Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies.
Engineering machinery, tools, and implements, Technological innovations. Automation
Plant-derived materials from Salvia officinalis L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from Staphylococcus aureus, with a value of −33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R2 = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems.
Surfactants are chemical compounds present in a large number of products that people use on a daily basis, starting with detergents for washing clothes, dishes, personal hygiene products, etc. Some products also contain certain heavy metals. Their uses cause heavy contamination of wastewater that must be purified before discharge into receivers. Given that some types of surfactants are very persistent and heavy metals are non-biodegradable and toxic even in small concentrations, the purification process requires a complex approach and a combination of different methods. Bioremediation, as an environmentally acceptable and economically clean technology, has great potential. It is based on the use of indigenous microorganisms that have developed different mechanisms for breaking down and removing or detoxifying a large number of pollutants and are excellent candidates for bioremediation of wastewater. Bacteria can degrade surfactants as sole carbon sources and exhibit tolerance to various heavy metals. This paper summarizes the most significant results, highlighting the potential of bacteria for the biodegradation of surfactants and heavy metals, with the aim of drawing attention to their insufficient practical application in wastewater treatment. Bioreactors and microbial fuel cells are described as currently relevant strategies for bioremediation.
The strong uncertainty of the high proportion of new energy and the gradual decrease in the proportion of thermoelectric units have led to a shortage of system flexibility resources. System-level energy storage can efficiently alleviate the pressure of peak shaving and frequency regulation. Effective aggregation of flexibility resources is a key technical foundation for enhancing economic operation and advanced user-side response strategies of new power systems. However, the decentralization and heterogeneity of flexibility resources across generation, grid, load, and storage sides pose dual challenges of aggregation speed and accuracy. In view of this, this paper proposes a large-scale multi-dimensional flexibility polymerization method based on different response time scales. First, the flexibility resource definitions and response characteristics of generation, grid, load, and storage sides were analyzed and categorized according to their response time scales. Second, flexibility regulation models for resources on each side were established. On this basis, an improved Minkowski aggregation algorithm is proposed to precisely quantify the regulation capabilities of multi-dimensional flexibility resources at different time scales, enabling efficient resource aggregation. Finally, the results of the case analysis show that the proposed method can accurately aggregate the flexibility resource adjustment capabilities at different time scales to respond to the multi-time scale flexibility requirements of the system.
Engineering machinery, tools, and implements, Technological innovations. Automation
Zdravko Šumić, Aleksandra Tepić Horecki, Lato Pezo
et al.
Dried peaches are widely consumed as a snack food product and used as an ingredient in cereals as well in chocolate and energy bars. Accordingly, the main objective of this investigation was to optimize the vacuum-drying process for peaches using a combination of three different statistical methods: principal component analysis, the standard score method and an artificial neural network approach. Applied input drying parameters were temperature (50–70 °C), pressure (20–120 mbar) and time (6–10 h), while the investigated output parameters were moisture content, water activity, total color change, phenolic and flavonoid contents and antioxidant activity. It was noted that all investigated output parameters constantly decreased (moisture content, water activity) and increased (total color change, total phenolic and flavonoid contents and antioxidant activity (FRAP, DPPH and ABTS assays)) in accordance with the applied drying temperature. The key variables accounted for 86.33% of data variance based on the PCA results, while the SS and ANN method resulted in the same optimal drying conditions: 60 °C, 70 mbar and 6 h, which indicated the effectiveness of the applied statistical methods.
Since their invention by Akachi in 1990s, pulsating heat pipes (PHPs) have attracted widespread interest and application in practice, e.g., grinding, chip cooling, the thermal management of batteries, etc., owing to their notable efficiency in heat transfer and their simplicity and flexibility in structure. Key factors influencing the heat transfer efficacy of pulsating heat pipes are mainly attributed to the thermophysical properties of the working fluid, the structural parameters, and the operating conditions. Research on pulsating heat pipes is conducted through theoretical investigations, numerical simulations, and visual experiments. In this paper, the research on PHPs in recent decades is reviewed with the consideration of the heat transfer performance mechanism and application of pulsating heat pipes, especially research under operation conditions such as with “status with motion” and with “inconsistent heat flux”.
