class level based on inventory data from practice. Two main modeling approaches are conducted and compared: i) fine-tuned linear models differentiated per diameter class, ii) an artificial neural network (multilayer perceptron) trained on all diameter classes. The models are trained on the inventory data of the Canton of Neuchâtel (Switzerland), which are area-wide data without individual tree-level growth monitoring. Both approaches produce convincing results for predicting future diameter distributions. The linear models perform better at the individual diameter class level with test R2 typically between 50 and 70% for predicting increments in the numbers of stems at the diameter class level. From a methodological perspective, the multilayer perceptron implementation is much simpler than the fine-tuning of linear models. The linear models developed in this study achieve sufficient performance for practical decision support.

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

To determine the adequacy of management practices in Social Health Organization (SHO) in face of compliance guidelines established by Brazilian organizations. This was qualitative research. Data were gathered through interviews and documentary analysis and were analyzed through the interpretation of significances and content analysis. The entity structures the compliance function based on three axes: a) normative - institutionalization of the integrity program, ethical conduct, internal controls and risk management; b) commercial - institutionalization of mechanisms aimed at transparency and accountability of the funds raised. However, the Covid-19 pandemic generated unforeseen contingencies such as accountability for funds from private donations; c) organizational - adherence by top management in maintaining a structure for inspection and control of the institution’s processes and behaviors in the market in which it operates. Based on the analysis of documents there is 76% adequacy of adherence of management practices to the compliance guidelines. It is concluded that organizations that depend on resources and understand that the subsidies they receive, whether government, whether public or private donations, will make the necessary efforts to ensure high levels of compliance, their choices and conduct preserve their image and the achievement of greater credibility and legitimacy.

The relaxation process of lithium batteries caused by load variation is considered. It is shown that such processes have strong dependence on internal physical and chemical processes and battery technical conditions. Theoretical expressions of the relaxation process caused by a step-like load variation have been obtained for 1^st and 2^nd order equivalent electrical circuits. The experimental investigations show that the obtained models fit the real relaxation processes and the behavior of the identified parameters could be explained by specific features of physical and chemical processes within the lithium battery. It should be noted that the obtained results can be generalized for a different type of electrochemical power source. The proposed approach makes it possible to provide means for electrochemical power source characterization and diagnostic, the main advantages of which are good time localization of measurement procedures and inexpensive apparatus implementation.

As is known, the construction industry has one of the highest occupational accidents incidence rates among all economic sectors. Currently, Building Information Modeling (BIM) appears as a tool that addresses occupational safety issues throughout the construction life cycle projects, avoiding hazards and risks and, consequently, increasing safety. This work investigates BIM methodology and related technologies application for building safety planning in construction and demonstrates the potential of this technology for the integrated implementation of safety measures during the design phase and the construction site management. The first step consisted of a literature review on the application of BIM in safety in the design and planning phases. Following, to show the potentialities of construction simulation, a case study based on BIM 4D to prevent falls from height was developed. With BIM 4D, it is possible to follow the construction process over time, giving the construction safety technicians’, designers, supervisors and managers the capability to analyse, in each phase, the potential risks and which safety measures should be implemented. BIM can effectively integrate safety measures from the design phase to the construction and use phase and do integrated safety planning within construction planning, leading to reliable safety management throughout the construction process.

The Urban Eco-Sustainable Index for Upscaling Water Security at Catchment Level in Langat River, Malaysia has developed by using the Modified Watershed Sustainability Index or MicroWSI (MWSI), which was based on the Participation, Design and Management Components. The study has successfully applied spatial and social dimensions on ecohydrology of the selected Langat River reach for stormwater management, natural ecosystems health and quality of life. The planning and public participation aspects of the study have evaluated the surrounding neighborhood area of Langat. The conceptual design of rehabilitation works implementation related to Urban Stormwater Management Manual for Malaysia (MSMA) Stormwater Management Ecohydrology (SME) within the study area has been developed with four components of MSMA-SME to be implemented in the study area i.e Bioretention System, Porous Pavement System, Constructed Wetland and Slope Protection Works. These components were proposed to be applied in the development of Langat Riverfront Community Park (LRCP) which has taken into account the components of Design, Management and Participation of Community and Stakeholders in Langat River Basin, Malaysia. This study analyzed the MWSI for the Upscaling of MSMA Ecohydrology at Catchment Level of Langat River and has found the medium level of sustainability for the level of participation, proposed design, and management. Thus, there is a need to increase the level of readiness in the community and stakeholder participation in the Langat River towards sustainability of river conservation and rehabilitation programmes in this basin.

