Composites with HDPE and PLA matrix have been tested to analyse the effect of natural fillers (wood flour, recycled waste paper and a mix of both fillers) and temperature on polymer degradation. Composting tests have been performed in both mesophilic (35°C) and thermophilic (58°C) conditions. Degradation development has been evaluated through mass variation, TGA and DSC. HDPE, as expected, did not display any relevant variation, confirming its stability under our composting conditions. PLA is sensibly influenced by temperature and humidity, with higher reduction of Mw when composting is performed at 58°C. Natural fillers seem to influence degradation process of composites, already at 35°C. In fact, degradation of fillers at 35°C allows a mass reduction during composting of composites, while neat PLA do not display any variation.

Hereditary polyposis syndromes are rare but potentially devastating conditions which require multidisciplinary care from an early age. Societal guidelines serve as the framework for disease diagnosis, surveillance and treatment. Guidelines are based on expert opinion and ideally are evidence-based; however, recommendations may vary among different societies and can result in discordant care. This paper aims to summarize key differences in management based on societal guidelines for pediatric polyposis syndromes and identify some of the factors which may contribute to divergence in care.

The efficiency success status of organization built environment assets management needs robust and comprehensive maintenance processes which depends on the efficiency of the contract information documents for the maintenance management within the assets life cycle and achieve positive return of the investments. Therefore, this paper highlights on the appropriate interactive approach to structuring information flow scope of the asset facilities management contract for construction projects based on GIS and BIM integration processes, sustainability standards, and As-built contractual documents of the project to support the organization owner and stakeholder to improve the asset management processes. Using expert interviews with comprehensive understand for the information type in several facility management contracts conducted in common Organization types based on reviewing available international facility management standards manual and contract of procedures for different services types, and required information flow inside international facility management firms. The study classify and build significant integrated information and data flow model contract drafting with measures and indicators of performance efficiency as guideline for monitoring, technically evaluating, and financially procedures for performance efficiency and quality of services provided in the facilities management contract for King Faisal University campus projects as a case study.

Laryngopharyngeal reflux (LPR) is a prevalent disease in the general population and may have acute or chronic clinical presentation. LPR may be misdiagnosed in primary care medicine regarding the lack of gastroesophageal reflux disease symptoms and the lack of findings at the gastrointestinal endoscopy. Depending on the physician field of expertise and experience, LPR may be clinically over- or under-diagnosed. The management of LPR is possible in primary care medicine but primary care physician has to consider some red flags that requires to address the patient to otolaryngologist or gastroenterologist. The use of patient-reported outcome questionnaire such as reflux symptom score-12 and the consideration of some oral and pharyngeal findings visualized through the mouth opening may help the primary care physician to evaluate the LPR findings at the diagnosis time and throughout treatment. In this review, we provide a practical algorithm of management of LPR for primary care physician or other specialists that cannot perform fiberoptic examination. In this algorithm, physician has to exclude some confounding conditions such as allergy or other causes of pharyngolaryngitis and red flags. Physician may prescribe an empirical treatment based on diet and behavioral changes with or without medication, depending on the complaint severity of the patient. In case of prescription of medication, proton pump inhibitors and alginate have to be considered in association to protect the upper aerodigestive tract mucosa from acid, weakly acid and alkaline pharyngeal reflux events.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.

Today's major challenge for smart Microgrids is to ensure the security of communications in a large number of changing data sets that are vulnerable to attacks by denial of services in constant evolution. The Internet Protocol Traceback defines a set of methods that help identify the source of an attack with minimal requirements for memory and processing. However, the concept of Traceback is not yet being used in smart Microgrids. As a result, the main challenge of this article is to incorporate a new Traceback approach into the cybernetic system of a smart mesh Microgrid, which can be tested using a network simulator (NS-3) based on delay, debit, and packet loss rate parameters. In fact, the simulation results show the efficacy of this approach compared to others existing in the literature. Furthermore, using the proposed Traceback technique and the mesh nodes, we were able to create a smart meshed Microgrid. Moreover, using the Traceback approach given for merging Intel Galileo Gen.1 nodes with the Compex WLE200NX.11a/b/g/n to establish a secure test bench, which is deployed as a prototype at the Sfax Digital Research Center in Tunisia, we were able to create an intelligent Microgrid. In fact, by identifying all attack vectors and revealing their origins, we could boost the efficiency of our operation by 100%.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

Some researchers in Taiwan argue that the current mechanism by which government subsidies are allocated for smart city demonstration projects warrants improvement. A comprehensive literature review determined that the development potential of smart cities should be prioritized in site selection for such demonstration projects. This study developed an evaluation framework on the basis of multi-criteria evaluation methods to enable the identification of suitable smart community demonstration sites. Evaluation criteria were first identified through the Delphi method. Next, the weights of each criterion were derived through the analytic hierarchy process. Furthermore, the capability of the proposed evaluation model was determined through simulation testing.

In this article, we report how we constructed a dataset that contains the responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset has been collected in May-July 2020 and is a sample of over 500 respondents of various different ethnicities and backgrounds. These data have been used for modelling the ways people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of the users and via a variety of attitudes and expectations the users invest in their devices and in themselves as users. This article presents the survey items as well as some preliminary data insights. The data have been collected in English and answers have been anonymized in order to ensure GDPR compliance. They are stored in a .csv file containing the respondents’ answers to the questions.

Accidents and mishaps in industrial environments like construction, mining, and transport are rampant - mainly due to human negligence and improper monitoring of the workplace. In this paper, we address the safety of workers operating in dangerous environments by improving their situational awareness. According to Occupational health and safety rules, everyone must wear hard hats while on site. Our main idea is to make the hard hats smart by incorporating miniature-sized Doppler radars sensing the users’ surroundings. These Doppler radars are lightweight, rugged, and consume low-power compared to vision-based solutions. This paper discusses the observability of range from Doppler frequency measurements and the magnitude of estimation errors introduced by the human head, walking, and working motions. We present the framework to estimate the position of walls and targets surrounding the worker. For testing, we simulated an indoor environment with randomly moving workers. Experiments showed that once observability conditions are met, human head and walking movements can be handled through added noise in the system. We also present an innovative idea of using two Doppler radars to obtain the estimators’ initial estimates, reducing the estimation error to less than 5cm and convergence time by more than 80

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

The UK food industry faces significant challenges to remain sustainable. With major challenges such as Brexit on the horizon, companies can no longer rely on a low labour cost workforce to maintain low production costs and achieve economic sustainability. Smart Systems (SS) is being seen as an approach towards achieving significant improvements in both economic and environmental sustainability. However, there is little evidence to indicate whether UK food companies are prepared for the implementation of such systems. The purpose of this research is to explore the applicability of Smart Systems in UK food manufacturing companies and, to identify the key priority areas and improvement levers for the implementation of such systems. A triangulated primary research approach is adopted and includes a questionnaire, follow up interviews and visits to thirty-two food manufacturing companies in the UK. The questionnaire and interviews are guided by the development of a unique measuring instrument created by the authors that is focusses upon SS technologies and systems. This paper makes an original contribution in that it is one of few academic studies to explore the implementation of SS in the industry and, provides a new perspective on the key drivers and inhibitors around its implementation. Findings suggest that the current turbulence in the industry could be bringing food companies closer to the adoption of such systems, hence it is a good time to define and develop the optimum SS implementation strategy.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

In recent years, the internet of things has been widely utilized in various fields, such as in smart factories or connected cars. As its domain of application has expanded, it has begun to be employed using multi-server architectures for a more efficient use of resources. However, because users wishing to receive IoT services connect to multi-servers over wireless networks, this can expose systems to various attacks and result in serious security risks. To protect systems (and users) from potential security vulnerabilities, a secure authentication technology is necessary. In this paper, we propose a smart card-based authentication protocol, which performs the authentication for each entity by allowing users to go through the authentication process using a smart card transmitted from an authentication server, and to login to a server connected to the IoT. Furthermore, the security of our proposed authentication protocol is verified by simulating a formal verification scenario using AVISPA, a security protocol-verification tool.

Despite the lack of evidence that suggests hemodiafiltration (HDF) offers a better survival outcome than standard hemodialysis (HD), the number of patients initiating HDF in Japan continues to rise. This study examined the temporal change in the number of HDF incidents, evaluated factors associated with all-cause mortality, and compared the mortality risk and survival time of patient on HDF with patients receiving standard HD in three sets of 2-year cohorts. The primary analyses included the insurance claims data of 460 HDF patients and propensity score-matched 903 standard HD patients who initiated dialysis therapy between April 1, 2012 and March 31, 2018. Patient follow-up was censored at the time of death or the end of the 2-year study period. The influence of comorbidities on all-cause mortality was investigated, and the survival outcomes between HDF and standard HD patient groups throughout cohorts were compared. The number of HDF patients was increasing throughout cohorts, but the proportions of mortality cases across cohorts slowly decreased. Dementia status increases the mortality risk of patients regardless of dialysis modality. The mortality risk among HDF patients with dementia was more significant than HD patients. Adjusting for all covariates, we observed that HDF patients had a reduced mortality risk and longer survival time than patients on standard HD. This study supports the notion that HDF reduces all-cause mortality compared with standard HD in an incident dialysis population in Fukuoka Prefecture, Japan.

