Miracle-dz/UniFAR-FTUs huggingface dataset:
An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm.
Where is the public of ‘networked publics’? A critical analysis of the theoretical limitations of online publics research

The study of online media use has been elemental in shaping the research on publics within communication and media studies in the last few decades. This article takes a critical view of this research by asking to what extent it has been informed by the long history of theoretical work on the concept of the public. Reviewing the literature on ‘networked publics’ as an illustrative example, we demonstrate how the lack of public-theoretical engagement creates both conceptual and empirical limitations to the study of online publics. We also indicate how the sociological, political theory and cultural studies traditions on the concept of the public can contribute to widening the perspectives of online publics research within communication and media studies.
Creating Mindful Spaces: Educational Practices for Interior Design

At the beginning of the day, in the cool of the early morning, as light and color are beginning to come back into the world, bring attention to the quality of inner space, the space in this room and the space in the world around us. Developing that attention, we notice the space that’s always here, the space around things, the space within things. This helps us to recollect, to awaken to the inner space, the space of our minds which receives and contains, which encompasses all thoughts, feelings, perceptions, moods (Amaro, 2020, p. 543).
A Dynamic Thermodynamic Sea Ice Model

Abstract A numerical model for the simulation of sea ice circulation and thickness over a seasonal cycle is presented. This model is used to investigate the effects of ice dynamics on Arctic ice thickness and air-sea heat flux characteristics by carrying out several numerical simulations over the entire Arctic Ocean region. The essential idea in the model is to couple the dynamics to the ice thickness characteristics by allowing the ice interaction to become stronger as the ice becomes thicker and/or contains a lower areas percentage of thin ice. The dynamics in turn causes high oceanic heat losses in regions of ice divergence and reduced heat losses in regions of convergence. TO model these effects consistently the ice is considered to interact in a plastic manner with the plastic strength chosen to depend on the ice thickness and concentration. The thickness and concentration, in turn, evolve according to continuity equations which include changes in ice mass and percent of open water due to advection, .
Tensor networks for interpretable and efficient quantum-inspired machine learning

It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML). Tensor network (TN), which is a well-established mathematical tool originating from quantum mechanics, has shown its unique advantages on developing efficient ``white-box'' ML schemes. Here, we give a brief review on the inspiring progresses made in TN-based ML. On one hand, interpretability of TN ML is accommodated with the solid theoretical foundation based on quantum information and many-body physics. On the other hand, high efficiency can be rendered from the powerful TN representations and the advanced computational techniques developed in quantum many-body physics. With the fast development on quantum computers, TN is expected to conceive novel schemes runnable on quantum hardware, heading towards the ``quantum artificial intelligence'' in the forthcoming future.
Measurement and Prediction of Signal Strength of Wireless Sensor Network

—This paper utilizes an eﬃcient prediction model using the concept of ray-tracing based on the Theory of Geometrical Optics (GO) to predict the signal strength between two wireless sensor nodes within an indoor environment, which can provide aid to designers in the implementation of Wireless Sensor Networks (WSNs). WSN is a technology that is widely used for functions such as collecting and processing data, then transmitting it wirelessly within the network. WSNs are typically autonomous and self-organizing networks of nodes that communicate wirelessly with each other and collaborate to perform tasks such as data processing, sensing, aggregation, and forwarding. With the increasing prevalence of WSNs in indoor environments, installations of numerous sensor nodes are necessary to collect and transmit data in certain areas, which builds up to a single network. Thus, to ensure the functionality of the WSNs, it is of utmost importance to ensure a reliable connection between the nodes, which is directly aﬀected by its location and placement. The prediction model developed in this work is built using MATLAB software, which is then implemented into a Graphical User Interface (GUI) using MATLAB App Designer, which allows modiﬁcations to be made to the prediction model as to ﬁt the user’s environment. The results of our prediction model are compared against experimental ones obtained through physical measurements using wireless communications technologies such as ZigBee and Bluetooth Low Energy (BLE).
Artificial intelligence in social science: A study based on bibliometrics analysis

