Flexible Solution Research Articles

An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector

The multidisciplinary field of occupational health and safety (OHS) aims to identify, assess, and mitigating hazards that arise in the workplace or due to the execution of the work. The Fine-Kinney method is a quantitative risk assessment widely applied in many industries to identify, evaluate and prevent potential hazards. Despite the widespread usage of the classic Fine-Kinney approach in several research, its effectiveness in addressing the issue of objective risk appraisal among decision-makers is limited. Therefore, this study proposes a comprehensive occupational risk assessment framework that integrates the Fine-Kinney method with the analytical hierarchy process (AHP) and weighted aggregated sum product assessment (WASPAS) under the Fermatean fuzzy environment. The Fermatean fuzzy set (FFS) offers a more flexible solution in determining the uncertainty and vagueness of information, as it covers membership and non-membership degrees in a wider area compared to intuitionistic and Pythagorean fuzzy sets. The AHP approach is used to calculate the weightage of each Fine-Kinney risk parameter (probability (P), exposure (E), and consequence (C)), and WASPAS method is used to find the ranking of the hazards. In the proposed model, Fermatean fuzzy weighted geometric (FFWG) operator is used for aggregation of expert opinions. Finally, sensitivity and comparative analyses are also performed to further highlight the adaptability and efficiency of the proposed occupational health and safety risk assessment (OHSRA) model, and a case study analyzing occupational hazards for aquaculture operations is presented to demonstrate the model's rationality and feasibility.

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.

Secure digital wallet by using QR code: mapping of mobile payment in partially connected environment

Mobile payment is rising attention worldwide to pay for items from sender to receiver. QR (Quick Response) code technology relies on scan-and-pay techniques, does not require specific hardware, and allows a sender to easily scan the destination address of the payment directly from the receiver’s smartphone. Existing QR (Quick Response) Code-based payment solutions require full internet connectivity for transactions. Sometimes, the user does not have internet for mobile payment. To resolve this issue an innovative hybrid method is proposed which uses web and mobile-based applications for E-payment and transactions using QR (Quick Response) Code in a partially connected environment. Online senders can pay by scanning the QR code provided by the offline receiver. A prototype of the proposed model has been developed as Cloud Sarafa System (CSS) which offers a flexible solution. A Prototype of the proposed model tested via use cases and test cases. Data was collected from 350 participants to evaluate the performance report against the prototype of the proposed model which endorsed positive.

PitViper: a software for comparative meta-analysis and annotation of functional screening data.

Recent advancements in shRNA and Cas protein technologies have enabled functional screening methods targeting genes or non-coding regions using single or pooled shRNA and sgRNA. CRISPR-based systems have also been developed for modulating DNA accessibility, resulting in CRISPR-mediated interference (CRISPRi) or activation (CRISPRa) of targeted genes or genomic DNA elements. However, there is still a lack of software tools for integrating diverse array of functional genomics screening outputs that could offer a cohesive framework for comprehensive data integration. Here, we developed PitViper, a flexible and interactive open-source software designed to fill this gap, providing reliable results for the type of elements being screened. It is an end-to-end automated and reproducible bioinformatics pipeline integrating gold-standard methods for functional screening analysis. Our sensitivity analyses demonstrate that PitViper is a useful tool for identifying potential super-enhancer liabilities in a leukemia cell line through genome-wide CRISPRi-based screening. It offers a robust, flexible, and interactive solution for integrating data analysis and reanalysis from functional screening methods, making it a valuable resource for researchers in the field.

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment.

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Modular Systems for Building Renovation

The article discusses the methodology and technology of installing a new facade system on the old facade of the building, improving the appearance of the building and protecting the old facade, including the prevention of many problems. This research employs an analytical descriptive approach by gathering information on the methodology and technology used in installing modular facade systems on existing buildings. Data is collected through literature review and case studies of successfully implemented building renovation projects. The analysis results indicate that the use of modular systems in building renovation provides numerous benefits, including improved construction efficiency, cost and time reduction, as well as enhanced durability and aesthetics of the building. Moreover, the use of modular systems enables more flexible design adjustments according to the existing architectural context. The utilization of modular systems in building renovation brings positive impacts in enhancing the building's appearance while safeguarding the original facade and preventing various construction issues. By providing more efficient and flexible solutions, modular systems unlock significant potential in sustainably and aesthetically renewing existing building structures.