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Lessons learned and risk-based outcomes from modernising the Australian Standard for post-mortem inspection and disposition judgement of beef, sheep, goat and pig carcases are described. Initial Risk Profiles identified priorities for quantitative assessments. The main difficulty encountered was the paucity of quantified performance of current inspection. Resolving this involved acquiring gross abnormality data representing regional production/proportional abattoir volumes, the range of gross abnormalities appearing nationally, proportional occurrence at carcase sites, and seasonality to enable comparisons. Evidence and discussion are provided for associated challenges experienced including preventing contamination, use of food chain information to support amendment, inspection as a part of industry Quality Assurance and opportunities to improve inspector training. Criteria considered by the Competent Authority for determination of equivalence of alternative post-mortem inspection techniques included comparisons of public health risk, non-detection rates for gross abnormalities, and microbial contamination resulting from inspection activities, as appropriate. Most gross abnormalities detected arose from animal health and welfare conditions affecting wholesomeness and did not present as food safety hazards. Non-detection rates between current and alternative inspection (observation) were negligible. A Quantitative Risk Assessment for Cysticercus bovis was conducted. Carcases with multiple gross abnormalities predominantly reflected historic infections (prior septicaemia) where trimming achieved wholesomeness unless cachexic.

Internships can foster educational alliances between industry (business environment) and academic institutions, which can facilitate both flexibility and accelerated innovation. The purpose of this research is to search for the factors that determine HRD through the implementation of the Internship program among the students of Democritus University of Thrace and reveal the correlation between the factors. Also, this paper investigated the impact of the COVID-19 Eras on Human Resource Development (HRD) Factors and finally, investigated the correlation between the two variables "Recruitment in the specialization" and "Covid Eras". The sample of the research consisted of 1398 students of DUTh who implemented Internships during the academic years 2015-2016 to 2021-2022. A questionnaire with 60 variables was created for data collection. A random sampling method was applied to collect the sample. From the results of the survey, ten factors were extracted for human resource training that explained 75.512% of the total variance and for each one, Cronbach's Alpha reliability was tested, which was high. In detail, the factors revealed were Learning Outcomes, Efficacy, Emotional Commitment, Satisfaction, Participation, Empowerment, Role Ambiguity, Role Conflict, Supervisor and Collaboration. From the results of the study, it was evident that HR development - training is a multi-dimensional concept and is a necessary condition for the competitiveness of the firms. Training can help to improve the flexibility of the firm, but also to motivate the staff by implementing multidisciplinary training programs. In general, internship programs are valuable ways for students to acquire broad competences, where the practical knowledge gained supports and complements the theoretical studies acquired in educational institutions.

Human activities (e.g., exploitation, utilization, and conservation) exert substantial impact on forest ecological functions (FEF). The human influence on FEF significantly varies across developmental stages, which are attributed to the temporal and spatial in nature. This study evaluates some notable FEF indices (FEFI) in the 448 counties within the Yellow River basin (YRB) using panel data from the Seventh to Ninth National Forest Inventory. Data analysis involves employing the residual trends, geographical and temporal weighted regression. Results indicate that, firstly, the overall forest ecological function in the YRB is moderate to inferior, with superior FEF in counties endowed with more natural forest resources. Afforestation demonstrates a short-term improvement, but its effectiveness diminishes over time. Secondly, the positive spatial correlation is stronger among counties, characterized by both high-high and low-low agglomeration effects. Scattered planting, simple stand structure, and concentrated harvesting of forests at the identical stand ages hinder the formation of complex and large-scale FEF agglomerations. Thirdly, significant spatial-temporal differences exist in the impact of human activities on FEF. In the upper reaches of the basin, increased vegetation coverage through agricultural and forestry production benefited FEF, however, some over-exploitation of forests and grasslands is observed there. In the middle reaches, the economic development, expanding population and the greening activities help to improve FEF effectively, but excessive water using in agricultural production brought more difficulties to FEF improvement. In the lower reaches of the basin, improved wastelands and forest protection positively influence FEF. This study recommends that county governments should prioritize forest management with multi-species and multi-layered complex forests. Each county should define its position in regional development and ecological protection, considering the potential impacts on neighboring regions. This holistic approach promotes more effective integrated regional management.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

The measurement of discrimination in employment is a key variable in understanding dynamics in the nature of and change in ‘race relations’. Measuring such discrimination using ‘situation’ and ‘correspondence’ tests was influenced by John Rex’s sociological analyses, begun in in England in the 1960s, and replicated in Europe and America in later decades. This literature is reviewed, and the methodologies of testing for employment discrimination are discussed. Recent work in Britain and The Netherlands is considered in detail in the light of changing social structures, and the rise of Islamophobia. Manchester, apparently the city manifesting the most discrimination in Britain, is considered for a special case study, with a focus on one individual, a Muslim woman seeking intermediate level accountancy employment. Her vita was matched with that of a manifestly indigenous, white Briton. Submitted vitas (to 1,043 potential employers) indicated significant discrimination against the Muslim woman candidate. Results are discussed within the context of Manchester’s micro-sociology, and Muslim women’s employment progress in broader contexts, drawing on our work in Jordan and Palestine. We conclude with the critical realist comment that the “hidden racism” of employment discrimination shows that capitalist societies continue to be institutionally racist, and the failure to reward legitimate aspirations of minorities pushes ethnic minorities into a permanent precariat, with implications for social justice and social control, which denies minority efforts to “integrate” in society’s employment systems.

The research aims to present an innovative methodology to execute the Heritage conservation processes in a collaborative and interdisciplinary BIM project, with an effective management of the deterioration suffered over time, emphasizing the structures and coatings. The research begins with the architectural survey using scanning techniques (TLS) and terrestrial photogrammetry (SfM) and taking as a study the Duomo of Molfetta (Italy), a unique Romanesque architecture of Puglia (Italy). The methodological process is mainly aided by the precise semantic segmentation of global point clouds, a semi-automatic process assisted by classification algorithms implemented in the post-processing software, and which has allowed the classification of the unstructured information provided by the remote sensing equipment when identifying the architectural-structural systems of a building with high historical values. Subsequently, it has been possible to develop an efficient Scan-to-HBIM workflow, where the Heritage-BIM (HBIM) project has fulfilled the function of a database by incorporating and organizing all the information (graphic and non-graphic) to optimize the tasks of auscultation, identification, classification, quantification and, in turn, facilitating the parametric modeling of unique structures and architectural elements. The results have shown great effectiveness in the processes of characterization of architectural and artistic heritage, focusing on the deformations and deterioration of the masonry in columns, and making multidisciplinary conservation work in an HBIM project more flexible. But there are still some difficulties for the automatic interpretation of 3D point cloud data, related to specific systems of the historical architecture. In conclusion, human action and interpretation continues to be a fundamental pillar to achieve precise results in a heritage environment.

This study focuses on evaluating and improving the effectiveness of village fund management in Merauke Regency, South Papua. Village fund management faces unique challenges due to the region's geographical and socio-economic conditions. Organizational behavior transformation is critical, particularly in addressing weaknesses in organizational structure, communication, work culture, and monitoring systems, to achieve sustainable development outcomes. A qualitative case study approach was employed, with data collected through in-depth interviews, participant observation, and document analysis between May and August 2024. Merauke Regency was selected for its diversity in management practices. Thematic analysis and triangulation techniques were used to ensure the reliability and validity of findings. The findings reveal that transforming the organizational structure from hierarchical to flexible team-based models, improving communication, and fostering a positive work culture significantly enhanced village fund management. These changes led to better coordination, responsiveness, efficiency, and transparency. Increased community participation and strengthened monitoring and evaluation also contributed to more effective development outcomes.This study provides practical recommendations for policymakers to design more sustainable and inclusive village fund management strategies. Data-driven and participatory approaches are crucial for maximizing development impacts and ensuring socio-economic benefits at the village level.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

Bacterial cellulose (BC) has emerged as a sustainable biomaterial with diverse industrial applications. This paper examines BC production through a circular economy framework, focusing on organic waste as a primary feedstock. It compares static and agitated cultivation methods for BC production, highlighting their advantages and limitations. Static cultivation using Gluconacetobacter xylinum yields high-quality cellulose films but is constrained by lower yields and longer incubation times. Agitated cultivation accelerates production but may affect fiber uniformity. The paper emphasizes sustainability by exploring organic waste materials such as coffee grounds, tea leaves, and food scraps as cost-effective nitrogen and carbon sources. These materials not only lower production costs but also support circular economy principles by converting waste into valuable products. BC produced from these waste sources retains key properties, making it suitable for applications in the textile and other industries. In addition, BC production can align with vegan principles, provided that all additives and processing methods are free of animal-derived components. The paper discusses BC’s potential to replace synthetic fibers in textiles and reduce environmental impact. Case studies show successful BC integration into textile products. In conclusion, the paper calls for more research to optimize BC production processes and explore new industrial applications. Using organic waste in BC production can help industries adopt sustainable practices, reduce environmental footprints, and create high-value materials.

Domestic heating systems in the UK and across northern Europe are responsible for a substantial fraction of their countries’ carbon footprints. In the UK the vast majority of home space heating is via natural gas boilers with ’wet’ hydronic radiator systems. Most of those use TRVs (Thermostatic Radiator Valves) to avoid overheating, improve comfort and save energy. To meet Net Zero targets 20 million such UK gas systems may be retrofitted with heat pumps. Heat pump system designers and installers are cautious about retaining TRVs in such retrofitted systems in part because of worries that TRV temperature setbacks that lower heat demand may actually raise heat pump electricity demand in a "bad setback effect", thus wasting energy. This paper presents a new view of heat pump control and provides the first exploration of this issue, modelling one such industry claim, and finds that though real it should not apply to typical UK retrofits with weather compensation. The energy efficiency benefits of TRVs for older and partly-occupied homes, and to keep bedrooms cooler, remain valid. Comfort-seeking householders and installers should know that setting ’stiff’ temperature regulation may invoke the bad setback effect and cost dearly in energy and carbon footprint.

