Amid increasing environmental and stakeholder pressures, organizations are challenged to transform corporate social responsibility into a sustainable competitive advantage. While prior studies have explored this relationship primarily through linear models, limited attention has been given to the configurational pathways through which CSR and related learning processes yield sustainable outcomes. This study addresses this gap by integrating fuzzy-set qualitative comparative analysis (fsQCA) with partial least squares structural equation modeling (PLS-SEM) to capture both linear and configurational effects. Using data from 515 professionals across small- and large-scale mining firms in Ghana, the study examines how CSR directly and indirectly influences sustainable competitive advantage through exploratory and exploitative learning, and how environmental dynamism conditions these relationships. The results reveal that CSR promotes sustainable advantage through dual learning mechanisms, with exploratory learning exerting a stronger effect. Moreover, fsQCA uncovers multiple equifinal configurations of CSR, organizational learning, and environmental dynamism that jointly lead to a high level of competitive advantage. The findings extend CSR and sustainable development research by highlighting fsQCA as a novel analytical lens for uncovering complex causal interactions and offering practical insights for firms seeking adaptive, socially responsible strategies in dynamic contexts. Theoretically, the study advances CSR and learning research by identifying distinct mechanisms and boundary conditions. In practice, it offers evidence-based guidance to firms in volatile contexts on transforming CSR investments into adaptive capabilities that sustain long-term competitiveness.

Power system operation modes rely on massive operation data, yet they are frequently compromised by missing values and anomaly contamination, significantly reducing decisionmaking reliability. To address this challenge, this paper proposes an anomaly identification and restoration strategy based on the IDSA-MAE. By integrating the Centrality-Weighted Distribution Map (CWDM) and Diagonally-Masked Self-Attention (DMSA), the model effectively captures complex spatiotemporal features. Significantly, an iterative Score-Identify-Correct closed-loop is adopted, utilizing latent space Mahalanobis distance for precise anomaly correction. Furthermore, a Dual-Constraint Performance Protocol comprising the Statistical Restoration Index (SRI) and Power Flow Consistency (PFC) is established. Results indicate that this method significantly improves statistical accuracy and physical authenticity, providing high-fidelity support for power grid operation.

To address the challenge faced by power grid companies in accurately detecting changes in user industry information, which has been complicated by the increasing variability of industry characteristics in recent years, a data-driven approach for identifying anomalies in load characteristics is proposed. Initially, a two-stage methodology for developing typical load patterns for various industries is presented. The hierarchical density-based spatial clustering of applications with noise (HDBSCAN) technique is utilized to extract typical daily load curves for users under different scenarios. Subsequently, these extracted daily load curves are clustered using an improved K-means algorithm to establish typical load patterns for the respective industries. In the second phase, a multidimensional intelligent diagnostic method for load characteristic anomalies is introduced. User load characteristics are constructed, and the entropy weight method is employed to evaluate the relative significance of typical industry scenarios. The one-class support vector machine (OCSVM) algorithm is then utilized to quantify the degree of anomaly present in user load characteristics across each scenario. Comprehensive suspicion scores are calculated and ranked to accurately identify users exhibiting abnormal load characteristics. The effectiveness of the proposed method is validated through the analysis of actual user data from a specific region. The results demonstrate that the method is both feasible and practical for constructing typical industry load scenarios and for the identification of load characteristic anomalies.

High-quality operation mode samples serve as the foundation for data-driven power system analysis. To address the challenge that existing generative models struggle to accurately construct high-dimensional operating boundary samples and that the computational cost of security margin evaluation is prohibitively high, this paper proposes a physics-guided generation framework based on Reinforcement Learning and Conditional Generative Adversarial Networks (RL-CGAN). First, a Deep Reinforcement Learning (DRL) agent is constructed for active boundary exploration. Secondly, an Interior Point Method (IPM) combined with a lightweight proxy network is utilized to achieve rapid and precise security margin labeling for massive random samples. Subsequently, a Conditional Generative Adversarial Network (CGAN) is trained using the full-space dataset to generate samples in batches. Finally, a pre-trained DRL model is introduced to perform local fine-tuning and manifold projection on the pseudo-boundary samples, ensuring their precise convergence to physical security boundaries. Case studies indicate that this method significantly improves both sample generation efficiency and boundary fidelity.

The study’s main goal is to identify the leadership practices, knowledge sharing, and organizational cultures that positively impact organizational performance through innovation capability in Pakistan’s textile sector. The quantitative study included about 400 lower- and middle-level managers from Pakistan’s textile industries. The analysis employs the Structural Equation Model(SEM), approach using Smart PLS-SEM ( Partial Least Squares), a more widely accepted method in social science research. Prior research has demonstrated that organizational culture and leadership significantly impact how well a company performs, while innovation is necessary for survival. Results of the research show that higher organizational success outcomes are obtained when led by effective leadership behavior, a cooperative work environment, and open knowledge-sharing practices, all of which play a key role in boosting overall organizational performance. The study recommends that government regulators, top management, and policymakers adopt innovation capability by using transformative leadership, knowledge sharing, and supportive culture to boost organizational performance.

