Flexible Strategy Research Articles

Особливості бізнес-планування у сфері гостинності

The article concentrates on the evolving context of business planning in the hospitality industry, exploring flexible strategies for addressing the negative industry-wide factors that have an impact. The hospitality industry is subject to several operational factors, including revenue generation, cost management, human resources, and marketing, which are influenced by seasonal variations. This necessitates the implementation of strategies to mitigate the impact of fluctuating demand. The high level of competition in the hospitality sector requires a clear competitive advantage, which may be achieved through factors such as location, the provision of high-quality service, innovative design, or a thematic focus. These factors must be strategically employed to distinguish businesses from one another. External factors, including economic conditions, political stability, and environmental risks, significantly influence the hospitality industry. Implementing robust scenario planning, risk management, and service diversification strategies is essential to mitigate these risks. Financial planning is critical to business planning and success, as financial obligations in the hospitality industry underscore the importance of a comprehensive investment analysis. The hospitality industry’s primary asset is its human capital. , strategies should be employed to create a positive working environment, invest in staff training, and implement incentive programs to attract, develop, and retain talent. Guests’ safety and maintaining a positive reputation for hospitality establishments are contingent upon monitoring reviews, responding to negative comments, and utilizing a positive image. The study highlights the necessity for an integrated methodology to be employed in planning business operations within the hospitality sector. Keywords: business planning, hotel and restaurant complex, hospitality industry, stages of organizational culture formation, business.

Resolution of Optimal Mitochondrial and Nuclear DNA Enrichment in Target-Panel Sequencing and Physiological Mitochondrial DNA Copy Number Estimation in Liver Cancer and Non-Liver Cancer Subjects.

Mitochondria generate energy to support cells. They are important organelles that engage in key biological pathways. The dysfunction of mitochondria can be linked to hepatocarcinogenesis, which has been actively explored in recent years. To investigate the mitochondrial dysfunction caused by genetic variations, target-panel sequencing is a flexible and promising strategy. However, the copy number of mitochondria generally exceeds nuclear DNA, which raises a concern that uneven target enrichment of mitochondrial DNA (mtDNA) and nuclear DNA (ncDNA) in target-panel sequencing would lead to an undesirably biased representation of them. To resolve this issue, we evaluated the optimal pooling of mtDNA probes and ncDNA probes by a series of dilutions of mtDNA probes in both genomic DNA (gDNA) and cell-free DNA (cfDNA) samples. The evaluation was based on read count, average sequencing depth and coverage of targeted regions. We determined that an mtDNA:ncDNA probe ratio of around 1:10 would offer a good balance of sequencing performance and cost effectiveness. Moreover, we estimated the median physiological mtDNA:ncDNA copy ratio as 38.1 and 2.9 in cfDNA and gDNA samples of non-liver cancer subjects, respectively, whereas they were 20.0 and 2.1 in the liver cancer patients. Taken together, this study revealed the appropriate pooling strategy of mtDNA probes and ncDNA probes in target-panel sequencing and suggested the normal range of physiological variation of the mtDNA:ncDNA copy ratio in non-liver cancer individuals. This can serve as a useful reference for future target-panel sequencing investigations of the mitochondrial genome in liver cancer.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

The Rise of Her Economy in the New Media Era: An Innovative Study of Women's Consumption Power and Marketing Strategies

With the advent of the new media era, the rise of her economy has become a phenomenon that cannot be ignored, and the economic influence of women in the consumer market is also increasing. Starting from the perspective of consumer culture and based on domestic and international research results, this paper explores the behaviours and preferences of female consumers in the digital realm, derives the study of the alienation of female aesthetics and consumption in the consumer society, and grasps the behavioural characteristics of female consumption in general by linking theory to practice, as well as analyzing the effects of marketing strategies on social media platforms to reveal the evolution of the relationship between women, new media and the consumer market. The results of this study demonstrate the key role of new media in shaping women's consumption behaviour and highlight the importance of flexible marketing strategies in effectively engaging the female consumer base. Finally, the article reflects on the negative impact of the media's focus on women's issues in an era of proximity to the female psyche. The study provides practical insights for a deeper understanding of the she-economy phenomenon and for companies wishing to capitalise on the growing power of the female consumer in the digital age. In turn, it promotes socially virtuous consumption and the realisation of a truly equal, respectful and caring consumer market.