As one of the important wastes, waste PET pollutes human living and natural environment seriously. PET is an important solid waste that needs to be recycled at present. The complete degradation of PET was realized at low temperature. The lipophilic hydrophobic membrane was formed on the surface of stainless steel mesh (SSM) by a simple dip coating method, and the oil-water separation material was successfully prepared. Due to the load of degradation products, the surface roughness of SSM increased from 19.09 μm increased to 62.33 μm. The surface changed from hydrophilic to hydrophobic, and the water contact angle increased to 123o. The oil-water separation flux of modified SSM is 9825 L/(m2·h) and the separation efficiency is 98.99%. The modified SSM has good reuse performance. This hydrophobic modification method can also be used to modify other porous substrates, such as activated carbon, filter paper, foam, and other materials. In this study, the porous substrate modified by the degradation product of waste PET was used to prepare oil-water separation materials, which not only solved the problem of white pollution, but also reduced the dependence on non renewable resources in the conventional preparation methods of oil-water separation materials. The research provided new raw materials and methods for the industrial production of oil-water separation materials, and had important application prospects.

This preliminary study introduces the incorporation and chemical stabilization of sulfur waste into geopolymers matrix and explores the concept of material production for further environmental and engineering solutions. Geopolymers were synthesized by dissolving sulfur in the alkaline activator of sodium hydroxide and sodium silicate in different proportions. The Alkaline solution was then mixed with metakaolin to synthesize the geopolymer matrix. Adding sulfur from 0 wt.% to 5 wt.%, compared with metakaolin, was found to lead to an increase in the compressive strength of the geopolymer from 22.5 MPa to 29.9MPa. When sulfur is between 5 wt.% and 15 wt.%, a decrease in compressive strength was observed to 15.7 MPa, which can be explained by defects and voids in the geopolymer microstructure through the solubility of excess sulfur and thus the formation of defects and voids in the geopolymer microstructure. Because of the incorporation of sulfur into the geo-polymer, a compact and dense microstructure is formed as reported in the SEM analysis. XRD analysis shows that besides quartz and Analcime, a new phase, Al2·H10·O17·S3, is also formed as a result of sulfur dissolution in alkaline activator of geopolymer.

An activated carbon manufacturing process using winemaking waste is analyzed and designed at industrial scale. Starting from experimental research, the chemical transformations and thermodynamics during pruning wood conversion are studied as a basis for plant design. In this way, mass and energy balances of hydrothermal carbonization and physical activation are fulfilled and a thermoeconomic methodology is applied to develop an energy-integrated plant. To achieve this target, a network of heat exchangers is allocated to minimize heat consumption and supply hot domestic water, while a cogeneration cycle is designed to provide electricity and satisfy the remaining heat demand. Furthermore, a sensitivity analysis is carried out to determine the influence of the production scale and other operation parameters, such as annual workload, service life, and capital and feedstock costs, on the economic viability of the plant. The energy balance of the plant indicates that the energy integration design manages to provide 48.9% of the overall process energy demand by crossing hot and cold streams and recovering heat from residual flue gas. On the other hand, the exergy cost analysis identifies the combustion of pruning wood used to provide heat demands as the main source of exergy destruction, confirming the suitability of integration to improve the thermodynamic performance. Including activated carbon production, electricity, and hot domestic water, the exergy efficiency of the plant stands at 11.5%.

Waste orange peel represents a heavy burden for the orange juice industry, estimated in several million tons per year worldwide; nevertheless, this by-product is endowed with valuable bioactive compounds, such as pectin, polyphenols and terpenes. The potential value of the waste orange peel has stimulated the search for extraction processes, alternative or complementary to landfilling or to the integral energy conversion. This study introduces controlled hydrodynamic cavitation processes, as a new route to the integral valorization of this by-product, based on simple equipment, speed, effectiveness and efficiency, scalability, and compliance with green extraction principles. Waste orange peel, in batches of several kg, was processed in more than 100 L of water, absent any other raw materials, in a device comprising a Venturi-shaped cavitation reactor. The extractions of pectin, endowed with a very low degree of esterification, polyphenols (flavanones and hydroxycinnamic acid derivatives), and terpenes (mainly d-limonene) were effective and fast (high yield, few min of process time), as well as the biomethane generation potential of the process residues was effectively exploited. The achieved results proved the viability of the proposed route to the integral valorization of waste orange peel, though wide margins exist for further improvements.

In recent years, with the increasing research and development of the LED industry which contains GaN, it is expected that there will be a large amount of related wastes in the future. Especially the gallium has extremely high value of economic, therefore, it is necessary to establish the recycling system of the GaN waste. However, GaN is a direct-gap semiconductor and with high energy gap, high hardness, and high melting point make it difficult to recycle. Therefore, this study will analyze the physical characteristics of LED wastes containing GaN and carry out various leaching method to leach the valuable metals from the waste optimally. Different acids are used to find out the best reagent for leaching the gallium. Different experimental parameters are discussed such as the effect of the different acid agents , concentration, pressure, solid-liquid mass ratio, temperature, and time which influence the leaching efficiency of the gallium. In this study, various leaching methods which effect the leaching efficiency of the gallium are compared and the advantages and disadvantages are discussed. Finally, pressurized acid leaching method is preferred to leach the GaN waste, and hydrochloric acid is used as the leaching solution because of its better leaching efficiency of gallium. Eventually, the leaching efficiency of the gallium can reach to 98%.

This research demonstrates the optimization and production of biochar from barley husk (BH), corn cob (CC), and Agave salmiana leaves (AL) via pyrolysis in a muffle furnace. Taguchi experimental design (L9) was applied to conduct the experiments at different levels by altering four operating parameters. Carbonization temperature (300–500 ºC), carbonization time (30–90 min), precursor mass (2–5 g) and N₂ flow rate (100–200 cc/min) were the variables examined in this study. The effect of the parameters on the biochar yield was investigated, and the important parameters were identified employing analysis of variance (ANOVA). The optimum conditions for maximum biochar yield were: carbonization temperature of 400 ºC, carbonization time of 30 min, precursor mass of 2 g, and N₂ flow rate of 150 cc/min. The biochars produced under optimum conditions was characterized physically and chemically. Biochar yields of 19.75% for corn cob (CCB), 32.88% for barley husk (BHB), and 31.14% for agave leaves (ALB) were obtained.

M184V is a single-base mutation in the YMDD domain of reverse transcriptase (RT). The M184V resistance-associated mutation is related to virological unresponsiveness to lamivudine (3TC) and emtricitabine (FTC) and induces high-level resistance to these two antiretroviral agents. M184V is rapidly selected in the setting of non-suppressive antiretroviral therapy (ART) and accumulates in the HIV reservoir. There were continuous efforts to evaluate the impact of M184V mutation on the treatment outcomes in people with HIV (PWH). Since lamivudine remains an extensively used part of recommended antiretroviral combinations, M184V is commonly detected in patients with virological failure. ART guidelines do not recommend the use of drugs impacted by resistance-associated mutations as they have been confirmed to comprise a risk factor for virological failure. However, previous authors suggest that 3TC/FTC can remain active even in the presence of M184V. Obtaining the viral resistance profile for each individual both treated or ART-naïve is a key challenge for lifelong treatment management.Given the potential benefits of lamivudine in the ART combinations, the investigation of M184V remains of high interest for clinicians and researchers. This is a review of literature for the challenges in treating both naïve and experienced individuals carrying the M184V resistance mutation, including virological failure, virological suppression, and resistance to ART.

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.

In the digital age, Artificial Intelligence (AI) is emerging as a transformative force in various sectors, in-cluding medicine. This article explores the potential of AI, akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like ma-chine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medi-cal communities is essential to address these challenges and unlock the full potential of AI in revolution-izing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq's manufacturing context. The study's primary data sources were 360 manager respondents. The structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the result, the researcher used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The result reveals that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company's non-financial performance. Management participation and planning formality positively and statistically significantly affect a business's non-financial performance at the 10 percent level. The domain of strategy and technique has not impacted the company's non-financial performance.

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners’ site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.

Buildings are rapidly becoming more digitized, to a large extent due to developments in IoT. This will provide many opportunities but also a number of challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor the building’s status effectively. This monitoring is essential for various services that rely on information about the presence and activities of individuals within different areas of the building. Occupancy information, including people counting, occupancy detection, location tracking, and activity detection, plays a vital role in the management of smart buildings. In this article, our primary focus is on the use of Passive Infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to many others. Despite numerous literature review articles in the field of occupancy information, there currently isn’t a specialized literature review dedicated to occupancy information derived specifically from PIR sensors.Therefore, this study analyzes articles that specifically explore the application of PIR sensors for occupancy information. This article provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, with a focus on applications in people counting, activity detection, and localization (traking and location). It consolidates findings from a number of articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information.These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects.These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The paper seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics plays a crucial role in proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This research underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. The paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates ETL/ELT processes, AI techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. By employing an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios. This approach enables users to visualize, analyze, and predict the response of the urban system at the neighborhood level. Consequently, antecedent and predictive patterns, trends, and correlations are visualized at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Workplace exercise programs are imperative for improving employee health and wellbeing. Employee cognition and productivity can also benefit from such programs; however, the practical application of interventions to influence these outcomes is poorly understood. This research aimed to assess the suitability of a university Staff Fitness Program for improving employee productivity and cognition. Forty-seven participants were included in the study, comprising 27 Staff Fitness participants and 20 control group participants. A two (groups) x three (time-points) between-subjects/within-subjects design was employed over 14 weeks, comprising ten weeks of once-weekly exercise intervention for Staff Fitness participants. All participants completed the Victoria Stroop Colour and Word Test and the Health and Work Questionnaire at each time-point, to assess cognition and productivity, respectively. Participants also completed the International Physical Activity Questionnaire (short form) at time-point one, three and during training weeks (excluding week one) to measure physical activity. Appropriate T-tests and non-parametric equivalents were used to assess group differences in outcome measures and across time-points. There were no significant between group or longitudinal differences indicative of improved productivity or cognition. The Staff Fitness group demonstrated a non-significant improvement (Z = 73.00, p = .055) in physical activity from time-point one (M = 1540.93 MET-min-week, SD = 1083.74) to three (M = 2077.32 MET-min-week, SD = 1401.48) with a large effect size (d = 0.97), while control group physical activity significantly decreased (Z = 10.00, p = .013). The present Staff Fitness Program benefited employees by improving physical activity, despite no improvements in productivity or cognition. Future research should further explore the application of workplace exercise programs and the manipulation of exercise parameters to show improvements in employee productivity and cognition.