Intestinal flora affects the maturation of the host immune system, serves as a biomarker and efficacy predictor in the immunotherapy of several cancers, and has an important role in the development of colorectal cancer (CRC). Anti-PD-1/PD-L1 antibodies have shown satisfactory results in MSI-H/dMMR CRC but performed poorly in patients with MSS/pMMR CRC. In recent years an increasing number of studies have shown that intestinal flora has an important impact on anti-PD-1/PD-L1 antibody efficacy in CRC patients. Preclinical and clinical evidence have suggested that anti-PD-1/PD-L1 antibody efficacy can be improved by altering the composition of the intestinal flora in CRC. Herein, we summarize the studies related to the influence of intestinal flora on anti-PD-1/PD-L1 antibody efficacy in CRC and discuss the potential underlying mechanism(s). We have focused on the impact of the intestinal flora on the efficacy and safety of anti-PD-1/PD-L1 antibodies in CRC and how to better utilize the intestinal flora as an adjuvant to improve the efficacy of anti-PD-1/PD-L1 antibodies. In addition, we have provided a basis for the potential of the intestinal flora as a new treatment modality and indicator for determining patient prognosis.

With the increasing popularity of electric vehicles,the number of electric vehicle users in industrial parks is increasing,and their charging and discharging behaviors pose great challenges to the planning and operation of park integrated energy system(PIES).A two-layer optimal scheduling of PIES considering the charging and discharging willingness of electric vehicles is proposed.Firstly,a charging and discharging willingness model is established based on factors such as dynamic real-time electricity price,battery charge capacity,battery loss compensation,and additional participation incentives.An improved electric vehicle charging and discharging model is obtained on this basis.A two-layer optimal scheduling model is established with the goal of minimizing the charging cost of the car,and the inner model is transformed into the constraints of the outer model through the Karush-Kuhn-Tucker(KKT)condition,so as to quickly and stably solve the single-layer model.Finally,the simulation solution is performed,and three different scenarios are set up.The proposed model is compared with the general charging and discharging willingness model.The effectiveness and feasibility of the two-layer optimal scheduling of PIES proposed in this paper are verified.

Green procurement plays a crucial role in mitigating emissions, promoting sustainable practices, and achieving the objectives of keeping global warming below the 2°C threshold. However, the implementation of green procurement faces significant challenges. Despite the recognized importance of sectoral collaboration, cross‐sectoral engagement (CSE) has not yet been widely acknowledged as an essential principle in green procurement implementation. This study aims to address these implementation challenges by developing a comprehensive decision‐making framework for CSE in public–private partnership projects, with a focus on advancing sustainable development goals. The research identifies and prioritizes barriers to CSE from multiple theoretical perspectives, employing an integrated approach that combines the Method Based on the Removal Effects of Criteria (MEREC) and the Combinative Distance‐Based Assessment (CODAS) method. The results reveal three primary factors hindering cross‐sectoral engagement: conflicting stakeholder interests and priorities, power imbalances, and communication gaps and language barriers. To promote effective collaboration, policymakers are urged to invest in capacity building initiatives that enhance stakeholders' understanding of green procurement principles and encourage their active participation in achieving sustainable development objectives. Theoretically, this study has, for the first time, developed a CSE framework that can be used to promote green practice implementation, integrate four theories to holistically access barriers to cross‐sectoral collaboration, and show the integration of the MEREC CODAS MCDM technique.

Aiming at the problems of low locating accuracy, data anomalies and missing in the extracted feature of the existing source localization methods, a data-driven voltage sag source localization is proposed based on multiple localization features and Kernel Naive Bayesian (KNB) classification. Firstly, in order to explore embedded information of the sag events, the causing mechanism of five type voltage sag is analyzed by simulation results, and seven kinds of localization features are extracted. These features include disturbance power and energy, impedance real polarity, system trajectory slope, current real-part polarity, and positive and negative sequence disturbance power. Then, considering that voltage sag source localization is actually a binary classification task, this paper adopts a simple and efficient kernel naive Bayesian classifier combined with multiple localization feature vectors to construct an upstream and downstream localization model of voltage sag sources for improving the accuracy and robustness of method. Finally, simulation verification is carried out in the IEEE33n ode system. The results show that, compared with common source localization methods, the proposed method has a high localization accuracy, as well as good reliably in locating various types of voltage sag sources when the feature data contains abnormal or missing data. It is applicable in complex practical applications.