Science is addressing global societal challenges and, due to limitations in research financing, scientists are turning to public at large to jointly tackle specific environmental issues. Citizens are therefore increasingly involved in monitoring programs, appointed as citizen scientists with potential to delivering key data at near no cost to address environmental challenges, so fostering scientific knowledge and advise policy- and decision-makers. One of the first and most successful example of marine citizen science in the Mediterranean is represented by the integrative and collaborative implementation of several jellyfish spotting campaigns in Italy, Spain, Malta, Tunisia started in 2009. Altogether, in terms of time coverage, geographic extent, and number of citizen records, these represent the most effective marine citizen science campaign so far implemented in the Mediterranean Sea. Here we analyzed a collective database merging records over the above four Countries, featuring more than 100,000 records containing almost 25,000 observations of jellyfish specimens, collected over a period of 3 to 7 years (from 2009 to 2015) by citizen scientists participating in any of the national citizen science programs included in this analysis. Such a wide citizen science exercise demonstrates to be one of the so far available most valuable and cost-effective tools to understanding ecological drivers of jellyfish proliferations over the Western and Central Mediterranean basins, and a powerful contribute to develop tailored adaptation and management strategies, mitigate jellyfish impacts on human activities in coastal zones, and support implementation of marine spatial planning, Blue Growth and conservation strategies.

The southern extent of the boreal forest in North America has experienced intensive human disturbance in the past decades. Among these, forest harvesting leads to the substantial loss of late-successional stands that include key habitat attributes for several avian species. The American Three-toed Woodpecker, Picoides dorsalis, is associated with continuous old spruce forests in the eastern part of its range. In this study, we assess the influence of habitat characteristics at different scales on the occupancy of American Three-toed Woodpecker in a heavily managed boreal landscape of northeastern Canada, and we inferred species occupancy at the regional scale. We conducted 185 playback stations over two breeding seasons and modelled the occupancy of the species while taking into account the probability of detection. American Three-toed Woodpecker occupancy was lower in stands with large areas recently clear-cut, and higher in landscapes with large extents of old-growth forest dominated by black spruce. At the regional scale, areas with high probability of occupancy were scarce and mostly within protected areas. Habitat requirements of the American Three-toed Woodpecker during the breeding season, coupled with over-all low occupancy rate in our study area, challenge its long-term sustainability in such heavily managed landscapes. Additionally, the scarcity of areas of high probability of occupancy in the region suggest that the ecological role of old forest outside protected areas could be compromised.

The construction of sustainable urban forests follows the principle that well-being in people is promoted when exposed to tree population. Facial expression is the direct representation of inner emotion that can be used to assess real-time perception in urban forests. The emergence and change of facial expressions for forest visitors are in an implicit process. The reserved character of oriental races strengthens the requirement for the accuracy to recognize expressions through instrument rating. In this study, a dataset was established with 2,886 randomly photographed faces from visitors in a constructed urban forest park and a promenade at summertime in Shenyang City at Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions as angry, contempt, disgusted, happy, neutral, sad, scared, and surprised emotions. The FireFACE ver. 3.0 software was used to test hit-ratio validation as the accuracy (ac.) to match machine-recognized photos with those identified by experts. According to Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac.=0.40%, P=0.0038) and scared (ac.=25.23%, P=0.0018) expressions cannot pass the validation test. Therefore, the FireFACE can be used as an instrument to analyze facial expression from oriental people in urban forests but contempt and scared expressions cannot be identified.