Participant Assessment of an Alternative Flexible Congregate Nutrition Meal Program for Older Adults

Congregate Nutrition Services have long been a pillar of public health assistance, championing the independence and community engagement of older Americans. The advent of COVID-19, however, restricted access to these services due to the closure of physical locations. In response, Lanakila Meals on Wheels initiated a virtual congregate meal program, Kūpuna U, in collaboration with community partners in Honolulu County. The program combined grab-and-go or home-delivered meals with virtual and in-person classes to improve both nutrition and socialization for older adults. This study aimed to capture participant feedback to assess and enhance the Kūpuna U program, developing it as a flexible and scalable congregate meal solution applicable nationwide. Five focus group discussions were conducted with program participants (n = 34). The majority of participants were female (74%), Asian (73%), and living alone (56%). Participants found the program beneficial, enhancing their nutrition, social engagement, and learning experiences on various topics tailored for older adults. Supportive staff played a crucial role in motivating participants to stay engaged. Participants also identified potential enhancements to the program, including more activities and courses, expanded hours, additional in-person options at various locations, and culturally tailored meals.

Arc Length Spacing-Driven Cooperative Elliptical Encircling Control of Robots: Design and Experiments

Most studies of cooperative enclosing control are centered around moving along the shared circle with available position information. Note that Global Positioning System (GPS) is typically inaccessible under hostile scenar- ios, and elliptical circumnavigation may provide a more flexible and high-efficiency entrapping solution, whilst the non-orthogonality of axial and tangential velocities and the lack of GPS enable elliptical entrapping to be more challeng- ing, and the related outcomes are still rare. Following this line, a novel coordinated error in terms of arc length spacing is skillfully formulated and included in elliptical encircling control to characterize cooperative behaviors without trigg- ering frequent speed variation and unstable observation. Particularly, to remedy the dependence on accessible global position, a new bearing-based estimator under a local frame is proposed to recover the relative coordinate, then a modified cooperative elliptical encircling control strategy is formulated without entailing global position data. Lyapunov stability analysis demonstrates that all error variables are ultimately bounded. Both simulations and experiments are implemented to illustrate the efficacy of proposed control algorithm.

An impedance transformer for UHF RFID and WiFi cross‐technology communication‐based tourism crowd counting

AbstractThis letter proposes a novel cross‐shaped structure‐based parallel coupled line transformer and its application to tag antenna for 920‐MHz radio frequency identification (RFID) and 5‐GHz WiFi Cross‐Technology Communication (CTC). This transformer can be tuned by some lumped capacitors and the equivalent transmission line length for high order harmonic matching. The WiFi is applicable for tourism crowd counting and its counting accuracy may be increased with an additional number of the virtual WiFi Access Points contributed by 5‐GHz resonance of said tags attached with touristic metallic objects. To study the 5‐GHz resonance of these tags the tag antenna is a square patch with a rectangular slot built with 3D acrylonitrile butadiene styrene printing material. The working frequency of the antenna is around 920 MHz, which can achieve a gain of 5.4 dBi and there is also a resonance at 5.6 GHz with a gain of −6.7 dBi. When the capacitor is tuned, the 5‐GHz band of WiFi is varied from 5.2 to 5.6 GHz applicable to emerging WiFi 5 and 6 CTC applications. It is anticipated that the prototype tag provides a flexible tourism crowd counting solution using CTC in the future.

Design of General Parametric Repetitive Control Using IIR Filter With Application to Piezo-Actuated Nanopositioning Stages

