Aspects Of Performance Research Articles

Unveiling the multifaceted public interest in ChatGPT

The present study was aimed at identifying key topics in online discussions about the use of ChatGPT by examining a large dataset extracted from Reddit social media using natural language processing. A corpus of 159,971 posts about ChatGPT were extracted from a custom-made python-coded Reddit content scraper for posts in the r/ChatGPT subreddit discussions. After cleaning the data, the sample was reduced to 119,853 posts which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the cooccurrence of texts. These clusters were named by a panel of social psychology experts (n=3) by reading typical text segments within each cluster. Four thematic clusters emerged, categorized into two main topics: “Society and AI Integration”, focusing on ethical concerns (32.1%), and “Operational Aspects and Applications”, which delves into technical and practical facets (67.9%). The latter includes clusters like “AI Technical Framework”, “Casual AI Interactions”, and “Human-AI Etiquette”. The Reddit discourse provides a comprehensive understanding of ChatGPT, revealing user priorities like system capabilities and ethical considerations. Notably, the “Human-AI Etiquette” cluster is a new topic less covered in existing literature. The findings underscore the importance of effective prompting for meaningful user engagement with ChatGPT.

Improving Safety in High-Altitude Work: Semantic Segmentation of Safety Harnesses with CEMFormer

The symmetry between production efficiency and safety is a crucial aspect of industrial operations. To enhance the identification of proper safety harness use by workers at height, this study introduces a machine vision approach as a substitute for manual supervision. By focusing on the safety rope that connects the worker to an anchor point, we propose a semantic segmentation mask annotation principle to evaluate proper harness use. We introduce CEMFormer, a novel semantic segmentation model utilizing ConvNeXt as the backbone, which surpasses the traditional ResNet in accuracy. Efficient Multi-Scale Attention (EMA) is incorporated to optimize channel weights and integrate spatial information. Mask2Former serves as the segmentation head, enhanced by Poly Loss for classification and Log-Cosh Dice Loss for mask loss, thereby improving training efficiency. Experimental results indicate that CEMFormer achieves a mean accuracy of 92.31%, surpassing the baseline and five state-of-the-art models. Ablation studies underscore the contribution of each component to the model’s accuracy, demonstrating the effectiveness of the proposed approach in ensuring worker safety.

A block α-circulant based preconditioned MINRES method for wave equations

In this work, we propose an absolute value block α-circulant preconditioner for the minimal residual (MINRES) method to solve an all-at-once system arising from the discretization of wave equations. Motivated by the absolute value block circulant preconditioner proposed in [E. McDonald, J. Pestana, and A. Wathen. SIAM J. Sci. Comput., 40(2):A1012-A1033, 2018], we propose an absolute value version of the block α-circulant preconditioner. Since the original block α-circulant preconditioner is non-Hermitian in general, it cannot be directly used as a preconditioner for MINRES. Our proposed preconditioner is the first Hermitian positive definite variant of the block α-circulant preconditioner for the concerned wave equations, which fills the gap between block α-circulant preconditioning and the field of preconditioned MINRES solver. The matrix-vector multiplication of the preconditioner can be fast implemented via fast Fourier transforms. Theoretically, we show that for a properly chosen α the MINRES solver with the proposed preconditioner achieves a linear convergence rate independent of the matrix size. To the best of our knowledge, this is the first attempt to generalize the original absolute value block circulant preconditioner in the aspects of both theory and performance the concerned problem. Numerical experiments are given to support the effectiveness of our preconditioner, showing that the expected optimal convergence can be achieved.

3D printed feathers with embedded aerodynamic sensing

Bird flight is often characterized by outstanding aerodynamic efficiency, agility and adaptivity in dynamic conditions. Feathers play an integral role in facilitating these aspects of performance, and the benefits feathers provide largely derive from their intricate and hierarchical structures. Although research has been attempted on developing membrane-type artificial feathers for bio-inspired aircraft and micro air vehicles (MAVs), fabricating anatomically accurate artificial feathers to fully exploit the advantages of feathers has not been achieved. Here, we present our 3D printed artificial feathers consisting of hierarchical vane structures with feature dimensions spanning from 10-2to 102mm, which have remarkable structural, mechanical and aerodynamic resemblance to natural feathers. The multi-step, multi-scale 3D printing process used in this work can provide scalability for the fabrication of artificial feathers tailored to the specific size requirements of aircraft wings. Moreover, we provide the printed feathers with embedded aerodynamic sensing ability through the integration of customized piezoresistive and piezoelectric transducers for strain and vibration measurements, respectively. Hence, the 3D printed feather transducers combine the aerodynamic advantages from the hierarchical feather structure design with additional aerodynamic sensing capabilities, which can be utilized in future biomechanical studies on birds and can contribute to advancements in high-performance adaptive MAVs.