The aim of this research is forwarded for assessment of provision possibilities of the multi-componential and heterogeneous services in the fast-changing topology of cargo transportation processes. The mobile intelligent services in nowadays transport means require the development of complex infrastructure for multi-compositional service support. Our objectives are related to the investigation in data-transfer capabilities for heterogeneous service support, by offering some improvements for developing the infrastructure of transportation of vehicles by helping in the administration of transport processes. This research aims to develop an approach for assessment of infrastructure for sustainable management of cargo transportation processes by roads. Such assessment is multi-layered by including the management possibilities of cargo transportation logistic processes and electronic (smart, mobile) services by implementing of nowadays innovative software and hardware of information communication technologies (ICT). Special attention is paid for road safety, more environment cleanable, and paperless management by assessing the integration of potentials and prospects of wireless, vehicle Ad-hoc communication networks (VANET), and other communication possibilities. Some requirements are revealed for such type of infrastructure for the provision of heterogeneous services. The results of the development of infrastructure demonstrate the capacities of the potential of wireless networks for the provision of high-level of multi-component, heterogeneous services.

We reviewed the current status of a Vulnerable seagrass, Halophila beccarii from the coast of India using the published data from 1977-2020. We found that the seagrass, H. beccarii has a pan India distribution on both east and west coast. It is abundant in the intertidal silty-muddy region on the west coast, while on the east coast it is found on sandy habitats, with few exceptions of muddy habitat. H. beccarii was found to be associated with mangroves or smaller seagrass species within a depth limit of 1.7m. Low salinity and high nitrate levels were observed for the H. beccarii meadows of the west coast due to its association with mangroves. The nutrient levels in H. beccarii meadows of India were comparatively lower than other seagrass meadows. Most of the research on H. beccarii has focoused on its morphometrics (41%), reproductive (33%) and distribution (29%) along the coast of India. Reproductive traits such as flowering and fruiting varying according to the seasons of each coast due to the influence of monsoon and its associated temperature, salinity and nutrient influx. H. beccarii has a great potential of various bioactive compounds, which needs further investigation. Habitat disturbance, anthropogenic pollution and coastal development are the major cause of declining H. beccarii ecosystems in India. Significant loss of the seagrass was observed from the west coast of India due to increased coastal development activities. There is a significant need in quantifying H. beccarii population trends, impact of climate and anthropogenic stressors, economic values of ecosystem services and the role of ecological connectivity for better conservation and management of H. beccarii seascapes across India. There is a need for integration of research outcomes in policy framing for preventing the decline and further loss of this vulnerable seagrass ecosystem.

Consistent data is seldom available for whole-catchment flood modelling in many developing regions, thus this study demonstrates how the complementary strengths of open and readily available geospatial datasets and tools can be leverage to map flood risk within acceptable levels of uncertainty for flood risk management. Available fragmented remotely-sensed and in situ datasets (including hydrological data, altimetry, digital elevation model, bathymetry, aerial photos, optical and radar imageries) are systematically integrated using 2-dimensional CAESAR-LISFLOOD model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Experimental modelling, calibration and validation is undertaken for the whole Niger-South hydrological catchment area of Nigeria, then segmented into sub-domains for re-validation to understand how data variability and uncertainties impact on the accuracy of model outcomes. Furthermore, aerial photos are applied for the first time in the study area for flood model validation and to understand how different physio-environmental properties influence synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria.

This work can be considered as a first step of designing a future competitive data-driven approach for remaining useful life prediction of aircraft engines. The proposed approach is an ensemble of serially connected extreme learning machines. The results of prediction of the first networks are scaled and fed to the next networks as an additive features to the original inputs. This feature mapping allows increasing the correlation of training inputs with their targets by holding new prior knowledge about the probable behavior of the target function. The proposed approach is evaluated under remaining useful estimation using a set of “time-varying” data retrieved from the public dataset C-MAPSS (Commercial Modular Aero Propulsion System Simulation) provided by NASA. The prediction performances are compared to basic extreme learning machine and proved the effectiveness of the proposed methodology.