Nanofluidic sensing inspired by the anomalous water dynamics in electrical angstrom-scale channels

Manipulation of confined water dynamics by voltage keeps great importance for diverse applications. However, limitations on the membrane functions, voltage-control range, and unclear dynamics need to be addressed. Herein, we report an anomalous electrically controlled gating phenomenon on cation-intercalated multi-layer Ti3C2 membranes and reveal the confined water dynamics. The water permeation rate was improved rapidly following the application and rise of voltage and finally reached a maximum rate at 0.9 V. The permeation rate starts to decrease from 0.9 V. Below 0.9 V, the electric field affects the charge and polarity of water molecules and then leads to ordered and denser rearrangement in the two-dimensional (2D) channel to accelerate the permeation rate. Above 0.9 V, with the assistance of metal cations, the surge in current induced aggregation of water molecules into clusters, thereby limiting the water mobility. Based on these findings, a high-performance humidity sensor was developed by simultaneously optimizing the response and recovery speeds through electric manipulation. This work provides flexible strategies in intelligent membrane design and nanofluidic sensing.

Playtime for Cephalopods: Understanding the Significance of Play Behavior in Octopus bimaculoides.

Play is considered to be an essential part of development that supports learning, memory, and the development of flexible behavioral strategies. An increasing amount of non-mammalian species have been discovered to engage in play behavior, but there has been little research into play behavior in cephalopods specifically. Here we studied play behavior of wild-caught, laboratory-housed California Two-Spot Octopuses, Octopus bimaculoides. The behavior observed in these animals is consistent with established criteria of play behavior in animals. The evidence of play in O. bimaculoides provides further insight into the evolutionary development of play behavior as a whole, the characteristics of play in an under-studied species, and also has the potential to be used as a factor in determining the welfare state of octopuses to improve their management in captivity.

Reliability–flexibility integrated optimal sizing of second‐life battery energy storage systems in distribution networks

AbstractSecond‐life batteries (SLBs), which are batteries retired from electric vehicles (EVs), can be used as energy storage systems to enhance the performance of distribution networks. Two issues should be addressed particularly for the optimal sizing of SLBs. Compared with fresh batteries, the failure rate of SLBs is relatively high, and timely and preventive replacement is needed. In addition, the flexibility introduced by EVs and installed SLBs should be coordinated to achieve optimal economic benefits. This paper focuses on the efficient utilization of SLBs by highlighting reliability‐flexibility concerns in optimal sizing. The model is formulated as a bi‐level model. On the upper‐level, considering the operational reliability constraints of SLBs, decisions regarding the investment and replacement of SLBs are optimized. Distribution network operations are improved on the lowerlevel, with an effective spatiotemporal flexible dispatch strategy for EVs. Finally, a linearized process for the optimal sizing of SLBs is presented and efficiently implemented. The Sioux Falls network and IEEE 69‐node distribution network are coupled as the test system. According to the simulation results, when the state of health of the SLBs decreased to 70%, the conditions were unreliable. The differences in the optimal SLB size and costs considering reliability and flexibility are highlighted.

Mechanisms of ammonium detoxification in submerged macrophytes under shade conditions

Excessive ammonium disrupts the biological and physical characteristics of aquatic freshwater ecosystems, causing nutrient imbalances and toxicity. Different macrophytes exhibit varying tolerance levels to ammonium stress, influenced by species-specific adaptations. However, eutrophic water bodies not only have high nutrient loads but also exhibit low light transparency, necessitating an understanding of how submerged macrophytes cope with both high ammonium concentrations and low light conditions. In this study, we explored the tolerance of submerged macrophytes under these challenging conditions by testing various ammonium concentrations and light intensities. Our findings reveal that Myriophyllum spicatum demonstrates high ammonium tolerance under both optimal and low light intensities. Specifically, under optimal light, the primary ammonium assimilation pathway is catalyzed by NADH-GDH (Nicotinamide Adenine Dinucleotide-dependent Glutamate Dehydrogenase), with its activity increasing 4-fold at 50 mg L−1 [NH4+-N] compared to the control. Conversely, under low light intensity, the GS (Glutamine Synthetase)-catalyzed pathway becomes predominant, with GS activity rising 3-fold at 50 mg L−1 [NH4+-N] compared to the control. These results provide new insights into the adaptive mechanisms of M. spicatum, highlighting its flexible strategies for ammonium assimilation and its potential application in water restoration efforts. This study offers valuable information on the enzymatic pathways involved in ammonium detoxification, which is essential for developing effective strategies to manage and restore eutrophic aquatic systems.