Digital Transformation in Operations Management: Leveraging Technology to Improve Business Efficiency

Digital transformation in operations management has become an urgent need for companies to improve efficiency, productivity, and competitiveness in the face of an increasingly complex and competitive business environment. Digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data provide opportunities for companies to optimize various aspects of operations. This article aims to explore how digital transformation can be applied in operations management to support these goals. The research method used is a literature review, where various previous studies and industry reports are analyzed to identify the benefits, challenges, and impacts of digital technology implementation on business operations. The results show that the implementation of digital technology enables companies to speed up workflows, reduce costs, improve process accuracy, and prepare the foundation for continuous innovation. In the discussion, it is also explained that although there are challenges in terms of investment costs, training, and data security, companies that successfully overcome these barriers will gain a significant competitive advantage. The implications of these findings are the importance of continued investment in technology and human capital development, as well as the adoption of sustainability strategies to support long-term growth.

Automated Fingerprint Identification System

Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security

A Research Study on Effective Growth of Franchise Business Model in Cafe & Restaurant Sector in Ahemdabad City

The growth of the franchise business model in the café and restaurant sector in Ahmedabad is examined in this study, with a focus on franchisee perceptions across different age groups. Data from 172 franchisees were analyzed using a quantitative approach to test ten alternate hypotheses related to operational aspects such as training adequacy, brand recognition, and market competition. The results revealed consistent perceptions among franchisees, with no significant differences across age demographics. Specifically, franchisees uniformly accepted perceptions regarding the effectiveness of the franchise model, adequacy of support, and the influence of brand recognition, indicating a strong alignment in understanding the model's benefits. However, the findings also emphasized significant competition from local independent cafés, highlighting the need for strategic adaptations. The study's practical implications suggest that franchisors should prioritize standardized training and support systems, improve marketing strategies, and cultivate strong franchisee-franchisor relationships to drive growth. The research underscores the importance of addressing competitive dynamics and continuously improving support systems. Future research is recommended to explore longitudinal impacts, diverse demographic influences, and the integration of digital transformation within franchise operations. This study provides valuable insights into franchise effectiveness, offering guidance for practitioners seeking to optimize franchise performance in the café and restaurant sector

Preparation of A Map of Potential Areas For the Installation of A Sanitary Landfill in The Porto Murtinho/MS Region

Objective: The main objective is to prepare a map to identify areas with potential for the installation of a landfill in the region of Porto Murtinho/MS. Theoretical Framework: This study addresses the relevance of the careful selection of locations for the implementation of Sanitary Landfills, with an emphasis on environmental and social aspects, using geotechnologies as fundamental tools for a comprehensive and effective analysis. Method: The mapping was based on the acquisition and processing of geospatial and environmental data. The factors were classified as limiting or not, considering watercourses, areas subject to flooding, roads, land use and occupation, slope, soil and urban proximity. Data integration through map algebra was performed in QGIS software, and WRPLOT View was used for wind analysis. Results and Discussion: The results revealed that the majority of areas with high installation potential are located at a distance of 2 (two) to 5 (five) kilometers from the urban perimeter. The supplementary analysis of Rosa dos Ventos demonstrated that areas located to the east are more suitable for the implementation of sanitary landfills. Research Implications: The multifaceted approach, which includes environmental and operational aspects, makes it possible to identify areas suitable for the implementation of sanitary landfills. Originality/Value: This study demonstrates the relevance of geotechnologies in choosing locations for landfills, where limiting factors exclude unsuitable areas, and non-limiting factors classify the installation potential, being a methodology replicable in other locations for sustainable environmental management.