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p < 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p > 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

Vaccination is an important method of immunological preventive care required for health management of all animal including fish. More particularly, immunization is important in in-land fisheries for the management of diseases in fish brood stocks and healthy seed production. According to the latest statistics in 2020, 90.3 million tonnes of captured fish production was achieved from the aquaculture sector. Out of the above, 78.8 million tonnes from marine and 11.5 million tonnes from inland waters aquaculture sectors. About 4% decline in fish production was achieved in 2020 than 2018 from inland fisheries sectors. On the other hand, the digestive protein content, healthy fats and nutritional values of fish products are comparitively more affordable as compared to other meat sources. In 2014, about 10% aquatic cultured animals were lost (costing > 10 billion $ global annual loss) due to infectious diseases. Therefore, vaccination in fish especially in brood stocks is one of the most important approaches to stop such losses in the fisheries sector. Fish vaccines consist of either whole killed pathogens, subunit of a protein, a recombinant protein, DNA vaccines or live attenuated vaccines. Challenges existing are adaption of vaccination in fisheries sector, route of administration, use of effective adjuvants and most importantly lack of effective results. Use of autogenous vaccines, vaccination via intramuscular, intraperitoneal or oral routes, and most importantly adding vaccines in feed using top dressing methods or as a constituent in fish feed are now in emerging. These method shall lower the risk of using antibiotics in cultured water which in turn will reduce the environmental contamination.

The article explores the subject of information and accounting solutions for accounts receivable management that include appropriate accounting solutions and analytical techniques. The study focuses on the statutory framework for accounting for accounts receivable under the Russian Accounting Standards (RAP), International Financial Reporting Standards (IFRS), and the generally accepted accounting principles (GAAP). The analytical techniques are described in the context of the authors' view on the essence of accounts receivable management that implies analysis, the establishment of a credit policy and of a discount policy. The article places emphasis on the use of available information technology for accounts payable management, such as blockchain-based smart contracts.

Attention to the work-related musculoskeletal disorders (WMSDs) involve also statistical surveys showing an increasing trend in the incidence of WMSDs. Technological development retrieves new tools and methods for the assessment of physical load at work. In addition to the conventional questionnaires and scaling assessment systems, these methods are mostly based on the direct sensing of appropriate parameters, which allows more precise quantification. The aim of this paper is to compare the assessment of ergonomic risk through current legislative regulations. A Captiv wireless sensor system was used at a car headlight quality control assembly workplace for sensing, data acquisition and data processing. During the evaluation of the postures and movements at work, we discovered differences in the applicable standards: Decree 542/2007 Coll. (Slovak legislation), STN EN 1005-4+A1, French standards default in the Captiv system. Standards define the thresholds for hazardous postures with significant differences in several evaluated body segments, which also affects the final evaluation of the measurements. Based on the results of our research, we found that respondents were in an acceptable position for approximately 63% of the total duration of the work activity in terms of the Legislative method, 44% with Captiv and 27% with Standards method.

This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

The construction industry is a key driver of environmental change due to its extensive ‎use of resources and high levels of emissions, thus placing a significant burden on global ‎efforts towards sustainable development targets. A large portion of the environmental ‎footprint of buildings results from the energy required to sustain indoor comfort levels. ‎Therefore, enhancing the energy efficiency of existing buildings becomes critical in ‎reducing their environmental impact. This study examines the influence of increasing ‎thermal performance with the aim of reducing heating ‎demand. Thus, numerical modelling tools and different energy performance assessment meth-odologies have been used. ‎Specifically, it evaluates two existing residential buildings in Romania and compares ‎heating demand predictions using PHPP and Mc001-2022 methodologies across various ‎climatic data sets and case studies. The results reveal significant variability in heating ‎demand predictions, emphasizing the importance of methodological choice and climatic ‎data. The ‎study highlights that both PHPP and Mc001-2022 methodologies yield varying results ‎depending on the specific conditions and characteristics of each building. This variation in results underscores ‎the necessity for a nuanced and context-specific approach to energy performance ‎assessment, in-corporating multiple energy efficiency measures tailored to the unique ‎features of each building.

This comparative study aims to investigate environmental awareness, knowledge, and safety among students in Montenegro and North Macedonia, considering the unique socio-cultural and environmental contexts of both countries. A mixed-methods approach involving surveys and interviews was employed to gather data from students in educational institutions across Montenegro and North Macedonia. The study assessed various factors, including students' awareness of environmental issues, their perceived level of knowledge, and their attitudes towards safety measures. A comprehensive series of 400 face-to-face interviews was carried out and these interviews encompassed 200 students from Montenegro and an equal number from North Macedonia, taking place at two esteemed academic institutions: The University of Montenegro in Podgorica and The University of St. Clement of Ohrid, Bitola, Republic of North Macedonia. Our hypothetical conceptual framework proposes that a combination of variables, including gender (H1), age (H2), year of study (H3), and rate of study (H4), significantly influences the attitudes of students from Montenegro and North Macedonia towards environmental awareness, safety, and knowledge. Research findings indicate significant variations in environmental awareness and knowledge perception among students in the two countries. While both Montenegro and North Macedonia face environmental challenges, differences in educational systems and socio-economic factors contribute to distinct attitudes and behaviors towards environmental issues and safety practices. This study sheds light on the importance of understanding regional differences in environmental awareness and knowledge perception among students. By identifying areas of strength and areas needing improvement, policymakers and educators can develop targeted interventions to enhance environmental education and foster a culture of sustainability in both Montenegro and North Macedonia.

In the context of intelligent and integrative lighting, in addition to the need for color quality and brightness, the non-visual effect is essential. This refers to the retinal ganglion cells (ipRGCs) and their function, first proposed in 1927. The melanopsin action spectrum has been published in CIE S 026/E: 2018 with the corresponding melanopic equivalent daylight (D65) illuminance (mEDI), melanopic daylight (D65) efficacy ratio (mDER) and 4 other parameters. Due to the importance of mEDI and mDER, this work synthesizes a simple computational model of mDER as the main research objective, based on a database of 4214 practical spectral power distributions (SPDs) of daylight, conventional, LED, and mixed light sources. In addition to the high correlation coefficient R2 of 0.96795 and the 97% confidence offset of 0.0067802, the feasibility of the mDER model in intelligent and integrated lighting applications has been extensively tested and validated. The uncertainty between the mEDI calculated directly from the spectra and that obtained by processing the RGB sensor and applying the mDER model has reached ± 3.3% after an appropriate matrixing process and proper illumination characterization combined with the successful mDER calculation model. This result opens the potential of low-cost RGB sensors for applications in intelligent and integrative lighting systems to optimize and compensate the non-visual effective parameter mEDI using daylight and artificial light in indoor spaces. The goal of the research on RGB sensors and the corresponding processing method are also presented and their feasibility is methodically demonstrated. A comprehensive investigation with a huge amount of color sensor sensitivities is necessary in a future work of other researches.

The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

Emergency Management has not developed in a cohesive or comprehensive manner. Differing terms are used to name identical concepts. A standard comprehensive doctrine for the important development of emergency management is needed. In this paper I explain a doctrinal framework for emergency management. The paper states a clear identification of the five dimensions, and each of their components, necessary to the informed practice of Emergency Management. Although emergencies vary in cause and severity, the process of Emergency Management necessary for optimal handling of these emergencies varies little. The “All Hazards Approach” to emergency management establishes and reinforces commonality in processes, procedures, planning templates and organizational structure. The three types of agencies associated with emergency management activities are discussed, including the subject matter agency, coordinating agency, and supporting agency. The four critical functions of emergency management are detailed, and include mitigation, preparedness, response, and recovery, often all carried out concurrently. The ten activities, and seven resources of emergency management are highlighted. There are many ways to represent the integration of these five dimensions, to ensure completeness, while ensuring clarity; the two most common are described. When experts in Emergency Management follow the doctrine, they can coordinate all those involved to be sure that all aspects of any hazard are considered, and that all organizations/agencies, functions, activities, and resources of Emergency Management are coordinated and optimally active. Following this process is the only way to ensure the best outcomes from any emergency.

Consumer protection in the financial market has several dimensions. From a formal point of view, consumer rights are guaranteed by law. Market asymmetries are eliminated through the obligation to provide consumers with complete and objective information about financial products and services, honest marketing communication and professional sales practices. Programs are implemented in schools and the media to promote knowledge and responsible use of financial products and services. Despite the efforts made, the number of incorrect and suboptimal financial decisions is so high that the risk of households falling into excessive debt remains significant. The limited effectiveness of the law led to the claim that only effective education can reduce the risk of suboptimal financial decisions. Unfortunately, the efforts made in this area are not fully satisfactory. The aim of the article is to review scientific research on the financial knowledge of consumers in Central Europe and compare it to the results of a study conducted in Poland in January 2024. The results of the analysis could indicate the direction of further research on the possibilities of eliminating suboptimal financial decisions of consumers without limiting - for their better protection - access to financial products.

