Background: The global popularity of gluteal augmentation has risen significantly, driven by the desire for enhanced buttocks size and shape to align with individual patient preferences. This increased demand has prompted extensive research into diverse techniques and their safety. Methods: A retrospective analysis was conducted to evaluate the outcomes of a gluteal augmentation technique involving ultrasound-assisted liposuction, submuscular implants, and ultrasound-guided fat grafting. Our study involved a review of the medical records of 50 consecutive patients who underwent this procedure between February 2020 and July 2023. Results: Data related to patient demographics, the quantity of fat grafts, and any complications were analyzed. Additionally, a brief survey was conducted to evaluate patient satisfaction. The Polytech implants were used in forty-four patients, and Sebbin implants in six. The implant size varied from 285 to 560. Most of the Polytech implants were 390 cc (25/44; 56.8%). Two patients had a minor infection at the incision sites and subsequent wound dehiscence. No revision surgeries were needed. Conclusions: The presented technique incorporates ultrasonic liposuction, submuscular silicone implants, and ultrasound-guided fat grafting to achieve safe and aesthetic gluteal enhancements. This method is especially suitable for patients looking to augment both the central and lateral areas of the buttocks, particularly when they lack adequate fat tissue for augmentation through fat grafting. With the addition of ultrasound guidance, the fat grafting step is significantly safer. Full article

The transportation sector is an indispensable link in the industrial chain of an economic system. Considering the current push to comprehensively construct new patterns of development, the transportation sector is under huge pressure to achieve the goal of “carbon peak and carbon neutral”. Therefore, to develop low-carbon transportation in China, it is important to study the carbon emission linkages in its transportation sector in order to understand its potential and the factors affecting carbon mitigation. In this study, sectoral carbon emission linkages and dynamic changes during the period of 2002–2020 for the transportation sector in China were quantitatively investigated based on the input–output analysis and hypothetical extraction method (HEM). The results showed that the total carbon emission linkages for the transportation sector mainly experienced growth, increasing by 848.63 Mt from 2002 to 2020, with consistently stronger supply-side than demand-side carbon emission linkages. This indicates that the potential of the transportation sector for carbon reduction lies upstream in the chain of carbon emission linkages. Furthermore, the transportation sector was consistently a net export sector of carbon emissions, and net carbon transfer decreased with fluctuations. Regarding carbon emissions from the transportation sector, the construction sector was found to be main destination for the majority of these emissions, while the power supply sector was the largest source. The policy implications derived from this research can serve as a crucial lever for the low-carbon development of China’s transportation sector. Full article

Ongoing climate change is projected to intensify drought stress globally. Understanding the response mechanisms of Phyllostachys edulis (Carrière) J. Houz. (moso bamboo) to long-term drought is crucial, given its significance as a carbon sequestration resource. In this study, precipitation exclusion was implemented to simulate drought stress and we investigated the effects of long-term drought on the photosynthetic parameters, stomatal conductance, and water use efficiency of moso bamboo. The results showed that throughout all growth seasons, the maximum net photosynthetic rates (P_max) of bamboo at all ages under long-term drought conditions (after 8 years of precipitation exclusion treatment) were significantly lower than those of the control (p < 0.05). It can be concluded that long-term drought reduced the maximum photosynthetic capacity of the bamboo at all ages. Under long-term drought conditions, there were many seasons where the light saturation point (LSP) of first-degree (1–2 years old) bamboo and third-degree (5–6 years old) bamboo under drought was significantly lower than those of the control, while the LSP value of second-degree (3–4 years old) bamboo under drought was significantly higher than that of the control. This suggests that long-term drought reduced the ability of first-degree and third-degree bamboo to utilize strong light, while improving the ability of second-degree bamboo to utilize strong light in summer, autumn, and winter. Under long-term drought conditions, the light compensation point (LCP) and the apparent quantum efficiency (AQY) of the bamboo decreased. It can be concluded that long-term drought reduced the ability of first-degree bamboo to utilize weak light in all seasons, as well as the ability of second-degree bamboo to utilize weak light in spring and autumn; meanwhile, it improved the ability of second-degree bamboo to utilize weak light in summer and winter, and the ability of third-degree bamboo to utilize weak light in spring, summer, and autumn. In the high light range (PARi > 1000 µmol · m⁻² · s⁻¹), there were significant differences in stomatal conductance (g_s) among different the different treatments of bamboo, which were influenced by both the growing season and the forest age. Compared to the control, under drought conditions, the stomatal conductance of third-degree bamboo increased in spring and that of the second-degree bamboo increased in autumn. The correlation analysis showed that the relationship between the stomatal conductance and vapor pressure deficit (VPDL) of bamboo under long-term drought conditions showed a significant polynomial relationship in both high and low light ranges. The correlation between the instantaneous water use efficiency (iWUE) and VPDL for the drought and control treatments of bamboo also showed a significant polynomial relationship in high light ranges. It was found that long-term drought changed the photosynthetic parameters of the bamboo, reflecting its ability to tolerate and adapt to drought in different seasons. Age-related differences in photosynthetic parameters should be fully considered in forest age structure adjustments and forest thinning procedures to strengthen the light intensity and maintain the opening of the stoma. These results provide a theoretical basis for the efficient and sustainable cultivation of bamboo under global climate change. Full article