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

Analysis of the relationship between upper body speed capacity and lower body strength of elite handball players specialized in winger position

Background and Study Aim. The physical demands of the winger position in elite handball require a unique combination of upper body speed and lower body strength. Therefore, identifying the optimal relationship between these two physical characteristics could improve performance for players in this role. This study aims to analyze the relationship between upper body speed capacity and lower body strength in elite handball players specializing in the winger position. Material and Methods. The study included 16 elite handball players specializing in the winger position from various teams in the National Handball League (Liga Zimbrilor). Four of these players were members of the Dinamo Bucharest team. Tests for evaluating the explosive strength of the lower limbs included the Squat Jump (SJ), Counter Movement Jump (CMJ), Counter Movement Jump with Free Arms (CMJb), and the maximum strength test (1RM) in squats. Specific technical training tests included triangular movement, a 30-meter sprint, and long-distance handball throws, focusing on speed and execution technique. Data analysis was conducted using KyPlot 6.0 software. Statistical indicators included median, standard deviation, coefficient of variation, and non-parametric Spearman correlation tests. Statistical significance was set at p < 0.05. Results. The results show no statistically significant differences in lower limb strength between high-performance handball players (HPHP) and those specialized in the winger position (PSW) across various jump and squat tests (p > 0.05). The Countermovement Jump (CMJ) test approached statistical significance (p = 0.084), while the CMJ with free arms (CMJb) and squat strength test did not show significant differences (p = 0.231 and p = 0.789, respectively). In the specific fitness tests, no significant differences were observed in the Triangle Movement test (p = 0.826), the 30-meter sprint (p = 0.404), or the handball throw test (p = 0.147). Correlation analysis indicated that squats and CMJb had positive correlations with improved performance in speed and distance tests, while CMJ showed a negative correlation with technical performance in agility tests. Conclusions. The findings suggest that lower limb strength and technical fitness are similar between elite handball players, regardless of specialization. This supports a unified training approach across player roles. However, targeted exercises like squats and CMJb may enhance specific performance aspects. These exercises improve dynamic strength and stability, which can benefit wingers' technical execution and speed.

Multi-Objective Optimization and Design for Industrial Vinyl Chloride Reactor by Hybrid Model

The acetylene conversion rate and vinyl chloride production capacity are the main economic indexes for vinyl chloride synthesis, and the reaction temperature is an important operating parameter to prevent the Hg active component from being loss. These three factors have not been taken into consideration simultaneously in the traditional optimization process, making it difficult to achieve optimization targets perfectly for industrial application. To overcome this problem, an efficient strategy framework was proposed based on a hybrid model. Compared with conventional paradigms, the proposed framework could not only reduce computational expense but optimize these two economic indexes with a constrained reaction temperature simultaneously. In addition, a machine learning method was used to conduct a feature analysis, which can reveal the potential interaction between different variables so key variables of this reactor could be identified. To demonstrate and verify this framework, multi-objective optimization involving multiple variables with constrained conditions for the industrial reactor was conducted from design and operation perspectives, respectively. The proposed strategy could provide optimal operational direction for the industrial reactor from these design and operation aspects, which contribute to the sustainable and highly efficient process development in this field. For the first section of an industrial vinyl chloride reactor, this strategy could realize significant improvement in the acetylene conversion rate from 81.34% to over 95.00% and in the vinyl chloride production capacity from 2.60 to above 3.40 mol/h in the operation scenarios, which can meet production requirements. So, the second section of the traditional reactor system is not needed at all.

Dopamine in Sports: A Narrative Review on the Genetic and Epigenetic Factors Shaping Personality and Athletic Performance

This narrative review examines the relationship between dopamine-related genetic polymorphisms, personality traits, and athletic success. Advances in sports genetics have identified specific single nucleotide polymorphisms (SNPs) in dopamine-related genes linked to personality traits crucial for athletic performance, such as motivation, cognitive function, and emotional resilience. This review clarifies how genetic variations can influence athletic predisposition through dopaminergic pathways and environmental interactions. Key findings reveal associations between specific SNPs and enhanced performance in various sports. For example, polymorphisms such as COMT Val158Met rs4680 and BDNF Val66Met rs6265 are associated with traits that could benefit performance, such as increased focus, stress resilience and conscientiousness, especially in martial arts. DRD3 rs167771 is associated with higher agreeableness, benefiting teamwork in sports like football. This synthesis underscores the multidimensional role of genetics in shaping athletic ability and advocates for integrating genetic profiling into personalized training to optimize performance and well-being. However, research gaps remain, including the need for standardized training protocols and exploring gene–environment interactions in diverse populations. Future studies should focus on how genetic and epigenetic factors can inform tailored interventions to enhance both physical and psychological aspects of athletic performance. By bridging genetics, personality psychology, and exercise science, this review paves the way for innovative training and performance optimization strategies.