Recent advances in satellite technology have brought enormous potential to ecosystem mapping, which is one of the fundamental components of environmental studies. In this paper, a Random Forest classifier is applied for the strict assessment of the efficiency of ecosystem mapping through a detailed comparative analysis between combined Sentinel-1 and Sentinel-2 data and stand-alone Sentinel-2 imagery over three priority ecosystems, including wetlands, riverine areas, and mangroves in Bangladesh. The collocated images, based on the integration of Sentinel-1 data with Sentinel-2 data, would do better than Sentinel-2 imagery alone over various ecosystems. Particularly, in this study, attention focused on the Hakaluki Haor area for the wetlands, the Padma-Jamuna River confluence for the riverine ecosystem, and the Sundarban forest for mangroves. By leveraging Synthetic Aperture Radar (SAR) data in C-band dual-polarization from Sentinel-1 and four spectral bands (blue, green, red, and near-infrared) from Sentinel-2, the study analyzes imagery from December 2022 to February 2023. A 5% cloud masking filter is applied to optical data to enhance accuracy. In this methodology, 70% of the total signature values are used for training the classification model and the remaining 30% for testing. It can be noticed from the results that with the use of fused data, remarkably high accuracy in classification has been improved, such as overall accuracies of 94.17% for mangroves, 87.30% for riverine, and 85.96% for wetland ecosystems. In contrast, the use of singular Sentinel-2 imagery yields lower accuracies of 91.56%, 85.21%, and 82.51% for the respective ecosystems. The integration of radar data is shown to provide critical information, especially in environments with dense vegetation or cloud cover, where optical data alone may be insufficient. The findings of this study underline the limitations of relying on Sentinel-2 imagery to capture complex details of diverse ecosystems and highlight the need to include Sentinel-1 data for a more holistic analysis. This fusion allows improved accuracy to be achieved, which not only brings in more depth of ecological knowledge but also underpins more effective conservation strategies.

To evaluate dryland wheat genotypes' performance to different pre-crop and residue management under dryland conditions, a split-split plot experiment based on RCB design with three replications was conducted for two years (2017-2018 and 2018-2019). The site of study's long-term average precipitation, temperature, and relative humidity are 376 mm, 9 ◦C, and 50%, respectively. Wheat-wheat and vetch-wheat cropping systems were considered in the main plots, different wheat and vetch residue levels including 0, 2, and 4 t ha-1 were located in subplots, and five dryland wheat genotypes including Sadra, Hashtroud, Baran, Varan, and Ohadi were allocated in sub-sub plots. Results indicated that leaf chlorophyll content index (CCI) and stomatal conductance (gs) were greater in the vetch-wheat cropping system compared to the wheat monoculture system for all genotypes. Normalized differences vegetative index (NDVI) of genotypes improved by applying the crop residue. Over two years, the application of crop residues resulted in higher variable fluorescence at J and I steps as well as an increase in the photosynthesis performance index (PI). Varan and Baran genotypes stood out as the superior genotype, exhibiting the highest values physiological characteristics and grain yield under the application of 4 t ha-1 of vetch residue. Grain filling rate (GFR) was reduced, while grain filling duration (GFD) was increased with increasing the crop residue levels. The enhanced grain yield of wheat genotypes grown under vetch residue attributed to the factors such as improvement in leave pigments and photosynthetic efficiency which facilitate longer grain filling duration with high grain weight. As a result, it is advisable to adopt a vetch-wheat cropping system with a high proportion of crop residue in dryland regions to achieve increased and sustainable wheat production.

This paper aims to explore the effect of humble leadership on project success by integrating the mediating role of employee creativity. Top management support moderates the direct relationship (humble leadership and project management effectiveness) and indirect relationships through employee creativity. Time-lagged data were obtained from 332 persons working in the matrix organization across the information technology. The results showed that humble leadership enhance project management effectiveness by mediating and moderating processes. This study provides a solution to an underlying research question that has gone unanswered in prior studies. What are the strategies proposed for humble leadership in fostering the effectiveness of the project?

Organic fertilizers are fertilizers made from organic materials which are partly or wholly derived from waste materials, the function of these fertilizers is to improve the physical, biological, and chemical properties of the soil, these fertilizers are processed through the composting process, these fertilizers are very much needed because they are difficult to obtain at a high price. expensive. This study aims to obtain data on the growth and yield of sweet corn, due to the use of agricultural waste as organic fertilizer. The research method was experimentally consisting of 5 treatments and 5 replications; ie P0 = Without organic fertilizer (control) P1 = Dosage of organic fertilizer 2.5 tons/ha, P2 = Dosage of organic fertilizer 5 tons/ha P3 = Dosage of organic fertilizer 7.5 tons/ha, P4 = Dosage of organic fertilizer 10 tons /ha using a Randomized Block Design (RBD) The results showed that organic fertilizer had a significant effect on plant height aged 30, 40 and 50 DAP, had a very significant effect on cob length at a dose of 10 t.ha-1 and, had no significant effect on cob weight, will still have a high weight of 350 g.cob-1 From this description it can be concluded that organic compost can be used as a substitute for chemical fertilizers which provides an increase in the growth and production of sweet corn plants at the use of 10 tons/ha.

The agricultural ecological environment provides an important resource guarantee for social development. The extensive management mode of agriculture in China has not fundamentally changed; the contradiction between production and governance is still prominent, and the management of agricultural surface pollution has a long way to go. Based on the data of 547 rural households in Jiangxi province, this paper uses the Ordered Logit, 2SLS, and moderation effect model to analyze the mechanism between social norms (SNs) and rural households’ (RHs) disposal of pesticide packaging waste (PPW) and to test the moderation effect of environmental regulation (ER) in the influence path of SN to RHs’ disposal of PPW. The results show that (1) descriptive and directive norms can promote RHs not to litter PPW. However, the role of surrounding crowd supervision (directive norms) is more obvious than the behavior of the surrounding crowd (descriptive norms). The consistent conclusion was still obtained after the robustness test and endogeneity treatment. (2) The results of the moderation effect test showed that reputational incentives strengthened the promotional effect of directive norms on RHs' behavior of not littering PPW; punitive regulation hindered the promotional effect of descriptive norms on RHs' behavior of not littering PPW. Based on the findings of the study, the following policy recommendations are put forward: continue to strengthen the guidance and soft binding force of informal institutions such as SNs; accurately locate the target groups and formulate differentiated measures; pay attention to the complementary nature of formal and informal institutions, and reasonably formulate environmental regulations to fit in with SNs.

Plastics are highly beneficial for the day-to-day activities of human beings; however, their decomposition is limited due to their strong covalent bonding. The concept of degradation of these big molecules into smaller ones or monomers was attempted by several researchers in the preceding decades with limited success. Pyrolysis is one of the ideas used to convert plastics into fuel, rather than small molecules, compared to the crowded structure of polymers. Among these plastics, low-density polyethylene (LDPE) is largely used as carry bags throughout the world, and, herein, the results of catalytic pyrolysis of the conversion of LDPE into fuel are reported. Different dosages of zinc oxide (ZnO) were used as a catalyst to do the pyrolysis at a specific temperature in a batch reactor specially designed at our laboratory. 0.6 g of ZnO was found to be the optimal dosage for a 50 g waste LDPE batch to get the maximum oil yield. The yielded oil was analyzed chemically through Fourier transform infrared spectroscopy (FTIR) and Reformulyzer M4 Hydrocarbon Group Type Analyzer. Evaluation of physical and chemical exergy along with exergetic efficiency of the process was done.

The presence of metal ions, in high concentrations, in the effluents results from industrial metal coating is a well-known fact. Most of time, such metal ions, once they reach the environment, contribute significantly to its degradation. Therefore, it is essential that before such effluents are discharged into the environment, the concentration of metal ions is reduced (as much as possible), to minimize the negative impact on the quality of ecosystems. Among all methods that can be used to reduce the concentration of metal ions, sorption is one of the most viable options, due to its high efficiency and low cost. Moreover, due to the fact that many of the industrial wastes have sorbent properties, this method is in accordance with the principles of circular economy. Based on these considerations, in this study, mustard waste biomass (resulting after oil extraction) was functionalized with an industrial polymeric thiocarbamate (METALSORB), and used as an sorbent to removal Cu(II), Zn(II) and Co(II) ions from aqueous media. The best conditions for the functionalization of mustard waste biomass were found to be: mixing ratio biomass : METASORB = 1g : 1.0 mL and temperature of 30 C. The experimental sorption capacities of functionalized sorbent (MET-MWB) were 0.42 mmol/g for Cu(II), 0.29 mmol/g for Zn(II) and 0.47 mmol/g for Co(II), and were obtained in the following conditions: pH of 5.0, 5.0 g sorbent/L and temperature of 21 C. Modeling of isotherms and kinetic curves, but also the analysis of the results obtained from desorption processes, demonstrates the usefulness of this sorbent in the treatment of effluents contaminated with metal ions. In addition, tests on real wastewater samples highlight the potential of MET-MWB for large-scale applications.

More than 800 million tons of fish are utilizing in a year and 25-30% become waste. The waste amount is beneficial source for extraction of collagen. But procedure of extraction is still to be optimized. The current study was designed to extract collagen from fish through fish waste fermentation under various conditions. For collagen extraction we did lactic acid fermentation in which yogurt and Dough bacteria was added with fish sample and placed it in incubator at 30ºC for one month. In yogurt and dough culture have at least 10 type of lactic acid bacterial species. After every week we check PH of each sample and take soup of that sample. After centrifugation of that sample’s TCA (Trichloroacetic acid) precipitation was done of yogurt and dough sample and kept at -20ºC. then did SDS-PAGE) using 6% resolving and 5% stacking gel of already stored sample. Then HaCaT cells (1 × 10⁴ cells/well) were cultured in 96-well flat-bottom culture plates and treated with appropriate doses of FFCP (fermented fish collagen peptide) for 24 and 48 hours.The SDS-PAGE revealed that the collagen protein of fish had doublet pattern for α1 and α2 chains at corresponding to 145 kDa and 132 kDa respectively. The density for α1 twice as compared to α2. Our result agrees that The fish collagen consists mostly of α-chain as well as little amount of inter and intra molecular cross-linked components of α-chains; b (dimmer) and c (trimer). Different biochemical tests were done for identification of lactic acid bacteria catalase positive, citrate positive, urease negative. Our fermented fish collagen and peptides mixture were test for cellular cytotoxicity and proliferative effect on HaCaT cells. Current result shows that fermented extracted collagen are nontoxic and induce the proliferation of HaCaT cells. Current study is supported by various studies that revealed the medical application of fish extracted collagen as underline.

