Częstochowa University of Technology

The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Abstract Lithium‐ion battery is a mature technology that is used in various electronic devices. Nowadays, this technology is a good candidate as energy storage for electric vehicles. Therefore, much research is focused on the development of high‐density power lithium‐ion batteries. Government regulations force manufacturers to recycle the batteries for safety and health reasons but recycling could also be interesting from an economic viewpoint since cathodes in lithium‐ion batteries contain valuable metals. The electrodes in lithium‐ion batteries will evolve to provide more energy and the recycling processes will have to fit with this evolution. Leaching, bioleaching and solvent extraction are at the centre of these processes. In this paper, recent leaching and solvent extraction strategies for recovering valuable metals from spent lithium‐ion batteries are reviewed and the evolution of these processes is discussed. © 2013 Society of Chemical Industry

Technology adoption is always a difficult task for Small and Medium-sized Enterprises (SMEs) due to lack of resources and other market issues. Many technology challenges adversely affect the sustainable business performance of SMEs. However, the incorporation of Industry 4.0 can overcome various technology issues. The goal of Industry 4.0 is to attain an advanced level of operational effectiveness and productivity, as well as a higher level of automatization. Thus, the objective of this study is to identify the role of Industry 4.0 to promote sustainable business performance in SMEs in Thailand. A survey has been prepared to collect the data from managers of SMEs and analyzed with the help of Partial Least Square. The questionnaire was used to collect the data and questionnaires were distributed by using simple random sampling. A total of 500 questionnaires were distributed amongst the managerial staff of SMEs located in Thailand. From these distributed questionnaires, 280 were returned and 270 valid responses were found. Data were analyzed by using Partial Least Square (PLS)-Structural Equation Modeling (SEM). Findings reveal that Industry 4.0 is a key to the growth of sustainable business performance among SMEs. Elements of Industry 4.0 such as big data, Internet of Things and smart factory have a positive role in promoting information technology (IT) implementation, which contributes to sustainable business performance. Moreover, organization structure and process strengthen the positive relationship between Industry 4.0 and IT implementation.

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

In the postmodern era of industrialization, sustainable business performance is vital for success in a competitive environment. In order to attain sustainable business performance, Malaysian Small and Medium-sized Enterprises (SMEs) are facing various social and technological challenges. The objective of this study was to examine the roles of social and technological challenges in achieving a sustainable competitive advantage and sustainable business performance. To accomplish this objective, first-hand data were collected from Malaysian SMEs. Opinions of managerial staff of these SMEs were preferred regarding the roles of social and technological challenges in achieving a sustainable competitive advantage and sustainable business performance. An email survey was carried out to collect data. A total of 500 questionnaires were distributed among managerial staff of SMEs. Questionnaires were distributed by using simple random sampling. By using structural equation modeling, findings of the study revealed that social and technological challenges played major roles in boosting sustainable competitive advantage and sustainable business performance. Moreover, strategic alignment was a key in reflecting the positive roles of social and technological factors on sustainable competitive advantage. Findings of the study are beneficial for practitioners and will allow their strategies to reflect sustainable competitive advantages and sustainable business performance.

With the spread of the COVID-19 pandemic and the increasing importance of e-commerce, the study of online consumer behavior is of particular relevance. The purpose of this study was to form a methodological approach to assess the relationships and the level of influence of the factors activating the purchasing behavior of online consumers against the background of the COVID-19 pandemic. The research methodology was based on the transformation of Cattell’s questionnaire and the implementation of correlation analysis. To determine the predisposition of online consumer behavior at the time of making a purchase decision, this study used the questionnaire method. The survey was conducted among online shoppers in the top 10 countries in terms of e-commerce market growth. The scientific contribution is the proposed methodological toolkit to assess the purchasing behavior of online consumers, which identifies the most influential factors in their purchasing behavior and provides an opportunity to assess the dynamics of their activity during the study period, to identify key trends and determine changes in their behavior. The research revealed what changes in online consumer buying behavior are typical in the COVID-19 pandemic. The impact of consumer awareness and experience has increased. Online consumers have become more experienced, which has influenced the activity of their buying behavior. This study proved the shifting influence of online consumer purchasing behavior factors during the pandemic. The increasing importance of the speed of decision making by consumers when purchasing goods and services online was determined.