The achievable performance with repetitive control is limited due to its inherent sensitivity to the frequency shift away from the intended periodic frequencies and the undesired gain amplification of the aperiodic disturbances. To address these limitations, the paper proposed a general parametric repetitive control (GPRC) method based on the IIR filter with the features of low-order and excellent magnitude responses to result in better tracking performance in diverse applications. By analyzing its sensitivity function, it is found that the design of GPRC can be converted to the explicit parametric design of an IIR high pass filter. The controller design process and the stability analysis are presented in detail. To show the effectiveness of GPRC, comparative experiments are conducted via tracking sinusoids, triangular trajectories and other complex trajectories with multi-frequency components. The experimental results show that, in contrast with the conventional repetitive control (CRC) and a modified repetitive control (MRC), the GPRC exhibits excellent robustness against the frequency shift and advanced performance at the aperiodic frequencies. The tracking results of the sinusoids show that the maximum tracking error obtained with GPRC for a frequency shift of 3 Hz decreases from 0.0259 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> (CRC) and 0.2207 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> (MRC) to 0.0101 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu m$</tex-math> </inline-formula> at the nominal frequency of 1000 Hz, demonstrating the merits of the proposed GPRC. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —To enable automation systems, one of the crucial requirements is to track repetitive references with high precision. Although the normal repetitive control (RC) based schemes are successfully applied to improve the tracking accuracy of periodic trajectories, the existing RC schemes suffer from the problems of lower robustness against frequency shift and the unwanted gain amplification at the aperiodic frequencies due to Bode’s sensitivity integral. To overcome this problem, this paper proposes a novel general parametric repetitive control (GPRC) method via characterizing the loop properties quantitatively based on the IIR high pass filter design. Focusing on specific issues, the detailed variations to handle the errors at only the odd-and even-harmonics are also demonstrated. This framework leads to a flexible solution in practical implementations. The experimental validation on a piezo-actuated nanopositioning stage is comparatively presented in terms of tracking accuracy and rejection ability of the gain amplification at the aperiodic frequencies. With its flexibility and effectiveness, the proposed GPRC can be easily implemented in diverse applications.

All-in-One BPSK/QPSK Switchable Transmission and Reception for Adaptive Free-Space Optical Communication Links

Adaptive free-space optical (FSO) communication links have been extensively studied in order to adapt to variable atmospheric channel environments due to factors such as atmospheric turbulence. As a supporting technology, an all-in-one BPSK/QPSK switchable transmission and reception method is proposed and experimentally demonstrated for adaptive modulation format switching in FSO links. The transmission and reception of both modulation formats are realized based on the same IQ modulator and single-photodetector coherent receiver. Simulation and experimental results show that the QPSK signal has a power penalty of about 3–4 dB compared to the BPSK signal with a BER of about 1 × 10−3. The basis for format switching is given according to the various atmospheric channel conditions. The proposed method provides a flexible and efficient solution for variable FSO communication environments to improve their performance.

EPOPTIS: A Monitoring-as-a-Service Platform for Internet-of-Things Applications.

The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications. To ensure a flexible and scalable solution, we leverage the FIWARE open-source framework, also incorporating blockchain and smart contract technologies to establish a robust integrity verification mechanism for aggregated monitoring and management data. Additionally, we apply automated workflows to filter and label the collected data systematically. Moreover, we provide thorough evaluation results in terms of CPU and RAM utilization and average service latency.

New exact solutions of the (3+1)-dimensional double sine-Gordon equation by two analytical methods

The (3+1)-dimensional double sine-Gordon equation plays a crucial role in various physical phenomena, including nonlinear wave propagation, field theory, and condensed matter physics. However, obtaining exact solutions to this equation faces significant challenges. In this article, we successfully employ a modified G′G2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\left( \\dfrac{G'}{G^2}\\right) $$\\end{document}-expansion and improved tanϕξ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ an \\left( \\dfrac{\\phi \\left( \\xi \\right) }{2}\\right) $$\\end{document}-expansion methods to construct new analytical solutions to the double sine-Gordon equation. These solutions can be divided into four categories like trigonometric function solutions, hyperbolic function solutions, exponential solutions, and rational solutions. Our key findings include a rich spectrum of soliton solutions, encompassing bright, dark, singular, periodic, and mixed types, showcasing the (3+1)-dimensional double sine-Gordon equation ability to model diverse wave behaviors. We uncover previously unreported complex wave structures, revealing the potential for complex nonlinear interactions within the (3+1)-dimensional double sine-Gordon equation framework. We demonstrate the modified G′G2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\left( \\dfrac{G'}{G^2}\\right) $$\\end{document}-expansion and improved tanϕξ2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ an \\left( \\dfrac{\\phi \\left( \\xi \\right) }{2}\\right) $$\\end{document}-expansion methods effectiveness in handling higher-dimensional nonlinear partial differential equations, expanding their applicability in mathematical physics. These method offers enhanced flexibility and broader solution categories compared to conventional approaches.

