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The need for environmental protection and involvement of ecological aspects in the business operations is forcing the organizations to re-examine their action plans and rebuild their supply chain activities. Many organizations are incorporating environmental rules and regulations in their everyday matters by focusing on green supplier selection. The proposed research paper develops a multi-objective interactive fuzzy programming model for the selection of suppliers. This model works on a business quartet of green appraisal score, cost, quality, and time. The model uses an environmental scale for different green parameters and all the suppliers are scored based on this scale. In this research model, Quality Function Deployment (QFD) methodology is integrated with the multi-objective interactive fuzzy programming. QFD technique is utilized to compute the weights of several green factors used for the selection of suppliers. The model uses a Fuzzy linguistic scale and a triangular membership function to link expert opinions along with their experience to solve the problem. Finally, the model is validated on a numerical case study of the textile industry for green supplier selection which achieves a 100% satisfaction for cost and time, 75% satisfaction for green appraisal score, and 93.95% for the quality. The proposed model assists the decision-makers in selecting green suppliers to improve the overall sustainability of their organizations.

This study examines the effects of embedding Quality Management practices (QMP), that is, quality training, employee relations, and management relations on Sustainable Performance (SP), and the mediating role of Organizational ambidexterity exploration (innovation exploration) and exploitation (innovation exploitation) between the relationship between QMP and SP in the manufacturing industry. Moreover, the role of Green Innovation is explored as a moderator in the ties of Organizational Ambidexterity Exploration (innovation exploration) and SP.  SmartPLS is used in this paper to analyze quantitative data collected from industry specialists and supply chain managers in the food, paint, and textile industries. The findings suggest that good management practices: management relations, employee training, and quality training, can help create a conducive environment for exploratory innovations, which can furthermore help improve the SP of the firm. In the case of exploration, incorporating green products and procedures can help the company achieve long-term success. QMP in ambidextrous manufacturing industries improves the firm’s social performance by meeting the needs of environmentally conscious stakeholders and ensuring healthy relationships, social welfare, customer satisfaction, and societal friendliness toward sustainable products through green product and process innovation.  Managers can take advantage of the inherent potential of quality management systems to integrate them into exploitative and exploratory activities and boost the company’s long-term profitability. This study explored a novel concept of green innovation as rare, valuable, inimitable, and organized, so it helps firms in the attainment of sustainable competitive advantage.

The automotive industry serves as a crucial support system for the economies of industrialized nations in their pursuit of international market competitiveness. Despite this industry's importance, most developing countries face the challenge of acquiring a reasonable economic position at the global level in the automotive sector for various reasons. The most salient reasons include inconsistent government policies, multiple taxes, investor insecurity, political instability, and currency devaluation. Identifying risks is crucial for a new entrant in the already-established automotive industry. The researchers have used multiple (qualitative and quantitative) techniques to identify and prioritize risks in setting up manufacturing plants. The efforts to tackle these identified risks are undertaken at the domestic and government levels to smoothen the establishment of industry. The risks are first identified, in the current study, by reviewing the previous literature and conducting interviews of the various stakeholders (automotive dealers, managers, and customers). Then this study uses Monte Carlo simulation (MCS) approach and develops a risk exposure (high, medium, or low) matrix for the automotive industry of Pakistan. The findings reveal that the depreciation of local currency against the foreign exchange, oligopoly nature of competition, and low market acceptability of new entrants due to their products' image are the most critical risks the automobile industry faces. These findings will help automotive research institutes in developing national policies that specifically aim to support new players in the automotive industry, particularly in addressing high-priority hazards. The results may also provide valuable insights for new participants seeking to identify and address the key challenges in the Pakistani automotive industry before entering it.

This study examines the impact of both random and anticipated disruptions on transportation costs within different stages of a downstream oil supply chain. Conducting a comprehensive literature review, a MILP model was developed to simulate a multifaceted refined oil supply chain, integrating refining and import facilities, storage depots, and customer demand nodes. The study unfolds in two phases: a deterministic model establishing a supply chain performance baseline, and a Monte Carlo simulation generating disruption scenarios. Results reveal increased transportation costs and significant flow modifications between entities. Imports of refined oil products surged to counter local production shortages, with increased use of cost-effective bulk cargo modes and a notable reliance on road transport to offset disrupted pipelines. The study highlights the substantial impact of disruptions on transportation costs, emphasizing diversified transportation methods where pipelines are constrained. Acknowledging study limitations focusing on a singular supply chain’s transport costs, it advocates for research on inventory management and alternate pipeline development to enhance supply chain resilience under disruption scenarios.