Engineering machinery, tools, and implements, Technological innovations. Automation
Sustainable communication refers to any communication related to corporate social responsibility, considering economic, social, and environmental concerns and the interests of all stakeholders. The fact that media culture has become a dominant force of socialization is discussed in the article. The other point here is that at the beginning of the twentieth century, there were about 3500 manor houses in the territory of present-day Lithuania. Currently, only about 600 of them have survived. The description of the communicative strategies that some of the manors apply, such as by Paliesius manor, can prompt far-reaching conclusions. The general idea of the article is that castles and estates located in priority areas of the country's tourist attraction can be decisive in promoting the nation's heritage, tradition, and culture. The strongest precondition for this influence can be the part of programming that those heritage objects undertake.
Vedran Milanković, Tamara Tasić, Milica Pejčić
et al.
Coffee is one of the most popular beverages, with around 10.5 million tons manufactured annually. The same amount of spent coffee grounds (SCGs) might harm the environment if disposed of carelessly. On the other hand, pesticide contamination in food and biowaste is a rising problem. Because pesticides are hazardous and can cause serious health consequences, it is critical to understand how they interact with food biowaste materials. However, it is also a question if biowaste can be used to remediate rising pesticide residues in the environment. This study investigated the interactions of SCGs with the organophosphate pesticides malathion (MLT) and chlorpyrifos (CHP) and addressed the possibility of using SCGs as adsorbents for the removal of these pesticides from water and fruit extracts. The kinetics of MLT and CHP adsorption on SCGs fits well with the pseudo-first-order kinetic model. The Langmuir isotherm model best describes the adsorption process, giving the maximal adsorption capacity for MLT as 7.16 mg g−1 and 7.00 mg g−1 for CHP. Based on the thermodynamic analysis, it can be deduced that MLT adsorption on SCGs is exothermic, while CHP adsorption is an endothermic process. The adsorption efficiency of MLT and CHP using SCGs in a complicated matrix of fruit extracts remained constant. The neurotoxicity results showed that no more toxic products were formed during adsorption, indicating that SCGs are a safe-to-use adsorbent for pesticide removal in water and fruit extracts.
Jānis Teivāns-Treinovskis, Nikolajs Jefimovs, Ruta Velika
et al.
Despite the high level of technological and civilisational development of Western society, its prosperity and sustainability of development most directly depend on energy policy. Quite often, the field of energy security determines which countries are leading and decisive in the world economy and global competitiveness. The latest geopolitical events, namely the Russian military aggression on Ukraine's territory, showed that most European countries, including Russia itself, increasingly depend on individual energy resources. Such a situation creates serious challenges that must be promptly and effectively responded to. One of the most severe challenges is energy blackmail, which is purposefully implemented by the leadership of the Russian Federation to achieve its goals in Ukraine. The threat of destroying Ukraine's energy system is no less severe, and a humanitarian disaster may also follow in this European and Western-oriented country. Unable to defeat Ukraine on the battlefield, the Russian Federation is trying to force the Ukrainian leadership to agree to an actual capitulation by methodically destroying its energy sector. This shows that the European Union must be able to respond to the problems of its energy sector and provide support to its allies to guarantee energy security. In addition, this type of response must comply with international and national law.