The term “risk” is connoted with divergent meanings in natural hazard risk research and the practice of risk management. Whilst the technical definition is accurately defined, in practice, the term “risk” is often synonymously used with “danger”. Considering this divergence as a deficiency, risk communication often aims to correct laypersons’ understanding. We suggest in reference to Breakwell (2001) to treat the variety of meanings as a resource for risk communication strategies instead. However, there is no investigation so far, of what laypersons’ meanings of risk actually comprise. To address this gap, we examine the meanings of risk applying a social representations approach (Moscovici, 2001) in a qualitative case study design. Results of the study among inhabitants of Swiss mountain villages show that differences in meanings were found according to hazard experience and community size. We found commonly shared core representations, and single peripheral ones. We conclude with suggestions on how to make usage of the knowledge on SR in risk communication.

The management of the electrical energy still raises a huge interest for end-users at the household level. Home electricity management systems (HEMS) have recently emerged both to warrant uninterruptible power and high power quality, and to decrease the cost of electricity consumption, by either shifting it in off peak time or smoothing it. Such a HEMS requires a bidirectional DC-AC converter, specifically when an energy transfer is required between a storage system and the AC-grid, and vice versa. This article points out the relevance of an innovative topology based on sinusoidal waveforms from the generation of sine half-waves. Such a topology is based on a DC-DC stage equivalent to an adjustable output voltage source and a DC-AC stage (H-bridge) which are in series. The results of a complete experimental procedure prove the feasibility to improve the power quality of the output signals in terms of total harmonic distortion (THD-values about 5%). The complexity of the proposed converter is minimized in comparison with multilevel topologies. Finally, wide band-gap semiconductor devices (SiC MOSFETs) are helpful both to warrant the compactness and the high efficiency (about 96%) of the bidirectional converter, whatever its operation mode (inverter or rectifier mode).

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

In networked driving simulation, two or more human drivers participate interactively within a shared virtual environment. Thereby, typical applications of driving simulation can be extended to consider multi-driver scenarios, where a much closer approximation of reality with its unpredictability is achieved. However, the utilized network is typically prone to a considerable amount of message traffic. In addition to restricted system scalability, the resulting degradation of network performance leads to invalid simulation outcomes or unacceptable system behavior. High-level architecture (HLA) is an IEEE standard for networked simulation. Data distribution management (DDM) is one of the service groups provided by HLA standard. The aim of the DDM service is to reduce network traffic and to save effort required to process unnecessary received data. However, different approaches for current DDM implementations show major drawbacks in terms of utilization complexity, inefficiency, and yet added network overhead. This paper presents a concept of an interest manager that takes over the DDM functionality and avoids these drawbacks. Simulation data is exchanged between the participating driving simulators only when it is necessary according to the driving situations. The concept is validated by analyzing the network load of two driving maneuvers with and without the interest manager.

Growing evidence suggests that healthcare provider advice may increase tobacco cessation, reduce alcohol use, and improve the adoption of desirable lifestyle behaviors among patients. However, how brief interventions and other provider-patient interactions can shape cumulative adoption of multiple modifiable behaviors is less well studied for diabetes prevention and control. Using weighted internet panel survey data from a large socio-demographically diverse urban population in the United States (n=1,003), the present study describes differences in group characteristics among those who had been “ever diagnosed” with prediabetes/diabetes versus those who had not. It also examines the associations between the cumulative adoption of lifestyle behaviors and each of the following: a) lifetime prediabetes/diabetes diagnosis; b) brief lifestyle intervention exposure (i.e., received provider advice/encouragement); and c) recent provider-patient communication about diabetes. There were several group differences in “ever diagnosed” prediabetes/diabetes status by age, employment status, health status, nutrition knowledge, blood pressure/hypertension diagnosis, and diabetes-related health behaviors (p<0.05). Each of the three provider-patient interactions of interest were positively associated with a higher cumulative sum of adopted modifiable lifestyle behaviors for diabetes prevention and management. Results suggest that provider advice/provider-patient interactions of any type can have a salutary impact on whether individuals with prediabetes or type 2 diabetes will engage in recommended lifestyle behavior modifications.