Geopolymer cement has been popularly studied nowadays compared to ordinary Portland cement because it demonstrated superior environmental advantages due to its lower carbon emissions and waste material utilization. This paper focuses on the formulation of geopolymer cement from nickel-laterite mine waste (NMW) and coal fly ash (CFA) as geopolymer precursors, and sodium hydroxide (SH) and sodium silicate (SS) as alkali activators. Different mix formulations of raw materials are synthesized to produce a geopolymer based from an I-optimal design and obtained different compressive strengths. A mixed formulation of 50% NMW and 50% CFA, SH-to-SS ratio of 0.5, and an activator-to-precursor ratio of 0.429 yielded the highest 28-day unconfined compressive strength (UCS) of 22.10 ± 5.40 MPa. Furthermore, using an optimized formulation of 50.12% NMW, SH-to-SS ratio of 0.516, and an activator-to-precursor ratio of 0.428, a UCS value of 36.30 ± 3.60 MPa was obtained. The result implies that the synthesized geopolymer material can be potentially used for concrete structures and pavers, pedestrian pavers, light traffic pavers, and plain concrete.

Nowadays, inherently safer designs are considered as key priorities to prevent or mitigate serious incidents with devastating consequences. The need for process safety assessment during early design phases has motivated the development of several contributions related to computer-aided assessment methodologies in order to measure the inherent safety of chemical processes. In this work, the large-scale production of chitosan from shrimp wastes was evaluated from process safety point of view using the numerical descriptive inherent safety technique (NuDIST).To this end, simulation of the chitosan production was performed using Aspen Plus ® to obtain extended mass and energy balances. The assessment of all the chemicals involved within the process was carried out for the following safety parameters: explosivity (EXP), flammability (FL) and toxicity (TOX). The safety assessment of the process included the parameters of temperature (T), pressure (P) and heat of reaction (HR). The maximum chemical safety score was estimated in 171.01 with ethanol as main contributor to the parameter of explosivity and flammability. The score associated with operating data was calculated in 209.30 and heat of reaction reported to be the most affecting parameter. The NuDIST score was estimated in 380.30. This NuDIST value revealed the low hazards associated with the handling of substances such as shrimp wastes, chitosan and water, as well as the non-extreme temperature and pressure conditions. In general, the large-scale production of chitosan from shrimp shells showed to be an inherently safe alternative of waste valorization.

This article examines the impact of climate change on the hydrometeorological state of some lakes and reservoirs in the Akmola and North Kazakhstan regions. Time series with values of various climate parameters for the period 1986-2023 are used for the climate analysis; the reliability of the results was assessed using the Fisher criteria. Hydrometric indicators were studied using the ArcGIG program. Using GIS to spatially analyze water climate data, 8-channel satellite images from 1995 to 2023 were used. During the study, it was found that the area of the main lakes has undergone changes.

Remote work as a new working pattern is considered of high importance in determining the competitiveness of the institutions that adopt this form of work. This trend toward remote working has benefited workers greatly in terms of flexibility and work-life balance and has had unanticipatedly positive effects on sustainability. The issue has grown in importance considering recent development of digitalization and high concern of countries to achieve the sustainable goals. This study attempts to examine the effect of three explanatory variables: remote work, institutional efficiency, and work management on the competitiveness of finance institutions in Saudi Arabia. To collect the primary data, a survey was conducted on a stratified random sample that comprised 1270 respondents. The sample covers the three finance sector segments: banks, insurance, and finance companies. Correlation analyses, one-sample t-tests, and multi-regression analyses are applied to analyze the data. The results of the study showed that there is a positive significant relationship between the three explanatory variables of the study and institutional competitiveness. Besides, the regression model revealed that the two explanatory variables; remote work and institution efficiency are statistically significant at 95% confidence intervals, while work management is not statistically significant. The study recommends promoting remote work to increase the finance institution's competitiveness by restructuring the work policies, designing work strategies, and introducing a new management system compatible with remote work.

This report aims to document the extent of plastic waste pollution in lorry stations in Accra, assess its impact on public health and propose actionable solutions for effective waste management in stations. By highlighting the severity of the issue as well as providing recommendations for solutions, we seek to inform and inspire municipal assemblies, policymakers and other stakeholders to take decisive action against plastic waste pollution in lorry stations in Accra. Plastic waste pollution is a growing concern in Ghana, with lorry stations being particularly affected due to high foot traffic and inadequate waste management infrastructure within stations. This report is significant because of its implications for addressing public health hazards, continuing the ongoing efforts to reduce plastic pollution in Ghana and addressing community concerns. Through this comprehensive analysis, Mckingtorch Africa aims to drive meaningful change and create a cleaner, healthier Ghana for present and future generations.

In the ongoing work, environmentally sound technologies for converting organic wastes into fertilizers, to improve soil sustainability and crop yield have been identified and assessed. Wet wastes were combined with 50% wood sawdust and 50% 50% wet wastes (Compost 1) or (10% Straw + 90% wet wastes) (Compost 2) to produce soil improvers with a balanced level of nutrients and their effectiveness on soil ecosystem functioning have been tested and compared to horse manure (HM) and nitrogen:phosphorous:potassium (NPK) fertilizers. Unfertilized soil was used as control. Soil chemical and biological properties, have been detected after the harvesting of broccoli and red cabbage (90 days from the initial treatments). Three independent experiments have been conducted in open field in a randomized complete block design with three replications (n=9). Results showed that Compost 1 had the highest C/N ratio and CSC, indicating a better humification of wet material. Compost 1 even if contained a minor amount of organic carbon, as well as a less activity of FDA and DHA than Compost 2, was the most effective in improving soil quality, significantly increasing the labile fraction of organic matter, the oxidative enzyme (DHA), microbial biomass and crop yield. Both composts increased crop productivity.

Cassava Solid waste (Onggok) is a solid waste from the cassava-based tapioca industry, the starch content in Onggok is still 50-80 wt.% of residual starch, which can be used to produce maltodextrin for foods application. Furthermore, using cassava solid waste is beneficial in terms of the cost of raw materials. The starch content in cassava can be modified through enzymatic hydrolysis using α-amylase and β-amylase enzymes at 80oC and pH 5.5. Enzymatic hydrolysis was done using mixed enzymes that varied the concentration of α-amylase at 0,.1% and the β-amylase enzymes at 0.05, 0.1, and 0.5%. the concentration of cassava waste (Onggok) varied from 4%, 7%, 10%, and 13%. The optimum concentration of α-amylase and β-amylase enzymes are 0.1% and 0.1% respectively at time intervals of 1 minute resulting in a dextrose equivalent (DE) value 0f 5.7. The characteristics of maltodextrin were studied using scanning electron microscopy (SEM) to determine the morphology of the starch structure which had changed due to the enzyme hydrolysis. Simultaneous thermal analysis (STA) was studied to determine the Thermal stability of hydrolysis products. Maltodextrin produced from cassava waste (Onggok) has a DE value < 10, which has the potential to be used in the pharmaceutical industry

E-waste is considered the fastest-growing global waste stream and has become an emerging environmental challenge. The widespread usage of digital devices has generated a large amount of e-waste. According to the Ministry of Commerce, India annually generates ~14.5 lakh metric tons of e-waste and recycles only ~5 % of e-waste. Annually, the global production of e-waste has been expected to increase by 4–5 %. Advancements in IT and communication divisions have improved the usage of electronic equipment. Heavy metals such as (Pb), mercury (Hg), arsenic (As), tin (Sn), and organic chemicals such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated biphenyls (PBBs), polybrominated dibenzo-p-dioxins (PBDDs), polybrominated diphenyl ethers (PBDEs) are subsequently released from e-wastes and contaminates soil and water sources. These toxic compounds are hazardous to human health and the environment. They are capable of entering various biological systems and causing alterations in respiratory functions of the lungs, DNA damage, impaired functioning of the thyroid, and increased risk of chronic diseases such as cancer and cardiovascular ailments. Such concerns raise the quintessential need to manage e-waste to prevent future hazards. This manuscript critically reviews the sources, health and environmental impact, disposal techniques, recycling, and regulation of e-waste.

Precise classification of sarcomas is crucial to drive optimal clinical management. In this prospective, multicenter, observational study within the Hellenic Group of Sarcoma and Rare Cancers (HGSRC) we assessed the effect of expert pathology review, coupled with the application of molecular diagnostics, on diagnosis and management of sarcoma patients. Newly diagnosed sarcoma patients were addressed by their physicians to one of the two sarcoma pathologists of HGSRC for histopathological diagnostic assessment. RNA next-generation sequencing was per-formed to all samples, using a platform targeting 86 sarcoma gene fusions. Additional molecular methods were performed on the opinion of the expert pathologist. Therefore, the expert pathologist provided a final diagnosis based on the histopathological findings and, when necessary on molecular tests. In total, 128 specimens from 122 patients were assessed. Among the 119 cases in which there was a preliminary diagnosis by a non-sarcoma pathologist, there were 37 modifications in diagnosis (31.1%) by the sarcoma pathologist, resulting in 17 (14.2%) modifications in management. Among the 110 cases in which molecular tests were performed, there were 29 modifications in diagnosis (26.4%) through the genomic results, resulting in 12 (10.9%) modifications in management. Our study confirms that expert pathology review is of utmost importance for the optimal sarcoma diagnosis and management and should be assisted by molecular methods in selected cases.