A Novel Multi-Dimensional Joint Search Method for the Compression of Medical Image Segmentation Models.

Due to the excellent results achieved by transformers in computer vision, more and more scholars have introduced transformers into the field of medical image segmentation. However, the use of transformers will make the model's parameters very large, which occupies a large amount of the computer's resources, making them very time-consuming during training. In order to alleviate this disadvantage, this paper explores a flexible and efficient search strategy that can find the best subnet from a continuous transformer network. The method is based on a learnable and uniform L1 sparsity constraint, which contains factors that reflect the global importance of the continuous search space in different dimensions, while the search process is simple and efficient, containing a single round of training. At the same time, in order to compensate for the loss of accuracy caused by the search, a pixel classification module is introduced into the model to compensate for the loss of accuracy in the model search process. Our experiments show that the model in this paper compresses 30% of the parameters and FLOPs used, while also showing a slight increase in the accuracy of the model on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset.

A Flexible Control Strategy for Multi-Functional PV Inverters with Load Compensation Capabilities Considering Current Limitations and Unbalanced Load Conditions

A Flexible Control Strategy for Multi-Functional PV Inverters with Load Compensation Capabilities Considering Current Limitations and Unbalanced Load Conditions

Femtosecond laser writing of ant-inspired reconfigurable microbot collectives

Microbot collectives can cooperate to accomplish complex tasks that are difficult for a single individual. However, various force-induced microbot collectives maintained by weak magnetic, light, and electric fields still face challenges such as unstable connections, the need for a continuous external stimuli source, and imprecise individual control. Here, we construct magnetic and light-driven ant microbot collectives capable of reconfiguring multiple assembled architectures with robustness. This methodology utilizes a flexible two-photon polymerization strategy to fabricate microbots consisting of magnetic photoresist, hydrogel, and metal nanoparticles. Under the cooperation of magnetic and light fields, the microbots can reversibly and selectively assemble (e.g., 90° assembly and 180° assembly) into various morphologies. Moreover, we demonstrate the ability of assembled microbots to cross a one-body-length gap and their adaptive capability to move through a constriction and transport microcargo. Our strategy will broaden the abilities of clustered microbots, including gap traversal, micro-object manipulation, and drug delivery.

Embedding assessment flexibilities for future authentic learning

ABSTRACT Studies reporting flexible assessment strategies and their impact across different modes of study remain limited with little emphasis placed on the role these arrangements play in devising authentic assessment processes. This paper synthesises recent research work depicting flexible design principles and practice strategies for how educators might effectively and with confidence move assessment forward in ways that prepare students for a multitude of future possibilities. Evidence is presented for how such assessment arrangements can be created to allow for a valid and authentic representation of student performance in increasingly digital learning environments. Authentically future-facing assessment in higher education needs to be adaptable, ideally offering an element of flexibility and agency to students which helps them to continuously connect what they are learning – ideas to thinking, principles to problems, theory to practice, learning to life.

Achieving nitrite shunt using in-situ free ammonia enriched by natural zeolite: Pilot-scale mainstream anammox with flexible nitritation strategy

The mainstream partial nitritation/anammox (PN/A) process represents a significant innovation in decarbonizing municipal wastewater treatment. However, its implementation is considerably hampered by the challenge of stable nitrite supply. In this study, a pilot-scale PN/A system receiving real sewage (20 m3) was operated at room temperature for nearly one year. Remarkable PN performance with relatively high nitrite accumulation ratio of 75.04 ± 10.05 % was obtained via in-situ free ammonia (FA) strategy. The ammonium concentration enriched in the zeolite increased significantly by 548.8 times compared to that in the aqueous phase by ion exchange. This substantial increase robustly inhibited nitrite-oxidizing bacteria (NOB), resulting in high relative abundance ratio of ammonia-oxidizing bacteria (AOB) to NOB of 37.93 ± 12.61 in the zeolite biofilm, compared to 10.22 ± 1.67 in suspended floc sludge. The significant differences in FA concentrations between zeolite biofilm and suspended floc sludge resulted in distinct spatial distribution disparities of AOB and NOB, which were central to achieving stable nitrite accumulation without complex multiple selective pressures. Consequently, compliant effluent with total nitrogen of 10.91 ± 4.23 mg N/L was achieved at 10.4-31.1 °C without external carbon source addition. The biocarriers in the anammox process played a key role in enhancing functional genes and electron flow, supporting anammox-dominated nitrogen removal. This study presents a flexible and adaptable strategy for mainstream nitrite shunting, highlighting its potential for large-scale implementation of mainstream anammox treatment.