Background: Anti-CD38 monoclonal antibodies (mAbs) have significantly changed the multiple myeloma treatment landscape. This meta-analysis compared the efficacy and safety of anti-CD38 mAb-based therapy versus standard therapy in newly diagnosed multiple myeloma (NDMM) patients. Methods: We performed a comprehensive literature search on PubMed, the Cochrane Database, and ClinicalTrials.gov. The primary outcomes were progression-free survival (PFS) and minimal residual disease (MRD) status. Dichotomous outcomes were pooled using risk ratio (RR) along with the 95% confidence interval (CI) in RevMan 5.4. Subgroup analysis and meta-regression analysis were performed. The RoB 2.0 tool was used to assess the risk of bias. Results: Our meta-analysis included 11 randomized controlled trials. There were 5270 patients; 3040 TEs and 2230 TIEs. Anti-CD38 mAbs significantly improved MRD negativity (RR 1.94, 95% CI: 1.59–2.37; p < 0.00001) and PFS (RR 0.51, 95% CI: 0.45–0.58; p < 0.00001). Subgroup analyses revealed better outcomes for both the TE (MRD: RR 1.52, 95% CI: 1.37–1.68; PFS: RR 0.43, 95% CI: 0.34–0.54) and TIE (MRD: RR 3.49, 95% CI: 2.65–4.61; PFS: RR 0.55, 95% CI: 0.47–0.64) populations. Meta-regression revealed that Eastern Cooperative Oncology Group (ECOG) score 0 significantly influenced MRD status (β = −0.015, p < 0.05), whereas ECOG scores 1 and 2 lacked statistical significance. Subgroup analysis revealed that PFS was significantly different between standard (RR 0.47) and high (RR 0.81) cytogenetic risk groups. Conclusion: In NDMM patients, anti-CD38 mAb-based therapy significantly improved MRD status, and PFS compared with standard therapy alone, in both TE and TIE patients, suggesting a favorable benefit–risk profile. Plain language summary How effective and safe are new anti-CD38 antibody treatments compared to standard therapy for patients with newly diagnosed multiple myeloma? A review and analysis Why was this study conducted? Anti-CD38 monoclonal antibodies (mAbs) have improved the course of treatment for multiple myeloma (MM), a type of blood cancer. These medications may provide better results since they target particular MM cells. In patients recently diagnosed with multiple myeloma (NDMM), this study compared the safety and efficacy of these novel treatments with standard therapy. What did the researchers do? Data from 11 clinical trials with 5,270 NDMM patients were examined by the researchers. They examined two primary outcomes: minimal residual disease (MRD), which looks at the remaining cancer in the body after treatment, and progression-free survival (PFS), which measures how long patients live without the disease getting worse. Patients were separated into two categories: those who qualified for a stem cell transplant (TE) and those who did not (TIE). What did the researchers find? The results showed that anti-CD38 mAbs significantly improved patient outcomes. More patients achieved MRD negativity (lower cancer levels) and had longer PFS compared to those on standard therapy. For TE patients, anti-CD38 mAbs improved MRD by 52% and PFS by 57%. TIE patients saw even greater benefits, with a 249% increase in MRD negativity and a 45% improvement in PFS. What do these results mean? This study demonstrates that, regardless of a patient’s eligibility for a stem cell transplant, anti-CD38 monoclonal antibodies are useful in the treatment of recently diagnosed multiple myeloma. These results imply that this treatment may slow the course of the disease and lower cancer levels in a large number of patients, demonstrating a positive benefit–risk profile for potential future therapeutic strategies.

Matrices are a unifying concept that permeates diverse fields of study and provides a common framework for expressing and solving problems in all fields, including decision-making. In most decision-making scenarios, addressing uncertainty is paramount as real-world scenarios often involve incomplete information, ambiguous data, and unpredictable factors. The aptness of a picture fuzzy hypersoft set as a parameterization tool becomes evident when dealing with the complexities and challenges associated with managing imprecise data. In this article, we have initially developed the notions of the picture fuzzy hypersoft matrices ( P F H S M ) and their basic operations based on the enhanced framework of the picture fuzzy hypersoft set. We then proposed the ideas of fundamental operational principles for picture fuzzy hypersoft numbers based on the structure of picture fuzzy hypersoft matrices. Furthermore, the concepts for picture fuzzy hypersoft geometric aggregation operators have been presented. The application of picture fuzzy hypersoft geometric aggregation operators to energy policy design signifies a bridge between theoretical advancements and practical decision-making. The inclusion of a decision-making approach, explanatory example, and comparative analysis enhances the understanding of how the developed theory can be effectively used and demonstrates its potential contributions to the field of informed decision-making using human intuitionistic data.