Considering the unquestionable role of education in economic development, it has an impact at a macroeconomic level—contributing to enhancing productivity and innovation, reducing poverty and promoting social cohesion—and at a personal level—ensuring the improvement of knowledge, skills and individual capabilities and promoting social values like empowerment and equality. Notably, quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study used a fuzzy set qualitative comparative analysis to assess the conditions for attaining higher levels of quality education based on the different indicators used to measure SDG 4. The analysis will allow comparisons of results among different countries and provide information about the initiatives that could be relevant to increasing quality education worldwide and for policymakers.

Waste biogenic materials from seafood exploitation are valuable resources of new compounds exploitation within the blue bioeconomy concept. Here, we describe the effectiveness of Raman technology implementation as an in-line tool for demineralization process control of crustaceans or gastropods. We produced transparent chitin polymer foils from three waste crustacean shells (C. sapidus, S. mantis and M. squinado) using a slow, green chemical approach employing acetic acid, and showed the progressive process of biogenic carbonate dissolution and increasing the Raman characteristic signal of chitin, in a time dependence manner. It turned that chitin foils obtaining is species-specific, and the demineralization bath of waste shell mixture can be effectively tracked by Raman spectroscopy, for solvent control and the recovery of the calcium acetate as valuable by-product. Comparatively, calcium acetate drug, a compound widely used in kidney failure diseases, or as additive in nutraceuticals and food industry, has been obtained here, following a green demineralization path of the sea snail Rapana venosa intact shell, assisted by Raman tech-nology, using 9% acetic acid (vinegar) solution for 14 days. To validate the final products identity and quality, The process of waste shells demineralization and the final products of the treatment were investigated using various Raman techniques and technologies, cross-validating the results with FT-IR, XRD and SEM-EDX techniques. NIR Raman spectroscopy revealed chitin bands in a time-dependent manner, while Resonance Raman spectroscopy showed the preservation of ca-rotenoids after two weeks of acetic acid treatment. A hand-held flexible TacticID Raman system with a 1064 nm excitation demonstrated its effectiveness as a rapid, in-line decision making tool during process control and revealed excellent reproducibility of the lab-based instrument signal, suitable for in situ evaluation of the demineralization status and solvent saturation control. The calcium acetate recovered from residual treatment solutions was assessed regarding its hydration status, purity, and suitability as recrystallized material for further use as pharmaceutical product or ingredient in complex formulations.

Sustainable supply chain management (SSCM) represents a critical strategy for the electronics industry to balance economic objectives with environmental and social responsibility across complex global production networks. This multiple case study analysis investigates SSCM integration within leading Korean electronics manufacturers Samsung, LG, and Hyundai. In-depth qualitative data, focus groups, and sustainability report analysis reveals the policies, practices, results and implementation barriers related to incorporating sustainability criteria across procurement, manufacturing, distribution and recycling functions. Findings show sustainability considerations increasingly driven by cost, risk, reputational and regulatory factors, with vertical coordination initiatives helping transfer knowledge to assist suppliers on compliance and emissions reductions. Yet substantial obstacles persist around monitoring, coordination, transparency and technical capabilities, especially amongst lower tiers. The paper discusses how common reporting standards, ethical sourcing certification and partnerships can accelerate electronics supply chain sustainability. By mapping integration achievements and gaps, this study informs managerial best practices and policy reforms toward promoting socially and ecologically responsible models for Korea's vital export industry as it navigates rising stakeholder pressures.

The rise in both the frequency of natural disasters and the ubiquity of artificial intelligence has led to novel applications of new technologies in improving disaster response processes, such as the labor-intensive assessment of disaster damages. Assessment of residential and commercial structure damages, a precursory step to government agencies being able to provide most of their financial assistance, has benefited from aerial and satellite imagery-based computer vision models; however, limitations of using such imagery include the ground structures being obscured by clouds or smoke, as well as the lack of resolution to distinguish individual structures from others. Using a different data source, we propose a damage severity classification model using ground-level imagery, focusing on residential structures damaged by wildfires. This classifier, a Vision Transformer (ViT) model trained on over 18,000 professionally labeled images of damaged homes from the 2020-2022 California wildfires, has achieved an accuracy score of over 95%. Further, we have open sourced the training dataset–the first of its kind and scale–as well as built a publicly available web application prototype, which we demoed to and received feedback from disaster response officials, both of which further contribute to the broader literature beyond the proposed model.

Cognitive impairment is a condition which affects aging patient populations and is becoming more prevalent as the geriatric population expands. It is categorized by the extent of cognitive decline, memory loss, functional abilities, and the capability to perform daily tasks. Based on the degree of impairment, it is classified as mild cognitive impairment or dementia. Considering the debilitating nature of cognitive decline and its impact on quality of life, prevention is key. In addition, given the gradual progression of the underlying causes of cognitive decline, early diagnosis and appropriate management has the potential to offer clinical advantages and better patient outcomes. Management options discussed vary from pharmaceutical regimens to neurosurgical interventions, with differing outcomes based on etiology of cognitive impairment.

Achieving sustainable development will require improved water management, but the main framework to address it, Integrated Water Resources Management (IWRM), lacks clarity and acceptance among a diverse community of many stakeholders. This creates communication problems and obscures issues that people care about like drinking water and pollution prevention. As a process within IWRM, the concept of water resources assessment also suffers from a lack of clarity, which leads to ambiguity and reports that are too general to lead to action. This lack of acceptance of IWRM and water resources assessment saps support for essential data collection, analysis, and reporting. Clarifying IWRM will require work within the water community and better messaging to stakeholders. Methods for water resources assessment can be clarified by linking outcomes to higher level reporting of policy and issues. Acceptance of the role of water resources assessment can be facilitated by adding it to the management instruments in the IWRM Toolbox. Stovepipes among water management agencies require coordination to achieve this, usually through framework studies. Improved messaging will involve what people and stakeholders care about and will require changed approaches to presentation of outcomes by categories, such as drinking water, pollution prevention, and flood risk reduction. Researchers can report outcomes with technical messaging that trends toward uniform language and definitions and the water resources management community can nudge its media outlets to carry unifying messages.

Type two diabetes mellitus (T2DM) represents a chronic condition with increasing prevalence worldwide among the older population. T2DM condition increases the risk of micro and macro-vascular complications as well as the risk of geriatric syndromes as falls, fractures and cognitive impairment. The management of T2DM in the older population represents a challenge for the cli-nician, and a Comprehensive Geriatric Assessment should always be prioritized, in order to tailor the glycate haemoglobin target according to functional and cognitive status comorbidities, life ex-pectancy and type of therapy. According to the most recent guidelines, older adults with T2DM should be cathegorized in three groups: healthy patients with good functional status, patients with complications and reduced functionality and patients at the end of life; for each group the target for the glycemic control is different, also according to the type of treatment drug. The therapeutic ap-proach should always begin with lifestyle changes; after that, several lines of therapies are available, with different mechanism of action and potential effect other than glucose level reduction. Partic-ular interest is growing around sodium-glucose cotransporter-2 inhibitors, due to their effect on the cardiovascular system. In this review we evaluate the therapeutic options available for the treat-ment of older diabetic patients, to ensure a correct treatment approach

The functioning of the parents’ emotional sphere is very important to a child’s mental and physical health. This study focused on investigating the association between mothers’ emotional intelligence (EI) and paediatric type I diabetes (T1DM) disease management in their children. We hypothesized that mothers’ EI is associated with T1DM outcomes. Mothers of children with T1DM aged 6-12 years were surveyed. One hundred and thirty-four mothers, the main caregivers of their diabetic children, provided measures of EI and completed a demographic questionnaire. The primary indicator of diabetes management was haemoglobin A1c (HbA1c; the main form of glycosylated haemoglobin). EI scales and subscales were associated with glycaemic management indices. Logistic regression analysis was applied for the assessment of the association between parents’ EI and their paediatric with T1DM disease management. The analysis demonstrated a statistically significant correlation between T1DM management and mothers’ ability to understand and control own emotions, to transform their own negative emotions into positive and to control own negative emotions. Mothers’ EI scales and subscales of understanding and regulating their own emotions, subscales of transforming their own negative emotions into positive ones and controlling their own negative emotions were statistically reliable predictors of glycaemic control in children with T1DM.

It is well known that the event of hepatitis B virus reactivation can occur among patients undergoing treatment for hematological malignancies. In this paper we will present the available data regarding the risk of hepatitis B virus reactivation in this special population of immunosuppressed patients and explore the relevance of an accurate prevention and management of this condition. A computerized literature search was performed using appropriate terms arrangement, including English-written literature only or additional relevant articles. The evaluation of hepatitis B reactivation risk is a multidimensional process, which includes conducting an accurate clinical and physical history, considering the virological categories, the knowledge of the medication chosen to treat these hematological malignancies and the induced grade of immunosuppression. Adopting adequate preventive strategies and surveillance according to the current international recommendations is crucial to prevent HBVr and its dire clinical consequences (hepatitis, liver failure, interruption of lifesaving anti-neoplastic treatments). Universal HBV screening of patients scheduled to undergo treatment for hematological malignancies should be the chosen policy, and clinicians should be aware of the inherent risk of viral reactivation among the different virological categories and the classes of immunosuppressive drugs.