Physical processes play important roles in controlling eutrophication and oligotrophication. In stratified lakes, internal waves can cause vertical transport of heat and nutrients without breaking the stratification, through boundary mixing events. Such is the case in tropical Valle de Bravo (VB) lake, where strong diurnal winds drive internal waves, boundary mixing and hypolimnetic warming during stratification periods. We monitored VB during 18 years (2001-2018) when important water-level fluctuations (WLF) occurred, affecting mixing and nutrient flux. Mean hypolimnetic temperature increase (0.06–1.04°C month-1) occurred in all the stratifications monitored. We analyzed temperature distributions and modeled the hypolimnion heat budget to assess vertical mixing between layers (26,618–140,526 m-3h-1), vertical diffusivity coefficient KZ (6.2x10-7–3.3x10-6 m2s-1) and vertical nutrient entrainment to epilimnion on monthly scale. Stability also varied as a function of WLF. Nutrient flux to the epilimnion ranged 0.36–5.99 mg m-2d-1 for soluble reactive phosphorus (SRP) and 5.8–97.1 mg m-2d-1 for dissolved inorganic nitrogen (DIN). During low water-level years, vertical nutrient fluxes increase and can account for up to &gt;40% of the total external nutrients load to the lake. Vertical mixing changes related to WLF affect nutrient recycling, their flux to sediments, ecosystemic metabolic balance and planktonic composition of VB.

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

Databases are by far the most valuable asset of companies. Since the need was seen not only to count but also to have some type of record of elements such as crops, animals, money, properties and that this record could be consulted and modified according to the situation, that is where the first database was born. , and after that, these databases cannot be disorganized, they also need to be managed and administered under established standards that facilitate their understanding and management not only by their creators but by the other people who subsequently administer them. Databases and database management systems have an interesting evolutionary history that deserves to be analyzed and this is the objective of this document, where it is sought to understand. Along with databases and their management systems, data mining or Data mining arises that in order not to extend ourselves so much, it is the job of finding common patterns in various data sources and in what way they can be used to predict situations or results of various circumstances; We also focus on the other topic that we will present, Oracle data mining, which roughly is to merge data mining with Oracle, which makes it a powerful tool for obtaining information and predicting results based on statistics.In this article we will study and analyze the ideas, concepts and basic examples that make up SGBD and Data Mining and, we will try to go deeper into this topic, the use of decision techniques such as advanced statistical algorithms. We also present a fictitious example of the application of these techniques: predicting which products can be sold based on their relationship with others. we will give a brief explanation of association rules, data mining cycle and the types of learning and the evolution that data mining has had.

This study analyzed the relationship between contextual factors, personal factors, and entrepreneurial intentions of business area students from the perspective of the Theory of Planned Behavior. Data were collected using a questionnaire applied to 229 entering the courses, and the analysis was carried out using Structural Equation Modeling (PLS-SEM). The results demonstrated that: a) H1a: education had a positive influence on the choice of an entrepreneurial career, and b) H1b: business-friendly environments reinforce this choice, influenced by broader social and cultural support. This demonstrates that environmental, factors enhance cognitive factors and the intention to undertake. The results also showed that: c) H2c: the perception of abilities to start a business, and d) H2a: the favorable evaluation of choosing an entrepreneurial career, are determining elements of entrepreneurial intentions in the studied context. However, the negative relationship between e) H1c: government incentives and the ability to start a business, and f) H2b: the influence of social and environmental on entrepreneurial intentions requires attention from universities and governments in promoting environments conducive to entrepreneurship, the choice of an entrepreneurial career and the decision of young people to get involved in sustainable business projects. In conclusion, environmental and psychological aspects have positive and significant effects on the entrepreneurial career intentions of higher education students, opening space for governments and universities to promote student entrepreneurship with the sustainability business as beliefs and values.