Flexible load can optimize the load curve, which is an important means to promote renewable energy consumption. The peculiarities of electricity, heat, cooling and gas loads are analyzed in this paper, considering the fuzzy degree of human perception for water temperature, and the characteristic model of hot water load is established. Considering the fuzzy degree of human perception of ambient temperature, the characteristic model of cooling load is established by using PMV and PPD index. Meanwhile, considering four combinations of cut load, translatable load, transferable load and alternative load, and considering the coupling relationship of composite parts, different response models of load are established respectively. With the minimum cost of the system, including operation and compensation costs as the objective function, the optimization scheduling model of the regional integrated energy system is established, and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment. The results show that the load curve can be optimized, the flexible regulation ability of the regional integrated energy system can be enhanced, the energy loss of the system can be reduced, and the wind power consumption ability of the system can be increased by considering the integrated demand response.

BackgroundAlthough the use of anti-PD-1 antibodies has fundamentally changed traditional cancer treatment, most patients are resistant to anti-PD-1 treatment. Glucocorticoids (GCs) play an important role in tumorigenesis and tumor progression, but the role of endogenous GCs in resistance to anti-PD-1 antibody therapy remains unclear.MethodsSingle cell-derived cell lines (SCDCLs) were generated from a colorectal cancer cell line (CT26) using limiting dilution. We analyzed tumor tissues from anti-PD-1 antibody-treated and untreated mice inoculated with SCDCLs via transcriptome sequencing and flow cytometry to detect pathway activity and immune cell composition changes in the tumor microenvironment.ResultsFive SCDCLs were inoculated into wild-type BALB/c mice (all tumorigenic). Single-cell clone (SCC)-2 exhibited the slowest growth rates both in vivo and in vitro compared to other single-cell clones, and better long-term survival than SCC1 and CT26. Flow cytometry showed that SCC2 tumor-bearing mice exhibited significantly higher infiltration of T cells within the tumor tissue, and higher expression of PD-1 on these T cells than the other groups in vivo. However, the SCC2 group showed no response to anti-PD-1 therapy. Transcriptome analysis revealed that the SCC2 group exhibited increased expression of genes related to GC (Hsd11b1, Sgk3, Tgfbr2, and Il7r) compared to SCC2-anti-PD-1 treated tumors.ConclusionsGC pathway activation is related to resistance to anti-PD-1 therapy.

Adoptive cell therapy (ACT) has revolutionized the treatment of patients with cancer. The success of ACT depends largely on transferred T cell status, particularly their less-differentiated state with stem cell-like properties, which enhances ACT effectiveness. Stem cell-like memory T (TSCM) cells exhibit continuous self-renewal and multilineage differentiation similar to pluripotent stem cells. TSCM cells are promising candidates for cancer immunotherapies, whereas maintenance of a more stem-cell-like state before transfer is challenging. Here, we established a highly efficient protocol for generating CD8+ TSCM cells from peripheral blood mononuclear cells (PBMCs). The process involved activating PBMCs using anti-CD3 monoclonal antibody and RetroNectin, followed by a transient-resting culture period (24 h) and subsequent long-term expansion in vitro with interlukien-2. We report that this transient-resting culture after activation preserves CD8+ T cells in a stem memory phenotype (CD95+ CD45RA+ CCR7+) compared to the conventional culture method. Further, this approach reduces the expression of T cell immunoglobulin mucin-3, an exhaustion marker, and increases the expression of T cell factor-1, a master regulator of stemness even after long-term culture compared to the conventional culture method. In conclusion, our study presents a simplified and cost-effective method for generating and expanding CD8+ TSCM cells ex vivo. This approach streamlines the optimization of cancer immunotherapy using ACT.

With the popularization and extensive use of power grid, people’s lives and many factories generally rely on electricity, and once the power grid is damaged and fails, it will seriously endanger the social order and economic benefits. At present, the research on the rescue dispatch of electric vehicles as an emergency power source mainly focuses on path planning, and does not consider the comprehensive rescue ability of the rescuers dispatched, nor does it involve the matching degree of rescuers and rescue vehicles. In this paper, the rescue urgency is obtained through the analysis and evaluation of the fault point, the characteristics of the rescue vehicles and rescue personnel are analyzed to screen out the appropriate rescue vehicles and personnel, the Euler distance is used to solve the matching coefficient between the rescue vehicle and the rescue personnel, and finally the multi-objective power grid fault emergency rescue function considering the matching of human and vehicle characteristics is established.