Previous literature has linked working for pay while attending university with negative academic and health outcomes, yet working students are often resilient when experiencing such adverse circumstances. This makes it crucial to explore potential psychological mechanisms which transform adverse experiences into sources of motivation and persistence for working students. We explore one mechanism – social identification – and its different foci – identifying as a student, employee, working student, or as a student of one’s discipline of study – as potential predictors of important academic (academic self-efficacy, approaches to learning, and academic achievement) and social (status in society) outcomes in three cross-sectional studies. In Study 1, part-time working hours (but not identification processes) were associated with academic self-efficacy. In Study 2, discipline identification and part-time working hours were associated with using deep approaches to learning. In Study 3, student identification was associated with increased status in society. Overall, discipline identification may be solely linked to academic outcomes, yet student identification should be explored further as a potential enhancer of social and graduate outcomes. We discuss additional mechanisms which can help to transform working students’ experiences through their social identities and suggest boundary conditions which can affect the link between these identities and important outcomes.

The aim of the study is to obtain a more complete picture of the blood plasma melatonin concentration in the donkey species. To this purpose sampling and statistical processing were carried out in such a way that allowed to establish the annual and daily rhythm. Based on human observations, according to the hypothesis of the authors, the blood plasma melatonin concentration of pregnant individuals rises during late gestational period, before parturition. To confirm this, the melatonin concentration of pregnant and non-pregnant mares was monitored and compared. In regard to the circannual rhythm, the significantly lowest midnight melatonin value (27.67 pg/ml) is typical for the summer solstice. Under consideration of circadian changes, a significantly strongest melatonin production (45.16 pg/ml) can be observed on the night of the winter solstice (p &lt; 0.001). Considering the gestational age, the blood plasma melatonin concentration (around 38 pg/ml) does not change as pregnancy progresses (p = 0.136). The results obtained in the study population of the domestic ass usefully expand the little knowledge previously gathered about the development of blood plasma melatonin concentration of this species.

This review highlights recent innovations in food packaging, emphasizing the shift from conventional petroleum-based materials to bio-based alternatives and smart packaging systems. Bio-based materials, such as starch, cellulose, and polyhydroxyalkanoates (PHA), offer sustainable solutions due to their biodegradability and reduced environmental impact. These materials are positioned as eco-friendly alternatives to traditional plastics, but face challenges related to production costs and scalability. Additionally, advancements in smart packaging technologies, including sensor and indicator systems, provide real-time food quality monitoring, enhancing food safety and reducing waste. Active packaging technologies, incorporating natural antioxidants and moisture control, extend product shelf life and improve food preservation. Also, these biopolymers usually present lower CO2 footprint, energetic cost, and water consumption during their production, in comparison to traditionally used synthetic plastics. The review identifies critical challenges, such as regulatory barriers and technological limitations, but also outlines significant opportunities for future research and innovation in the food packaging sector, aiming for more efficient, safer, and environmentally sustainable packaging solutions.

The digital world is increasingly invading our reality, which leads to the formation of a significant reflection of the processes and activities taking place in the smart city. Such activities include well-known urban events, celebrations, and those with a very local character. Due to the mass occurrence, events have a comparable influence on the formation of the spirit and the urban atmosphere. This work presents an enhanced semantic version of the ConvTree algorithm - SemConvTree. It allows considering the semantic component of the data obtained by using semi-supervised learning of topic modeling ensemble (consisting of improved models BERTopic, TSB-ARTM, SBert-Zero-Shot). We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results lead in the long term to talk about the potential perspective in creating a semantic platform for the analysis and monitoring of urban events in the future.

Problem:Palestinian demographic landscape is undergoing an increase in the elderly population like many regions across the world. The elderly Palestinians face a unique challenges and problems of limited access to traditional care due to restricted movement, checkpoints, and also for limited healthcare resources. Therefore, the need arose to develop a sensitive and adaptable smart health monitoring system for Palestinian elderly to provide accurate and timely health data to improve well-being and prevent complications. Method: The proposed system (SHMS) leverages the Internet of Things (IoT) with appropriate sensors to track vital signs like heart rate, blood oximeter, body temperature and signal from a patient heart. Advanced algorithms analyze the data, generating alerts for potential health risks, triggering timely interventions, and enabling remote consultations. Results: This system has the potential to contributes to SDG 3 to significantly improve the health and well-being of elderly Palestinians by providing an accurate, low-power, and low-cost system for remote health monitoring. The system, being wearable, reads vital signs in real-time, overcoming geographical barriers and facilitating remote monitoring, especially in areas with limited healthcare centers and suffered from barriers along the way. The Doctor can suggest the patient’s prescription needed based on the momentary condition of the elderly and on previous readings of the sensors, if necessary. Which will also be notified via messages. Moreover, the doctor can take immediate intervention in case of an emergency. By implementing the system, it will be unnecessary for more than 50\% of the elderly to visit health care, thus reducing the cost to 50\%.}

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in Smart Cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G Smart Cities IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low Round-Trip Time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

Digital technologies have been present in manufacturing and industry for a long time, and today they represent the basis for the application of the Industry 4.0 model in practice. The transformation marked by the Industry 4.0 model is based on the application of a large number of models of communication technologies, solutions for connecting entities (machines, parts, ...) in the factory within Internet of Things (IoT), as well as the possibility of big data analysis (BDA) throught the artificial intelligence tools (AI/ML). These technologies provide great opportunities to create more flexible and profitable manufacturing processes based on data, and using the Internet. As more and more factories and their equipment are IoT enabled, the volume of data will only grow, hence the application of digital technologies is inevitable. To truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into cognitive manufacturing, which is the base model of smart manufacturing. This paper provides an overview of the development of the concept of digital manufacturing, from different aspects, with examples of application in real manufacturing. The goal is to create a good practice of digital manufacturing with the elements of Industry 4.0, as a basis for the development and application of the smart manufacturing (SM) model.

This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on the information security and smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies, and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smarty city areas and makes it possible to assist new research that may contribute to further advances. The smart city topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart city research productivity to assist researchers in better understanding the current emerging of the area.

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on the strategy for renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with life-cycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and environmental impact. It was further observed that optimizing the building design with the goal to reduce embodied energy and carbon in compliance with modern energy standards, was crucial to improve overall energy performance. This work demonstrated that the developed BIM-based framework to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In Wireless Body Area Network (WBAN), for example, the patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity rendering the data transmission slower. Cognitive Radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors rendering the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

The Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform that supports a large array of multi-spectral and hyper-spectral sensors. It provides accurate aerosol optical depths and remote sensing reflectances (Rrs estimates) that can be used to generate products such as absorption coefficients due to phytoplankton and detritus/Gelbstoff as well as backscattering coefficients due to particulate matter. The OC-SMART platform yields improved performance in complex environments by utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

This research seeks to understand the impact of digital platforms on the governance of smart cities. It uses the Scaffold of Popular Participation to classify digital platforms and better understand participatory governance in cities, its benefits and complications, correlating them with the SDGs of the 2030 Agenda. It was also possible to list points of attention for the development of digitization in the public sector, including in Brazil. It concluded that the digitization of public services is in progress, pressured by the civil body for greater transparency and participation. In addition, the platforms must be guided by civil inclusion in the discussion of public policies, by the training of intelligent citizens and by sustainable guidelines that guide the themes addressed. In contrast, platforms can also facilitate deception and popular manipulation, which represents a high risk if used for this purpose.

Base on building information modeling(BIM) plus with Robotic process automation (RPA) technology is rapid development in smart building operations and maintenance (O&M). RPA is a workflow-based automated execution tool that is particularly well suited for replacing manual execution of simple and repetitive tasks using computer systems. The use of RPA technology can saves man-hours and increases productivity, which is important for the automation of building operations and maintenance operations.. The BIM technology, as an advanced technology in the construction industry, it can provide new ideas and methods for building operation and maintenance based on its visualization characteristics. That’s integrating RPA with BIM technology which will enhance the digitalization and virtualization of smart building operation and management. In this article will first introduce the features of RPA and BIM technology and then discuss the technical roadmap for remote automated operation and maintenance of smart building equipment room to implemention a low-cost and high efficient building equipment room operation and maintenance solution.

The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks' efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem's significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. In this way, the proposed system fully automates the strategic security planning of energy networks with computational intelligence methods. It allows the complete control of the digital strategies of the potential infrastructure that frames it, thus contributing to the timely and valid decision-making during cyber-attacks.

Blade Health Monitoring (BHM) is often necessary in power plants and in aviation to prevent excessive blade vibration and cracks. This article proposes a network of blade tip timing sensors operated in a distributed BHM system. A number of cooperating agents is implemented in smart conditioning units which can autonomously operate in an adverse environment and communicate with other nodes via a serial interface. The project uses special versions of reduced instruction set chips that are able to operate near the hot section of the engine. Due to the limited number of types of microprocessors available in the extended temperature range grading, it was necessary to fully utilize the limited hardware resources and implement preemptive multitasking. For this purpose, a custom operating system and communication protocol were designed. The protocol hosts the middle layer which hides the implementation of the distributed system. The presented architecture ensures the sufficient computational capacity in individual nodes of the network operated in adverse conditions. It is scalable and resistant to transmission errors.

With the continuous emergence and application of new technologies, the construction of smart cities has entered the practical promotion period. Since 2012, the pilot construction of smart city has been promoted by the government in China. On the basis of these practical experiences, this paper presents an overview of the latest technologies and applications for smart city construction in China and demonstrates that smart city strategy needs to be implemented according to local conditions, adhering to the people-oriented concept and using scientific and effective top-level design and planning. The construction of smart city is comprehensive system engineering, including the integration of geographic information sharing service platform, full-cycle management and control system of urban planning, construction and social management, as well as intelligent business information management system of gardening, water conservancy, environmental protection and other industries and departments. The information system (GIS), satellite remote sensing (SRS), global navigation satellite system (GNSS), Internet of things, mobile applications, cloud computing, visualization technology ware used to promote urban construction and sustainable development, and to meet the needs of future smart city development. Results show that centralized management is very important for the construction of smart city. The government plays a major role in the construction of smart city, which will be conducive to the development of new technologies and the effective use of smart city construction resources.