This paper uses the numerical results of surveys sent to Huelva’s (Andalusia, Spain) households to determine the degree of knowledge they have about the urban water cycle, needs, values, and attitudes regarding water in an intermediary city with low water stress. In previous research, we achieved three different households’ clusters. The first one grouped households with high knowledge of the integral water cycle and a positive attitude to smart devices at home. The second cluster described households with low knowledge of the integral water cycle and high sensitivity to price. The third one showed average knowledge and predisposition to have a closer relationship with the water company. This paper continues with this research line, applying Classification and Regression Trees (CART) to determine which hierarchy of variables/factors/ independent components obtained from the surveys are the decisive ones to predict the range of household water consumption in Huelva. Positive attitudes towards improved cleaning habits for personal or household purposes are the highest hierarchy component to predict the water consumption range. Second in the hierarchy, the variable Knowledge Global Score about the integral urban water cycle, associated with water literacy, also contributes to predicting the water consumption range. Together with the three clusters obtained previously, these results will allow us to design water demand management strategies (WDM) fit for purpose that enable Huelva’s households to use water more efficiently.

Ascending aorta aneurysm (AsAA) is a complex disease, currently defined an inflammatory disease. In the sporadic form, AsAA has, indeed, a complex physiopathology with a strong inflammatory basis, significantly modulated by genetic variants in innate/inflammatory genes, acting as independent risk factors and as largely evidenced in our recent studies performed during the last 10 years. Based on these premises, here, we want to revise the impact of reactive oxygen species (ROS) and oxidative stress on AsAA pathophysiology and consequently on the onset and progression of sporadic AsAA. This might consent to add other important pieces in the intricate puzzle of the pathophysiology of this disease with the translational aim to identify biomarkers and targets to apply in the complex management of AsAA, by facilitating the AsAA diagnosis currently based only on imaging evaluations, and the treatment exclusively founded on surgery approaches.

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

Persistent Spinal Pain Syndrome Type 2 (PSPS-T2), (Failed Back Surgery Syndrome), dramatically impacts on patient quality of life, as evidenced by Health-Related Quality of Life (HRQoL) assessment tools. However, the importance of functioning, pain perception and psychological status in HRQoL can substantially vary between subjects. Our goal was to extract patient profiles based on HRQoL dimensions in a sample of PSPS-T2 patients and to identify factors associated with these profiles. Two classes were clearly identified using a mixture of mixed effect models from a clinical data set of 200 patients enrolled in “PREDIBACK”, a multicenter observational prospective study including PSPS-T2 patients with 1-year follow-up. We observed that HRQoL was more impacted by functional disability for first class patients (n=136) and by pain perception for second class patients (n=62). Males that perceive their work as physical were more impacted by disability than pain intensity. Lower education level, lack of adaptive coping strategies and higher pain intensity were significantly associated with HRQoL being more impacted by pain perception. The identification of such classes allows for a better understanding of HRQoL dimensions and opens the gate towards optimized health-related quality of life evaluation and personalized pain management.

Though Cyber Physical Systems (CPS) become very popular in last the decade, dependability of CPS is still a critical issue and related survey is rare. We try to spell out the jigsaw of technologies and figure out the technical trends of dependable self-managing CPS. This survey first recalls the motivation and the similar concepts. By analyzing four generic architectures, we summarize the common characteristics and related assurance technologies, and propose a more generic environment-in-loop processing flow of CPS and a formal interaction flow between physical space and cyber space. Further, the similarity between correctness and dependability is formally analyzed and the new five research questions of dependable self-managing CPS are presented. Then we review the critical technologies and related correctness verification & validation (V&V) methods, the architectures for dependable self-managing CPS. Further, the detail dependability management and V&V technologies are surveyed, which covers the areas of running-time fault management methods and whole life cycle V&V technologies, maintenance and available tool sets. For holistic CPS development, Modeling techniques and MDE (model driven engineering) based V&V methods are analyzed in detail. Then we complete the jigsaw of technologies and figure out the missing part. Further, we propose the technical challenges and the further direction. To our best knowledge, this is the first comprehensive survey on dependable self-managing CPS development and evaluation.