Artificial intelligence (AI) is gradually changing the planet. Data digitisation, computing infrastructure and machine learning are helping AI tools to spread across all sectors of society. This article presents the results of a bibliometric analysis of AI-related publications in the social sciences over the last ten years (2013-2022). Most of the historical publications are taken into consideration with the aim of identifying research relevance and trends in this field. The results indicate that more than 19,408 articles have been published, 85% from 2008 to 2022, showing that research in this field is increasing significantly year on year. Clear domains or disciplines of research related to AI within the social sciences can be grouped into sub-areas such as law and legal reasoning, education, economics, and ethics. The United States is the country that publishes the most (20%), followed by China (13%). The influence of AI on society is inevitable and the advances can generate great opportunities for innovation and new jobs, but in the medium term it is necessary to adequately face this transition, setting regulations and reviewing the challenges of ethics and responsibility.
A Resource Allocation Scheme for Packet Delay Minimization in Multi-Tier Cellular-Based IoT Networks

With advances in Internet of Things (IoT) technologies, billions of devices are becoming connected, which can result in the unprecedented sensing and control of the physical environments. IoT devices have diverse quality of service (QoS) requirements, including data rate, latency, reliability, and energy consumption. Meeting the diverse QoS requirements presents great challenges to existing fifth-generation (5G) cellular networks, especially in unprecedented scenarios in 5G networks, such as connected vehicle networks, where strict data packet latency may be required. The IoT devices with these scenarios have higher requirements on the packet latency in networking, which is essential to the utilization of 5G networks. In this paper, we propose a multi-tier cellular-based IoT network to address this challenge, with a particular focus on meeting application latency requirements. In the multi-tier network, access points (APs) can relay and forward packets from IoT devices or other APs, which can support higher data rates with multi-hops between IoT devices and cellular base stations. However, as multiple-hop relaying may cause additional delay, which is crucial to delay-sensitive applications, we develop new schemes to mitigate the adverse impact. Firstly, we design a traffic-prioritization scheduling scheme to classify packets with different priorities in each AP based on the age of information (AoI). Then, we design different channel-access protocols for the transmission of packets according to their priorities to ensure the QoS in networking and the effective utilization of the limited network resources. A queuing-theory-based theoretical model is proposed to analyze the packet delay for each type of packet at each tier of the multi-tier IoT networks. An optimal algorithm for the distribution of spectrum and power resources is developed to reduce the overall packet delay in a multi-tier way. The numerical results achieved in a two-tier cellular-based IoT network show that the target packet delay for delay-sensitive applications can be achieved without a large cost in terms of traffic fairness.
DISTRIBUTION OF GOBIID SPECIES (GOBIIDAE, PISCES) IN THE YANTRA RIVER (DANUBE BASIN, BULGARIA)

Recently, a rapid spread of some species of family Gobiidae upstream the Danube River and its tributaries has been observed. The distribution of gobiid species in the Yantra River (Danube tributary in Bulgaria) was studied. Four gobiid species (out of six species occurring in the Danube) were recorded: Neogobius fluviatilis, N. gymnotrachelus, N. melanostomus, and Proterorhinus marmoratus. Most frequently occurred N. melanostomus reaching as far upstream as Petko Karavelovo. It was followed by N. gymnotrachelus reaching as far upstream as Polsko Kosovo, and P. marmoratus, upstream to Petko Karavelovo. N. fluviatilis was found only at the site near Krivina not far from the river estuary. Most abundant were N. melanostomus and N. gymnotrachelus. The former dominated at sites upstream of Byala, while the latter at sites close to estuary. The potential reasons for the range expansion of gobiid species upstream the Yantra River were discussed.
Synthesizing social insurance research: A bibliometric analysis

Social insurance has been a pivotal tool in implementing social security. The purpose of the study is to analyze the existing information clusters (areas) in the field of social insurance. Clusters define related and unrelated groups in the field of social insurance. These groups will help streamline and identify areas where little or no research has been conducted to present. To achieve the objective, the study employed a precise and systematic procedure to gather 562 journal articles published in Scopus-indexed journals from 1926–2022. Subsequently, VOSviewer, Science of Science (Sci2), and Gephi were utilized to conduct bibliometric analysis (such as keyword co-occurrence and bibliographic coupling) and network analysis tests (such as citation and co-citation analysis). The results of keyword co-occurrence and co-citation analysis suggest there are three knowledge clusters: welfare provisions, benefits provided by social insurance, and social insurance operational aspects. Through analysis found top article-based Inequality, social insurance, and redistribution with 408(LC) and 1042(GC) and its page rank value is 0.010574 through prestigious analysis. Additionally, it is also observed that I. Nielsen had made the most substantial contributions as an author, with R. Smyth and C. Nyland following closely in the rankings. Also, observed maximum total link strength with 109 value on social security variable. The study also drawn attention to specific deficiencies, including regional concentration of research, insufficient research in developing and underdeveloped countries, inadequate knowledge sharing among researchers, limited methodological diversity, and a lack of research on the role of social insurance in facilitating society’s recovery from the pandemic.
Thermohaline Circulation Enhanced by Wind Forcing