A framework for integrated resource planning in surgical clinics

The problem under study is based on the challenges faced by the Orthopaedic Clinic at St. Olav’s Hospital in Trondheim, Norway. Variations in demand and supply cause fluctuating waiting lists, and it is challenging to level the activities between the clinic’s two units, the outpatient clinic and the operating theater, to obtain short waiting times for all activities. Based on these challenges, we describe and present a planning problem referred to as the Long-term Master Scheduling Problem (LMSP), where the objective is to construct an integrated Long-term Master Schedule (LMS) that facilitates short waiting times in both units. The LMS can be separated into two schedules, one cyclic high-level schedule, and one non-cyclic low-level schedule. The demand for outpatient clinic consultations and surgeries is stochastic, as are the waiting lists. To account for this, we propose a planning framework consisting of an optimization model to solve the LMSP, and a two-level planning procedure. In the planning procedure, we first solve the LMSP to construct the LMS for the upcoming planning horizon. Then, to adjust to the fluctuating waiting lists, we periodically refine the low-level schedule by solving a constrained LMSP. We also develop a simulation-based evaluation procedure to evaluate the planning framework in a real-life setting and use this to investigate different planning strategies. We find that imposing flexible, dynamic and agile planning strategies improve waiting time outcomes and patient throughput. Furthermore, combining the strategies yields additive improvements.

Feasibility study of a single-primer extension-based microhaplotype NGS system.

DNA degradation has been a thorny problem in forensic science. Shortening the amplicon length of the genetic markers improves the analysis of degraded DNA effectively. Microhaplotype (MH) has been proposed as a potential genetic marker that can be used for degraded DNA analysis. In the present study, a 146-plex MH-next-generation sequencing (NGS) system with an average Ae of 6.876 was constructed. Unlike other MH studies, a single-primer extension (SPE)-based NGS library preparation method was used to improve the detection of MH markers for degraded DNA. SPE employs a locus-specific and universal primer to amplify target fragments, reducing the necessity for complete fragment sequences. SPE might effectively mitigate the impact of degradation on amplification. However, SPE produces amplicons of varying lengths, posing challenges in allele calling for SPE-NGS data. To address this issue, this study proposed a flexible allele-calling strategy to improve amplicon detection. In addition, this study evaluated the forensic efficacy of the system using 12 low-template samples (from 1ng to 7.8pg), 10 mock-degraded DNA with various degrees of degradation, and 8 forensic casework samples. When the template is as low as 7.8pg, our system can accurately detect at least 37 loci and achieves a random match probability (RMP) of 10-30 using the complete allele-calling strategy. Eighty-two loci can be detected, and RMP can reach 10-54 using a flexible allele-calling strategy. After 150min of 98°C treatment, 36 loci can still be detected, and an RMP of 10-5 can be obtained using the flexible allele-calling strategy. Furthermore, the number of single nucleotide polymorphism detected at different DNA amounts and degradation levels suggests that the SPE method combined with a flexible allele-calling strategy is effective.

THE MARKETING EFFECTIVENESS IN START-UP BUSINESS: A SYSTEMATIC LITERATURE REVIEW FROM 1973-2023

In today's rapidly evolving business environment, understanding dynamic marketing effectiveness is essential for startups. This study explores how flexible marketing strategies can adapt to technological advancements, shifting consumer behaviors, and global market changes. A systematic literature review of 740 scholarly articles identified 35 relevant studies. These articles address various facets of marketing effectiveness, including technology integration, strategic learning, social media use, and internationalization. The findings provide startups with practical guidance on enhancing marketing effectiveness by adopting innovative technologies and aligning with market trends. This study serves as a valuable resource for entrepreneurs and marketers, offering actionable insights into successful marketing strategies in a dynamic business landscape.