ASPECTS OF TOURISM PERFORMANCE IN V4 COUNTRIES UNDER THE INFLUENCE OF THE COVID 19 PANDEMIC FOR FUTURE MANAGEMENT AND QUALITY

Tourism is a very dynamic industry and can change rapidly under the influence of various factors. This causes pressure for flexible solutions in terms of managing tourism at the national level and ensuring its quality, and thus its sustainable attractiveness for both domestic and foreign tourism. Tourism performance has changed significantly under the influence of the Covid-19 pandemic, and based on this, the aim of this paper was to analytically evaluate the development of tourism performance in the Visegrad Four countries. Based on the use of the approximation method and the regression method, it evaluates the performance in the period 2013-2021 (latest available data) with the TTCI and TTDI Indexes and predicts the possible performance for the next year 2023 under the influence of the aforementioned pandemic. The results of the study point to different performance within the four countries. The country with the most stable tourism management potential and potentially the best performance appears to be the Czech Republic, and the worst performing is the Slovak Republic.

Assessing the probability of extremely low wind energy production in Europe at sub-seasonal to seasonal time scales

The European energy system will undergo major transformations in the coming decades to implement mitigation measures and comply with the Paris Agreement. In particular, the share of weather-dependent wind generation will increase significantly in the European energy mix. The most extreme fluctuations of the production at all time scales need to be taken into account in the design of the power system. In particular, extreme long-lasting low wind energy production events constitute a specific challenge, as most flexibility solutions do not apply at time scales beyond a few days. However, the probability and amplitude of such events has to a large extent eluded quantitative study so far due to lack of sufficiently long data. In this letter, using a 1000-year climate simulation, we study rare events of wind energy production that last from a few weeks to a few months over the January–February period, at the scale of a continent (Europe) and a country (France). The results show that the fluctuations of the capacity factor over Europe exhibit nearly Gaussian statistics at all time scales. A similar result holds over France for events longer than about two weeks and return times up to a few decades. In that case, the return time curves follow a universal curve. Furthermore, a simple Gaussian process with the same covariance structure as the data gives good estimates of the amplitude of the most extreme events. This method allows to estimate return times for rare events from shorter but more accurate data sources. We demonstrate this possibility with reanalysis data.

Enhancing Energy Efficiency in Electrical Systems with Reinforcement Learning Algorithms

Improving the energy efficiency of electricity systems is important for lowering environmental damage and promoting sustainable growth. In recent years, reinforcement learning (RL) methods have become useful for finding the best ways to use energy in many areas. The point of this study is to look into how RL algorithms can be used to make electricity systems more energy efficient. The study looks into how RL algorithms can be used to make electricity systems more efficient by lowering waste, making the best use of energy, and maximizing energy use. The study suggests a new way to use RL methods to change things like power sharing, load scheduling, and resource allocation on the fly in order to keep system performance high while using as little energy as possible. Some important parts of the study method are creating RL models that work with electricity systems and their limitations, as well as coming up with the right payment functions to help people learn how to behave in ways that use less energy. Extensive models and real-world studies on sample electrical systems are used to test how well the suggested method works. According to the study's results, using RL algorithms can lead to big changes in how efficiently energy is used, with cuts in energy use running from [insert exact number range]. The study also shows how flexible and scalable RL-based solutions are when it comes to different system setups and operating scenarios. Overall, this study adds to the growing amount of research on energy efficiency by showing how RL algorithms can be used to solve difficult problems in electrical systems. Practical plans can be made to improve energy efficiency and promote sustainability in a wide range of businesses and uses based on what this study has taught us.

An Analysis of Cloud-based Accounting Software: A Literature Review on Features, Performance, and User Satisfaction

Cloud-based accounting software has become increasingly popular among businesses seeking flexible and efficient financial management solutions. This paper presents a comprehensive literature review aimed at comparing various cloud-based accounting software platforms based on their features, performance, and user satisfaction. Through an analysis of existing research articles, this paper synthesizes key findings, identifies common trends, and highlights differences among different software solutions. The review provides valuable insights for businesses and practitioners in selecting the most suitable cloud-based accounting software for their needs.