Abstract A thermohaline circulation enhanced by wind forcing is demonstrated in an idealized basin model, and a mechanism that provides a connection between wind forcing and a thermohaline circulation is clarified. A rectangular ocean that extends over the Northern and Southern Hemispheres is driven by differential heating and wind stress at the sea surface. The differential heating is so distributed that the deep water is formed at the southern end of the model ocean. The wind stress is so distributed that there are three wind-driven gyres in the Northern Hemisphere, and it is not imposed in the Southern Hemisphere. Comparison is made between the cases with and without the wind stress. When the wind forcing is imposed, the basin-scale meridional circulation increases in intensity. This is due to the enhanced surface heating in the cyclonic wind-driven gyre with the Ekman upwelling and the accompanying enhanced surface cooling in the deep-water formation region. In the cyclonic wind-driven gyre, the Ekman.
User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference

Deep learning as a service (DLaaS) that promotes deep learning-based applications by selling computing services from IT companies to end-users has introduced potential privacy leaks from users and cloud servers. Federated learning (FL) provides an emerging distributed paradigm that enables numerous users to collaboratively train deep-learning models while protecting user privacy and data security. However, many FL-related existing works only focus on improving communication bottlenecks due to frequent model parameter transmission, but ignore the performance degradation incurred by imbalanced user distribution and high inference latency due to the high complexity of deep-learning models in the emerging IoT-edge-cloud FL. In this paper, we propose an efficient user-distribution-aware hierarchical FL for communication-efficient training and fast inference in the IoT-edge-cloud DLaaS architecture. Specifically, we propose a user-distribution-aware hierarchical FL architecture to cope with the performance degradation owing to the imbalanced user distribution. The proposed architecture also features a lightweight deep neural network that adopts the designed lightweight fire modules as components and has a side branch for communication-efficient training and fast inference. Extensive experiments demonstrate that the proposed schemes significantly boost the accuracy by up to 67.12%, save 47.98% communication costs, and accelerate inference by up to 87.24<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math><alternatives><mml:math><mml:mo mathvariant="bold">×</mml:mo></mml:math><inline-graphic xlink:href="wei-ieq1-3327513.gif"/></alternatives></inline-formula> compared to benchmarking methods.
Perceived External CSR from Employees’ Perspective in Nepalese Banking Industry

In order to better understand how external corporate social responsibility (CSR) efforts in the Nepalese banking industry affect different employee-related outcomes, this paper examines how employees perceive these activities. In this study, staff of Nepalese commercial banks were given questionnaires as part of a descriptive research design in order to gather primary data. The sample size is 120 individuals from an actual sample. This finding suggests that workers have a strong understanding of external CSR activities taking place outside of the organization. It is also clear that employees are conscious of the effects of the CSR efforts and give them a favorable evaluation. The study also investigates the link between work happiness, organizational engagement, and overall views of CSR and employee perceptions of external CSR actions outside the firm. This finding suggests that workers in Nepal's banking industry may see external CSR efforts as positively affecting their loyalty to the firm and job happiness. The relevance of this poll emphasizes how crucial an external CSR strategy is to successfully raising employee happiness and fostering organizational engagement. The report's suggestions for commercial banks are meant to help them match their CSR initiatives with employee expectations and values to foster a productive workplace. Future study objectives include looking at ways to increase employee involvement in CSR initiatives, assessing the long-term effects of CSR efforts, and doing industry comparison studies. Overall, this study helps understand how Nepalese banking industry employees feel about external CSR initiatives.
Characterisation of macrophage infiltration and polarisation based on integrated transcriptomic and histological analyses in Primary Sjögren’s syndrome