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions.

This qualitative study explores Supplier Relationship Management (SRM) in the context of digital transformation within e-commerce businesses, focusing on the implications of emerging digital technologies. Through semi-structured interviews and documentary analysis, the study examines how organizations leverage technologies like artificial intelligence (AI), blockchain, and Internet of Things (IoT) to enhance SRM practices. Findings reveal that digitalization enables improved operational efficiencies, real-time data analytics, and expanded supplier networks through platforms such as Amazon Business and Alibaba. Strategic shifts are noted towards collaborative supplier relationships, emphasizing joint innovation, technology co-investment, and shared risk management strategies. However, the implementation of digital SRM strategies presents challenges, including data privacy concerns, integration complexities, cybersecurity threats, and regulatory compliance issues. Organizations must navigate these challenges by developing robust governance frameworks and cybersecurity protocols to protect sensitive information and ensure regulatory adherence. Key performance indicators (KPIs) such as supplier performance, cost savings, innovation impact, risk management effectiveness, and sustainability integration are critical for evaluating the success of digital SRM initiatives. Strategically, businesses are advised to prioritize leadership commitment, cross-functional collaboration, and continuous learning to optimize digital SRM practices effectively. Future research should explore emerging trends in digital SRM, investigate technological impacts on supplier dynamics and organizational performance, and examine ethical considerations in digitalization. By integrating these insights into strategic decision-making, businesses can enhance supplier relationships, mitigate operational risks, and achieve sustainable growth in the global marketplace.

This qualitative research investigates the influence of Corporate Social Responsibility (CSR) on Supplier Relationship Management (SRM) within the e-commerce sector. Through semi-structured interviews and secondary data analysis, the study explores motivations, challenges, impacts, and strategic outcomes of integrating CSR practices into supplier relationships. Motivations for adopting CSR include ethical considerations, regulatory compliance, and meeting stakeholder expectations, reflecting a strategic alignment with corporate values and societal demands. Challenges such as resource constraints, regulatory complexities, and cultural differences highlight operational hurdles and strategic considerations e-commerce firms face in implementing CSR initiatives effectively. The study reveals that CSR enhances trust, collaboration, and risk mitigation within e-commerce supply chains through transparent communication, shared values, and ethical practices. Strategically, CSR contributes to enhanced brand equity, competitive advantage, and long-term value creation by differentiating firms in competitive markets and attracting socially conscious consumers and investors. However, economic trade-offs, stakeholder divergence, and operational complexities require firms to adopt adaptive strategies that balance short-term financial goals with long-term sustainability objectives. The findings suggest avenues for future research exploring longitudinal impacts of CSR on supplier relationships, cross-sectoral comparisons, and technological advancements in CSR-driven SRM practices. Practically, e-commerce firms should prioritize strategic alignment of CSR initiatives with core business objectives, engage stakeholders in CSR strategy development, and embrace continuous improvement to foster ethical conduct, stakeholder trust, and sustainable business practices.

Strategic procurement practices within global supply chains are crucial for organizations aiming to navigate complex challenges and achieve sustainable competitive advantage. This qualitative research explores how procurement has evolved from a cost-centric function to a strategic enabler of broader organizational objectives, including risk management, sustainability, and innovation. Through semi-structured interviews with procurement managers and supply chain executives across diverse industries, the study investigates key themes such as supplier relationship management, technological integration, and the impact of sustainability considerations on procurement decision-making. Findings indicate that effective procurement strategies, such as strategic sourcing and collaborative supplier partnerships, are pivotal in enhancing supply chain resilience and operational efficiency. Technological advancements, particularly artificial intelligence and big data analytics, are transforming procurement operations by optimizing processes and enhancing decision-making capabilities. However, the study identifies significant challenges, including regulatory complexities, geopolitical uncertainties, and internal resistance to change, which require adaptive approaches to procurement management. Sustainability emerges as a critical driver, with organizations adopting ethical sourcing practices, reducing carbon footprints, and promoting supplier diversity to meet environmental and social responsibility goals. Leadership qualities such as visionary thinking and emotional intelligence are essential for navigating these challenges and driving innovation within procurement practices. Overall, this research contributes to a deeper understanding of strategic procurement's role in organizational strategy and offers practical insights for enhancing procurement effectiveness and resilience in a competitive global market landscape.