Doxorubicin (DOX), widely used as a chemotherapeutic agent for various cancers, is limited in its clinical utility by its cardiotoxic effects. Despite its widespread use, the precise mechanisms underlying DOX-induced cardiotoxicity at the cellular and molecular levels remain unclear, hindering the development of preventive and early detection strategies. To characterize the cytotoxic effects of DOX on isolated ventricular cardiomyocytes, focusing on the expression of specific microRNAs (miRNAs) and their molecular targets associated with endogenous cardioprotective mechanisms such as the ATP-sensitive potassium channel (KATP), Sirtuin 1 (SIRT1), FOXO1, and GSK3β. We isolated Guinea pig ventricular cardiomyocytes by retrograde perfusion and enzymatic dissociation. We assessed cell morphology, Reactive Oxygen Species (ROS) levels, intracellular calcium, and mitochondrial membrane potential using light microscopy and specific probes. We determined the miRNA expression profile using small RNAseq and validated it using stem-loop qRT-PCR. We quantified mRNA levels of some predicted and validated molecular targets using qRT-PCR and analyzed protein expression using Western blot. Exposure to 10 µM DOX resulted in cardiomyocyte shortening, increased ROS and intracellular calcium levels, mitochondrial membrane potential depolarization, and changes in specific miRNA expression. Additionally, we observed the differential expression of KATP subunits (ABCC9, KCNJ8, and KCNJ11), FOXO1, SIRT1, and GSK3β molecules associated with endogenous cardioprotective mechanisms. Supported by miRNA gene regulatory networks and functional enrichment analysis, these findings suggest that DOX-induced cardiotoxicity disrupts biological processes associated with cardioprotective mechanisms. Further research must clarify their specific molecular changes in DOX-induced cardiac dysfunction and investigate their diagnostic biomarkers and therapeutic potential. Full article

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 10⁴ plant/ha and 161.05 kg·ha⁻¹ for the large-spike type variety of rice, 75.08 × 10⁴ plant/ha and 159.52 kg·ha⁻¹ for the intermediate type variety of rice, and 75.08 × 10⁴ plant/ha and 133.47 kg·ha⁻¹ for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China. Full article

This paper describes a solution for the image density classification problem (DCP) using an entirely distributed system with only local processing of information named cellular automata (CA). The proposed solution uses two cellular automata’s features, density conserving and translation of the information stored in the cellular automata’s cells through the lattice, in order to obtain the solution for the density classification problem. The motivation for choosing a bio-inspired technique based on CA for solving the DCP is to investigate the principles of self-organizing decentralized computation and to assess the capabilities of CA to achieve such computation, which is applicable to many real-world decentralized problems that require a decision to be taken by majority voting, such as multi-agent holonic systems, collaborative robots, drones’ fleet, image analysis, traffic optimization, forming and then separating clusters with different values. The entire application is coded using the C# programming language, and the obtained results and comparisons between different cellular automata configurations are also discussed in this research. Full article

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being. Full article

With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R² (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction. Full article

Ceramic fibers have the advantages of high temperature resistance, light weight, favorable chemical stability and superior mechanical vibration resistance, which make them widely used in aerospace, energy, metallurgy, construction, personal protection and other thermal protection fields. Further refinement of the diameter of conventional ceramic fibers to microns or nanometers could further improve their thermal insulation performance and realize the transition from brittleness to flexibility. Processing traditional two-dimensional (2D) ceramic fiber membranes into three-dimensional (3D) ceramic fiber aerogels could further increase porosity, reduce bulk density, and reduce solid heat conduction, thereby improving thermal insulation performance and expanding application areas. Here, a comprehensive review of the newly emerging 2D ceramic micro-nanofiber membranes and 3D ceramic micro-nanofiber aerogels is demonstrated, starting from the presentation of the thermal insulation mechanism of ceramic fibers, followed by the summary of 2D ceramic micro-nanofiber membranes according to different types, and then the generalization of the construction strategies for 3D ceramic micro-nanofiber aerogels. Finally, the current challenges, possible solutions, and future prospects of ceramic micro-nanofiber materials are comprehensively discussed. We anticipate that this review could provide some valuable insights for the future development of ceramic micro-nanofiber materials for high temperature thermal insulation. Full article