All around the world, inequalities persist in the complex web of social, economic, and ecological factors that mediate food security outcomes at different human and institutional scales. There have been rapid and continuous improvements in agricultural productivity and better food security in many regions of the world during the past 50 years due to an expansion in crop area, irrigation, and supportive policy and institutional initiatives. However, in Sub-Saharan Africa, the situation is inverted. Statistics show that food insecurity has risen since 2015 in Sub-Saharan African countries, and the situation has worsened owing to the Ukraine conflict and the ongoing implications of the COVID-19 threat. This review looks into multidimensional challenges to achieving the SDG2 goal of "End hunger, achieve food security and improved nutrition, and promote sustainable agriculture" in Sub-Saharan Africa and the prosper policy recommendations for action. Findings indicate that weak economic growth, gender inequality, high inflation, low crop productivity, low investment in irrigated agriculture and research, climate change, high population growth, poor policy frameworks, weak infrastructural development, and corruption are the major hurdles in the sustaining food security in Sub-Saharan Africa. Promoting investments in agricultural infrastructure and extension services together with implementing policies targeted at enhancing the households' purchasing power, especially those in rural regions, appear to be essential drivers for improving both food availability and food access.

The purpose of this study was to investigate the factors determining the behavioral intention of using food delivery apps (FDAs) during COVID-19 pandemics, under a case study of Bangkok, Thailand. The study was necessitated by the increased use of FDAs during the lockdown; online transactions were considered important in preventing the spread of the virus. The study used quantitative techniques involving structural equation model (SEM) to evaluate the effects of exogenous variables on endogenous variables. Primary data were collected from people who had installed and used FDAs. The findings indicated that performance expectancy, effort expectancy, social influence, timeliness, task technology fit, perceived trust, and perceived safety significantly influence the behavioral intention to use (BIU) to use food delivery apps during the COVID-19 pandemic. To this end, effort should be intensified to study and understand FDAs as it pertains to safety and usage.

Industry 4.0 is a current trend of automation and digitalization of industries. The impacts and importance of Industry 4.0 are reflected in all aspects of our lives. The purpose of this article is to analyze the literatures based on a scoping review method. A lack of digital culture, training, knowledge, and language are also challenges faced by Industry 4.0 while implementing its operations. Digital English and Education 4.0 are also employee competencies of Industry 4.0. The authors have reviewed the literature related to Digital English, Education 4.0, and Industry 4.0 from various resources. Astonishingly, the results show that the studies conducted in these areas are so specific focusing only one of the above-mentioned areas; no research article was identified that detailed the interconnections among these areas. From the scoping review, the study has identified the gaps in the literature. Thus, the study concludes that filling up the gaps and conducting research in these areas are useful to sort out a few of the challenges of Industry 4.0 and it recommends that in future, researchers conduct studies based on the interconnections of Digital English and Education 4.0 for Industry 4.0.