Sustainability of fruit production is becoming a necessity in the time of climate changes and severe environmental issues, including decreasing water availability and biodiversity loss. To overcome these difficulties in sweet cherry production, we aimed to investigate the autochthonous cherry germplasm as a source of adaptable, size-controlling and productive rootstocks. The performance of sweet cherry cultivar ‘Summit’ grafted on six rootstock candidates and ‘Gisela 5’ as a control has been assessed in semi-arid climate, in conditions without irrigation and pruning, and with minimal herbicides’ application. The qualitative (anchorage, suckering, vitality), vegetative (trunk cross sectional area—TCSA, tree dimensions) and generative (potential and achieved yielding, fruit quality) characteristics were investigated. All candidates provided adequate anchorage while three candidates did not form suckers. Trees on ‘Gisela 5’ showed the lowest vitality. The scion TCSA in the fifth vegetation ranged from 16.7 to 47.2 cm2, while tree height, crown width and depth were up to 293, 150 and 175 cm, respectively. In sixth vegetation, the yield reached 4.1 kg. The average fruit mass in the trial of 2020–2021 was 8 g, fruit width was up to 27.5 cm, while the dry matter content reached 19%. The study showed that with the proper rootstock selection, sweet cherries could achieve satisfactory growth and yield without harming the environment and with minimal orchard’s maintenance practices. Within investigated autochthonous material, candidate PC_02_01/4 induced the best performance of ‘Summit’ cultivar.
<p class="first" id="d265116e77">Digital sharing of research data is becoming an important research integrity
norm. Data sharing is promoted in different avenues, one being the scholarly
publication process: journals serve as gatekeepers, recommending or mandating
data sharing as a condition for publication. While there is now a sizeable
corpus of research assessing the pervasiveness and efficacy of journal data
sharing policies in various disciplines, available research is largely piecemeal
and mitigates against meaningful comparisons across disciplines. A major
contribution of the present research is that it makes direct across-discipline
comparisons employing a common methodology. The paper opens with a discussion of
the arguments aired in favour and against data sharing (with an emphasis on
ethical issues, which stand behind these policies). The websites of 150
journals, drawn from 15 disciplines, were examined for information on data
sharing. The results consolidate the notion of the primacy of biomedical
sciences in the implementation of data sharing norms and the lagging
implementation in the arts and humanities. More surprisingly, they attest to
similar levels of norms adoption in the physical and social sciences. The
results point to the overlooked status of the formal sciences, which demonstrate
low levels of data sharing implementation. The study also examines the policies
of the major journal publishers. The paper concludes with a presentation of the
current preferences for different data sharing solutions in different fields, in
specialized repositories, general repositories, or publishers' hosting area.
</p>
As per newspapers and national news reports, small and medium-sized enterprises (SMEs) in Indonesia, currently 26,574 in number, are spreading widely. The transformation of SMEs to large, mature business entities is slow. This study investigates the sluggish transformation of SMEs in Indonesia and offers new explanations for the sluggishness, focusing on knowledge empowerment instead of financial aspects. A new development model was constructed with an aim to enhance SMEs. This study serves as research with a paradigm of constructivism, prioritizing knowledge empowerment. Executive officers of SMEs should aim for the construction of conceptual knowledge of social networks and social cognition in a sequential-ordered logic to achieve organizational readiness and readiness for change. They, moreover, internalize these concepts to transform SMEs into large, mature business entities with high levels of competitiveness, leadership engagement, dynamic capabilities, and profound sustainabilities.
Sundaravaradan Navalpakkam Ananthan, Alvaro Furlani Bastos, Surya Santoso
et al.
Post-fault event report analysis is a crucial skill set for electric power engineers in the protection industry. This paper serves as a reference which elucidates the preprocessing procedures involved in transforming data present in event reports to phasors that can be used in various post-fault analysis application algorithms. The paper discusses key elements of this process such as interpreting the data and calculating voltage and current phasors from instantaneous sample values present in a fault record. A crucial component of event report analysis is choosing the appropriate time instant for calculating phasors for event report analysis. Conventionally, protection engineers manually perform event report analysis and arbitrarily select time instants after certain cycles of fault inception for this purpose. This approach prevents the process from being successfully automated. Furthermore, arbitrary selection of time instant does not utilize the entire fault data and may fail in several cases such as short time fault scenario and evolving fault scenario. For this purpose, this paper proposes an adaptive novel technique which utilizes the entire data present in the event report to select the most suitable time instant for event report analysis. The superiority of the proposed algorithm over conventional methods is demonstrated using three real-world scenarios.
Engineering machinery, tools, and implements, Technological innovations. Automation