In previous research, we presented an apparatus designed for comprehensive and systematic surveillance of trees against borers. This apparatus entailed the insertion of an uncoated waveguide into the tree trunk, enabling the transmission of micro-vibrations generated by moving or digging larvae to a piezoelectric probe. Subsequent recordings were then transmitted at predetermined intervals to a server, where analysis was conducted manually to assess the infestation status of the tree. However, this method is hampered by significant limitations when scaling to monitor thousands of trees across extensive spatial domains. In this study, we address this challenge by integrating signal processing techniques capable of distinguishing vibrations attributable to borers from those originating externally to the tree. Our primary innovation involves quantifying the impulses resulting from the fracturing of wood fibers due to borer activity. The device employs criteria such as impulse duration and a strategy of waiting for periods of relative quietness before commencing the counting of impulses. Additionally, we provide an annotated large-scale database comprising laboratory and field vibrational recordings, which will facilitate further advancements in this research domain. Full article

A novel co-bonded octyl and pyridine silica (OPS) sorbent was prepared and applied for the solid phase extraction (SPE) of cyclopiazonic acid (CPA, a type of mycotoxin) in feed and agricultural products for the first time. A simple mixed-ligand one-pot reaction strategy was employed for OPS sorbent preparation. Nitrogen adsorption–desorption measurements, elemental analysis (EI), thermal gravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FT-IR) analysis demonstrated the successful immobilization of octyl and quaternary ammonium groups onto the surface of silica gel. The large specific surface area, high-density functional groups, and mixed-mode anion-exchange characteristics of these silica particles made them the ideal material for the efficient extraction of CPA. Additionally, the OPS sorbents displayed excellent batch-to-batch reproducibility, satisfactory reusability, and low cost. The SPE parameters were optimized to explore the ionic and hydrophobic interactions between CPA and the functional groups, and the ultra-high performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UPLC-MS/MS) parameters were optimized to obtain a desirable extraction efficiency and high sensitivity to CPA. Meanwhile, the OPS sorbent presented a satisfactory extraction selectivity and low matrix effect. Under the optimized conditions, our developed CPA detection method was used to determine CPA level in rice, wheat flour, corn flour, peanut, and feed samples, exhibiting a lower detection limit, better linearity, higher sensitivity, and satisfactory extraction recovery rate than previously reported methods. Therefore, our method can be preferentially used as a method for the detection of CPA in agricultural products and feeds. Full article

Facial nerve injury can cause significant functional impairment, impacting both the peripheral and central nervous systems. The present study evaluated changes in facial motor function, numbers of cholinergic neurons and microglia, and nNOS levels in the facial nucleus of the central nervous system (CNS) following peripheral facial nerve injury. Facial nerve function, as determined by eyeblink and whisker-movement reflexes, was evaluated at baseline and 1, 2, 3, 4, 8, and 12 weeks after inducing facial nerve injury through compression or axotomy. The expression of choline acetyltransferase (ChAT), ionized calcium-binding adaptor molecule 1 (Iba-1), and neuronal nitric oxide synthase (nNOS) in the facial nucleus of the CNS was analyzed 2, 4, and 12 weeks after peripheral facial nerve injury. Compression-induced facial nerve injury was found to lead to temporary facial motor impairment, whereas axotomy resulted in persistent impairment. Moreover, both compression and axotomy reduced ChAT expression and increased Iba-1 and nNOS expression in the facial nucleus, indicating upregulation of an inflammatory response and neurodegeneration. These results indicate that, compared with compression-induced injury, axotomy-induced facial nerve injury results in greater facial motor dysfunction and more persistent microglial and nitric oxide activation in the facial nucleus of the CNS. Full article

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure inspection, security surveillance, environmental monitoring, archaeological research, mining applications, etc. The paper analyzes in detail the effectiveness of UAVs in specific tasks, including power line and bridge inspections, search and rescue operations, police activities, and environmental monitoring. The focus is on the integration of advanced navigation algorithms and artificial intelligence technologies with UAV surveillance and the challenges of operating in complex environments. Looking ahead, this paper predicts trends in cooperative UAV surveillance networks and explores the potential of UAVs in more challenging scenarios. This review not only provides researchers with a comprehensive analysis of the current state of the art, but also highlights future research directions, aiming to engage and inspire readers to further explore the potential of UAVs in surveillance missions. Full article