Novel species of microfungi described in the present study include the following from South Africa: Camarosporium aloes, Phaeococcomyces aloes and Phoma aloes from Aloe, C. psoraleae, Diaporthe psoraleae and D. psoraleae-pinnatae from Psoralea, Colletotrichum euphorbiae from Euphorbia, Coniothyrium prosopidis and Peyronellaea prosopidis from Prosopis, Diaporthe cassines from Cassine, D. diospyricola from Diospyros, Diaporthe maytenicola from Maytenus, Harknessia proteae from Protea, Neofusicoccum ursorum and N. cryptoaustrale from Eucalyptus, Ochrocladosporium adansoniae from Adansonia, Pilidium pseudoconcavum from Greyia radlkoferi, Stagonospora pseudopaludosa from Phragmites and Toxicocladosporium ficiniae from Ficinia. Several species were also described from Thailand, namely: Chaetopsina pini and C. pinicola from Pinus spp., Myrmecridium thailandicum from reed litter, Passalora pseudotithoniae from Tithonia, Pallidocercospora ventilago from Ventilago, Pyricularia bothriochloae from Bothriochloa and Sphaerulina rhododendricola from Rhododendron. Novelties from Spain include Cladophialophora multiseptata, Knufia tsunedae and Pleuroascus rectipilus from soil and Cyphellophora catalaunica from river sediments. Species from the USA include Bipolaris drechsleri from Microstegium, Calonectria blephiliae from Blephilia, Kellermania macrospora (epitype) and K. pseudoyuccigena from Yucca. Three new species are described from Mexico, namely Neophaeosphaeria agaves and K. agaves from Agave and Phytophthora ipomoeae from Ipomoea. Other African species include Calonectria mossambicensis from Eucalyptus (Mozambique), Harzia cameroonensis from an unknown creeper (Cameroon), Mastigosporella anisophylleae from Anisophyllea (Zambia) and Teratosphaeria terminaliae from Terminalia (Zimbabwe). Species from Europe include Auxarthron longisporum from forest soil (Portugal), Discosia pseudoartocreas from Tilia (Austria), Paraconiothyrium polonense and P. lycopodinum from Lycopodium (Poland) and Stachybotrys oleronensis from Iris (France). Two species of Chrysosporium are described from Antarctica, namely C. magnasporum and C. oceanitesii. Finally, Licea xanthospora is described from Australia, Hypochnicium huinayensis from Chile and Custingophora blanchettei from Uruguay. Novel genera of Ascomycetes include Neomycosphaerella from Pseudopentameris macrantha (South Africa), and Paramycosphaerella from Brachystegia sp. (Zimbabwe). Novel hyphomycete genera include Pseudocatenomycopsis from Rothmannia (Zambia), Neopseudocercospora from Terminalia (Zambia) and Neodeightoniella from Phragmites (South Africa), while Dimorphiopsis from Brachystegia (Zambia) represents a novel coelomycetous genus. Furthermore, Alanphillipsia is introduced as a new genus in the Botryosphaeriaceae with four species, A. aloes, A. aloeigena and A. aloetica from Aloe spp. and A. euphorbiae from Euphorbia sp. (South Africa). A new combination is also proposed for Brachysporium torulosum (Deightoniella black tip of banana) as Corynespora torulosa. Morphological and culture characteristics along with ITS DNA barcodes are provided for all taxa.

The article explores the impact of the quality and volume of energy consumption of the population on the human development index using a sample of a number of countries as an example. The hypothesis concerning the relationship between the amount of energy consumed, the human development index (HDI), and the environment (CO2 emissions into the atmosphere) has been verified. The study results show that the size and rating of the HDI are influenced by such factors as urbanization growth, gross domestic product (GDP), gross national income (GNI) per capita, the share of “clean” energy consumption by the population and business in total energy consumption, the level of socio-economic development, and R&D expenses. In the course of building the model, the recommendations by the United Nations (UN) and the Organization for Economic Co-operation and Development (OECD) were used. The results show that the volume of energy consumption not only affects the human development index in a particular country, but is also an important factor in determining the level of sustainable development. The results, obtained in the course of the study and described in the article, may be applicable in the practice of research related to the assessment of human development and sustainable development.

This review aimed to analyse the current state of management practices for poultry manure in Poland and present future perspectives in terms of technologies allowing closing the loops for circular economy, and thus recovery of nutrients and energy. The scope of the review focused primarily on: (1) the analysis of poultry production and generation of poultry manure with special references to quantities, properties (e.g. fertilizing properties), seasonality, etc.; (2) the overview of current practices and methods for managing poultry manure including advantages and limitations; (3) the analysis of potential and realistic threats and risk related to managing poultry manure, and also (4) the analysis of promising technologies for converting poultry manure into added value products and energy. The review addressed the following technologies: composting of poultry manure to obtain fertilizers and soil improvers, anaerobic digestion of poultry manure for energy recovery, and also pyrolysis of poultry manure into different types of biochar that can be applied in agriculture, horticulture and industry. Poultry manure is rich in macro- and micronutrients but also can contain various contaminants such as antibiotics or pesticides, and thus posing a realistic threat to soil and living organisms when applied to soil directly or after biological treatment. The main challenge in poultry manure processing is to assure sufficient closing of carbon, nitrogen and phosphorous loops and safe application to soil.

Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data. Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features: -Provides a framework for unification, construction and development of neuro-fuzzy systems; -Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation, -Covers not only advanced topics but also fundamentals of fuzzy sets, -Includes problems and exercises following each chapter, -Illustrates the results on a wide variety of simulations, -Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

With the rapid enhancement in the design and development of the Internet of Things creates a new research interest in the adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of industrial Internet of Things and the security-related resource constraints of industrial 5.0. This conducts new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is an emerging limitation between the inadequate performance of industrial IoT and connected nodes, and the high resource requirement of permissioned private blockchain ledger has not yet been tackled with the complete solution. Due to the introductions of NuCypher Re-Encryption infrastructure, hashing tree and allocation, and deployment of blockchain proof-of-work required more computational power as well. This paper is divided into three different folds; first, we studied various related literature of blockchain-enabling industrial Internet of Things and its critical implementation challenging aspects along with the solution. Secondly, we proposed a blockchain hyperledger sawtooth-enabled framework. This framework provides a secure and trusted execution environment, in which service delivery mechanisms and protocols are designed with an acknowledgment, including the immutable ledger storage security, along with the peer-to-peer network on-chain and off-chain communication of industrial activities. Thirdly, we design pseudo-chain codes and consensus protocols to provide smooth industrial node streamline transactions and broadcast content. The proposed multiple proof-of-work investigated and simulated using Hyperledger Sawtooth-enabled docker for testing to exchange information between connected devices of industrial Internet of Things within the limited usage of resource constraints.

We report on the capillary rise of water in three-dimensional networks of hydrophilic silica pores with 3.5 nm and 5 nm mean radii, respectively (porous Vycor monoliths). We find classical square root of time Lucas-Washburn laws for the imbibition dynamics over the entire capillary rise times of up to 16 h investigated. Provided we assume two preadsorbed strongly bound layers of water molecules resting at the silica walls, which corresponds to a negative velocity slip length of -0.5 nm for water flow in silica nanopores, we can describe the filling process by a retained fluidity and capillarity of water in the pore center. This anticipated partitioning in two dynamic components reflects the structural-thermodynamic partitioning in strongly silica bound water layers and capillary condensed water in the pore center which is documented by sorption isotherm measurements.

Industry is currently undergoing a revolution (called Revolution 4.0) related to the far-reaching integration of all production areas through the digitization and the creation of new communication channels. The Polish economy generated a GDP of USD 524.5 billion in 2017, of which small and medium enterprises generated about 50% of revenue and in which microenterprises accounted for the largest share in generating GDP; i.e., around 30.2%. The aim of the research is to determine the adaptation possibilities of the small and medium-sized enterprises (SME) sector to Industry 4.0 solutions. Pilot research was carried out in the Czestochowa Industrial District. Enterprises from the SME sector were asked to provide an information about the used technologies and to determine the level of their organization’s readiness for sustainable development through technological transformation. Financial resources as well as a lack of specialized support in obtaining new technologies were defined as problems as far as transformation is concerned. The solution of the diagnosed problem is the development of a platform aimed at integrating the potential of enterprises from the SME sector in order to undertake joint actions for sustainable development.

In this paper, we propose a new class of probabilistic neural networks (PNNs) working in nonstationary environment. The novelty is summarized as follows: 1) We formulate the problem of pattern classification in nonstationary environment as the prediction problem and design a probabilistic neural network to classify patterns having time-varying probability distributions. We note that the problem of pattern classification in the nonstationary case is closely connected with the problem of prediction because on the basis of a learning sequence of the length n, a pattern in the moment n + k, k > or = 1 should be classified. 2) We present, for the first time in literature, definitions of optimality of PNNs in time-varying environment. Moreover, we prove that our PNNs asymptotically approach the Bayes-optimal (time-varying) decision surface. 3) We investigate the speed of convergence of constructed PNNs. 4) We design in detail PNNs based on Parzen kernels and multivariate Hermite series.

Industrialization and population growth have significantly increased the demand for lightweight, high-strength materials for construction and automotive applications, ultimately increasing the demand for eco-friendly materials. Due to its environmental acceptability, technological feasibility, and economic viability, natural fiber-reinforced composite exhibits many potential engineering applications. However, the production and recycling of natural fibers are expensive. Researchers are now comparing natural fiber-reinforced composites with synthetic composites to determine the best materials, especially for construction and automotive engineering applications. This review paper focuses on natural fiber reinforced epoxy composites’ physical, mechanical, and thermal characteristics. These properties are critical for the effective design and use of composite materials such as construction and automotive applications. This review begins with a background of epoxy and natural fibers. The physical and chemical treatment for natural fiber composites to improve their properties is also briefly discussed, along with the critical factors affecting the physical, mechanical, and thermal properties of natural fiber-reinforced composites. Finally, concluding remarks and suggestions for future works are given.

In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.