PurposeClinical application of immunotherapy represented by Programmed Death-1 (PD-1) monoclonal antibody has changed the treatment paradigm for colorectal cancer (CRC), and tumor-infiltrating T lymphocytes are critical for anti-PD-1 therapy in CRC. However, there are few studies on the relationship between the expression CXCR3 on T lymphocytes and the clinical aspects of CRC. In this study, we analyzed the expression levels of CXCR3 and PD-1 in CD8+ and CD4+ T lymphocytes in healthy donors (HDs) and patients with CRC.MethodsWe detected the expressions of CXCR3 and PD-1 on T lymphocytes in peripheral blood of healthy donors as well as peripheral blood, tumor tissue and para-cancerous tissues of patients with CRC using flow cytometry. We also analyzed the relationship between the expressions of CXCR3 and PD-1 on T lymphocytes and the pathological characteristics of CRC using t test.ResultsExpression of CXCR3 on tumor-infiltrating T lymphocytes was lower, whereas the expression of PD-1 was higher than that on para-cancerous tissues and PB in patients with CRC. In patients with lymph node metastasis of CRC, the expressions levels of CXCR3+ PD-1+ on tumor-infiltrating CD8+ and CD4+ T lymphocytes were higher than those in patients without lymph node metastasis. The levels of CXCR3+ PD-1+ expressions differed depending on the primary tumor site.ConclusionExpressions of CXCR3 and PD-1 on tumor-infiltrating T lymphocytes are related to the development of CRC and metastasis, providing clues for exploring the pathogenesis of CRC and developing new strategies for tumor immunotherapy.

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces “deep learning” technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved.

With the increase of electric vehicle ownership, how to accurately dispatch electric vehicles has become an urgent problem to be solved. Scientific evaluation of users by analyzing user behavior trends is a key link in the implementation of accurate scheduling. In this paper, considering the characteristics of user behavior trends, a research method of electric vehicle friendly charging evaluation system based on hierarchical fuzzy comprehensive evaluation is proposed: Firstly, a comprehensive evaluation index system for user-friendly charging of electric vehicles is constructed to provide a reference standard for comprehensive evaluation. Secondly, a combined weighting algorithm based on EWM-CRITIC and improved AHP is proposed to achieve scientific empowerment by modifying the subjective and objective weights. Finally, a hierarchical fuzzy comprehensive evaluation method is proposed to evaluate the user's charging friendliness in a hierarchical manner, which provides a basis for accurate scheduling. The simulation is carried out using real data, and the results show the effectiveness and rationality of the user-friendly charging evaluation system method for electric vehicles.

<p>Pie graph shows the composition of TIL samples of MM-3.</p>

In order to give full consideration to the interests of charging stations and electric vehicles（EV） users,and improve charging congestion and charging imbalance,an EV dynamic charging pricing strategy considering bilateral interests is proposed. The interest demands of charging stations and electric vehicle users are defined,and the EV dynamic charging pricing scheme is designed. A multi-objective function is constructed with the goal of minimizing the user charging cost,minimizing the user waiting time,maximizing the revenue of the charging station,and maximizing the balance of the service rate of the charging station equipment. The entropy weight method is used to analyze the charging record data in some urban areas of a city to obtain the weight of each objective function,so as to establish a comprehensive optimal charging pricing model. The improved adaptive mutation particle swarm optimization（AMPSO）algorithm is used to solve the problem. The simulation results show that the proposed pricing strategy can establish a reasonable real-time charging price,improve the service efficiency of the charging station,so as to achieve a win-win situation for the charging station and users.

In order to extract the regulation potential of demand side,a demand response regulation strategy of power grid accessed with high proportion of renewable energy is proposed considering industrial load characteristics. A rolling scheduling framework based on industrial load demand response is designed,the demand response potential of industrial load is extracted by analyzing the production characteristics of different types of industrial loads. Aiming at the uncertainty of renewable energy and load,a conditional deep convolution generative adversarial network scenario generation method combined with feature loss is proposed to provide typical scenario sets under different time scales for system regulation. Based on the generated scenario set,a multi-scenario stochastic programming combined with stochastic model predictive control method is proposed with the minimum total system operating cost as the object,a multi-time scale rolling scheduling optimization model is constructed,and the optimal strategy of industrial load demand response in different stages is obtained. The simulative results of improved IEEE 30-bus and IEEE 118-bus systems verify the applicability and effectiveness of the proposed model and strategy.

Reactive power balance in the power system is an important task in the planning and design of the power system. Its purpose is to determine the configuration and type of reactive power compensation devices in the power system, and to maintain the voltage level, power quality, and other quality factors of each node in the power grid within a reasonable range under various operating modes. This article selects areas with high permeability of distributed power sources and takes reactive power balance as the starting point to conduct research on the distribution network after the integration of distributed power sources through simulation analysis, instance verification, and other methods. The relevant results confirm the feasibility of the research and the correctness of the conclusions, providing technical support for the subsequent implementation of distributed power supply integration work in the entire county or district.