A novel operational protocol for the establishment of a medical facility for receiving returning hostages: structure, process and outcomes

BackgroundOn October 7, 2023, Hamas carried out an unprecedented attack on the State of Israel and kidnapped 251 people into captivity to the Gaza Strip. Several months later, as part of a humanitarian exchange deal, 105 hostages were released in five phases and admitted to one of six hospitals throughout the country for treatment. Shamir Medical Center (SMC) was one of these facilities. This study aims to describe the structure, process and outcomes of establishing a comprehensive, multi-step, operational protocol for receiving hostages returning from captivity.MethodThe process of preparing SMC as a receiving center, the establishment of procedures for implementation of the medical protocol, and the assessment of multi-disciplinary team preparedness and implementation and outcomes in an institutional protocol are described.Results24 returning hostages were received at SMC. Social workers, dietitians and translators were used by 100% of the majority group of returning hostages from the same country of origin and the sole individual from the other country of origin utilized a dietitian, social worker, ENT consultations, and a hearing test. Among the majority group, orthopedic and dermatological consultations were utilized by 17.4% and 13% received an ENT consultation. Of the administered imaging, 13% received a chest X-ray, 8.7% received a limb X-ray, 17.4% received a head CT scan, and 4.3% received an abdominal CT. In addition, 21.7% were provided antibiotic therapy. Protocol efficacy was measured by assessing time to various operational aspects of protocol implementation and medical procedures such as mean hours to room assignment, primary physician evaluation and social worker session. No correlation between age and operational variables was found.ConclusionThis novel operational protocol was successfully implemented and may serve as a framework for managing similar unpredictable sensitive events in the case of future need.

Experimental study on seismic performance of glulam structural columns with anchored connections

In current timber structures, the low initial stiffness, and weak bending resistance of the connections result in significant structural deformation and failures. To improve the mechanical capabilities of timber structural joints, a novel timber-component anchorage system with robust bending resistance was introduced. Thirteen scaled-down specimens of glulam columns with anchored connections were meticulously fabricated for testing, scaled at a ratio of 1/2. Cyclic loading tests were conducted, considering three types of the volume compression percentage of confined wood perpendicular to the grain; and five axial-load levels. Subsequently, various seismic performance aspects of glulam columns with anchored connections were examined, including the failure mode, hysteresis behavior, envelope curve, strength degradation, stiffness degradation, energy dissipation capacity, and critical points. During the analysis, the influence of the P–Δ effect on the results was discussed and a damage-based hysteretic model was presented. Finally, a comparison of the mechanical performance for various types of timber structural joints was conducted. The findings revealed that the failure mode of the anchored glulam columns occurred as cracking perpendicular to the grain, and the anchored columns exhibited superior bending resistance and overall seismic performance.

Biosensing technology for detection and assessment of pathogenic microorganisms.

At present, the prevalence of infectious diseases is rising annually, making it an important risk factor for human health that should not be neglected. Consequently, infection control and prevention have become even more important. The key to determining and designing the most effective anti-infectious medication depends upon the immediate and accurate identification of the causative agent. The standard techniques used for routine infection screening and surveillance tests are shifting toward biosensors. Furthermore, biosensors are projected to be employed for microbiological detection to satisfy the higher accuracy required for clinical diagnosis. This is because of their compact size, real-time monitoring and ability to analyze large sample numbers with less sophistication and manpower requirement, which have allowed them to develop quickly with extensive uses. Biosensors have multiple applications in food safety, environmental surveillance, drug sensing and national security because they offer several advantages such as quick response, outstanding sensitivity, remarkable selectivity, high degree of accuracy and precision, ease of use and affordable price. This review highlights the performance aspects of recently developed biosensors for the detection of infectious bacteria and viruses in biological and environmental samples and emphasizes the significance of nanotechnology in signal amplification for enhanced biosensor performance and dependability.

Leveraging digital platforms to improve Public Health in hospital smart buildings