This research tries to integrate fuzzy neural networks with penalty function to address the quadratic programming based on the mean‐variance Markowitz portfolio model. The fuzzy quadratic programming problem with penalty function consists of the lower, central, and upper models. The models utilize fuzzy neural networks to solve the models. The proposed method has been implemented on the six leading stocks in the Pakistan Stock Exchange. The approach identifies the ideal portfolios for potential investors in the Pakistan Stock Exchange. Data of the six popular stocks trading on the stock exchange from January 2016 to October 2020 are taken into consideration. The optimizers are RMSprop, Momentum, Adadelta, Adagrad, Adam, and gradient descent, respectively. The findings of all the optimizers at all three phases (lower, central, and upper) agree on identifying the optimal investment portfolios for investors. The optimizers recommend investing in either one of the two categories. The first group recommends investing in the FFC, ARPL, and UPFL portfolios. The second group recommends LUCK, AGTL, and IGIHL. The first group tends to enhance return, variability, and risk. It is a high‐risk group. The second group aims to reduce return variability while lowering risk. It is a risk‐averse group. It is evident that all of the optimizers recommend investing in FFC, ARPL, and UPFL, with the exception of the Adam and Adadelta optimizers, which recommends investment in IGIHL, AGTL, and LUCK. RMSprop, Momentum, Adagrad, and gradient descent increase variability, risk, and returns. Adam proves the best optimizer, then RMSprop, and finally, Adagrad. Adam, Adadelta, and RMSprop are sensitive, whereas momentum and gradient descent are irresponsive to fuzzy uncertain data. The percent improvement in the objective is 0.59% and 0.18% for the proposed Adagrad and Adadelta, respectively.

This study develops an integrated production-inventory model for a two-echelon supply chain network with controllable probabilistic deterioration. The investment in preservation technology is considered a decision variable to control the deteriorated quantity of an integrated system. The objective of the study is to optimize preservation investment, the number of shipments and shipment quantity, so that the total cost per unit of time of the supply chain is minimized. The study proposes a solution method, and the results show that investment in preservation technology reduces the total supply chain cost by 13%. Additionally, preservation increases the lot size, thus increasing the production cycle length, which reduces the ordering cost of the system. Furthermore, this study shows that preservation leads to a reduction of solid waste from deteriorated products. Total deteriorated products reduced to 8 units from 235 units, hence, preservation generates positive environmental benefits along with economic impacts. The robustness of the proposed model is illustrated with a numerical example, sensitivity analysis, and graphical representations. Moreover, comparative study and managerial insights are given to extract significant insights from the model.

Objective The primary objective of this study was to evaluate the effectiveness and safety profile of various surgical interventions used to manage pulmonary hydatid cysts, comparing patient outcomes such as postoperative morbidity, hospital stay duration, postoperative mortality, and complication rates across different surgical techniques. Methods This retrospective observational cohort study was conducted at the Department of Thoracic Surgery at Lady Reading Hospital, Peshawar, Pakistan, from January 1, 2023, to December 31, 2023. Patients with surgical treatment of pulmonary hydatid cysts were included. Surgical techniques ranged from lung-sparing procedures, such as cystotomy with capitonnage, enucleation, and pericystectomy, to more extensive resections, including wedge resection, segmentectomy, lobectomy, and pneumonectomy. Primary outcomes included postoperative morbidity, hospital stay duration, pain scores, and 30-day mortality. Statistical analysis was performed using analysis of variance (ANOVA) and Fisher's exact test, with a significance threshold of p < 0.05. Results A total of 180 patients were included. The mean age of patients was 37.45 ± 11.28 years; 105 (58.33%) were men, and 75 (41.67%) were women. Cystotomy with capitonnage was performed in 97 patients (53.89%), showing the shortest hospital stay (9.4 ± 3.1 days) and the lowest morbidity (18 patients, 18.56%). Pneumonectomy was conducted in four patients (2.22%) and was associated with the longest hospital stay (17.8 ± 7.1 days) and the highest morbidity (three patients, 75.00%). Postoperative mortality occurred in three patients (1.67%), all of whom underwent extensive resections. The overall postoperative complication rate was 23.89% (43 patients), with chest pain in 99 patients (55.00%), cough in 81 (45.00%), and fever in 27 (15.00%). Statistically significant differences in hospital stay (ANOVA: p = 0.001) and morbidity (Fisher's exact test: p = 0.01) were found, favoring lung-sparing techniques. Conclusion Lung-sparing procedures, particularly cystotomy with capitonnage, were associated with superior outcomes, including shorter hospital stays, lower morbidity, and no mortality, in this cohort. These findings support the prioritization of conservative surgical approaches in managing pulmonary hydatid disease (HD), especially in endemic and resource-limited settings.

Lung disease classification plays a significant part in the early discovery and determination of respiratory conditions. This paper proposes a novel approach for lung disease classification utilizing two advanced deep learning models, MedViT and Swin Transformer, applied to the Lung X-Ray Image Dataset that includes 10,425 X-ray images categorized into three classes: Normal with 3,750 images, Lung Opacity with 3,375 images, and Viral Pneumonia with 3,300 images. A series of data augmentation methods, including geometric and photometric augmentation, are applied to improve model performance and generalization. The results illustrate that both MedViT and Swin Transformer accomplish promising classification accuracy, with MedViT showing particular strength in medical image-specific feature learning due to its hybrid convolutional and transformer design. The impact of different loss functions is also examined, where Kullback-Leibler Divergence yields the highest accuracy and effectively handles class imbalance. The best-performing MedViT model achieves an accuracy of 98.6% with a loss of 0.09. These findings highlight the potential of transformer-based models, particularly MedViT, for reliable clinical decision support in automated lung disease classification.