Implementation of Digital Transformation and Government Enterprise Architecture in Improving the Performance of Integrated Social Services

This study addresses the subject matter of declining public confidence in local governments in Indonesia, primarily due to issues in public service delivery, highlighting the urgent need for improved integrated social services. The objective is to analyze the mediating effect of enterprise architecture orientation on the influence of environmental turbulence, flexibility strategy, business model innovation, and digital transformation on social service performance in West Java. Methods involved a survey explanatory approach with all 27 social service providers in West Java Province, employing whole population sampling. Data were analyzed using descriptive statistical analysis and hypothesis testing using Partial Least Square Structural Equation Modeling (PLS-SEM). Results indicate that environmental turbulence, flexibility strategy, business model innovation, and digital transformation significantly impact social service performance when mediated by enterprise architecture orientation. The conclusions suggest that a robust model emphasizing strategic architectural orientation and effective implementation of flexibility, innovation, and digital transformation can enhance social service performance amidst environmental challenges. This study contributes to understanding the complex dynamics of social service provision in local governments, advocating for strategic investments in enterprise architecture and adaptive strategies to improve public service delivery and restore public trust.

Project -based Learning and Students’ Performance

Project-based learning (PBL) is an educational model that is believed to enhance student learning experience and academic achievement. The Buck Institute for Education (BIE) is the promoter of PBL. The Chinese Ministry of Education emphasizes the importance of project-based learning in its curriculum standards. Research shows that PBL improves student academic achievement, engagement, critical thinking, and promotes collaboration and innovative thinking. However, the implementation of PBL faces many challenges, such as project design complexity, individual student differences, time management, assessment standards development, teacher role changes, resource constraints, and curriculum integration. Addressing these challenges requires teachers with keen insight, flexible teaching strategies and continuous professional development. There are differences between project-based learning and traditional teaching in student performance, such as initiative and engagement, critical thinking and problem solving, teamwork and communication skills, as well as in-depth understanding and transfer of knowledge. This study uses quantitative research design to numerically describe the implementation of investigation-based project-based learning and analyze its relationship with student achievement. The subjects were teachers and students (172 teachers, 2,154 students) from three schools in Guangdong, China, and the sampling was purposeful, focusing on teachers and students using project-based learning.

Connecting the green to the digital: Integrating Eco Cities and Smart Regions

<p>This article explores the integration of Eco Cities and Smart Regions to create sustainable and innovative urban environments. Eco Cities focus on environmental sustainability through renewable energy, waste management, and green infrastructure, aiming to reduce ecological footprints and promote healthy lifestyles. Smart Regions, on the other hand, utilize advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to enhance public service efficiency, optimize resource use, and foster economic development. The study highlights how combining these two approaches can lead to significant benefits, including improved operational efficiency, better quality of life, environmental resilience, and active citizen participation. Case studies of cities like Copenhagen and Singapore demonstrate successful integration, showcasing achievements in sustainability and technological innovation. The article also identifies the main challenges in this integration, including technical compatibility, cybersecurity risks, high initial investments, digital inequality, and the need for robust governance frameworks. Overcoming these challenges requires collaborative efforts among governments, private sectors, and civil society, as well as innovative financing strategies and capacity-building programs. Future considerations emphasize the need for a holistic approach to urban planning that incorporates emerging technologies, flexible and adaptive strategies, and inclusive citizen engagement. By addressing these factors, cities and regions can become more resilient, sustainable, and inclusive, meeting the needs of both current and future generations. This integrated approach promises to transform urban environments into hubs of sustainability and innovation, driving long-term positive outcomes for society and the environment.<strong></strong></p>

Visible–infrared person re-identification via patch-mixed cross-modality learning

Visible–infrared person re-identification (VI-ReID) aims to retrieve images of the same pedestrian from different modalities, where the challenges lie in the significant modality discrepancy. To alleviate the modality gap, recent methods generate intermediate images by GANs, grayscaling, or mixup strategies. However, these methods could introduce extra data distribution, and the semantic correspondence between the two modalities is not well learned. In this paper, we propose a Patch-Mixed Cross-Modality framework (PMCM), where two images of the same person from two modalities are split into patches and stitched into a new one for model learning. A part-alignment loss is introduced to regularize representation learning, and a patch-mixed modality learning loss is proposed to align between the modalities. In this way, the model learns to recognize a person through patches of different styles, thereby the modality semantic correspondence can be inferred. In addition, with the flexible image generation strategy, the patch-mixed images freely adjust the ratio of different modality patches, which could further alleviate the modality imbalance problem. On two VI-ReID datasets, we report new state-of-the-art performance with the proposed method.