Abstract The integration of digital platforms into hospital infrastructure represents a significant leap forward in public health management. This session explores how smart hospital buildings, equipped with advanced digital technologies, can enhance public health outcomes through more effective disease surveillance, improved environmental controls and enhanced patient care management. Smart hospital buildings utilize a range of new technologies such as IoT sensors, AI-driven analytics and big data solutions to monitor and optimize various aspects of hospital operations and care. These technologies enable real-time data collection on environmental conditions, patient health metrics and resource utilization, creating an ecosystem that not only supports the immediate health needs of patients but also contributes to broader public health goals. The session will address four key areas where digital platforms can make an impact: -Disease Surveillance and Management: Digital tools can track health data trends and patterns within hospital settings, enabling early detection of infectious outbreaks and more precise tracking of patient outcomes. -Environmental Monitoring: IoT sensors can continuously assess conditions such as air quality, temperature and humidity, which are critical for preventing hospital-acquired infections and ensuring a safe environment for patients and staff. -Resource Optimization: AI algorithms can analyse data to optimize resource allocation, ensuring that medical supplies and human resources are used efficiently, which is crucial in health emergencies. -Patient-Centred Care: Digital platforms can facilitate personalized patient care through real-time health monitoring and data analysis, ensuring timely medical interventions and better health management. This session will highlight case studies demonstrating successful implementation of digital technologies in smart hospital buildings and discuss the challenges and lessons learned from these initiatives.

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

A Process Evaluation in a Randomized Controlled Trial among Adults with Acetabular Dysplasia

Abstract Introduction The 2021 framework from the UK’s Medical Research Council defines evaluation as a theory-driven approach to examine how well an intervention works. However, the application of these guidelines can be challenging. Therefore, using a process evaluation case study on an exercise and patient education intervention for adults with acetabular dysplasia, we reflect on which specific aspects were relevant for the evaluation of the intervention and why. We focus on detailing methods to assess implementation (process, dose, reach), acceptability, mechanisms of change, and the impact of contextual factors. Methods Two hundred trial participants aged 18-50 years will be recruited from a University Hospital in Denmark and randomised to intervention and control groups. The process evaluation adopts a concurrent mixed-methods design involving self-report questionnaires at baseline and 6-month follow-up, training records and semi-structured focus groups with intervention providers (n = 10) and healthcare managers (n = 4-6). The mechanisms of change will be explored through semi-structured one-to-one interviews (at baseline and 6-month follow-up) with 15-20 purposefully sampled trial participants, and additionally, through exploratory examinations of associations between dose and change in health outcomes at 6-month follow-up), via simple linear regression models. The acceptability and contextual factors will be explored through one-to-one participant interviews, plus focus groups with 4-6 healthcare managers. Thematic analyses of the interviews will focus on expectations, experiences, events, personal understandings and interpersonal and organisational interactions. Conclusions The presentation will explore how the operational aspects of the intervention can be assessed through a process evaluation and how these findings may improve and expand any future implementation efforts.

A new digital model for Italian Integrated Home Care: strengths, barriers, and future implications

Abstract Issue/Problem After the COVID-19 pandemic, Italy’s National Health Service (NHS) is transforming by adopting telemedicine through its National Recovery and Resilience Plan, aiming to modernize healthcare delivery and ensure equity. In this plan Territorial Coordination Centers (COT) were designed to improve patient access and continuity of care. However, regulatory challenges hinder the widespread adoption of telemedicine. Our work describes the technical, structural, and operational aspects of the new Italian Integrated Home Care (IHC) digital model, addressing strengths, barriers, and future implications. Description of the problem The implementation of the IHC digital model faces multiple challenges. Italy’s poor technical infrastructure, especially in rural areas, hampers access to critical services. Resistance to change among rural systems and the elderly, coupled with low digital literacy, inhibits telemedicine adoption. Fragmentation in the NHS leads to resource disparities, particularly disadvantaging southern regions. Italy also grapples with a severe shortage of healthcare professionals, posing a threat to the model’s sustainability. Results The National Digital Health Agency enacts the IHC model by overseeing telemedicine services through a cloud-based protocol. Each Region provides “Basic Telemedicine Services”: healthcare professionals’ and patient teleconsultation, telemonitoring, teleassistance. Communication between services, infrastructures, and local health authorities is facilitated by a modular interoperability layer, ensuring data standards and quality. A national telemedicine dissemination portal will contain data on all telemedicine solutions provided. Lessons Clear communication is essential for telemedicine acceptance. COTs enhance coordination and management. Bridging the technology gap for disadvantaged groups, supporting frontline employees and addressing adoption factors is vital to overcome the challenges faced. Key messages • The National Digital Health Agency drives telemedicine services under the IHC model. Each region offers Basic Telemedicine Services, ensuring data standards, quality and interoperability of data. • Italy’s telemedicine adoption faces poor infrastructure, resistance and workforce shortages. Focusing on equity, healthcare workers support and bridging the technology gap will be key.

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max-min planning investigates energy efficiency only from the links/nodes perspective. The max-min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max-min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max-min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.