This qualitative study explores how businesses are adapting their Supplier Relationship Management (SRM) strategies to address the challenges and capitalize on the opportunities presented by evolving e-commerce trends. Through in-depth interviews with key industry stakeholders, the research investigates the integration of advanced technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT) into SRM practices. Findings reveal that these technologies enhance efficiency, transparency, and responsiveness within supply chains, enabling businesses to navigate complexities such as supply chain dynamics, fluctuating consumer demand, and regulatory compliance. The study identifies effective strategies employed by businesses, including enhancing communication channels with suppliers, diversifying supplier bases, and investing in technological advancements. These strategies enable businesses to mitigate risks, improve collaboration, and drive innovation, thereby maintaining competitive advantage in the digital marketplace. Collaborative practices such as joint product development, shared risk management, and co-investment in technology emerge as critical drivers of successful SRM. Ultimately, this research contributes to a deeper understanding of SRM in the context of e-commerce, offering insights into how businesses can optimize their supply chain operations amidst technological disruptions and market fluctuations. The findings underscore the importance of proactive adaptation and strategic alignment with suppliers to foster resilient and agile supply chains. As e-commerce continues to evolve, businesses must leverage these insights to refine their SRM strategies and sustain growth in an increasingly competitive environment.

This qualitative research investigates how supply chain innovations drive marketing differentiation in consumer goods companies. In a competitive global marketplace, firms are increasingly leveraging advanced supply chain management (SCM) practices to enhance operational efficiency and create distinctive market positions. The study explores five key supply chain innovations—digitalization, sustainability practices, predictive analytics, agile supply chain models, and collaborative partnerships—and their impact on marketing differentiation strategies. Data were collected through semi-structured interviews with 20 executives and managers from leading consumer goods companies, analyzing themes related to innovation adoption, challenges, and outcomes. Findings indicate that digital technologies such as IoT, AI, and blockchain are pivotal in improving supply chain visibility, optimizing inventory management, and enabling real-time decision-making, thereby supporting personalized customer experiences and agile responses to market dynamics. Sustainability practices, including sustainable sourcing and green logistics, emerge as critical drivers of brand reputation and consumer trust, aligning with growing consumer preferences for eco-friendly products. Predictive analytics facilitate better demand forecasting and pricing strategies, while agile supply chain models enhance flexibility and responsiveness in delivering products faster to market. Despite benefits, challenges include integrating innovations with legacy systems, managing resistance to change, and addressing data security concerns. Strategies for overcoming these barriers include leadership commitment, cross-functional collaboration, talent development, and strategic partnerships. By embracing these strategies and innovations, consumer goods companies can strengthen their competitive positioning, enhance customer satisfaction, and achieve sustainable growth in a rapidly evolving marketplace.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

Machine learning has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of machine learning models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9,584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg-1, which favor enhanced organic matter in the soil. Different models identified different impacts of nutrients on the organic matter level in the soil. The SHAP method is practical for assessing whether different machine learning models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.

This study considers the values of the credit payments that are made at random values for both payment size and number. The simultaneous existence of the two random variables increases the total value of the volatility of the payment process. The dependence for the simultaneous influence of the two random variables is derived by applying conditional probabilistic formulations. Formal relations apply to animal husbandry credit payments. The credit policy was analyzed for the cases of regular payments and with a stochastic number of payments. A recommendation for the upper level of credit payments is proposed based on a model predictive approach using historical payment data. A credit management algorithm is derived, in which decision-making for credit payments is formally derived and numerically discussed. The application of derived quantitative relations is empirically applied to real data on animal husbandry. The recommendations from the dependencies obtained allow a reduction in the mean values of credit payments, which correspond to a reduction in the credits used for business management, without a change in business policy.