Background Primary Sjögren’s syndrome (pSS) is a progressive inflammatory autoimmune disease. Immune cell infiltration into glandular lobules and ducts and glandular destruction are the pathophysiological hallmarks of pSS. Macrophages are one of the most important cells involved in the induction and regulation of an inflammatory microenvironment. Although studies have reported that an abnormal tissue microenvironment alters the metabolic reprogramming and polarisation status of macrophages, the mechanisms driving macrophage infiltration and polarisation in pSS remain unclear. Methods Immune cell subsets were characterised using the single-cell RNA sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) from patients with pSS (n = 5) and healthy individuals (n = 5) in a public dataset. To evaluate macrophage infiltration and polarisation in target tissues, labial salivary gland biopsy tissues were subjected to histological staining and bulk RNA-seq (pSS samples, n = 24; non-pSS samples, n = 12). RNA-seq data were analysed for the construction of macrophage co-expression modules, enrichment of biological processes and deconvolution-based screening of immune cell types. Results Detailed mapping of PBMCs using scRNA-seq revealed five major immune cell subsets in pSS, namely, T cells, B cells, natural killer (NK) cells, dendritic cells (DCs) and monocyte-macrophages. The monocyte-macrophage subset was large and had strong inflammatory gene signatures. This subset was found to play an important role in the generation of reactive oxygen species and communicate with other innate and adaptive immune cells. Histological staining revealed that the number of tissue-resident macrophages was high in damaged glandular tissues, with the cells persistently surrounding the tissues. Analysis of RNA-seq data using multiple algorithms demonstrated that the high abundance of pro-inflammatory M1 macrophages was accompanied by the high abundance of other infiltrating immune cells, senescence-associated secretory phenotype and evident metabolic reprogramming. Conclusion Macrophages are among the most abundant innate immune cells in PBMCs and glandular tissues in patients with pSS. A bidirectional relationship exists between macrophage polarisation and the inflammatory microenvironment, which may serve as a therapeutic target for pSS.
Comparing Metabolic Cost and Muscle Activation for Knee and Back Exoskeletons in Lifting

Performing heavy manual lifting jobs can be extremely strenuous, often resulting in high rates of injury at overloaded joints. Lower-body exoskeletons have potential to mitigate this fatigue and injury through targeted joint assistance. A variety of current wearable devices aim to provide this kind of support, but their effects on both directly and indirectly targeted joints have not been fully investigated. In this study, a powered bilateral knee exoskeleton (research device), and passive back exosuit (HeroWear Apex) were compared across 10 individuals through a battery of manual lifting tasks. Metabolic, electromyographic, and subject-reported outcome measure data were used to determine which exoskeleton provided the best assistance for each task type. It was found that the knee exoskeleton significantly reduced metabolic cost by $\mathbf {9.6\%}$ compared to the no-exoskeleton baseline $(\mathbf {p < .05})$ . Both the knee and back exoskeletons reduced activation in up to 6 of the muscles measured (out of 16). Importantly, each device primarily reduced muscle activation around the joint it targeted, not around adjacent joints. These findings suggest that exoskeletons that target the knee or lower back joint during lifting can provide significant assistance in reducing exertion.
Improved Leukemia Clearance After Adoptive Transfer of NK Cells Expressing the Bone Marrow Homing Receptor CXCR4R334X

A cute myeloid leukemia (AML) is a poor-prognosis bone marrow (BM) malignancy. Standard treatment relies on chemotherapy and for selected patients hematopoietic stem cell transplantation. New treatments are highly warranted. Immunotherapy, including adoptive natural killer (NK) cell transfer, represents a new treatment modality. Here, we report that expanded human NK cells, genetically engineered to express the gain-of-function BM homing receptor CXCR4 R334X , have superior homing capacity to AML-containing BM compartments resulting in improved treatment efficacy. NK cells are lymphocytes with an innate ability to kill tumor cells. Studies have shown that donor NK cells can prevent AML relapse following HSCT 1 and that adoptive NK cell transfer can induce remission in patients with relapsed/refractory AML and high-risk myelodysplastic syndromes (MDS). 2,3 Although methods to further enhance the antitumor efficacy of NK cells are currently explored, few have focused on improving NK cell homing to the tumor. 4,5 This is highly relevant for targeting BM–residing malignancies as infused NK cells have poor BM homing. 6 The significance of improved BM homing was recently highlighted in a clinical trial where infusion of NK cells with high CXCR4 surface expression was associated with increased response rates in AML and MDS patients. 3 Using mRNA transfection, it was demonstrated that NK cells expressing gain-of-function CXCR4 R334X have higher propensity for BM compartments in mice, but it is yet to be shown that this single modification results in better leukemia control by the NK cells. 7