The versatile electrocaloric (EC) behaviors of the (1-x)Pb(Mg_1/3Nb_2/3)O₃-xPT (PMN-100xPT) single crystal are closely related to the multiple phase transitions under the multiple fields of electric field and temperature. In this work, the EC effect of <001>-oriented PMN-30PT single crystals with chemical composition at morphotropic phase boundary has been studied during the phase transformation process from the ferroelectric rhombohedral (R) phase to the tetragonal (T) phase. Two consecutive negative EC peaks have been achieved for the first time. Based on the projection of the EC effect in the electric field-temperature phase diagram, the relationship between the EC behaviors and the phase transitions is further established. It was found that the monoclinic (M) phase actually existed during the transformation from the R phase to the T phase, and the related R-M phase transition and M-T phase transition could both induce negative EC peaks. Under the electric field of E = 10 kV/cm, the first negative EC peaks induced by the R-M phase transition is at 57 °C with ΔT_max = −0.11 K. And the M-T phase transition can produce a higher negative EC peak, and its value can reach −0.22 K at 68 °C. Based on thermodynamic calculations, the relationship between the entropy change in different phase transitions and the EC behaviors has been further elucidated. The negative EC effect originates from the structural entropy increase in the electric field-induced phase transition process. This work not only advances the research on the electrical properties of relaxor ferroelectric single crystals but also provides a new insight into high-performance ferroelectric materials design. Full article

Climate change has brought several natural disasters to South Africa in the form of floods, heat waves, and droughts. Neighbouring countries are also experiencing tropical cyclones, almost on a yearly basis. The insurance sector is faced with an increased level of climate change risk with individuals, corporates, and even the government approaching it for financial cover. However, with an increased level of competition in the insurance sector, (re)insurers must engage in massive product research and development. Therefore, this paper looks at the possibility of the insurance industry developing new products in the form of catastrophe resilience bonds (CAT R Bonds). A qualitative approach is used following content analysis of (re)insurers’ product development policies, marketing documents, company reports, and risk management reports as well as the Conference of Parties 27 and 28 resolution papers. The findings reveal that (re)insurers’ underwriting capacity, reinsurance protection, and innovative and creative product development increase because of CAT R Bonds. CAT R Bonds enhance the interaction between the capital market and money market, thereby giving speculative investors another investment option. Increased investment into new product development such as CAT R Bonds must continue in South Africa in pursuit of climate change resilience goals.  Full article

Background: Amyotrophic lateral sclerosis (ALS) is an incurable disease. There are vigorous attempts to develop treatments to reduce the effects of this disease, and among these treatments is the transplantation of stem cells. This study aimed to retrospectively evaluate a mesenchymal stem cell (MSC) therapy cohort as a promising novel treatment modality by estimating some additional new parameters, such as immunological and biochemical factors. Methods: This study was designed as an open-label, one-arm cohort retrospective study to evaluate potential diagnostic biomarkers of repeated infusions of autologous-bone marrow-derived mesenchymal stem cells (BM-MSCs) in 15 confirmed patients with ALS, administered at a dose of 1 × ${10}^6$ cells/kg BW with a one-month interval, in equal amounts in both an intravenous (IV) and intrathecal (IT) capacity simultaneously, via various biochemical (iron (Fe), ferritin, total-iron-binding capacity (TIBC), transferrin, and creatine kinase (CK)) and immunological parameters (tumor necrosis factor-alpha (TNF-α), neurofilament light chain (NFL), and glial-cell-derived neurotrophic factor (GDNF) levels, evaluated during the three-month follow-up period in serum and cerebrospinal fluid (CSF). Results: Our study indicated that, in the case of immunological biomarkers, TNF-α levels in the CSF showed a significant decrease at month three after transplantation compared with levels at month zero, and the p-value was p < 0.01. No statistically significant changes were observed for other immunological as well as biochemical parameters and a p-value of p > 0.05. Conclusions: These results can indicate the potential benefit of stem cell transfusion in patients with ALS and suggest some diagnostic biomarkers. Several studies are required to approve these results. Full article