Eye Gaze for Monitoring Attention Through Hybrid Ensemble Learning

One of the countless tasks that call attention to monitoring is necessary for comprising healthcare, education, transportation safety, and human-computer interaction. This research describes novel work done in attention monitoring by fusing a hybrid eye gaze model with deep learning to monitor a driver's attention level. The hybrid eye gaze model proposed is described and its results are produced in this paper. The proposed model uses an augmented dataset where data augmentation techniques like rotation, shifting, shearing, and flipping are applied together with adjustments like changing the fill mode in terms of zooming into the image and rescaling. These are all crucial aspects in reliable and consistent training of the model. Our model is built on modern pre-trained architectures which include VGG16, VGG19, InceptionV3, EfficientNetB0, EfficientNetB7, and InceptionResNetV2. To aid in capturing very minute attention dynamics, we modify these architectures and then incorporate more layers. Later, we used a model ensemble to increase the accuracy and efficiency of the model. Later, the XGBoost model is integrated with all other models used before in the hybrid model technique to obtain better accuracy and efficiency of the model. The model performance is adequately evaluated using various evaluation measures like accuracy, precision, recall, F1 Score, and support. These metrics provide a holistic understanding of the model's capability to detect and predict attention patterns in different contexts. After using the models, we could get the best accuracy from VGG19 and InceptionResNetV2, i.e., 84.6% and 83.6% respectively. VGG16 hybrid models recorded 82% in the accuracy test. With deep learning and pre-trained architectures, the Hybrid Eye Gaze Model shows a strong and flexible attention monitoring solution for varying types of applications.

ANALYSIS OF DATA ORGANIZATION SYSTEM ARCHITECTURES OF THE SMART EUROPEAN REGION "CENTER OF EUROPE" BASED ON THE DATA LAKE TECHNOLOGICAL SOLUTION

In the contemporary landscape of digital transformation, the concept of a "smart region" emerges as a key strategy for regional development, utilizing data integration technologies to enhance the quality of life, optimize service delivery, and promote sustainable growth. The designation of the "Centre of Europe" as a smart region is justified by its strategic geographical location, encompassing Transcarpathia and adjacent areas of neighboring countries, coupled with a relatively low population density. This unique context creates compelling premises for rethinking traditional urban frameworks of smart cities on a broader scale, transforming them into a comprehensive smart region model tailored to the specific needs and opportunities of Central Europe. The rationale for implementing the "Centre of Europe" as a smart region stems from its advantageous position at the heart of the continent, which generates unprecedented opportunities for cross-border collaboration, innovation, and connectivity. The low population density of the region further highlights the potential for the adoption of advanced technological solutions capable of addressing a wide array of challenges and needs. By achieving the status of a smart region, the "Centre of Europe" aspires to become a leading example of how digital and technological innovations can be utilized to create an inclusive, efficient, and progressive regional ecosystem. Objective: The primary objective of this research is to conduct a comparative analysis of advanced data organization system architectures and their role in facilitating the transformation of the "Centre of Europe" into an intelligent European region. It also aims to explore the capabilities of utilizing data lakes for the effective management of large data sets and regional development, taking into account the unique geographical and demographic characteristics of the region. The subject of the study encompasses the architectures of big data organization systems in the smart region of the "Centre of Europe". The object of the study involves the processes of storing and processing large data within the smart region. Main characteristics: This article illuminates the main aspects of implementing big data processing technologies in the smart region of the "Centre of Europe", necessitating scalable and flexible solutions for large data storage, the use of efficient data management practices, and the integration of advanced technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI) to enhance data processing and analytical capabilities. Furthermore, recommendations for regional authorities on adopting these technologies have been formulated, highlighting the importance of collaboration, support for investments in the region's digital infrastructure as key components for achieving the smart region vision. The construction of modern, high-tech data storage facilities using the Data Lake architecture enables the simultaneous use and processing of large volumes of data from various fields and sources. The authors have proposed conceptual models for forming data complexes from different functional areas of the smart region.

Future of Workspaces: Real Estate Investments in Flexible and Remote Work Environments

The contemporary transformation of workspaces, propelled by technological innovation and accelerated by the COVID-19 pandemic, has irrevocably altered the concept of conventional work environments. This upheaval has necessitated a strategic pivot within the real estate sector towards flexible and remote work solutions. The paper identifies a burgeoning trend towards remote work, conduct a case study using remote work data from Europe and China before and after the pandemic, and uses this to discuss the new challenges and developments faced by the real estate markets in Asia and Europe in the post-pandemic era. Results indicate that investments in adaptable workspaces can yield substantial benefits, mitigating initial market perturbations and offering long-term profitability. Furthermore, the study highlights the critical role of innovative workplace designs in enhancing productivity and work-life balance, positing that the future of real estate will be increasingly intertwined with the provision of versatile and employee-centric work environments. The implications of these findings extend to policymakers and business leaders, who must navigate the complexities of a rapidly evolving labor market, ensuring that investments align with the emerging paradigms of work.