In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were analyzed by thin-section analysis and XRD tests, and the effect of intermediate principal stress on the mechanical properties of marble was investigated. Both SEM and microfracture analysis were coupled to reveal the failure mechanisms. The results highlight that the crack initiation strength, damage strength, peak strength, and elasticity modulus of Jinping marble exhibit an increasing trend with an increase in intermediate principal stress, while the peak strain initially increases and subsequently decreases. Moreover, this study established negative correlations between marble strength, brittleness characteristics, and fracture angle with grain size, whereas positive correlations were identified with the content of quartz, sodium feldspar, and the magnitude of the intermediate principal stress. The microcrack density in marble was found to increase with larger grain sizes and decrease with elevated quartz and sodium feldspar content, as well as with increasing intermediate principal stress. Notably, as the intermediate principal stress intensifies and grain size diminishes, the transgranular tensile failure of marble becomes more conspicuous. These research findings contribute to the effective implementation of disaster prevention and control strategies. Full article

The article presents the results of obtaining a composite material by sintering nickel-containing raw materials mixed with carbon-containing materials, namely using coke and semi-coke. The sintering process was performed at a charge layer height of 240 mm and the temperature of the lower layer was T = 1200 °C. The results of the sieve analysis showed (a fraction of 10 mm) that the yield of a suitable composite material using coke was 68.3% and with semi-coke 67.0%. The average nickel and chromium content in the composite materials was 1.42% and 3.07%, accordingly. As a result of determining the strength characteristics of the obtained composite materials with various reducing agents by dropping from a height of 2 m onto a steel pallet, it was found that the obtained composite materials have high mechanical properties in terms of strength of 81% and 89.2%. The results of the elemental composition at the studied points and the thermal analysis of the studied composite material are presented. The mineralogical composition of the composite material is presented in the form of serpentine and nontronite, and the empty rock is made of quartz and talc. The activation energy of thermal analysis by the method of non-isothermal kinetics were calculated. The results of experiments on the production of composite materials from nickel-containing raw materials will be recommended for obtaining the optimal composition of composite materials at the stage of pilot tests and industrial development of the developed technology for processing nickel ores of the Republic of Kazakhstan. For the processing of nickel-poor nickel ores, it is of great importance to obtain optimal technological and technical and economic indicators that ensure low cost of nickel in the resulting product. Full article

Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.4 increase in F-1 score from RF. Full article

In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains. Full article

Quinoa (Chenopodium quinoa Willd.) is a facultative halophyte renowned for its importance in enhancing food security, and it supports forage production across diverse climatic regions. The objective of this study is to examine the impacts of multiple pre-treatment methods on C. quinoa seed (Titicaca cultivar) germination parameters, identify the optimum pre-treatment to diminish the consequence of salinity, and promote the productivity of this crop, especially in marginal environments. For this purpose, a spectrum of sodium chloride (NaCl) concentrations spanning from 0 to 500 mM and gibberellic acid (GA3) concentrations ranging from 0 to 300 ppm were tested, and mechanical scarification (MS) was carried out. The effect of a combination of these pretreatment NaCl/GA3 and NaCl/MS on the germination parameters of C. quinoa seed was also investigated. The results showed that the total germination, vigor index, and germination index decreased progressively with an increase in salinity. Hence, salinity exhibited a notable influence on most germination parameters. Moreover, seeds scarified with 500 mM of NaCl negatively affected all measured parameters. In contrast, gibberellic acid applied at 200 ppm was effective on most of the parameters measured, particularly under 100 mM of NaCl. These findings indicate that immersing seeds in gibberellic acid could mitigate the adverse impacts of salinity. Full article

CSR decoupling refers to the misalignment between a company’s stated CSR policies and its actual practices, resulting in issues like diminished financial performance and heightened risk. While initially explored in developed economies such as the US, recent research has shifted focus towards developing nations like China. However, a comprehensive review of CSR decoupling literature in the Chinese market remains lacking. Previous research typically examines the decoupling phenomenon at the general level of CSR, without considering the distinct impacts of its three key components: environmental, social, and governance pillars. Our study seeks to address this gap by conducting a comprehensive review of CSR decoupling covering 82 related studies, specifically analyzing its environmental, social, and governance dimensions within the context of China. Our findings offer valuable insights for both future research on CSR decoupling in China and policymaking. Firstly, there is a pressing need to prioritize investigations into means–ends decoupling, given the constraints on policy–practice decoupling imposed by stringent regulations. Secondly, the role of government policies in shaping CSR practices is pivotal. Future research could delve into the impacts of policy shocks using quasi-experimental designs. Thirdly, emerging issues like workplace safety, the executive pay gap, and gender diversity are gaining prominence in China’s CSR landscape. Lastly, the dominance of state ownership presents significant challenges to corporate governance, warranting further exploration. Full article